Just as LinkedIn, Google and Facebook are businesses started with a vision, so do Global BI Projects. The background of this paper is Gartner's 2012 BI Report that empirically states --> less than 30% of BI projects will meet business expectations in 2012-14 period. Please send an email for your free copy of the ‘Gartner 2012 BI report Deconstructed’
Here are seven tips about how to simply start and deploy a successful Business Intelligence Program/Project. Here are the top seven tips I’ve picked up over the years.
1. Listen More to Business and Align Deliverables Accordingly
It has been proven beyond reasonable doubt that involving actual business owners and stakeholders in BI projects is the only way to assured BI success. IT is critical but so is business. To be a good BI leader one has to listen to business and then use the IT alternatives to meet, and sometimes exceed business expectations. First and foremost an IT leader has to be a good listener and less of a talker of technocratic solutions. The collective knowledge of the Deployment team must be leveraged as great ideas can spring from the most unlikely sources. Keep your ears open to even the shrewdest advice. Get into the midst of business users, identify leaders and analytics owners, listen to them, draw business into the BI development process and cooperate to build the ultimate BI solution as a team.
As Gartner stated in 2010 ‘Without business in business intelligence, BI is dead’
2. Eliminate Surprises
Define your BI success criterias very early and very clearly, then track and report it on a weekly basis. I call it BVA or Business Value Attainment.
Most BI Projects do not track ‘Meet Business Expectations’ scores through the lifecycle of the project, yet are surprised when it does not meet their requirements towards the end of the project. The key to a successful BI project is to eliminate any scope of surprise after ‘Go-Live’
BI Valuenomics reported in 2010: ‘98% of BI Projects are declared successful in week 1 after go-live, yet less than 50%* remain successful by week 10’. So planning must ensure that the BI project success does not fall off the cliff as it nears or passes the ‘Go-Live’ date.
*Note: this was published in 2010 when, according to Gartner, failure rate of BI projects was reported at over 50%. In 2012 the failure rate is now estimated at over 70%. So today you must read this as less than 30%..
3. Think BVA**
The scientific principles of BI mandate that BI projects plan for ‘**Business Value Attainment’, versus simply deploying a BI technology. The decision is [1] Meet Business Expectations in BI; or [2] Deploy a technocratic BI application. Some key considerations
• Your charter must conform to BVA definitions
• At each stage key stakeholders must review a BVA Checklist and prioritize what needs to be accomplished when
• Your PM should conform to BVA project reporting components
• Your project must be mentored by a ‘BI Business Value Architect’ whose focus is aligning all technology decisions with a ‘Business First’ process of filtration
• Your project must include a ‘BI Business value Owner’ that has to be an internal resource
• Your Project RACI must be clearly communicated prior to commencement of the project with clear business ownership and accountabilities
Current projects are run on a technocratic assumption that 'technology alone can provide all the answers' to business needs. However, the past decade and a half has clearly proven this assumption to be erroneous.
4. Based on Proven Scientific Principles
Large and small customers, alike, need to analyze their business with competitive positioning as a fundamental background. What makes your business unique, and successful, is the way you do business and your business stakeholders.
In BI your analytical skills have to be radically different from your competition in order for your business to stand out in the competitive landscape. But this does not have to be technocratically complex. Each business stakeholder and each enterprise has critical business analysis needs just waiting to be solved in a more efficient manner. For example running a national CO-PA report once a month in a 20 hour run vs. having it run in real-time in under a few minutes. Maintain a focus on ‘True Business Needs’ and innovation. Don’t try to reinvent the wheel and never believe that quantity of reports is better than quality of business focused analytics. Remember that a simple change for the better is far more effective than five complicated changes for the worse.
5. Take pride in your work
Last two years I enjoyed my favorites of all BI projects. In one we scored 96% business satisfaction, based on user feedback, in week 1, 10 and 20 and did not have any emergency transport, i.e. defect mitigation, in the first two weeks. In the other we scored 102% project success, based on user feedback on, week 1, 22 and 30. In both projects we deployed the BVA process and it was a team effort of each individual going an extra mile to make the project a success. In a Gartner reported background of fewer than 30% success this is exemplarily solid outliers from the average. With so many different business units, business needs, divisions, nationalities under one project it was interesting that the only bonding link to all developments was our scientifically documented ‘Global Enterprise BI Cookbook’. Remember that your business users are your final judge, jury and executioners and your biggest advocates. Focusing on them, helping them take ownership with pride will always shine through how they treat and use your delivered BI content.
6. Keep it Simple & Have Fun
Dont get dazzeled by flashing lights and technocratic promizes. focus first on business needs, develop a business vision then proceed with an alternatives analysis. After that document. By documenting clear directives for standards, processes, Architecture, automated modeling, etc guidelines in your ‘Global Enterprise BI Cookbook’ we eliminate the element of subjective assumptions and replace them with a solid global methodology. This simplifies not only the deployment but also the job of auditors and stewards. By keeping it simple team members tend to have fun.
If your team is not having fun, then we are all doing it wrong. In such cases it is critical to stop, pause and review. If your resources get up in the morning thinking their work is a chore, then the methodology is wrong, and they should be trying something else. When employees are having fun and there is a work-life balance in the project then the project is indeed on the right path.
Great project are nourishment, give a chance to be positive and are good for all parties and individuals concerned.
7. When you fail – Sit Back and Analyze Next Steps
This one is far, far easier said than done. When BI projects fail it seems far easier to immediately start unplanned projects to meet business expectations. This is not only costly but also counterproductive.
When, and if, your BI Projects is going, or has already gone, south:
• Welcome to the club: more than 70% of BI projects actually do not meet business expectations. You are certainly not alone. Every BI PM and resource has experienced some flavor of this, some will accept it others not. The ones that accept actually learn, the ones that done continue on their same technocratic path to the next failure. Don’t panic, don’t get disheartened, instead dust off the errors and get yourself a trusted ‘BI Business value Architect’. Someone with a solid business background and an equally solid technology understanding. Find out what and why things went wrong. This can be accomplished in a matter of weeks. Identify the negatives, or defects, and avoid them. Identify the positives and work on them. This is then your starting point.
• Sometimes it is shorter to ‘Rip it Up and Start Again: Once again a difficult decision if the company has spent a few million dollars and the end result is dissatisfaction with business users. But it is important to remember the Einstein quote
• Albert Einstein stated that insanity is ‘.. doing the same thing over and over again and expecting different results’: All too often failed BI projects are the result of failed business participation, processes and methodologies. When BI projects fail it is not uncommon to find companies have one or more of the following patterns:-
o Doing the same thing that got you in trouble at the start. Low/ No business ownership
and accountability. Lack of Methodology, standards or processes.
o Terminate the BI Team, i.e. replace the BI SI with a new one
o Offshore the BI development
o Hire lower cost contractors and make them work from 8am till 11pm every night and
through weekends
o Loose all the good resources in the process
o See an escalation in cost, business dissatisfaction and budgets
o See a decrease in business sponsors, true solutions and strategic BI developments
Pause, rewind and plan before you take the next leap of faith into another round of lunacy. Conduct a short ‘Strategic BI Health-check’ and then prioritize the next steps in a professional and planned manner.
Sep 27, 2012
How to Fail Your Way to BI Success
“Insanity”, according to Albert Einstein, is “doing the same thing over and over again and expecting different results”
In Feb 2012 Gartner reported that more than 70% of BI projects will fail in the 2012-14 period. this is alarming to say the least, and it is time to get concerned and review the path we are all walking on. It is time to check whether you are rushing on a path of insanity, or willing to stop for a while and get your bearings straight.
Decisions taken in a state of fear are reported to consistently choose higher risks and lead to an almost certain failure.
The biggest difference between you and Picasso, or Einstein, or whoever your heroes are that they spent lots of time in whatever they did. They spent more time in front of a canvas, or guitar, or computer, working away at applying their minds and souls to specific things. According to Malcolm Gladwell’s book ‘Outliers’, patterns only form when someone has done similar tasks for 1,000 hours, which in his score equals to 1. I have spent over 30,000 hours in BI projects ranging from Oracle, Teradata, Informix and SAP BI (97%) so this paper comes with an outlier score of 3. Looking back it is the failed BI projects, which I was assigned to, that taught me more than the ones that were successful. In one project we scored 102% user satisfaction based on the 'BI Valuenomics principles. Looking at the last six years close to 70% of the projects I was assigned to were projects either going, or gone, due south. In aircraft terminology I would term it as a nose dive.
Here is the standard question I have adked the CIO, BI managers and technical leads from the client,
Me: So, how was/ is your BI project?
..and the answers I have received over the last few years.
Client:
[1] Our BI project was an IT success, and a total business failure; (scored 102% after applying SP (Scientific Principles of BI)
[2] Our System Integrator is working on a fixed bid project. They were supposed to finish in January, now its June so it’s their problem; (cancelled the project in July, will commence to fix with same SI)
[3] We are 6 weeks from go live, and right now if we get 40% of the reports we expect then I will be more than happy; (customer trying find they way out of this mess)
[4] We are in a Severity 1 status two to three times a day so I don’t have time to answer your question; etc (customer continues on this path till date)
[5] My users have stopped using out BI totally and gone back to the legacy reports. I want you to bring the user confidence in our BI back (scored 98% after applying SP)
In one recent BI Project, where I found a basic naming conventions missing, when we asked the customer if they had a BI –Center of Excellence they responded a strong 'Yes'. Taken aback I took a little deeper dive and found that their SI was calling their offshore support staff COE – and even had the customer executives convinced that they indeed had an operational COE. So wat their COE did was daily load checks and low cost developments in BI across the planet.
One thing all these respondents were missing is that it is possible not to fail in BI projects today. But then you have to choose this path. Most BI projects fail because the client does not even try. Both clients and SI’s seem to get entangled into an endless spiral downwards where(a) initially the euphoria of utopian promises, which is followed by (b) the fear of failure, which together overturns all conventional logic- as both continue to proceed speeding down the path of failure at full speed.
Today with a sound global methodology and documented scientific principles we can keep delivering world class BI projects but somehow the choice, made by 70% of respondents, is a consistent path of failure. If this statement were wrong 70% of BI projects would not be speeding towards imminent failure even as we read this paper. If your BI is sick then stop all activities, which I know seems almost impossible to imagine, but if you are heading for an accident then the best advice is to stop hard. Now, that you have stopped you need to conduct a serious Strategic Health-check from a trusted and qualified BI Business Value Architect. If you somehow choose not-to then you shall once again have chosen to continue on your predictable path to failure. The solutions, though rarified, are written on the wall, but one has to take time document and read it.
More often than not, the fear of failure is so haunting that most of us continue to do what we have been doing, hoping that somehow the future will change. That, we have now clearly established, is walking right into the Einstein’s lunacy definition.
So what is the solution:
Briefly, the first step should be to try and never get your BI initiative on any path to failure. However, once you think that your BI is on a path to failure – the first step is to accept it. Then proceed to write down on a piece of paper ‘I will now face my fears and take the following steps to achieve (and fill in these blanks) in 3 months. In each step there must be a quantifiable goal that mitigates attribute of the fear of failure). If you cannot get a mentor with solid business background and BI experience to match- high business focus. I call them 'BI Business Value Architects'
• Step one:
• Step two:
• Step three:
Now sit back and imagine your life three months from now when you have managed to achieve your goals and actually achieved finding a remedial path and suddenly there is a light at the end of the tunnel. (Feels good, right?) So what's stopping you?
The two greatest reasons for BI failures are [1] Assumption and [2] Fear of Failure
Assumptions that your partner is going to provide you all the solutions, that if you involve business in your BI project they will cause unnecessary delays and increase costs, that the BI project can chug speed full-speed-ahead without any formal documented methodology- i.e. a ‘Global BI Cookbook’, etc.. This is like a Ready-Fire-Aim kind of a BI project.
Fear of failure is the second biggest obstacles to success and we all have to battle it. What separates success from failure is the ability to accept the fear, find out if you are qualified to find the solution and then applying a scientific method out of the mess with consensus of all key stakeholders. The failures prefer to manage in the state of crisis, I’ve met a few that seem to thrive on the daily adrenalin of multiple Sev 1 issues on a daily basis sometime for months on an end. Sev 1 issues should not happen more than a few times a year. Other continues on their current path hoping things will somehow change- which they rarely do.
It is critical to realize that the failure of a BI project is the failure of the enterprise, of its capability to make decisions (till it is fixed) and least of all of the SI.
Often I have sat in ‘Lessons learned’ meetings and have found that people who are afraid of failure treat them as mistakes. Mistakes are not failures. Mistakes are actually your path to success, but only if you avoid them the next time. Mistakes look like failure but are actually solutions in disguise. True failure is not recognizing failure in a timely manner, continuing on a path of failure without changing any of the variables, or blaming someone else for the failure. True failure is quitting. Everything else is a path of learning.
If humans never made mistakes evolution and development would stop. In pharmaceutical and R&D mistakes is the path to success. As Bernard Shaw had stated “Show me a man who has never made a mistake, and I’ll show you a fool”. Anyone who tries will make mistakes and I have made my honest share. However, we must also learn from mistakes. It is ill advised to make the same mistake again and continuously over time and space. In almost every sport it is advisable to firstly go beyond the ‘pain point’ and then go to the edge of failure. This is done to establish a breaking point. The best then take this breaking point further and further and succeed where no person has before. Gold medals are not won by athletes who work within constraints not ones who continue to make the same mistakes.
Great players will focus on the times they have failed to remind themselves not to add to that number. Lesser athletes will focus on the times they have won and try very hard to forget the ones they lost. In my life I have learned a hundred times more in failed projects, and my BI Valuenomics book is all the lessons learned from failure- as the scientific principle of all management established in 1897 that by eliminating all failure points our only option is success.
The Silicon Valley is full of the 10th time. i.e. 1 out of 10 ideas will succeed. The ones who succeed wither have uncanny luck or persistence, the ones who never make the same mistake twice and work as a team. The valley is also full of inventory who have held on to their ideas, not sharing, not teaming and they are left standing where they were 5 to 10 years ago.
Successful people look at points of failures, learn from it, establish some form of rule to avoid it the next time and succeed. Failures for them are opportunities to learn. In life one can either lead or follow, but fearful leaders try to do both at the same time and at the most critical moments it is the fear of failure that totally paralyzes their thinking. Henry Ford quoted: "Failure is the opportunity to begin again more intelligently."
So with a failure rate of 70% start with accepting that the probability of failure in your BI initiative is 70%. Recognize that 70% of the BI projects in the known universe will continue to fail all around you (unless you firmly believe that Gartner is a junk company with analyst smoke all kinds of weeds in strange bars and do not know what they write).
Your ‘advisors’ are going to bombard you only with what to do and how they have done it in other customers. However you need to start reading Gartner reports more carefully, speaking to fellow customers on what they did wrong. Follow this through and hire yourself a reputable ‘BI Business Value Architect’ and learn what not to do from them. Train your business stakeholders and arm them with checklists of failure and success criteria’s.
To be ultra successful you have to be able to find your failure points in your mind, find them often and predictably, and then avoid these pitfalls. If unable to do so accurately find yourself a reliable advisor who you can trust to enhance your business deliverables and not just deploy still another BI appliance.
1. Accept Failure is a high possibility in BI (currently at 70% failure rate)
2. Develop and get hold of BI Checklists by each phase
3. Anticipate and avoid failures. When failure happens welcome it and learn from it
4. Apply only scientific principle in BI, not because they are handed to you as such but because they are.
In Feb 2012 Gartner reported that more than 70% of BI projects will fail in the 2012-14 period. this is alarming to say the least, and it is time to get concerned and review the path we are all walking on. It is time to check whether you are rushing on a path of insanity, or willing to stop for a while and get your bearings straight.
Decisions taken in a state of fear are reported to consistently choose higher risks and lead to an almost certain failure.
The biggest difference between you and Picasso, or Einstein, or whoever your heroes are that they spent lots of time in whatever they did. They spent more time in front of a canvas, or guitar, or computer, working away at applying their minds and souls to specific things. According to Malcolm Gladwell’s book ‘Outliers’, patterns only form when someone has done similar tasks for 1,000 hours, which in his score equals to 1. I have spent over 30,000 hours in BI projects ranging from Oracle, Teradata, Informix and SAP BI (97%) so this paper comes with an outlier score of 3. Looking back it is the failed BI projects, which I was assigned to, that taught me more than the ones that were successful. In one project we scored 102% user satisfaction based on the 'BI Valuenomics principles. Looking at the last six years close to 70% of the projects I was assigned to were projects either going, or gone, due south. In aircraft terminology I would term it as a nose dive.
Here is the standard question I have adked the CIO, BI managers and technical leads from the client,
Me: So, how was/ is your BI project?
..and the answers I have received over the last few years.
Client:
[1] Our BI project was an IT success, and a total business failure; (scored 102% after applying SP (Scientific Principles of BI)
[2] Our System Integrator is working on a fixed bid project. They were supposed to finish in January, now its June so it’s their problem; (cancelled the project in July, will commence to fix with same SI)
[3] We are 6 weeks from go live, and right now if we get 40% of the reports we expect then I will be more than happy; (customer trying find they way out of this mess)
[4] We are in a Severity 1 status two to three times a day so I don’t have time to answer your question; etc (customer continues on this path till date)
[5] My users have stopped using out BI totally and gone back to the legacy reports. I want you to bring the user confidence in our BI back (scored 98% after applying SP)
In one recent BI Project, where I found a basic naming conventions missing, when we asked the customer if they had a BI –Center of Excellence they responded a strong 'Yes'. Taken aback I took a little deeper dive and found that their SI was calling their offshore support staff COE – and even had the customer executives convinced that they indeed had an operational COE. So wat their COE did was daily load checks and low cost developments in BI across the planet.
One thing all these respondents were missing is that it is possible not to fail in BI projects today. But then you have to choose this path. Most BI projects fail because the client does not even try. Both clients and SI’s seem to get entangled into an endless spiral downwards where(a) initially the euphoria of utopian promises, which is followed by (b) the fear of failure, which together overturns all conventional logic- as both continue to proceed speeding down the path of failure at full speed.
Today with a sound global methodology and documented scientific principles we can keep delivering world class BI projects but somehow the choice, made by 70% of respondents, is a consistent path of failure. If this statement were wrong 70% of BI projects would not be speeding towards imminent failure even as we read this paper. If your BI is sick then stop all activities, which I know seems almost impossible to imagine, but if you are heading for an accident then the best advice is to stop hard. Now, that you have stopped you need to conduct a serious Strategic Health-check from a trusted and qualified BI Business Value Architect. If you somehow choose not-to then you shall once again have chosen to continue on your predictable path to failure. The solutions, though rarified, are written on the wall, but one has to take time document and read it.
More often than not, the fear of failure is so haunting that most of us continue to do what we have been doing, hoping that somehow the future will change. That, we have now clearly established, is walking right into the Einstein’s lunacy definition.
So what is the solution:
Briefly, the first step should be to try and never get your BI initiative on any path to failure. However, once you think that your BI is on a path to failure – the first step is to accept it. Then proceed to write down on a piece of paper ‘I will now face my fears and take the following steps to achieve (and fill in these blanks) in 3 months. In each step there must be a quantifiable goal that mitigates attribute of the fear of failure). If you cannot get a mentor with solid business background and BI experience to match- high business focus. I call them 'BI Business Value Architects'
• Step one:
• Step two:
• Step three:
Now sit back and imagine your life three months from now when you have managed to achieve your goals and actually achieved finding a remedial path and suddenly there is a light at the end of the tunnel. (Feels good, right?) So what's stopping you?
The two greatest reasons for BI failures are [1] Assumption and [2] Fear of Failure
Assumptions that your partner is going to provide you all the solutions, that if you involve business in your BI project they will cause unnecessary delays and increase costs, that the BI project can chug speed full-speed-ahead without any formal documented methodology- i.e. a ‘Global BI Cookbook’, etc.. This is like a Ready-Fire-Aim kind of a BI project.
Fear of failure is the second biggest obstacles to success and we all have to battle it. What separates success from failure is the ability to accept the fear, find out if you are qualified to find the solution and then applying a scientific method out of the mess with consensus of all key stakeholders. The failures prefer to manage in the state of crisis, I’ve met a few that seem to thrive on the daily adrenalin of multiple Sev 1 issues on a daily basis sometime for months on an end. Sev 1 issues should not happen more than a few times a year. Other continues on their current path hoping things will somehow change- which they rarely do.
It is critical to realize that the failure of a BI project is the failure of the enterprise, of its capability to make decisions (till it is fixed) and least of all of the SI.
Often I have sat in ‘Lessons learned’ meetings and have found that people who are afraid of failure treat them as mistakes. Mistakes are not failures. Mistakes are actually your path to success, but only if you avoid them the next time. Mistakes look like failure but are actually solutions in disguise. True failure is not recognizing failure in a timely manner, continuing on a path of failure without changing any of the variables, or blaming someone else for the failure. True failure is quitting. Everything else is a path of learning.
If humans never made mistakes evolution and development would stop. In pharmaceutical and R&D mistakes is the path to success. As Bernard Shaw had stated “Show me a man who has never made a mistake, and I’ll show you a fool”. Anyone who tries will make mistakes and I have made my honest share. However, we must also learn from mistakes. It is ill advised to make the same mistake again and continuously over time and space. In almost every sport it is advisable to firstly go beyond the ‘pain point’ and then go to the edge of failure. This is done to establish a breaking point. The best then take this breaking point further and further and succeed where no person has before. Gold medals are not won by athletes who work within constraints not ones who continue to make the same mistakes.
Great players will focus on the times they have failed to remind themselves not to add to that number. Lesser athletes will focus on the times they have won and try very hard to forget the ones they lost. In my life I have learned a hundred times more in failed projects, and my BI Valuenomics book is all the lessons learned from failure- as the scientific principle of all management established in 1897 that by eliminating all failure points our only option is success.
The Silicon Valley is full of the 10th time. i.e. 1 out of 10 ideas will succeed. The ones who succeed wither have uncanny luck or persistence, the ones who never make the same mistake twice and work as a team. The valley is also full of inventory who have held on to their ideas, not sharing, not teaming and they are left standing where they were 5 to 10 years ago.
Successful people look at points of failures, learn from it, establish some form of rule to avoid it the next time and succeed. Failures for them are opportunities to learn. In life one can either lead or follow, but fearful leaders try to do both at the same time and at the most critical moments it is the fear of failure that totally paralyzes their thinking. Henry Ford quoted: "Failure is the opportunity to begin again more intelligently."
So with a failure rate of 70% start with accepting that the probability of failure in your BI initiative is 70%. Recognize that 70% of the BI projects in the known universe will continue to fail all around you (unless you firmly believe that Gartner is a junk company with analyst smoke all kinds of weeds in strange bars and do not know what they write).
Your ‘advisors’ are going to bombard you only with what to do and how they have done it in other customers. However you need to start reading Gartner reports more carefully, speaking to fellow customers on what they did wrong. Follow this through and hire yourself a reputable ‘BI Business Value Architect’ and learn what not to do from them. Train your business stakeholders and arm them with checklists of failure and success criteria’s.
To be ultra successful you have to be able to find your failure points in your mind, find them often and predictably, and then avoid these pitfalls. If unable to do so accurately find yourself a reliable advisor who you can trust to enhance your business deliverables and not just deploy still another BI appliance.
1. Accept Failure is a high possibility in BI (currently at 70% failure rate)
2. Develop and get hold of BI Checklists by each phase
3. Anticipate and avoid failures. When failure happens welcome it and learn from it
4. Apply only scientific principle in BI, not because they are handed to you as such but because they are.
'Red-Line' all Your Future BI Projects
Henceforth, REDLINE your BI Projects and Safeguard your corporate Assets.
Listening to Netanyanu at the UN yesterday, Sept 26th, 2012, and considering the Gartners 2012 BI Report I propose it is time for global enterprise BI Customers to also draw a clear 'Red-Line' of acceptable colateral damage in their BI initiatives.
In Netanyanu's case he requested the UN to draw a clear Red line of a globally acceptable tolerance. He cited similar Red-Lines that have delivered exceptional results. The Red Line drawn by NATO which declared that any attack on any of the NATO alliance nations would be considered an attack on all NATO nations. This simple Red Line still maintains peace in Europe.
In a very similar manner Companies and executives need to now draw a 'Red-Line' of acceptable tolerance in BI projects.
In a world dominated with under 30% "Meet Business expectations in BI" results, source Gartners 2012 BI report on one side, and the solution now published almost 2 years ago in 'BI Valuenomics- The Story of Meeting Business Expectations in BI' on the other, we have both critical components required for any strategic decision.
(ps. If you'd like to read the gartner 2012 report and feel its too expensive then send me an email and I'll send you my free copy of 'Gartners 2012 report Deconstructed')
So now that you have both critical components the first of them projecting what your BI Success factor will be if you use your traditional BI partners and methodologies, i.e. less than 30% success. While at the same time you have the solution documented in a book readily available. The final decision for your step forward now rests in your own hands.
While you are at it, I would also recommend drawing a 'Clear-Red-Line' of enterprise acceptance in your BI contracts. Your Redline document would look something like this
1. The SI must provide all advice, recommendations and expertize to ensure the BI Project MBE's (Meets Business Expectations in BI)
2. The minimum acceptable MBE score is 70%
3. If the minimum MBE score is not achieved then..... (you decide)
Once this red-line is established let the SI come back with their prosesses and proposals and review each through the lens of: [1] Is this scientific or not? [2] Will this align to our strategic Business Goals? Do I personally believe that their proposal will deliver over 70% MBE?
Background: over the last eight years or so I have spent over 80% of my time fixing BI projects that are either heading due south, or have gone past that. Over the years I have accumulated over 30,000 hrs assisting customers with their BI initiatives, out of which over 70% of this time has been spent in Strategic alignment, Global BI Methodologies and Fixing 'BI-Projects-Gone-Bad'
I shared my experience in BI Valuenomics and will be publishing 'The Sceintific Principles of Information Delivery" hopefully in Q4 of 2012.
So think about defining your own acceptable tolerance to success, and take pro-active action starting Next Monday.
Start today and get returns by Next monday
Listening to Netanyanu at the UN yesterday, Sept 26th, 2012, and considering the Gartners 2012 BI Report I propose it is time for global enterprise BI Customers to also draw a clear 'Red-Line' of acceptable colateral damage in their BI initiatives.
In Netanyanu's case he requested the UN to draw a clear Red line of a globally acceptable tolerance. He cited similar Red-Lines that have delivered exceptional results. The Red Line drawn by NATO which declared that any attack on any of the NATO alliance nations would be considered an attack on all NATO nations. This simple Red Line still maintains peace in Europe.
In a very similar manner Companies and executives need to now draw a 'Red-Line' of acceptable tolerance in BI projects.
In a world dominated with under 30% "Meet Business expectations in BI" results, source Gartners 2012 BI report on one side, and the solution now published almost 2 years ago in 'BI Valuenomics- The Story of Meeting Business Expectations in BI' on the other, we have both critical components required for any strategic decision.
(ps. If you'd like to read the gartner 2012 report and feel its too expensive then send me an email and I'll send you my free copy of 'Gartners 2012 report Deconstructed')
So now that you have both critical components the first of them projecting what your BI Success factor will be if you use your traditional BI partners and methodologies, i.e. less than 30% success. While at the same time you have the solution documented in a book readily available. The final decision for your step forward now rests in your own hands.
While you are at it, I would also recommend drawing a 'Clear-Red-Line' of enterprise acceptance in your BI contracts. Your Redline document would look something like this
1. The SI must provide all advice, recommendations and expertize to ensure the BI Project MBE's (Meets Business Expectations in BI)
2. The minimum acceptable MBE score is 70%
3. If the minimum MBE score is not achieved then..... (you decide)
Once this red-line is established let the SI come back with their prosesses and proposals and review each through the lens of: [1] Is this scientific or not? [2] Will this align to our strategic Business Goals? Do I personally believe that their proposal will deliver over 70% MBE?
Background: over the last eight years or so I have spent over 80% of my time fixing BI projects that are either heading due south, or have gone past that. Over the years I have accumulated over 30,000 hrs assisting customers with their BI initiatives, out of which over 70% of this time has been spent in Strategic alignment, Global BI Methodologies and Fixing 'BI-Projects-Gone-Bad'
I shared my experience in BI Valuenomics and will be publishing 'The Sceintific Principles of Information Delivery" hopefully in Q4 of 2012.
So think about defining your own acceptable tolerance to success, and take pro-active action starting Next Monday.
Start today and get returns by Next monday
Sep 4, 2012
More Reports do not Result in Better Decisions
Strategic BI Considerations: Enhance your BI IQ series
Recent research from Stanford and Princeton, confirms Gartner’s recommendations ‘Without Business in business intelligence, BI is dead’. For, when you leave you BI projects solely to your System Integrator/s their possible path may become delivering a large quantity of reports and analytics. While what your business might need is high quality analytics with self service capabilities.
Recent research clearly demonstrates that though this, large number of reports, seems alluring the path could lead to lower quality decisions.
When a business user needs to make a critical decision there are three scenarios they normally go through
1. The Good: they know exactly what report they need to access to get the required information. Or, they know how to build their analytics with modern ‘Self Service’ reporting applications
2. The Bad: They have over 40 reports that may contain the information they need and they will probably need to go through tens of reports to find it any of them or all of them contain the information they need. some of these reports may be junk reports. Such a situation leads to further confusion if each of the reports gives numbers than contradict other reports.
3. The Ugly: They have hundreds of reports in the BI environment but most of them cannot be used by business to take any kind of decision, leave aside critical decisions. The SI came, delivered hundreds of reports and they left by week 2, leaving business users hyperventilating with unusable reports and analytics.
According to Gartner’s 2012 report over 70% of BI initiatives live in scenario 3; less than 30% live in scenarios 2 and 1. You need to conduct a thorough ‘Strategic BI Health-check’ to see where you actually are. Automated solutions are available to accomplish this internally.
In almost every situation where users are subjected to information overload they tend to make worse decisions, according to a fascinating and new research from Stanford and Princeton psychologists.
The classic study from Anthony Bastardi of Stanford and Eldar Shafir of Princeton presented research subjects with scenarios in which they needed to choose between one of two alternatives. The example was totally related to the students as it dealt with credit applications for student loans for a course they wanted to take.
In these experiments sometime all the required information was supplied and at other times key information (for the required decision) was eliminated from the information provided. However, after the decision was taken these key information elements were exposed to the respondents. So from an emperical point of view all the respondents got exactly the same information but with one key difference. In the first group all the information was provided at the same time. With the second group there was a delay to providing the key information elements.
The result was astonishing from one side, but quite predictable from another. The question being asked was whether both groups finally took the right decision. The answer – Not at all.
It was clearly established that the group that had to wait for the key information elements at a later stage they consistently said ‘No’ to the course they wanted to take and refused loans at a higher rate.
So what is the business impact for a information consumer, or a decision maker, in an enterprise. What is the lesson to be learned?
It clearly establishes that when humans are made to wait for information, or if it is not readily available they assume it to be more valuable, even if the information bits are not relevant or important. So decision makers get into a ‘freeze’ moment if they assume their information is incomplete as they then believe the missing information is the most critical bit required to take their decision.
The Human mind is wired to instinctively collate unstructured information in situations and make logical sense out of it. When we hear a sound in the forest we can pretty accurately guess if it is make by a human, a squirrel or a potential threat like a cougar. We are wired to logically reduce uncertainty during hazardous times.
Howevr, in enterprise decisions there is a subtle difference. The first is that in todays flat world each decision could have tremendous impact on the strategic health of any company, i.e. a tiger in every bush scenario. Add to this the fact that we instinctively add more value to data that is missing. There is an inherent counter-benefit need to look for additional information- sometimes even if the information provided is adequate for taking the right decision.
In such cases if there are 40 additional reports that also deal with what sounds like similar information the user will be inclined to review all these reports hoping to make a better decision. However, if some of these reports are ‘junk’ or ‘unusable’ reports these will lead to add to the confusion or non-decision attributes of thinking. If this ‘junk’ number is high it can potentially freeze enterprise decision capabilities.
In the experiment ever the group that was provided with all the relevant information could not help but ask for more information. In enterprises where such additional information is available they become forced to go through it all and often end up more confused that when they started.
In a badly designed BI environment there are unused, unnecessary and test reports that sit in production BI environments, i.e. the ones business actually uses. The mind of most decision makers hates information gaps and often their attention gets focused on the wrong information elements. For example as the respondents got confused their attention got focused on the loan amount , i.e. 5,000 or 25,000 and not on the most important aspect that the student had a history of defaulting on their loans. In such a situation the loan amount was a minor detail.
If your user, in their quest to fill the information gaps, starts to click on additional reports, which by definition are ‘junk’ reports they will only end up more confused as some of these reports may hijack their mind to inconsequential details that do not really matter.
Reality is that users will want to read just one more report, one more analysis which today is only a click away. But if such reports are not harmonized for data and information consistency it may actually degrade the decision capabilities by clouding their information assets, or worse actually make them loose confidence in the BI reports.
So next time you want to make a good BI make sure it does not only deliver quantity, but quality
The next time you want to make a good decision make sure you spend more time in defining exactly what type of information do you need to take the decision and focusing on just that.
The next time you go into your BI environment and find ‘Junk’ reports, put into place a process to eliminate them all from your production envirionment.
Don’t trip yourself with junk reports. Don’t trip yourself with redundant analytics and most importantly of all don’t trip yourself with non-value-add information.
Lesson: 'Conduct a 'Stratgic BI Health-Check' starting next monday.
Question: How do I start a process by Next Monday: to find what reports in my BI environment are 100% usable in my DI environment. Read BI Valuenomics
Recent research from Stanford and Princeton, confirms Gartner’s recommendations ‘Without Business in business intelligence, BI is dead’. For, when you leave you BI projects solely to your System Integrator/s their possible path may become delivering a large quantity of reports and analytics. While what your business might need is high quality analytics with self service capabilities.
Recent research clearly demonstrates that though this, large number of reports, seems alluring the path could lead to lower quality decisions.
When a business user needs to make a critical decision there are three scenarios they normally go through
1. The Good: they know exactly what report they need to access to get the required information. Or, they know how to build their analytics with modern ‘Self Service’ reporting applications
2. The Bad: They have over 40 reports that may contain the information they need and they will probably need to go through tens of reports to find it any of them or all of them contain the information they need. some of these reports may be junk reports. Such a situation leads to further confusion if each of the reports gives numbers than contradict other reports.
3. The Ugly: They have hundreds of reports in the BI environment but most of them cannot be used by business to take any kind of decision, leave aside critical decisions. The SI came, delivered hundreds of reports and they left by week 2, leaving business users hyperventilating with unusable reports and analytics.
According to Gartner’s 2012 report over 70% of BI initiatives live in scenario 3; less than 30% live in scenarios 2 and 1. You need to conduct a thorough ‘Strategic BI Health-check’ to see where you actually are. Automated solutions are available to accomplish this internally.
In almost every situation where users are subjected to information overload they tend to make worse decisions, according to a fascinating and new research from Stanford and Princeton psychologists.
The classic study from Anthony Bastardi of Stanford and Eldar Shafir of Princeton presented research subjects with scenarios in which they needed to choose between one of two alternatives. The example was totally related to the students as it dealt with credit applications for student loans for a course they wanted to take.
In these experiments sometime all the required information was supplied and at other times key information (for the required decision) was eliminated from the information provided. However, after the decision was taken these key information elements were exposed to the respondents. So from an emperical point of view all the respondents got exactly the same information but with one key difference. In the first group all the information was provided at the same time. With the second group there was a delay to providing the key information elements.
The result was astonishing from one side, but quite predictable from another. The question being asked was whether both groups finally took the right decision. The answer – Not at all.
It was clearly established that the group that had to wait for the key information elements at a later stage they consistently said ‘No’ to the course they wanted to take and refused loans at a higher rate.
So what is the business impact for a information consumer, or a decision maker, in an enterprise. What is the lesson to be learned?
It clearly establishes that when humans are made to wait for information, or if it is not readily available they assume it to be more valuable, even if the information bits are not relevant or important. So decision makers get into a ‘freeze’ moment if they assume their information is incomplete as they then believe the missing information is the most critical bit required to take their decision.
The Human mind is wired to instinctively collate unstructured information in situations and make logical sense out of it. When we hear a sound in the forest we can pretty accurately guess if it is make by a human, a squirrel or a potential threat like a cougar. We are wired to logically reduce uncertainty during hazardous times.
Howevr, in enterprise decisions there is a subtle difference. The first is that in todays flat world each decision could have tremendous impact on the strategic health of any company, i.e. a tiger in every bush scenario. Add to this the fact that we instinctively add more value to data that is missing. There is an inherent counter-benefit need to look for additional information- sometimes even if the information provided is adequate for taking the right decision.
In such cases if there are 40 additional reports that also deal with what sounds like similar information the user will be inclined to review all these reports hoping to make a better decision. However, if some of these reports are ‘junk’ or ‘unusable’ reports these will lead to add to the confusion or non-decision attributes of thinking. If this ‘junk’ number is high it can potentially freeze enterprise decision capabilities.
In the experiment ever the group that was provided with all the relevant information could not help but ask for more information. In enterprises where such additional information is available they become forced to go through it all and often end up more confused that when they started.
In a badly designed BI environment there are unused, unnecessary and test reports that sit in production BI environments, i.e. the ones business actually uses. The mind of most decision makers hates information gaps and often their attention gets focused on the wrong information elements. For example as the respondents got confused their attention got focused on the loan amount , i.e. 5,000 or 25,000 and not on the most important aspect that the student had a history of defaulting on their loans. In such a situation the loan amount was a minor detail.
If your user, in their quest to fill the information gaps, starts to click on additional reports, which by definition are ‘junk’ reports they will only end up more confused as some of these reports may hijack their mind to inconsequential details that do not really matter.
Reality is that users will want to read just one more report, one more analysis which today is only a click away. But if such reports are not harmonized for data and information consistency it may actually degrade the decision capabilities by clouding their information assets, or worse actually make them loose confidence in the BI reports.
So next time you want to make a good BI make sure it does not only deliver quantity, but quality
The next time you want to make a good decision make sure you spend more time in defining exactly what type of information do you need to take the decision and focusing on just that.
The next time you go into your BI environment and find ‘Junk’ reports, put into place a process to eliminate them all from your production envirionment.
Don’t trip yourself with junk reports. Don’t trip yourself with redundant analytics and most importantly of all don’t trip yourself with non-value-add information.
Lesson: 'Conduct a 'Stratgic BI Health-Check' starting next monday.
Question: How do I start a process by Next Monday: to find what reports in my BI environment are 100% usable in my DI environment. Read BI Valuenomics
Aug 30, 2012
How to Fail Your Way to BI Success
“Insanity”, according to Albert Einstein, is “doing the same thing over and over again and expecting different results”
With Gartner now reporting that more than 70% of global BI projects will fail in the 2012-14 period, is it time to get really concerned and review the path we are walking on. It is time to check whether you are rushing on a path of insanity, or willing to stop for a while and get your bearings straight.
Decisions taken in a state of fear are reported to consistently choose higher risks, cost and lead to an almost certain failure. BI is no different.
The biggest difference between you and Picasso, or Einstein, Beethoven, Bill Gates or whoever your heroes are, is that they spent lots of time in whatever they did. They spent more time in front of a canvas, or guitar, or computer, working away at applying their minds passionately to specific subject areas and goals. According to Malcolm Gladwell’s book ‘Outliers’, patterns only form when someone has done a similar task for 10,000 hours, which in his score equals to 1. According to his research anyone that has spent less than 10,000 hours in any field should not be trusted as any sort of ‘advisor’.
I have spent over 30,000 hours in BI projects ranging from Oracle, Teradata, Informix and SAP BI (95%) so this paper comes with an outlier score of 3.
Looking back it is the failed BI projects, which I was assigned to, that taught me more than the ones I led and were successful. In one project we scored 102% user satisfaction score. Looking at the last six years close to 70% of the projects I was assigned to were projects either heading, or gone, due south. In flying terminology it is called a nose-dive.
Here is the standard question I asked the CIO, BI managers and technical leads from the client, in such projects and when I undertook a 'Strategic BI Healthcheck'
Me: So, how was/ is your BI project?
..and the answers I have received over the last few years.
Client:
[1] Our BI project was an IT success, and a total business failure;
[2] Our System Integrator is working on a fixed bid project. They were supposed to finish in December, now its June so it’s their problem not ours;
[3] We are only 6 weeks away from go live, and right now if we get 40% of the reports we expect then I will be more than happy;
[4] We are in a Severity 1 status two to three times a day so I don’t have time to answer your question;
[5] we have been clearly told by our SI, that according to best practices we must keep business stakeholders out of the doors of the BI project
In one recent BI Project strategic BI assessment, I found basic BI naming conventions missing. When we asked the customer if they had a BI –Center of Excellence they responded a strong ‘Yes’. Rather surprised, and after taking a very shallow dive we found that their SI was calling their offshore support staff COE – and even had the customer executives convinced that they indeed had an operational COE. So all their BI-COE did was daily load checks and low cost developments in BI from across the globe. Zero business participation. Zero standards and processes.
One thing all these respondents were missing is that today it is possible to not fail.
Most BI projects fail because the client does not even try. Both clients and SI’s seem to get entangled initially in an bright vision of hope, smoke and mirrors, followed by an endless spiral downwards where the fear of failure overturns all conventional logic- as both continue to proceed along a very predictable path of failure.
Currently with a sound global methodology and documented scientific principles we can keep delivering world class BI projects consistently ‘Right every time’. But somehow the choice remains towards a consistent path of failure. For the skeptics, were these statements wrong then 70% of BI projects would not be speeding towards imminent failure even as you read this paper. Unless you think Gartner writes all junk and their analysts smoke strange things in stranger bars. That thought would be self-cannibalistic in its very structure.
So your first step is to undertake a few ‘Acid-Tests’ depending on what stage your BI project is at. However the ‘Acid-Test of BI Success’ is pretty easy and can be conducted by the customer alone. So if your BI is sick then please stop all activities, which I know is almost impossible to imagine, but if your car is heading into an obvious accident situation the advice always is todo a very hard stop. Now that you have stopped, it is time to conduct a serious Strategic Health-check by a qualified BI Business Value Architect.
Should you choose not to do this , then you have personally, or collectively, once again consciously chosen to continue on your predictable path of failure. The solutions, though rarified, are written on the wall, but one has to take time to slow down and at least read it.
Important to remember' It will cost 4 to 60 times more to try and fix a BI fault after go live than during the planning phase' BI Valuenomics 2010
More often than not, the fear of failure is so haunting that most of us continue to do what we have been doing, hoping that somehow the future will change. That, by the way, is walking right into the Einstein’s lunacy definition at the begining of this paper.
The first step is to try and never get your BI initiative on any path to failure.
However, once you think that your BI is on a path to failure – the second step is to accept it. Once you think your project might be on a failure trajectory proceed to write down on a piece of paper ‘I will now face my fears and take the following steps to achieve success ( fill in these blanks). In each step there must be a quantifiable goal that mitigates attribute of the fear of failure)
• Step one:
• Step two:
• Step three:
Now sit back and imagine your life three months from now when you have managed to achieve your goals and actually managed finding a remedial path. When suddenly there is a light at the end of the tunnel. (Feels good, right?) So what's stopping you?
The two greatest reasons for BI failures are [1] Assumption and [2] Fear of Failure
Assumption that your partner is going to provide you all the solutions, that your advisor has over 10,000 hours ensuring their BI project is a success, that if you involve business in your BI project they will cause unnecessary delays and increase costs, that the BI project can crawl speed full-speed-ahead without any formal documented methodology- i.e. a ‘Global BI Cookbook’, etc.. This is like Ready-Fire-Aim kind of a BI project.
Fear of failure is the second biggest obstacles to success and we all have to battle it. What separates success from failure is the ability to accept the fear, find out if you are qualified to find the solution and then applying a scientific method out of the mess with consensus of all key stakeholders. The failure-types prefer to manage in the state of crisis, I’ve met a few that seem to thrive on the daily adrenalin-rush of multiple Sev 1 issues on a daily basis sometime for months on an end. Sev 1 issues should not happen more than a few times a year. While others continue on their current path of no-processes, no-standards, etc. hoping things will somehow change in the future- which they rarely do.
It is critical to realize that the failure of a BI project is the failure of the enterprise, of its assets, time, confidence but most important of all its capability to make decisions (till it is fixed) and least of all that of the SI.
Often I have sat in ‘Lessons learned’ meetings and have found that people who are afraid of failure treat them as mistakes. Mistakes are not failures. Mistakes are actually your path to success, but only if you learn from them and then avoid them the next time. Mistakes look like failure but are actually solutions in disguise. True failure is not recognizing failure in a timely manner, continuing on a path of failure without changing any of the variables, or blaming someone else for the failure. True failure is quitting. Everything else is a path of learning.
If humans never made mistakes evolution and development would stop. In pharmaceutical and R&D mistakes is the path to success. As Bernard Shaw had stated “Show me a man who has never made a mistake, and I’ll show you a fool”.
Anyone who tries will make mistakes and I have made my honest share. However, we must also learn from mistakes. It is ill advised to make the same mistake again and continuously over time and space. In almost every sport it is recommended to firstly go beyond the ‘pain point’ and then go to the edge of failure. This is done to establish a breaking point. The best of best then take this breaking point further and further and succeed where no person has before. Gold medals are neither won by athletes who work within constraints nor by ones who continue to make the same mistakes.
Great players will focus on the times they have failed to remind themselves on how not to add to that number. Lesser athletes will focus on the times they have won and try very hard to shout and promote only the few successes they have had. In my life I have learned a hundred times more in failed projects, and my BI Valuenomics book is all the lessons learned from failure- as the scientific principle of all management established in 1897- that by eliminating all failure points our only option is success. An ‘Acid Test’ for a SI is to ask them how many BI projects they have completed in the last 12 months and then try and speak to each one of them independently. A lot of SI’s have many BI customers but almost none are referenceable.
The Silicon Valley is full of the ‘10th time heroes’, i.e. 1 out of 10 ideas will succeed. The ones who succeed either have uncanny luck or sheer persistence, i.e. the ones who never make the same mistake twice and work as a team. The valley is also full of inventors who have held on to their ideas, not sharing, not teaming and they are left standing where they were 5 to 10 years ago and will probably be right there 10 years ahead.
Successful people look at points of failures, learn from it, establish some form of rule to avoid it the next time and succeed. Failures for them are opportunities to learn. In life one can either lead or follow, and fearful leaders try to do both at the same time and at the most critical moments it is the fear of failure that totally paralyzes their thinking. Henry Ford quoted: "Failure is the opportunity to begin again more intelligently."
So with an established BI failure rate of 70% start with accepting that there is a very high probability in your BI initiative/s will fail unless you take serious review of your situation. Recognize that 70% of the BI projects in the known universe will continue to fail all around . That you now need to eliminate all the failure points – for then failure gets eliminated as an option.
Again one way is read ‘BI Valuenomics’ . Up until mid August, that’s when I read the 2012 Gartner report, I had though this book was a nice read and a sound concept. After reading that report I today firmly believe that companies that do not read this book will have BI projects like dinosaurs and face a probable failure of around 70%. Right now this is the only book one can use as an actionable roadmap out of the Gartner 2012 BI nightmare- bar none.
Your ‘advisors’ are going to bombard you with what to do and how they have done it with other customers. However you need to start reading Gartner reports more carefully, speaking to fellow customers on what they did wrong. Follow this through and hire yourself a reliable ‘BI Business Value Architect’ and learn what not to do from them. Train your business stakeholders and arm them with checklists of failure and success criteria’s, i.e. what to avoid and what to ensure you have.
To be ultra successful you have to be able to find your failure points in your mind, find them often and predictably, and then avoid these pitfalls. If unable to do so accurately find yourself a reliable advisor who can undertake all these tasks on your behalf. This must under no condition replace your business stakeholders, be part of your current BI team, or be someone without solid business background and BI skills to match.
As a takeaway your scientific path to success should be:
1. Do your BI right the first time, every time.
2. Accept Failure is a very high possibility in BI (current at 70%)
3. Develop and get hold of BI Checklists by each phase
4. Anticipate and avoid failures. When failure happens welcome it and learn from it
5. Apply only scientific principle in BI, not because they are handed to you as such but because they are.
6. If you don’t know how find someone who does and has a proven string of successes that are referenceable
With Gartner now reporting that more than 70% of global BI projects will fail in the 2012-14 period, is it time to get really concerned and review the path we are walking on. It is time to check whether you are rushing on a path of insanity, or willing to stop for a while and get your bearings straight.
Decisions taken in a state of fear are reported to consistently choose higher risks, cost and lead to an almost certain failure. BI is no different.
The biggest difference between you and Picasso, or Einstein, Beethoven, Bill Gates or whoever your heroes are, is that they spent lots of time in whatever they did. They spent more time in front of a canvas, or guitar, or computer, working away at applying their minds passionately to specific subject areas and goals. According to Malcolm Gladwell’s book ‘Outliers’, patterns only form when someone has done a similar task for 10,000 hours, which in his score equals to 1. According to his research anyone that has spent less than 10,000 hours in any field should not be trusted as any sort of ‘advisor’.
I have spent over 30,000 hours in BI projects ranging from Oracle, Teradata, Informix and SAP BI (95%) so this paper comes with an outlier score of 3.
Looking back it is the failed BI projects, which I was assigned to, that taught me more than the ones I led and were successful. In one project we scored 102% user satisfaction score. Looking at the last six years close to 70% of the projects I was assigned to were projects either heading, or gone, due south. In flying terminology it is called a nose-dive.
Here is the standard question I asked the CIO, BI managers and technical leads from the client, in such projects and when I undertook a 'Strategic BI Healthcheck'
Me: So, how was/ is your BI project?
..and the answers I have received over the last few years.
Client:
[1] Our BI project was an IT success, and a total business failure;
[2] Our System Integrator is working on a fixed bid project. They were supposed to finish in December, now its June so it’s their problem not ours;
[3] We are only 6 weeks away from go live, and right now if we get 40% of the reports we expect then I will be more than happy;
[4] We are in a Severity 1 status two to three times a day so I don’t have time to answer your question;
[5] we have been clearly told by our SI, that according to best practices we must keep business stakeholders out of the doors of the BI project
In one recent BI Project strategic BI assessment, I found basic BI naming conventions missing. When we asked the customer if they had a BI –Center of Excellence they responded a strong ‘Yes’. Rather surprised, and after taking a very shallow dive we found that their SI was calling their offshore support staff COE – and even had the customer executives convinced that they indeed had an operational COE. So all their BI-COE did was daily load checks and low cost developments in BI from across the globe. Zero business participation. Zero standards and processes.
One thing all these respondents were missing is that today it is possible to not fail.
Most BI projects fail because the client does not even try. Both clients and SI’s seem to get entangled initially in an bright vision of hope, smoke and mirrors, followed by an endless spiral downwards where the fear of failure overturns all conventional logic- as both continue to proceed along a very predictable path of failure.
Currently with a sound global methodology and documented scientific principles we can keep delivering world class BI projects consistently ‘Right every time’. But somehow the choice remains towards a consistent path of failure. For the skeptics, were these statements wrong then 70% of BI projects would not be speeding towards imminent failure even as you read this paper. Unless you think Gartner writes all junk and their analysts smoke strange things in stranger bars. That thought would be self-cannibalistic in its very structure.
So your first step is to undertake a few ‘Acid-Tests’ depending on what stage your BI project is at. However the ‘Acid-Test of BI Success’ is pretty easy and can be conducted by the customer alone. So if your BI is sick then please stop all activities, which I know is almost impossible to imagine, but if your car is heading into an obvious accident situation the advice always is todo a very hard stop. Now that you have stopped, it is time to conduct a serious Strategic Health-check by a qualified BI Business Value Architect.
Should you choose not to do this , then you have personally, or collectively, once again consciously chosen to continue on your predictable path of failure. The solutions, though rarified, are written on the wall, but one has to take time to slow down and at least read it.
Important to remember' It will cost 4 to 60 times more to try and fix a BI fault after go live than during the planning phase' BI Valuenomics 2010
More often than not, the fear of failure is so haunting that most of us continue to do what we have been doing, hoping that somehow the future will change. That, by the way, is walking right into the Einstein’s lunacy definition at the begining of this paper.
The first step is to try and never get your BI initiative on any path to failure.
However, once you think that your BI is on a path to failure – the second step is to accept it. Once you think your project might be on a failure trajectory proceed to write down on a piece of paper ‘I will now face my fears and take the following steps to achieve success ( fill in these blanks). In each step there must be a quantifiable goal that mitigates attribute of the fear of failure)
• Step one:
• Step two:
• Step three:
Now sit back and imagine your life three months from now when you have managed to achieve your goals and actually managed finding a remedial path. When suddenly there is a light at the end of the tunnel. (Feels good, right?) So what's stopping you?
The two greatest reasons for BI failures are [1] Assumption and [2] Fear of Failure
Assumption that your partner is going to provide you all the solutions, that your advisor has over 10,000 hours ensuring their BI project is a success, that if you involve business in your BI project they will cause unnecessary delays and increase costs, that the BI project can crawl speed full-speed-ahead without any formal documented methodology- i.e. a ‘Global BI Cookbook’, etc.. This is like Ready-Fire-Aim kind of a BI project.
Fear of failure is the second biggest obstacles to success and we all have to battle it. What separates success from failure is the ability to accept the fear, find out if you are qualified to find the solution and then applying a scientific method out of the mess with consensus of all key stakeholders. The failure-types prefer to manage in the state of crisis, I’ve met a few that seem to thrive on the daily adrenalin-rush of multiple Sev 1 issues on a daily basis sometime for months on an end. Sev 1 issues should not happen more than a few times a year. While others continue on their current path of no-processes, no-standards, etc. hoping things will somehow change in the future- which they rarely do.
It is critical to realize that the failure of a BI project is the failure of the enterprise, of its assets, time, confidence but most important of all its capability to make decisions (till it is fixed) and least of all that of the SI.
Often I have sat in ‘Lessons learned’ meetings and have found that people who are afraid of failure treat them as mistakes. Mistakes are not failures. Mistakes are actually your path to success, but only if you learn from them and then avoid them the next time. Mistakes look like failure but are actually solutions in disguise. True failure is not recognizing failure in a timely manner, continuing on a path of failure without changing any of the variables, or blaming someone else for the failure. True failure is quitting. Everything else is a path of learning.
If humans never made mistakes evolution and development would stop. In pharmaceutical and R&D mistakes is the path to success. As Bernard Shaw had stated “Show me a man who has never made a mistake, and I’ll show you a fool”.
Anyone who tries will make mistakes and I have made my honest share. However, we must also learn from mistakes. It is ill advised to make the same mistake again and continuously over time and space. In almost every sport it is recommended to firstly go beyond the ‘pain point’ and then go to the edge of failure. This is done to establish a breaking point. The best of best then take this breaking point further and further and succeed where no person has before. Gold medals are neither won by athletes who work within constraints nor by ones who continue to make the same mistakes.
Great players will focus on the times they have failed to remind themselves on how not to add to that number. Lesser athletes will focus on the times they have won and try very hard to shout and promote only the few successes they have had. In my life I have learned a hundred times more in failed projects, and my BI Valuenomics book is all the lessons learned from failure- as the scientific principle of all management established in 1897- that by eliminating all failure points our only option is success. An ‘Acid Test’ for a SI is to ask them how many BI projects they have completed in the last 12 months and then try and speak to each one of them independently. A lot of SI’s have many BI customers but almost none are referenceable.
The Silicon Valley is full of the ‘10th time heroes’, i.e. 1 out of 10 ideas will succeed. The ones who succeed either have uncanny luck or sheer persistence, i.e. the ones who never make the same mistake twice and work as a team. The valley is also full of inventors who have held on to their ideas, not sharing, not teaming and they are left standing where they were 5 to 10 years ago and will probably be right there 10 years ahead.
Successful people look at points of failures, learn from it, establish some form of rule to avoid it the next time and succeed. Failures for them are opportunities to learn. In life one can either lead or follow, and fearful leaders try to do both at the same time and at the most critical moments it is the fear of failure that totally paralyzes their thinking. Henry Ford quoted: "Failure is the opportunity to begin again more intelligently."
So with an established BI failure rate of 70% start with accepting that there is a very high probability in your BI initiative/s will fail unless you take serious review of your situation. Recognize that 70% of the BI projects in the known universe will continue to fail all around . That you now need to eliminate all the failure points – for then failure gets eliminated as an option.
Again one way is read ‘BI Valuenomics’ . Up until mid August, that’s when I read the 2012 Gartner report, I had though this book was a nice read and a sound concept. After reading that report I today firmly believe that companies that do not read this book will have BI projects like dinosaurs and face a probable failure of around 70%. Right now this is the only book one can use as an actionable roadmap out of the Gartner 2012 BI nightmare- bar none.
Your ‘advisors’ are going to bombard you with what to do and how they have done it with other customers. However you need to start reading Gartner reports more carefully, speaking to fellow customers on what they did wrong. Follow this through and hire yourself a reliable ‘BI Business Value Architect’ and learn what not to do from them. Train your business stakeholders and arm them with checklists of failure and success criteria’s, i.e. what to avoid and what to ensure you have.
To be ultra successful you have to be able to find your failure points in your mind, find them often and predictably, and then avoid these pitfalls. If unable to do so accurately find yourself a reliable advisor who can undertake all these tasks on your behalf. This must under no condition replace your business stakeholders, be part of your current BI team, or be someone without solid business background and BI skills to match.
As a takeaway your scientific path to success should be:
1. Do your BI right the first time, every time.
2. Accept Failure is a very high possibility in BI (current at 70%)
3. Develop and get hold of BI Checklists by each phase
4. Anticipate and avoid failures. When failure happens welcome it and learn from it
5. Apply only scientific principle in BI, not because they are handed to you as such but because they are.
6. If you don’t know how find someone who does and has a proven string of successes that are referenceable
Aug 15, 2012
Put an end to the biggest lies in BI Implementations
Some of the lies found around BI project rooms
We have done this before and therein lies your success
By auditing and reporting on traditional PM (Budget, Time and Tasks) we are assured of success
We treat our employees well and that is the guarantee of your BI success
Business inclusion is one of the prime reason BI projects fail
There is little need for a methodology, standards or processes for companies like ours that have implemented this many times before
Just activate business content and it will meet eighty to ninety percent of your needs, and then provide you with reports that you never expected.
Our resources known what your business needs even better than your business folks
The global average is 50% and that is what you should expect from your BI project
Big is not necessarily beautiful
Traditional BI implementations assume that if your partner is a big ‘N’ and your contract is solid then your BI project is sure to succeed. Over the last six to seven years I have been sent to more BI projects that were heading south or had crashed through the floor of disappointment and continue to burrow downwards in an endless spiral of technocratic smoke and mirrors.
Business First- everything after that
We this not true then Gartner would not report that ‘Less than 50% of BI projects will meet business expectations’, or we would not surrounded by companies and business users gasping to get their day to day information and IT leaders hyperventilating to meet the promised expectations of business stakeholders.
If you have never seen this then stop reading this right now as we are possibly from two different planets.
Reality Check
What we see, sometimes under the rug, in enterprise BI initiatives is teams of ‘advisors’ who believe what they have been doing for the last twenty years or so is the gospel truth in business intelligence. They can write contracts that even your best lawyers cannot find fault with. They define the success criteria’s for your BI projects, they define the UAT test scenarios without business inclusion. This is reportedly applicable to more than 50% of BI projects worldwide which reportedly fail. The 2011 global BI spend was $11.3 billion and 50% of this was wasted.
Some signals your BI project is not on track
- Dissatisfied Business Users
- High churn of both SI partners and good resources
- High tension work atmosphere, where the blame game is common
- Unplanned Projects and higher than planned expenses
How to climb out of a BI Nose Dive
The new and modern (being deployed since 2007) BI methodology is based on ‘The Scientific Principles of Information Delivery’. Where the end-game of key stakeholders is not to complete a BI project or deploy a new flashy application and then blame the rest of the planet on a BI gone south. The goal of every BI project must be a singular focus of ‘Meet Business Expectations in BI’.
New Scientific mentors and methodologies are now ending with scores in the upper 90 percentile in user satisfaction and project success. Not one week but 20and 30 weeks after go-live. This is important because a 2010 BI Valuenomics poll indicated that ‘98% of BI projects are declared successful in week 1 after go live. Yet, less than 50% remain successful by week 10’. So if you want to assure success you need to reach week 10 with your partners and if it is still a nirvana kind of success then your must hug your partner and hang on to them for dear life- as they are a rare breed of partners in the world of BI.
However, what we find in reality is that despite continious failures many company continue to work with the same SI’s, or worse they go out and find someone at half the cost and half-way-across the planet. The problem here is neither the partner nor their proximity- but that the management continues on the very path that got them into the mess in the first place- a BI project that is not based on scientific principles, methodologies, standards and processes.
The foundations of the ‘Scientific Principles of Information delivery’ were born in the 2004-5 timeframe. By 2010 it was expressed in ‘BI Valuenomics – the story of meeting business expectations in BI’. It has since been further filtered and the end result is the new book ‘The scientific Principles of Information delivery’ as I realized the solution was not in deploying a BI application or in finishing a DW project, but in delivering decision capable information.
‘The Scientific Principles of Information delivery’ hatched about a year ago within the chaos of technocratic system integrators packing their new wine in old bottles. It was further exasperated when one of the technocratic leaders made a statement that the reason BI projects and BI strategies fail is because IT includes business in BI decision.
I was immediately reminded of a statement made in 2007 by a Greek maritime CEO I had worked with Alex Paleologoes who had calmly stated “Most partners forget two fundamental anchors of BI, the first is ‘business’ and the second ‘intelligence’”. Another statement that I wished I had coined was made by Gartner and is my all time favorite ‘Without business in business intelligence, BI is dead”. So if your ‘advisor’ comes in and makes any indications to keep business out of the door that is the very door you need to show them- in that is your strategic safety.
Finally all scientific principles, and this goes back to Taylor(Father of scientific principles of management) in 1890’s on to Ford (Assembly Line) in early 1910’s and onwards to Deming (JIT), Philips Kotler, Stephen Covey (excellence), Edwards Deming’s (Knowledge Worker)- we see one constant factor – Scientific and repeatable methodologies based on breaking down each step, removing inefficiencies and then automating anything rule based.
So say good bye to autocratic and technocratic BI Project management and say hello to ‘The scientific methodology of BI deployment’ based on global standards, processes, architecture, self-tuning & automated modeling, or in other words a documented global ‘Enterprise BI Cookbook’ that eliminated human interpretations, assumptions and the need to sell BI. Welcome to the marketing of Information Delivery and IDCM (Information Delivery & Consumption management), where technology has now become a service partner and a catalyst to ‘Meet Business Expectations in BI’
We have done this before and therein lies your success
By auditing and reporting on traditional PM (Budget, Time and Tasks) we are assured of success
We treat our employees well and that is the guarantee of your BI success
Business inclusion is one of the prime reason BI projects fail
There is little need for a methodology, standards or processes for companies like ours that have implemented this many times before
Just activate business content and it will meet eighty to ninety percent of your needs, and then provide you with reports that you never expected.
Our resources known what your business needs even better than your business folks
The global average is 50% and that is what you should expect from your BI project
Big is not necessarily beautiful
Traditional BI implementations assume that if your partner is a big ‘N’ and your contract is solid then your BI project is sure to succeed. Over the last six to seven years I have been sent to more BI projects that were heading south or had crashed through the floor of disappointment and continue to burrow downwards in an endless spiral of technocratic smoke and mirrors.
Business First- everything after that
We this not true then Gartner would not report that ‘Less than 50% of BI projects will meet business expectations’, or we would not surrounded by companies and business users gasping to get their day to day information and IT leaders hyperventilating to meet the promised expectations of business stakeholders.
If you have never seen this then stop reading this right now as we are possibly from two different planets.
Reality Check
What we see, sometimes under the rug, in enterprise BI initiatives is teams of ‘advisors’ who believe what they have been doing for the last twenty years or so is the gospel truth in business intelligence. They can write contracts that even your best lawyers cannot find fault with. They define the success criteria’s for your BI projects, they define the UAT test scenarios without business inclusion. This is reportedly applicable to more than 50% of BI projects worldwide which reportedly fail. The 2011 global BI spend was $11.3 billion and 50% of this was wasted.
Some signals your BI project is not on track
- Dissatisfied Business Users
- High churn of both SI partners and good resources
- High tension work atmosphere, where the blame game is common
- Unplanned Projects and higher than planned expenses
How to climb out of a BI Nose Dive
The new and modern (being deployed since 2007) BI methodology is based on ‘The Scientific Principles of Information Delivery’. Where the end-game of key stakeholders is not to complete a BI project or deploy a new flashy application and then blame the rest of the planet on a BI gone south. The goal of every BI project must be a singular focus of ‘Meet Business Expectations in BI’.
New Scientific mentors and methodologies are now ending with scores in the upper 90 percentile in user satisfaction and project success. Not one week but 20and 30 weeks after go-live. This is important because a 2010 BI Valuenomics poll indicated that ‘98% of BI projects are declared successful in week 1 after go live. Yet, less than 50% remain successful by week 10’. So if you want to assure success you need to reach week 10 with your partners and if it is still a nirvana kind of success then your must hug your partner and hang on to them for dear life- as they are a rare breed of partners in the world of BI.
However, what we find in reality is that despite continious failures many company continue to work with the same SI’s, or worse they go out and find someone at half the cost and half-way-across the planet. The problem here is neither the partner nor their proximity- but that the management continues on the very path that got them into the mess in the first place- a BI project that is not based on scientific principles, methodologies, standards and processes.
The foundations of the ‘Scientific Principles of Information delivery’ were born in the 2004-5 timeframe. By 2010 it was expressed in ‘BI Valuenomics – the story of meeting business expectations in BI’. It has since been further filtered and the end result is the new book ‘The scientific Principles of Information delivery’ as I realized the solution was not in deploying a BI application or in finishing a DW project, but in delivering decision capable information.
‘The Scientific Principles of Information delivery’ hatched about a year ago within the chaos of technocratic system integrators packing their new wine in old bottles. It was further exasperated when one of the technocratic leaders made a statement that the reason BI projects and BI strategies fail is because IT includes business in BI decision.
I was immediately reminded of a statement made in 2007 by a Greek maritime CEO I had worked with Alex Paleologoes who had calmly stated “Most partners forget two fundamental anchors of BI, the first is ‘business’ and the second ‘intelligence’”. Another statement that I wished I had coined was made by Gartner and is my all time favorite ‘Without business in business intelligence, BI is dead”. So if your ‘advisor’ comes in and makes any indications to keep business out of the door that is the very door you need to show them- in that is your strategic safety.
Finally all scientific principles, and this goes back to Taylor(Father of scientific principles of management) in 1890’s on to Ford (Assembly Line) in early 1910’s and onwards to Deming (JIT), Philips Kotler, Stephen Covey (excellence), Edwards Deming’s (Knowledge Worker)- we see one constant factor – Scientific and repeatable methodologies based on breaking down each step, removing inefficiencies and then automating anything rule based.
So say good bye to autocratic and technocratic BI Project management and say hello to ‘The scientific methodology of BI deployment’ based on global standards, processes, architecture, self-tuning & automated modeling, or in other words a documented global ‘Enterprise BI Cookbook’ that eliminated human interpretations, assumptions and the need to sell BI. Welcome to the marketing of Information Delivery and IDCM (Information Delivery & Consumption management), where technology has now become a service partner and a catalyst to ‘Meet Business Expectations in BI’
Jul 24, 2012
The Myth—and the Truth—About BI Success
The pace of change in Business Intelligence, coupled with the high degree of ‘Technical success and business failure’ establishes that the traditional BI planning may well be virtually meaningless. Even as you plan to optimize, turnaround or get into a new BI implementation be ready to support your technocratic assumptions with Scientific Principles, be ready for change based on empirical facts, and then embrace it.
When traditional BI gurus and technology experts talk about building and growing the Business Intelligence and analytical capabilities of any organization they mostly talk about the technology side of planning. Technical planning makes people feel safe, knowing there is a historical reference ability to all their actions and recommendations. Often BI technocrats feel that the only hindrance to a solid BI Strategy is to keep business totally out of the BI process. Gartner, oracle, SAP, Teradata all have established that this technology based planning is not conducive to a successful BI. Infact Gartner went as far as stating that ‘Without business in business intelligence, BI is dead’ – need we say more.
In Business Intelligence most business stakeholders are not doing the right kind of planning, and often not taking the scientific decision – and more than often they don’t even realize it.
Having a technology plan assures that a BI technology will be deployed on time, often in budget and with the resource. It has no assurances or guarantees that it will meet business needs and expectations. A technical plan is no guarantee of meeting business expectations. According to Gartner more than 50% of BI projects fail to meet business expecations after go live.
But knowing some simple scientific principles on how and what to plan for your BI initiative can make the difference between ensuring your BI becomes an asset or remains an expense with little business value.
Start with what’s important to your business.
A BI plan whetted by actual business users, that you and others in your company feel passionately about will serve you better that a technically superior product of plan that you or your business stakeholders do not feel strongly about. Know how to curb your business stakeholders so they don’t deluge the BI directives with unconditional demands that are not part of the deliverables.
BI Planning is more of a business art based on scientific principles than a technical science. By using the scientific principles have consistently scored in the upper 90% project success 20 weeks after go live since 2007. In one project even scored 102% as it exceeded business expectations. The key was a conference call with nine report owners each of who owned a number of reports to be delivered - as simple as that..
The big hurdle is how much business involvement is necessary. The answer lies in Roll-Up architecture design. Roll up your 0065ecutive dashboards from Management analytics, Roll up your management analytics from operational reports. From an architecture point of view roll up your management analytics from operational reports, roll up your management dashboards from your management reports – this ensures a daily data quality check from operational users. Most Bi projects fail and/or succeed on the operational users being able to use reports or not. The foundation of your BI is to excel in Operation decision capabilities with report that exceed business expectations
Work on a BI Framework that is scientific and accommodates change.
No BI plan or design can ever be a rigid final product. No BI methodology can be the mother of all methodologies. One of the highest growth areas for CIO’s has consistently been BI, and with new technologies no company can afford to design on 2007 planning processes. Your modern BI Framework is a series of Business Value GPS locations as guideposts: Keep you focus on ‘meet Business Expectations’ through each decision in the BI project- something BI projects just do not currently do or report on. Review your plans with a BI Business Value Architect- someone with a high business focus and exceptional technology experience. Welcome opportunities to revise and enhance your plans and methodologies with a formal ‘Key decision’ process. Dont follow a technocratic plan just because it has been planned, if your GPS tells you to take an alternative route do so immediately.
Mandate your business participation as a living and actionable process.
Make a conscious decision to appoint select business owners. Find yourself an internal ‘Business Value Owner’- someone who is trained for reporting ‘Meet business expectations in BI’ so that failure is not a big surprise after the project goes live and the implementation team has departed. Eliminate all post go-live surprises from the BI deployment process. Don’t let executive protocols, pride or inertia get into the mix. Give appropriate protocol to your internal Business Value Architect and mandate them to report weekly.
The probability of failures and the pace of technical change make traditional planning virtually meaningless. The wave of new technologies, big data, in-memory computing, columnar databases make traditional thinking retroactive. Social analytics, web mining, global emotion analysis and the management of huge amounts of external data call into question everything we have been doing in business intelligence and everything we believed to be true. What we have accepted as reality today, changes in the blink of an eye. Even as you plan your business intelligence it is critical to remember that with a little business involvement there has been constant success. When a change comes to your door, be ready to review all possible alternatives, let the vendors conduct a POC (their data); follow through with a Pilot (Your data and exact queries); see what else the Pilot delivers. Let business participate and select the final products. Be ready for the change and then when you have identified it – embrace it.
When traditional BI gurus and technology experts talk about building and growing the Business Intelligence and analytical capabilities of any organization they mostly talk about the technology side of planning. Technical planning makes people feel safe, knowing there is a historical reference ability to all their actions and recommendations. Often BI technocrats feel that the only hindrance to a solid BI Strategy is to keep business totally out of the BI process. Gartner, oracle, SAP, Teradata all have established that this technology based planning is not conducive to a successful BI. Infact Gartner went as far as stating that ‘Without business in business intelligence, BI is dead’ – need we say more.
In Business Intelligence most business stakeholders are not doing the right kind of planning, and often not taking the scientific decision – and more than often they don’t even realize it.
Having a technology plan assures that a BI technology will be deployed on time, often in budget and with the resource. It has no assurances or guarantees that it will meet business needs and expectations. A technical plan is no guarantee of meeting business expectations. According to Gartner more than 50% of BI projects fail to meet business expecations after go live.
But knowing some simple scientific principles on how and what to plan for your BI initiative can make the difference between ensuring your BI becomes an asset or remains an expense with little business value.
Start with what’s important to your business.
A BI plan whetted by actual business users, that you and others in your company feel passionately about will serve you better that a technically superior product of plan that you or your business stakeholders do not feel strongly about. Know how to curb your business stakeholders so they don’t deluge the BI directives with unconditional demands that are not part of the deliverables.
BI Planning is more of a business art based on scientific principles than a technical science. By using the scientific principles have consistently scored in the upper 90% project success 20 weeks after go live since 2007. In one project even scored 102% as it exceeded business expectations. The key was a conference call with nine report owners each of who owned a number of reports to be delivered - as simple as that..
The big hurdle is how much business involvement is necessary. The answer lies in Roll-Up architecture design. Roll up your 0065ecutive dashboards from Management analytics, Roll up your management analytics from operational reports. From an architecture point of view roll up your management analytics from operational reports, roll up your management dashboards from your management reports – this ensures a daily data quality check from operational users. Most Bi projects fail and/or succeed on the operational users being able to use reports or not. The foundation of your BI is to excel in Operation decision capabilities with report that exceed business expectations
Work on a BI Framework that is scientific and accommodates change.
No BI plan or design can ever be a rigid final product. No BI methodology can be the mother of all methodologies. One of the highest growth areas for CIO’s has consistently been BI, and with new technologies no company can afford to design on 2007 planning processes. Your modern BI Framework is a series of Business Value GPS locations as guideposts: Keep you focus on ‘meet Business Expectations’ through each decision in the BI project- something BI projects just do not currently do or report on. Review your plans with a BI Business Value Architect- someone with a high business focus and exceptional technology experience. Welcome opportunities to revise and enhance your plans and methodologies with a formal ‘Key decision’ process. Dont follow a technocratic plan just because it has been planned, if your GPS tells you to take an alternative route do so immediately.
Mandate your business participation as a living and actionable process.
Make a conscious decision to appoint select business owners. Find yourself an internal ‘Business Value Owner’- someone who is trained for reporting ‘Meet business expectations in BI’ so that failure is not a big surprise after the project goes live and the implementation team has departed. Eliminate all post go-live surprises from the BI deployment process. Don’t let executive protocols, pride or inertia get into the mix. Give appropriate protocol to your internal Business Value Architect and mandate them to report weekly.
The probability of failures and the pace of technical change make traditional planning virtually meaningless. The wave of new technologies, big data, in-memory computing, columnar databases make traditional thinking retroactive. Social analytics, web mining, global emotion analysis and the management of huge amounts of external data call into question everything we have been doing in business intelligence and everything we believed to be true. What we have accepted as reality today, changes in the blink of an eye. Even as you plan your business intelligence it is critical to remember that with a little business involvement there has been constant success. When a change comes to your door, be ready to review all possible alternatives, let the vendors conduct a POC (their data); follow through with a Pilot (Your data and exact queries); see what else the Pilot delivers. Let business participate and select the final products. Be ready for the change and then when you have identified it – embrace it.
Jul 1, 2012
Before leaping into Big Data ensure your Data is Solid
ProactiveExecutives and managers need the latest information to drive intelligentdecisions for assuring business success. Better, than competition, informeddecisions mean more revenue, less risk, decreased cost, and improvedoperational control for business agility and global competitiveness. In ourfast paced, technology-driven business processes, organizations are continuallystruggling to deal with growing data volumes and complexity to firstly usetheir own data efficiently. Now when we are bombarded with ‘big data’ the issueof global harmonization of data quality becomes all the more relevant.
Nowhere comes HANA
Constrained with a globally competitiveflattening-world and newer data complexities are COO, IT Managers and BusinessConsultants who are actually asking for less number of analytics and reportswhile simultaneously expecting easier access to smarter and fasterdecision-promoting informatics. They need information that is highly visual,surgically accurate, of extremely high quality, up-to-date and as close to truereal-time, personalized and secure. Most companies want their analytics on thego, i.e. on their smart phones, tables and available for instant access. Informationconsumers want their information literally on their fingertips no matter wherethey are. There is a growing need to untether business information from theconfines of the desk or cubicle. In a globally interconnected world all this isnow possible – only if we scientifically plan to do it right the first time.
Firstly: Rewind your memory to eachcompany where you have implemented DW, BI or MDM and we may recognize that wehave probably never seen a company that works all of their business from asingle source system. I have personally seen large companies with as many as2,500 plus global data source systems and as many as 1,500 reportingapplications across the corporation. Medium sized global enterprises could haveas many as 1,000 data sources globally and as many as 500 reportingapplications Even the smallest ERP company would have 50 or so data sources andas many as 20 to 30 reporting applications. A flat file is a data source as isa vendor input from their system. If any report is generated from an externalDW, or a PC that becomes a reporting application.
Note: Scientific research hasclearly demonstrated that the greatest risk of data quality is at the point ofexchange or transformation.
Secondly: In almost each of thesecompanies it has been noticed that each of their Master data elements likecustomer, vendor or product, may contain over 4,000 fields in the systemof original records. For example the ECC Customer Master table contains morethan 4,500 fields that can be used as does the material master data table.
Focus1: Let’s look at Productas it is the element that is most critical interface between the value chainpartners, the company, its customers and its vendors. Each product may containnumerous attributes like height, length, packing and packaging dimensions,weights and storage requirement. In some industries these could be as few as 30in others as numerous as 350. Each of these attributes is a data element or anindependent field in some system, preferably a single system of records thatgoverns data quality across the enterprise. Each of these attributes is part ofthe master data entity. If your company is a pipe manufacturer the entitiescould be as few 10, if retail or wholesale they could be close to a 100 and ifpharmaceutical this number could be close to 500.
Focus2: even in our current state,i.e. when we are looking at data within the walls of the enterprise and whereour largest data warehouses are in the 50 terabyte range - we are barely able to keep our global data quality in control. In mostcases each of the disparate data source system could either have their owninterpretation of data or a KPI, or manage it like a local asset with littleregard to global compliance. We continue to see data quality issues when allthe data is so much in our control.
Companies have barely been able to grasp theirdata quality issues within the walls of their enterprise, so opening the floodgates, to 10 Exabyte’s and above like accessing Facebook data pre-maturely orin a process that lacks scientifically de-cluttering may logically result inclouding the muddy waters all the more. We may traverse from our datacorruption environment right into a data anarchy situation.
Focus3: Add to this mix the factthat business today is dominated with acquisitions and new product launches andthe proactive and reactive process of global data governance becomes all themore imperative.
As stated by Claude Viman the global EnterpriseData Steward for J&J. ‘Proactive is always better than reactive” hecontinues, “ however, a strong data governance process has both” – but only ifplanned in a scientific manner.
The impact of bad data is more than familiar toall companies, especially to the report and analytics being churned to businessusers and decision makers, which we view as DW or BI. However, we must neverforget that BI and DW are the technologies that need to be leveraged to enhancethe business decision and operational performance of enterprises, and is neveran end by itself. According to the Experian QAS research close to 20 percent of customer contact datawith most companies remains flawed due to errors of data entry and 33% of thedata becomes naturally flawed or outdated within a year. Such inaccuracies injust the customer data can sway close to 18% of the corporate budgets andforecasts.
Now as SAP customers expand their analytics,customer and product lines across and outside their physical boundaries datafoundations and data governance needs to become a much higher priority forcustomers to ensure Information accuracy. As rightly stated by Dan Everett of Forbes he clearly statesthat in EIM solution marketing information governance is he elephant in theroom, he continues to state “To realize business value from bog data, companiesneed to have strong information governance, and few people seem to be talkingabout this”. Which translats into a fact that despite the big elephant standingin our BI rooms we seem to pretend it wither does not exist or we simply do notknow where to start.
Vimanfrom J&J has an advice to this dilemma “Unfortunately, not too manycompanies realize the importance of data governance in advance, and then theyhave to learn if the hard way”
Sowhat is the difference between Data and Information Governance?
While is is clear that data is the foundationof all information and we have more too often heard ‘Garbage in, Garbage out’,these statements are simply kindergarten statements for modern BI environmentsand systems that can often merge data from hundreds of sources for corporateanalytics. There is Master DataManagement,Rules and regulations for Data Quality. Localized and global TQM (Total QualityManagement) and the whole IDCM (Information Demand and Consumption Management) allof which together constitute the base for data governance. On top of this pyramidof data foundation stands information that needs to be governed on its ownaccount.
The question that must be asked, ‘Inan environment where all the base data is 100% clean can we still haveerroneous reports?’ and the answer is resounding yes. This is due tothe fact that a lot of information errors occur at the transformation layersand unless there is a high degree of informational governance there will beerrors in information. Just as an example if there are no naming standards eachdeveloper could churn out their own interpretation of a KPI or Metric. If thereis no change control in place a new developer could alter an existing KPI orMetric for a new request and an older user could continue to assume that thenumbers represent the older interpretation of information. Each of theseexamples leads to information error.
Information it has to be realized is not asupply management process but a demand and consumption management one.
The convergence of data quality standards andguidelines with rule based data governance with clear definitions of what typeof data is being accessed from what systems, what the DQS (Daa Quality Score)of each data source is, What information elements need to be stored, what isthe true system of records and whether global data has been physically orlogically cleansed, what systems will store what kind of data, how dataexchange will be accomplished in order to assure no terrorist data elementsenter the core Information repository, along with all the security mechanismsin place. Data governance is the foundation for information governance aswithout strict rule based data governance guidelines our information willalways be erroneous. The key to data governance is managing Master Data andtheir attributes.
Whoshould own data in an enterprise
One of the frustrating problems in anyorganization is assigning ownership for data quality. According to SOXdefinitions business owns data definitions and data guidelines as they knowbest what each data element represents and how every transformation must beconducted.
Everytime IT owns data, in isolation ofbusiness participants it constantly leads to a maddening game of “Whose information definition is right” atmeetings. The larger the enterprise the more maddening this delta becomes untilwe lose a global definition of any business attributes.
In almost every meeting when we ask respondents“How is your data quality” we alwaysreceive a consistent “Fine, I Guess”. If we follow this question with “How was your last BI initiative” itoften leads to “It was an IT success, buta business failure” in varying flavors and interpretations. All of this isa global slowly escalating time bomb.
The final solutions is to have a scientific mixof TDQM (Total Data Quality Management) initiative that consists of businessusers that understand business needs and definitions, SME’s (Subject MatterExperts) that understand the configurations in the source systems and MasterData Controllers whose sole job it is to manage global Master Data and changecontrol for all Master data elements across the enterprise.
Just as an example companies like Johnson andJohnson have 16 full time employees who are dedicated to enterprise Master Datamaintenance. But the overall accountability of data must lie within the TDQMGroup as defined above.
Part of the TDQM process should encompass acquisitions.Typically the new company has to be integrated and products normally startshipping out of the gate within 2 to 4 months. During this time each product,which may have anywhere from 100 to 400 attributes and to be integrated intothe operational systems. From an executive and management perspective each ofthese products has to be aligned / merged to product / information groups andexisting global analytics.
Overall the TDQM must also deploy six-sigmachecks and reporting to assure the level of data accuracy across the enterpriseis maintained at 99% and above.
Ifmy Information is bad why are we accelerating it?
This was a question asked at a meeting with aBI deployment where the fundamental reports were not meeting business needs andexpectations and the SI was trying to recommend installing a BW Accelerator tospeed query response. One of the business stakeholders asked the criticalquestion “If the information does notmeet our requirements, why are we wasting all this effort in making bad dataand information more efficient”. This is a question that organizations mustconsistently ask themselves before taking the leap of faith into newer technologieswith assumed benefits that later turn into IT successes and business failuresto varying degrees.
Like with all other Information Deliverysystems and applications the basic foundation of data remains critical. Therule of ‘Garbage in, Garbage Out’ still remains consistent.
If HANA is deployed in a scientific and plannedmanner then its advantages can be many.
1. MultiSource:HANA allows mixing of data from more than your SAP BW. Unlike the BWAaccelerator that could potentially only accelerate BW queries HANA accelerates allthe data and transformations.
2. DirectSource ELT:A Standalone HANA runs off direct extract from source tables, i.e. ECC CO-PAtables for example. In all such cases the issue of data quality and dataredundancy is eliminated instantly. In traditional BI and DW environments weoften landed with multiple versions of the same Data and each point of dataexchange and transformation represents a potential DQ failure point. Byeliminating multiplicity of data copies HANA removes DQ probability by a factorof declining copies in older models. For example If we take a single G/L Account:[1] Copy 1 is in the transaction; [2] Copy 2 is in the ledger; [3] Copy 3 is inthe extractor; [4] Copy 4 is in the PSA; [5] Copy 5 is in the Raw GL DSO; [6]Copy 6 is in the reporting DSO freight costs for example; [7] copy 7 is in the InfoCube;etc.. Each of these copies is technically subject to transformations andinterpretations – or DQ compromises. With HANA we can potentially eliminate DQ issuesonce and for all. The only control point is the modeling and transformationtool in HANA – by simply maintaining that we assure the highest data quality.
3. BW onHANA:Even with BW on HANA the advantage is that we can either accelerate all the reportsand data on our current BW, or using a proprietary ‘HANA Safe Passage’methodology where we can deploy HANA for selective BW objects – that will trulybenefit the need for HANA acceleration and true-real-time analytics for selectInfoProviders only.
4. ChangingDW Fundamentals: Thebig question is whether HANA and similar technologies have the potential tofundamentally eliminate the traditional DW concepts as for the first time SAPallows transformations and models to be created directly in their HANAdatabase. This is a 'Net New' privilege that most legacy technocrats have notfully wrapped their methodologies around as yet - The impact of this singlefunctionality is tremendous to say the least.
How big is HANA, am I the bleeding edgecustomer?
No HANA is huge. Is it just barely a year oldand already boasts of over 358 customer, 159 implementations, with 65,000competitive users getting their reports faster, and already having crossed $250million in revenues.
From an application side there are already over33 ‘Powered by HANA’ applications and many RDS (Rapid Deployment Solutions’that can be deployed in a few weeks. SAP is targeting to have the wholebusiness suite portfolio enabled on HANA by the end of 2012.
According to Bill McDermott SAP expects over1,000 customers to be on on HANA by the end of 2012 directly impacting therevenue growth for SAP and their global HANA Partners. BersteinResearch predicts that by 2015 HANA could be a $4.4 billion market for SAPand their partners. As of now there are over 1,800 HANA trained and certifiedconsultants – a number that continues to grow and will remain so as HANA movesfrom being a Stand Alone appliance to a SAP database platform for all SAPApplications. By 2015 HANA will have permeated all facets of SAP technologylandscapes, database management and business processes.
So as a customer one is fairly safe to commenceconsidering the HANA as a possible future option. The HANA methodology shouldbe business led and undertaken in a scientifically planned manner that isquality enhancing and cost mitigating at the same time.
It is now a question of whether your company will be migrating to HANA itseemingly is becoming a question of when.
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