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.

[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

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