Mar 26, 2013

Gartner's 2013 CLOUD Recommendations & your enterprise


The new Cloud Services Broker (CSB) is a service that more and more companies are adopting to to facilitate cloud adoption as IT organization in small, medium and large organizations adopt to internal and external CSB service offerings.

Some of the key reasons companies are adopting these trends are

1. As the pace of technology adoption accelerates internal IT departments are finding it challenging to accommodate all the demands of their business users and information consumers. As the delta between business expectations and deliverables increases more and more companies, from mid-sized to large companies, are adopting to private and secure CBS service providers.

2. Management, upgrades, modernization, integration and consolidations are becoming more and more important an dproactive companies are opting for deploying cloud strategies especially in areas where the competitive advantages of technology and speed are becoming the key competitive differentiators.

3. My input) Cloud service enable customer to remain on the current technology via a subscription service that is both predictable and strategically aligned to a principle of ‘each partner does what they are best at’ methodology. With IT costs becoming unpredictable cloud offerings are the option most companies are choosing to enhance their competitive advantage in a ever flattening world.

4. Note: When thinking Cloud for the enterprise think Security. Clouds are great but they are open to all so think private vs. public, think Fort Knox Security vs. loosing your crown jewels to an apology - like loosing all your employee social security details into a cloud offering.. For SAP customers think Afaria and partner with providers that know and understand Afaria.



For more reading Source: http://www.gartner.com/technology/cio-priorities/

Jan 13, 2013

Critical attributes of a Data Scientist in SAP BI

I have received many email on this topic and met some who claimed to be data scientists.

Here is an executive brief of what is takes to make one in the SAP BI environment

Here is a brief of what all you need to know / understand to become one, or claim to be one..

LEVEL 0

1. Understanding that Business Intelligence has been and is about two factors. The first being business and the second intelligence.

2. Resonating with Gartners 2010 comment ' without business in business intelligence, BI is dead'

3. Working on a 'Business First and Business Last' focus in all things business

4. Knowing that
   4.1 'Less that 50% of BI projects will meet business expectations' Gartner in 2003
   4.2 'Less that 30% of BI projects will meet business expectations by 2014' Gartner 2012
   4.3 '98% of BI projects declare success in week 1 of their go-live, yet less than 50% remain
         so by week 10' BI Valuenomics 2010

LEVEL A
1. Conceptual: As a data science it is critical that your conceptual understanding of Business Intelligence is solid not only from a technology standpoint but also from a business side of BI
2. Technical: It is critical to understand the technology that one is dealing with along with its capabilities and limitations. It is also critical to understand alternatives for the business need
3. Judgmental: Understand Data Architecture, TDQM, and the data impact from metadata to maser data. Understand the various statistical methodologies and the ability to build algorithms.

LEVEL B

SAP BI: Understand the fundamental of SAP BI. This includes deep understanding of SAP BW, BW Accelerator, BusinessObjects, BO Explorer and the recent HANA. It also includes understanding how each of these applications manages data along with strengths and weaknesses of each application area.
Global BI Architecture: Understanding Data Marts, Enterprise Data Warehouses with a singular purpose of building global FEDW environments (Federated Enterprise DW's) that allow global standards and 100% local independence.
Automated Modeling optimization: Just like most of us cannot put together a Rubik’s Cube in any optimized time we need to accept that we certainly cannot model a cube with 10 dimensions and 60 characters under any circumstance. Use automated modeling tools
Data Flow optimization: A clear understanding in the impact of differential data flows and the maintenance of Data Quality within each data element and the impact it has on the entire data warehouse.

LEVEL C

Basic Understanding:
0. Matching Algorithms: This won the 2012 Nobel Prize. Stable matching, Optimal pairing, Incentive compatibility, one and two sided matching, medical markets, experimental evidence, market designs..
1. Mathematical basics like an understanding of exponentials, logs, distribution types, continuous and random variations of data sources and elements
2. Econometrics and Modeling: The economics of language based system commands, descriptive statistics, Brownian movements in data, ARCH/GARC modeling, Monte Carlo Simulations, Auto regressive modeling, etc
3. Mean variance optimization: Quadratic optimization, Tracing out efficient frontiers, Covariance or combinations of portfolios, and other portfolio analytics
4. Textual data management: Extracting information from news and blogs; framework of textual data management, word count classifiers, vector distance classifiers, confusion matrix, accuracy, etc..
5. Bayesian modeling: joint probability administration, correlated default applications, Bayes net, Accounting fraud, etc.
6. Predictive modeling: Predicting growth in markets, product and services, Bass modeling, Peak growth calibration, artificial intelligence algorithms, organic growth modeling, etc.
7. Large data extractions: Discriminative analysis, Eigen systems, Factor analysis
8. Auctions financial models: Auctions methodology, Theory of auctions, Auction and bidder types, Optimization of bids, Discriminating pricing, Collusions in auctions, Advertising by auctions, Next price auctions, etc.
9. Network financial modeling: Graph theory, Strongly connected components, Shortest path algorithms, VC Web, Centrality, etc..
10. Financial Neural networks: Non linear regression, Perceptions, Squashing functions, Feedback/backward propagations, neural nets, etc
11. Mathematical Speculation: Gambling, Odds, Edge, Book makers, Kelly criterion, Entropy analysis, Casino games, day trading
12. Cluster analysis and Prediction trees: K-mean clustering, Hierarchical clustering, Prediction classification and regression trees, etc
13. Storage and speed in big data: Distributed computing from Hadoop, Map-reduce concepts, Parallel processing engines, Prototyping, advanced language usage,
14. Misc: Dynamic programming, Fourier analysis, artificial intelligence clustering, stable matching, optimal pairing, Incentive compatibility,

So welcome to the world of the data scientist in the new world of too much data and very little information.


From Big Baloney to Big Opportunity in Big Data


In early 2012 Big-Data was a most hyped concept in the tech industry with a lot of technocratic pundits wondering what the need for any big data was in the first place. Now it is becoming more than evident that big0-data is but starting.

The amount of data we create as a planet is growing exponentially. Data is bubbling forth from many sources including cameras, sensors, signals, blogs, social media, tweets, browsing trails we leave behind, and google searches we conduct. Include into these mix e-commerce transactions, product complaints via emails and texts, medical records, DNA details, drug contradictions, GPS locations of various smart devices and vehicles and we seem to be just scratching the surface.

The world has yet seen nothing of what big data informatics can actually yield. Proactive companies are already starting to invest into the new 360 degree informatics - analytics that mash-up external and enterprise data in an efficient manner.
Predictive analytics allows you to know the weather before you start your day, or before you head for the hills for a skiing holiday. Our GPS traffic tells us the traffic 10 to 50 miles ahead and provides actionable predictive solutions by providing alternative routes when traffic is very heavy, it also provides alternative routes when the information consumer takes a wrong turn for example. It is now routine to know what time the next train will arrive that we don’t event think of it as predictive analytics.

Take it one step further and we can today predict the next hotspot for a battle in Afghanistan simply by analyzing social, text and behavioral patterns to an accuracy where the nascent algorithms could predict altercations up to 24 hours ahead of it happening in 46% of cases.
Google Now, shows us the first glimpe on the predictive power of NOW.

Two things to remember

First start seeing patterns - as we move forward and merge human genome data, with weather, health, chemical contaminations, where each person lives we will certainly be able to predict the impact of genome, lifestyle, disease and life expectancy.

Second big data is the biggest data sets you require to meet your analytics reach. For an enterprise it could be 20 to a 100 terabytes. For Google it could be 20 to 100 Exabyte’s and for the NSA it could be a few Yotabytes
All individuals need not start thinking Yotabytes of data management right now. Focus on your needs only.
Within a few years, expect to be able to do Google-like searches to learn what diseases those with similar genetics have had and what medications worked for them. Eventually, when researchers combine the medical records of a hundred million people with their genome data, work habits, and other information, such as weather and pollution data based on where they live, they will all but certainly be able to determine the correlation between genome, disease and lifestyle.



Jan 3, 2013

HP's SAP HANA 2013 Vendor selection Benchmark

Welcome to the world of 'The Scientific Principles of Decision Enhancement'
Here is the HANA update from an HP perspective. Use this as your benchmark for selecting your 2013 HANA Partner.
 HW ATTRIBUTE                                                     Jan 2013 Facts 
01. SAP Certified Global HANA Partner                      Yes
02. No of HANA Installation to Date                           Over 280 installation (Nov 2012)
03. What does this represent                                   47% of global HANA Installations
04. SAP Certified in 'Disaster Recovery'                  Yes
05. SAP Certified in 'Scale-Out' HW                         Yes
06.Current HANA Scale Out Capabilities                    42 TB (first record)
07. SAP Certified for 'High Availability'                   Yes
08. SAP Certified HANA HW Partner                         Yes
09. SAP Certified HANA RDS Partner                         Yes
10. Has their own HANA RDS Solutions                      Yes (Fast Track)
11. SAP BI and HANA Hosting Partner                        Yes
12. SAP Cloud Solution Partner                                Yes
13. # of HANA Certified Consultants                         Over 80 (Dec 2012)
14. # of HANA certified consultants (Dec 2013)         Plan for 500
15. Certified for SAP BW Upgrade for4 HANA          Yes
CRITICAL NOTE: This is just 1 of 5 checklists, Hardware Parrner,  that each executive stakeholder needs to run in order to ensure they take strategically professional decisions

Others
2. BVA  ATTRIBUTE                       Jan 2013 Facts
3. Methodology ATTRIBUTE           Jan 2013 Facts
4. HANA SI Attributes                    Jan 2013 Facts
5. Current SAP BI State Check       Jan 2013 Fats


How 2 - ensure success in SAP HANA

By Jan 2013 as an executive you already have way too much on your plate. AS a Business Intelligence Leader, CIO and business owner you have a lot more on your plate. Staying focused on the technology alternatives can be a tough ask- with Gartner reporting that “..less than 30% of BI projects will meet business expectations..” in th 2011-14 period. . Staying focused can be tough when your business users are clamoring that they do not have access to their decision based analytics. Staying focused can be difficult if you just finished a $3, or 5, million BI project and your business users can hardly use the delivered reports, i.e. user satisfaction is low.


Is it the same for HANA projects?

The simple answer for January 2013 is that the same rules that have applied for your traditional BI deployments apply to a very large degree to your HANA BI Appliances.

Now as we enter the new world of HANA we need to minimize defects and thereby increase the probability of success. Proactive executives need to ascertain that we do not fall down the Albert Einstein chute of ‘Doing the same things and this time expecting different results” Which simply translates that if you continue to implement your HANA BI initiative as a technology only solution then your HANA results could be quite similar to your BI results of the past. As one CIO quoted “Our BI project was a technical success, but a total business failure”

So the advice for 2013, BI Implementations and HANA initiatives is 3 critical facts

1. Brutally Honest Partners and advisors

2. Without business in business intelligence BI is dead (Gartner 2010)

3. Take the 2x2 hour training for business stakeholders prior to moving ahead with your HANA initiatives. It is full of checklists, totally vendor agnostic and absolutely scientific in design.

Our brains are finely attuned to distraction, and in today's digital environment makes it especially hard to focus.

Uno: Brutally Honest HANA Advisors

1.1 Undertake a Brutally honest ‘Current BI State Strategic Workshop. Find out whare you currently are.

1.2 Understand where your business needs to be. Document their expectations and then design to meet their expectations

1.3 Get brutally honest findings with minimal interpretations, for unless we accept our current state of reality we may simply step into another fog.

Dos: ‘Without business in business intelligence, BI is dead” Gartner 2010

This statement was made by Gartner in 2010 and stands true from 1004 till date. Decisions enhancement and operational performance measures are not technical solutions but business ones.

The critical difference is between ‘Value’ and ‘BVA’, or Business Value Attainment.

The former is an external definition of value and what your business needs. It normally consists of generic measures that at best will make all companies working with the exact same method of measuring their business and its performance. No company will have any unique competitive differentiators.

BVA on the other hand is an internal and professional method of understanding your business expectations and your unique competitive advantages

Tres: Take HP’s 2x2 hour Executive Stakeholder training for HANA

Understand your weaknesses: We are used to working on our strengths. However, in BI it is critical to understand that the CIO, CFO, VP Sales, VP Procurement, i.e. your Key Stakeholders know little or nothing of SAP HANA or how to undertake a professional selection. Use the 2 x 2 hour HANA executive workshop

Strengthen your weaknesses: The workshop is basically a 2 hour session that comes with checklists and documents that can be used by non-IT stakeholders to verify that the selection process. The second 2 hour is to actually assist the team fill the checklists in a professional manner. It empowers stakeholders to undertake strategically professionally decisions.

Oct 2, 2012

Seven Critical tips to ‘Meet Business Expectations in BI’

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.

'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

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