3 Steps to making your BI - Business Smart

Just hiring the big partners is not enough. As business owners you have to ensure you derive business value from every investment – most importantly in BI

First of all start with keeping blood flowing in the body and thus your brain. Exercise is not only good for your health but it also makes you smarter. Think of it also as a form of mental nourishment, from just as your body requires food for energy your brain is replenished with oxygen carried by your blood. So as we exercise every muscle and cell in our body, including those between our ears, get invigirated.

According to Gretchen Reynolds ‘exercise does more to bolster thinking than thinking (alone) does’ The reason for that is possibly that if we just sit and think without external stimuli then we get into what is termed as tunnel mental vision, very much like the tunnel vision DMV warns us about if we constantly stare at one specific point of the road when we drive. Regular exercise is a mental foundation as it allows us to reach outside the fixed-framework of technology. Don’t get into a tunnel vision in BI methodologies from the past as they have resulted in less than 50% success. Get outside the standard framework and get scientific.

A.    Think Strategy not tactical solutions:  The first step in BI projects is to think what kind of reports would uses expect five years from now and what are the business goals. Align this with what your technology partners have in mind for the next five years and start your decisions based on this first foundational. Short-term BI thinks of deploying specific reports long term we need to enhance decisions.

B.     Start with a Foundation of Methodology: At the heart of all modern projects is methodology. If you construct anything there is a set methodology. Global methodologies ensure that when US companies construct anything across the planet the steel, design, concrete, coatings, architecture all are in accordance to global standards and processes. BI is no different.   

C.     Use Scientific Checklists: even the most experienced pilots use them. Astronauts, engineers, builders, construction workers, and all logical workers desiring perfection use checklists. Pilots don’t us this to remind them how to fly they use them for things that have been forgotten in the past. A checklist is a scientific method of avoiding failures. Use BI Staged checklists to ensure you have not missed anything. It starts with all the phases of a BI Project and a lot of sub-phases too.

And don’t forget to exercise. Exercise also makes brain cells more efficient. It is now discovered that exercise also creates new brain cells, as due new encounters and lessons. These new cells should empower you to  challenge technocratic dogmas just because they exist, like the one stating that the brain does not create any new cells. Challenge them and exercise your mind too. Each time you learn a new skill new brain cells are created. They only activate when you use these new skills, the more you use these new skills the stronger the cells get wired. While new ideas create new wires, exercise makes these cells more nimble. With exercise your brain will more readily wire many new cells into a neural network thus enhancing cognitive capabilities.

So take a thirty minute walk every day. If you have a dog, like I do, you can use the dog to assist save your head, your work and possibly your life.

How to extract both 'Business' & 'Intelligence' from BI projects

One of the greatest statements in 2010, from my side, was a Gartner statement made by Mark P. Donald, when he stated 'without business in business intelligence, BI is dead’. He condensed four years of my research brilliantly into a single sentence. By 2006 Gartner started reporting that BI projects were not meeting business expectations. Then in 2011 Gartner once again reported that the world spent $11.2 billion on BI in a fuzzy background that less than 50% of these projects would meet business expectations. Then in April 2012 ZDNet reported that there is a reported wastage of $6.2 trillion though it remains largely unverified by reliable resources. However, one thing is certain that BI projects are consistently failing to meet business expectations and with HBR publishing that good data alone does not assure good information all the pieces better fall into place.

Over my 15 years of BI experience I can state with fair confidence that there are few companies that do not feel their BI is in a deep crisis. Each executive and CIO is wondering whether it is the HW, SW or their implementation partner that is at fault. If you ask me the answer is both yes and No.

In order to find out why this costs more we will need to go back two decades when it took two people four to six weeks to prepare six reports for the board meeting and there was no questioning whatever the executives got once or twice a year. Each question took another week or two to answer. Fast forward to 2012 and we have invented hardware that can compute millions of time faster, and softwares that can perform technical miracles when compared to what it could do in 1987. In 1987 we needed two resources to provide corporate and executive reports, by 2012 we need a team of twenty to thirty experts to keep the complex technology simply humming. We are all specialist now and hold a lot of information about a small parts of the whole solution. In this way we have unfortunately created teams of Technocratic workers who have little idea of the forest as they continue to clip leaves on a single tree. What we need today is business value workers who are explorers and not technocratic cowboys, people who understand business value and the total Information Demand Consumption Chain (IDCM). Our research shows that information by the way is not a supply chain but a demand and consumption one.

It is also critical to understand that Planning on scientific principles will save companis millions of dollars in the near, mid and long term. Invest a step into planning and take a giant leap into strategic savings. This chart kind of explains it.

Our research indicates that the current technocratic designs mean that more than 50% of BI project live in a state of comatose existence, in some cases over 55% of BI Initiativs do not use the right alternatives for their business solution, 60% of the queries in production are not going to be used by information consumers, 70% of your Cubes will be modeled ineffciently, over 60% of our data warehouses are designed to strategically fail, and that ovr 65% of technical doctors that go to take care of sick data warehouses do not end with an improvement to the health of the information delivery capability. All this leads to performance degradation in Data load and Query Run times. The writing is clear on the wall that we need business value workers for two reasons, the first being the unmanageable costs of BI projects and the second the low business value being derived from them. In all this we must not forget that when we go from a world of PC's and two resources working on Lotus 123 for six weeks to prepare six reports to one were we generate hundreds of reports from tens of terabytes of data the costs cannot possibly remain the same.

The differences: In 1987 we used to deliver reports on what had happened last year, quarter or month and our management was only reactive. By 2002-3 we started delivering analytics where we started analyzing trends and moving more towards performance analytics that was summarized automatically. We effeciently merged planning data with global actual numbers or conducted spend and vendor performance. By 2009 we commenced on a path to predictive analytics and entered the world of informatics and bioinformatics. By this time though the technology had matured the systems to design it had not yet become scientifically driven. We could now summarize global data into daily views for our executives and came to the new world of predictive analytics. Now we did not wait for events to happen but could actually predict it in near real-time. For example it was then possible to to visually see the full global supply chain with connections to supplier systems and see a process weaken and immediately ripple across the supply chain. Companies were able to now react to the future. By 2011 we entered the world of true-real-real time. I use the double real-time for we had misused it in the past. Now we can conduct CO-PA analytics and see the situation of a campaign or trade promotion as it stands right now. We can see shelf space utilization as it stands right now across thousands of retail outlets across the state, country or planet. But unfortunately we still continue speeding the technology path where we deploy BI project successfully but they often do not deliver business expectations or values. This is where BVA, or Business Value Attainment, principles based on scientific standards and processes comes in.

Our solutions come from something as simple as data, pure empirical data. When we reviewed all the complexities in the data we suddenly realized that the most expensive BI projects did not result in the best information and vice versa, i.e. the best BI projects were not the most expensive ones.

What this means is that there is Hope for each and every one of us, because if only the most expensive projects were most successful then it would indeed be very disheartening for all but the largest corporation - but thankfully this is not the case.

When we analyzed the positive deviance a little further we found that the ones that were most successful looked more and more like scientific systems. All the evidence prove that simply having the most expensive technocratic components is never enough. In fact it turns out to be a big disadvantage as these projects then gedt driven by technocratic arrogance, based on an unbending faith that ‘technology is king’. These costly projects end up with a pile of expensive components that do not run like a well-oiled system and each issue has each group of experts pointing their fingers the other way.

Our research also established that scientific systems have inbuilt checks and measures.

Skill 1: Scientific systems have an ability to identify success and failure. The issue is that specialized technocrats can only see the small section of their specialization, and super specialists can see even less and both these workers can never do any form of predictive analytics – i.e. what will the impact of this step be 2 or 3 years from today to the enterprise information capabilities

Skill 2: Scientific systems devise solutions by eliminating defects. Tradition technocratic solution to any problem is to either buy a bigger HW, a
new SW or send their resources for more specialized training but when we viewed the DW and BI ecosystem all we found were more and more experts, who actually did not solve but exasperated the problem.

Skill 3: Scientific Systems eliminate defects with Checklists: When we analyzed how critical components were managed we found an abundance of checklists. Pilots use them, engineers use them and today even cowboys use them. Checklists do not tell pilots how to fly a plane but it is a reminder of critical things that are often forgotten or get missed and that can potentially lead to a disaster.

There is no clear recipe for success but there we did manage to build a checklist for eliminating failures. Over the last 6 years we have implemented these methodologies, that we call BVA or Business Value Attainment in 4 projects and achieved exceptionally high success. Each of our projects scored in the upper 90% project satisfaction in week 1 and week 30. I state this for one of our research indicated that ’98% of BI projects are declared successful in week 1, and less than 50% of them remain successful by week 10’ Gartner and BI Valuenomics research.

The last skill we collectively need is the ability to implement these established scientific methodologlies. Despite its established success we found it surprisingly slow to implement. Read my blog on Moneyball and BI’ as it explains this dilemma. These new 'scientific principles' (due for publishing in Q3 of 2012 in a book) challenge the status quo and current value systems. It challengs things the technocrats have been doing for too many years. It requires us to work with with humility and replace our technical arrogance, it requirs us to work with Business and IT as a single team and replace technocratic isolation, and it requires us to work with scientific principles and checklists where we though we knew everything and where a 50% failure rate was a done thing.

Today establishing scientific principles in everything is our greatest path forward in order to enhance quality and lower costs via a process of defect elimination. Taylor did it in the 1911, Ford carried it to the next level by 1921 and the flame was carried by Demings and Drucker created the knowledge worker. We have now come to an infliction point where we all need to become better Business Value owners and workers and less of technocratic dictators that march only to our own drum beats. The writings are clear it now only depends on who all are ready to read it.

New BI Healtcheck Rules - Part 2 of 2

Part 1 of 2: http://bivaluenomics.blogspot.com/2012/04/bew-bi-health-check-rules-part-1-of-2.html

“Do not dwell on assumption and expectations, bet the health of your BI environment on scientific checks and an official healt report annually” The Scientific Principles of Information Delivery, 2012

In part 1 of this paper we talked about standard health checks. The most critical on of them is is the foundation of the methodology which is your documented and communicated ‘Global BI Cookbook’

The additional recommendations of 'The Scientific Principles of Information Delivry', a book due for release around Q3 of 2012 has the following new recommendations and caveats.

a.    Conduct a BI Health check every year to ‘information freeze’ risk: Crossword and puzzles are great for the brain, but looking at the same report year after year can sometimes cause corporate tunnel vision. While it is necessary to have some corporate reports that remain strategically constant, it is still necessary to change reporting to suite market changes. Being overweight doubles your risk of information freeze and obesity quadruples it. The aim of all developments should be to keep your BI healthy for the life of the organization. “If you do nothing but keep feeding it garbage, the health of your BI has to deteriorate. According to Gartners report more than 50% of global BI’s are genetically predisposed to the disease of failure, just as 20% of Americans are predisposed to Alzheimer. An annual check will prevent a brain-seizure that gets out of control or costs your company millions of dollars to fix.

b.    Do not buy without alternatives analysis: Over the last year the author has visited BI projects that installed BWA but did not need it, Installed BOBJ but did it the wrong way, installed Teradata because their Oracle DW was designed all wrong, and the list goes on and on. Common sense mandates that when companies spend millions of dollars they conduct due alternatives analysis. But the fact is that they do not. So when your current initiative expires, or does not deliver in accordance to business expectations then the basic foundations were wrong. Hire a BI Business Value Architect to get this step right and save yourself and your company deep headaches strategically.

c.    Watch the numbers very closely: The 10 week brain seizurerisk assessment that’s been stated for ling may have been a BI paradigm until 2010 but it is no longer so. BI Valuenomics solved that. If watching numbers is elusive see ‘Moneyball’ the movie once again. ‘98% of BI projects are declared successful in week 1, yet less than 50% remain successful by week 10’ is a very numbers statement. Most companies let their vendors go after 4 weeks of support and fall off the cliff by week 10. Demand a 11 week support and things will change. Most BI installations demand quantity and most of them risk major medical surgery drowning in the numbers and gasping for quality. A recent study of may BI installations demonstrated that the risk of brain-seizure decreased by over 40% by simply applying the scientific principles of BVA (Business Value Attainment). This was constant and not influenced by the amount of money the company had invested into their BI initiative. 

d.    Cosmetic surgery is never beneficial: The most inefficient  point to conduct transformations is in the OLAP layer, yet over 80% of BI initiatives do it precisely there. The reason for this is technocratic data modeling which believes in collecting data as a means of information. This has been proven absolutely erroneous and most inefficient. Three independent studies indicate that OLAP transformation are most ineffecient tactically and strategically. “These studies are not casual” say Alex Paleologoes from Hellenic Technologies, “It degrades performance for all times, i.e. in its current state and even as you apply accelerating technologies”,  he continued, “For example if your quesr takes 60 seconds for database, and 30 seconds for OLAP transforms and you install a BWA. With the BWA your database access will go down to 1 second and your OLAP will still take 30 seconds. If you install HANA your database access will take 0.05 seconds and your OLAP will still take 30 seconds. So take care that your surgery is not cosmetic and tactical only.

e.    Personalize your Fitness Plan: The Scientific Principls guidelines now stress getting a fitness test between external partners in BI Initiatives. The triad partners and their plans, IT and business co-existance and the overall satisfaction ratings of each partner about every other partner. Most BI projects start with great expectations, but less than 50% of them reach their destinations. Some companies offer Global BI Health Checks , very much like you and I need metabolic rate analysis and fitness tests annually. Wise folks take this seriously and avert major disasters, some wiser ones feel it is a waste of time and effort to go for these annual expams and we all know how their lives end. The goal has to become fit, more fit than the competition, not just to go on a diet and loose weight. Being fit and fat is far superior to being unfit and thin.

f.     Don’t stop your medication in the middle: Just like a lot of patients stop taking their antibiotics midway, so too a lot of ‘smart’ executives stop following the advice of ‘BI Business Value Architects’ midway. We were told of an inhouse oofshore operation in Poland where the IT confidence in the group was at 16%. The author proposed a plan that would have taken them in 1 years to over 85%. However, within 4 months the Polish management felt then understood the scientific plans and stopped taking the medicine as they reached 33% satisfaction. They felt they could now do it on their own and took ownership. Penny wise pound foolish, because 16 months later they were still languishing at 31% satisfaction.

g.    Before the cancer risk, reduce the heart attack risk: As your queries become more and more slower your instant graticifation, and triad recommendation, will be to go and install a BW Accelerator. However, that will be an error for ‘it add little value to deliver reports, that business does not need, this time only faster’, based on the fact that 40-60% of the reports in your warehouse are useless trash. So before you go and spend millions of dollars on the new Hummer try a little remodeling. It will possibly meet your short term objectives and also slim your BI strategically for when you actually need a BWA.

h.    If you don’t understand the problem get a trusted expert: This is standad advice. If the recommendation is something you are willing to bet your job and reputation on then go for it. But, when it fails don’t go around pointing fingers, and firing , others. If in doubt get a second opinion. Not all doctors out there are looking for your good, some of them are also there to perform surgery on you even if you did not need one – just to make a fast buck.

So in order to keep your BI healthy do the right things from a scientific perspective, and not because some sales person convinced you into something that may not be in the best interest of your BI environment.

HANA exposes varying speeds of light at Oracle and SAP

On April 12th, 2012 Vishal Sikka announced a 100TB of data benchmark on a 100 billion sales records database. done on an IBM server costing around $640k.
Recently Oracle announced that they had approached "Lightening speed of 100 million records per second'. HANA was talking of 100 billion records in miliseconds, and 2 million inserts per second.

Gave examples of Oracle to HANA analytics running 10,000 times faster.

Watch the video: 

http://www.youtube.com/watch?v=FtrsPC2_52I

Big Data Flow Options in SAP BI

Depending on your data source and volume of Data SAP BI customers have various options.
This is a sample representation of the various alternatives available for handling Data flows from sources to Information Consumers.

NEW BI Health-Check Rules- Part 1 of 2

“The Latest Scientific BI wellness wisdom for ensuring your BI lives longer, healthier and smarter” The Scientific Principles of Information Delivery, 2012

“Our society has become so used to super-sizing we take big for granted. Big cars, bigger burgers and now unnecessary biggest BI systems. We have to redefine normal. It’s not normal to not delete your PSA so you become the largest BI environment, it’s not normal to supersize everything. That it’s unhealthy to be big, but its unhealthy and extremely unhealthy to be fat, podgy and believing that big is the secret to success.

Lose weight, don’t waste energy, build a regimen of global methodology, deploy FEDW Architecture,  use more automated modeling, lower data throughput pressure, conduct annual health-checks, watch your dirty data. A six-pack does not mean building six podgy BI environments across the planet. These health rules all still stand, but BI Value doctors have added new recommendations and caveats to the list. It’s not normal not to optimize your InfoCube models and just filling your BW with more and more data. Its not normal to put all your obese cubes into a BW Accelerator, as it will not be normal to put all your obese cubes now to HANA. Just like it’s not normal to always drive and never walk, so too it’s not normal to simply keep putting more and more data without running a BI Health Check to ensure that your data, ODS, Cubes and BI are lean and mean and built on muscles and not just pompous fat. Executive, budgets and corporations get numb to the concept of diet and exercise” Dr. BI Valuenomics.

Build a global regimen, lose weight, don’t waste energy(smoke), use less unnecessary data (Salt), lower data throughput pressure (blood pressure), conduct frequent automatic remodeling (exercise), watch your dead data (cholesterol), don’t install the wrong application because the salesperson told you so (marry the wrong person). These health rules all still stand,, but Dr Scientific Principles has added new recommendations and caveats to the list.

So let’s check if you understand the standard health rules.

a.    Build a global regimen: Corporations can no longer survive in their local ponds. The flat world converts each small company into a global multinational. Similarly BI projects are no longer built locally. It is a mixture of on-site, off-site and offshore. In order to ensure that all the teams work harmoniously the first step in all BI initiatives is to build a global methodology of standards and processes at a minimum. This will not only save your tactical life, but contribute to your strategic living.

b.    Lose weight to enhance performance: It has been adequately established that the average DSO or Cube in North America may contain 40 to 60% redundant objects. This is great if you collect data but pretty stressful for performance and support SLA’s (Service Level Agreements). So get an automatic audit of your Cubes and/or remodel your DSO’s to optimized Cubes so you perform at your capabilities. As a BW moves to the BW Accelerator phase the Cost of Ownership for each data element goes up considerably, due to the high cost of HW and support. By decreasing the BW size by 40 to 60% we have been able either decrease both the initial cost and annual support cost by the same proportion or allow 40-60% more cubes into the same BWA.

c.    Use Less Unnecessary Data: Our research indicates that an average north American ODS or Cube can contain anywhere from 20 to 60% data elements that are redundant.  By identifying and eliminating these elements we consume lesser system respurces and meet the objectives of a.

d.    Lower data throughput pressure: Simply by building a cube on top of an ODS we have been able to reduce query times from 500-700 seconds to under 20 seconds. By eliminating all the redundant objects we were further able to reduce this to under 13 seconds. Useless data does much more than occupy space. It clogs the system resources and slows data flow along with Query performance.

e.    Conduct frequent remodeling: Just as a car built in 1911 could not compete with the cars of today, nor a car built in 1997 have all the capabilities of ones built today. Similarly the cubes modeled in 2001 cannot be used as the information demand and priorities change. Do not try to do this manually as that can not only be costly but also extremely ineffecient. Go for the automatic route and finish this task in a few hours per cube.

f.     Watch your dead data: Our data warehouses are full of test elements, KPI’s and queries. This is dead-wood sittin gin your racing lance. Flush your system frequently so the engines perform optimally

g.    Don’t install the first application that comes your way: Just like don’t fall for the first person, or buy the first car you see. So too don’t decide on your final application just because someone told you so. We are finding more and more that the sheer competitiveness in the BI environment creates salespersons who truly don’t understaand their own products. Get the opinion of a trusted BI Business Value Architect prior to finalizing decisions.

So think scientifically and use your head to save your heart.

How to Simplify the Complexities of Big Data

Up until recently query response time determined a lot of what can or cannot be analysed. For example a recent company wanted to run ‘Intuitive Analytics’ in financial data for the month. This was a set of around 30-40 million GL records. Intuitive refers to when a financial analyst feels something is not right with a number and wants to dive deeper, with self-service capabilities, into the database to check their assumptions. I quote the user ‘I used to start a navigation step and I could go for coffee, talk to a friend and come back to still see the hour glass running. You must realize that it takes 20 to 40 navigation steps to find the truth’. We tracked and each step took anywhere from 500 to 1,300 seconds to run on 30-40 million records. When the company found a financial fraud they wanted to run these reports run faster because the current performance was a disincentive for analysts to conduct such research. After acceleration the report ran in 4 seconds.

·         Now remember we are only talking of finance reports and that too for a quarter. What if analysing the last 5 years of data would truly add value to the analysis. Suddenly we are talking of 700 million to a few billion records. What if we are talking of Cost & Profitability analysis where we not only need to look at a few billion records but also conduct calculations on these for cost and profit analysis? We are still talking only financial data within our enterprise walls.

·         The world and global competitiveness is changing fast. While we are talking of a few to tens of billions of records and the ability to handle that our BI infrastructure at this time starts to groan and creak with a 100 million records.

·         So we are faced with a decision dilemma. On one side we have the need to make globally comprehensive decisions to make our company more competitive. On the other hand the data volumes for such analysis tend to be rather large. Add to this that competition is also starting to mine social network data to analyse emotional scores on products and services of the company, including industry specific data that is now available from large subscription datasets. So from tens of terabytes we suddenly find ourselves entering data caverns in the thousands of terabytes, or Exabyte’s.

If we look at a scientific methodology to all this madness then here is an actionable roadmap with steps for advancement:

·         Step 1: Ensure you have a documented and communicated foundation of a global methodology in the form of a ‘Global BI Cookbook’ before you do anything else. Proceeding without this is another waste of time. Check 1: If you naming standards are ‘Z’ and ‘Y’ you have sub-optimal standards and these need to be changed.

·         Step 2: Clean up your current BI / W environment. Close to 90% of DW’s have redundant objects. Close to 80% of DW environments are architectured and modelled sub-optimally. First step is to clean all this unnecessary baggage in your existing DW/BI Environment.

·         Step 3: Benchmark your BI environments and selectively push objects into optimal environments. i.e. Never put all your cubes into BWA but select the ones that should be there.  

·         Step 4: Conduct an alternatives analysis for each step. i.e. don’t replicate your Cognos reports on a new BOBJ 4.x deployment. Don’t demand only WebI reports because you think they are great. Don’t think BWA or HANA will solve all your problems review Hadoop for social network data reduction.

·         Step 5: When thinking HANA think BWA and you are closer to the truth than you realize

 Modern systems create unbelievable amounts of data. Modern companies planning to track their company with a 360° will have to deal with extremely large volumes of data.

Let’s talk internal data. General ledger data can run anywhere from 40 to 500 million records. For large companies it can run deeper. Costing and Profitability analytics on these systems runs into many billions of records. Smart Meters are producing millions of records every ten or 15 seconds, so analysing a quarter of trends means billions of records. Retail outlets can create millions of records an hour. Airplane engine manufacturers want to conduct in-flight analytics so spares are on site even before the planes have landed this creates billions of records every hour. Homeland security needs to watch millions of passengers traveling inwards and outwards from their country this is billions of records every hour.

In almost all the above cases the speed of information is critical to goals. The general trend has been to either filter the data or view very small subsets of data because the technology was just not there to analyse very large volumes. Now the technology has finally arrived where we can analyse 40 billion or 200 billion records in less than 10 seconds. Such performance changes the competitive advantage of individuals, companies and nations. This speed enables ‘Trade promotions’ to be analysed on a daily basis, cost and profitability to be visualized as events happen, status of all the engines at this point of time across the planet, or a visual view of threat to our homeland at a second to second accuracy.

The digital economy has been here and now we can harness its power.

Lead this revolution with the following roadmap

·         Most critical: Build global methodologies and standards for harmonized data management

·         Collect and harness internal data for analysis

·         Then move to collecting many types of structured and unstructured data from outside the organization

·         Measure competitive advantage like emotional analysis of new product launch or behaviour predictability

·         Use this knowledge create proactive marketing targeted to synchronize with global emotions

The Dirty Little Secret of Tech BI Successes

“98% of companies declare their BI initiatives successful on week 1, yet less than 50% of them remain successful by week 10’ BI Valuenomics & Gartner

Just a few months ago stepped into a BI project only to be told still again that ‘Our BI project was an IT success, but a Business Failure’, which got me right back on the need to push away from the current belief of ‘Technology is King’ and move towards Gartner’s recommendations of ‘Without business in business intelligence, BI is Dead’. There are three ways to look at this dilemma in Business Intelligence worldwide.

On one side, our first category of customers,  of the equation are the mega corporations with seemingly unlimited resources and budgets. They have their pet vendors, will only deal with them and are willing to go through tens of cycles and as many lessons learned sessions while they continue to travel along the same path desperately hoping for a different outcome this time. These corporations have a blanket order for their global BI initiative with prime vendors. The belief with these corporations is that good quality must cost more, and thus spend many millions of dollars per annum of their BI initiatives alone. Their continue with a string of IT success and business failure deployments, followed by a long lessons learnt initiative, followed by still another round of ‘lets get it technically right this time around’ approach. For these corporations I would like to state that in our flattening world where efficiency is the prime competitive advantage this advantage could slowly erode not only your budgets but also the decision making capabilities of the executives therein. No corporation has infinite resource for infinity – not if it is underutilized consistently. (Probability of occurrence 3-5%)

The second category is medium to large enterprises that have resources but would like to utilize them optimally. These enterprises bid competitively and try and negotiate the lowest cost partner with the highest quality perceptions. What the companies often get is lowest cost associated with lower skills than what their business should have got. Low skills often result in misaligned roles:skill selection, which in turn results in a higher degree of technical only solution. These resources will only be able to give you what your business demands and if your business does not know enough they will only demand what they have, and that could be a waste. The belief in these companies is that quality can be maintained despite getting lower and lower skilled resources (due to cost restraints). These companies often miss both the IT and business goals, but more often meet II goals and miss business expectations. For these companies it is critical to get a solid BI Business Value Architect that works with the business owner and steering committee members to guide them to accurate decisions. It is recommended to first conduct an alternative analysis followed by the decision that the analytics are enabling- and not simply in replicating reports. These companies have a more than 50% rate of failure followed by desperate unplanned spending often as lower rates than even before. (Probability of occurrence: 80-85%.

The third category could be any company that have an unbending faith, very much like the category 1 customers but for different reasons. These companies bid competitively and then leave everything to the technology vendors to solve and deliver. What these companies get is a lot of content and reports but very little business alignment or reports that truly meet business expectations. These companies want to deploy a solution in the fastest time, with the least business contact and the maximum technology solution. They get what they demand. The belief in these companies is that standard best practice delivered content should be able to meet their needs – only to find out that in reality it never actually can. The belief in these companies is that the highest quality comes from minimum customization and maximum standard reports. In these companies IT always meets its goals and business rarely if at all. For these companies it is critical to point out that standard business content very rareluy meets business expectations other than for a few specific objects. It is recommended to let business view not just the title of the reports but also the content of each report being delivered. These companies have an over 70% probability of failure and that is optimistic. These companies almost always wonder if BI is a worthwhile investment. (Probability of occurrence 10-17%)

When customers launch their BI projects they often imagine a smooth journey with geniuses taking them to their promised land. However in over 50% of these BI projects we find that they are filled with crushing defeats, near-death experiences and very low user satisfaction. All this leads to rush order of unplanned projects.

When we look at some of the most successful BI implementations we consistently find a high level of business participation in these projects. Evidence substantiates that BI success is directly proportional to the participation by actual business stakeholders. In almost every case these were business lead and owner BI project. (Probability of occurrence Under 50% according to Gartner)

On the other side of the coin we have the most unsuccessful companies and in these we find project teams that shun business participation and assume they are already at the pinnacle of their deliverables. Unlike in successful projects there is little option to accept defeat or throw in the towel in these cases for three reasons. The first is that if you do you will lose your job. The second is for most companies it is very difficult to state that we just blew $4 million on a badly planned project. The third is that after the BI spends there is still pending business expectations that need to be fulfilled. All this leads to disruptive unplanned projects that rarely do the right stuff the second time around. (Probability of occurrence over 50% according to Gartner)

BI deployments should not be like UB-40 or the 40^th attempt that actually succeeded. We need to get this right the first time and right now we have enough scientific data to achieve this goal.  As an executive if ou have a mission to succeed then you need to take ownership and accountability for its success – do this with scientific principles behind your back and not technocratic assumptions.

So plan your work and only then work your plan can only succeed – as that leaves failure absolutely  no option.