Enterprise Data World is the business world’s most comprehensive vendor-neutral educational event about data and information management.I am honored to be invited to speak on Trends in Business Intelligence. Please join me wednesday, March 17th at 11:00 am in San Francisco, USA. Information is crucial to fight challenges but also in trying to identify chances and possibilities. What are the major trends in Business Intelligence and how is it impacting your organization? Capgemini explains how you can make use of data in order to make the necessary decisions under these fast changing circumstances.

A new book on BI will be published on January 26th during the Heliview Business Intelligence conference in FIGI ZEIST for which I wrote one chapter (On BI in Retail).

See also  http://bi.heliview.nl/

The title of the book is: Over business intelligence. Data is zilver, informatie is goud (About Business Intelligence. Data is silver, information is gold). 

The book can also be ordered for € 26,50 (excl. € 2,60 postage and packing) on www.uitgeverijtiem.nl.

You can also view the index and some summaries on this website.

Do you remember the 1995 movie Seven where Detectives Mills and Somerset jointly investigate a series of ritualistic murders inspired by the seven deadly sins? I vividly remember a library scene where Morgan Freeman goes through a set of books related to the seven deadly sins, like Dante’s Inferno (or hell). The seven deadly sins were: lust, gluttony, greed, sloth, wrath, envy and pride. Today I suddenly wondered, are there seven deadly sins for Business Intelligence? And if so, what are they? Inertia:The potential of BI is unbelievable. It allows companies to make smarter and faster decisions and simply outperform the competition. Lack of relevant information may even lead to a loss of market share, drop in profit or even bankruptcy.  There are numerous examples to support this claim. This is why BI is always in the top 5 of any CIO or CFO. However there are still companies that do not have an (official) BI initiative in their organization or lack focus on the information function. In today’s economy something that one simply cannot afford. So the first and most deadly sin must be something which has to do with the lack of relevant information or slow time to information. The use of BI must be stimulated at all times and no barriers should prevent the adaption of BI within any company. Costly:BI is often considered to be expensive. We all heard people say things like: “Why does it take 3 months to build a few simple reports”. In some cases these people are right. If BI is not organized in an efficient way it will lead to higher costs. For example if there are many local initiatives or an IT organization that does not understand business needs. For me, the second deadly sin has to do with the fixation on costs instead of thinking about return on intelligence. BI can have a hard dollar (or euro) return but the business case can also be qualitative or based on vision or innovation. Off course, we must always look for smart and cost effective solution. Lowering the TCO can be done, for example by standardization of tools and processes. Another possibility is outsourcing. However that can lead to new challenges. This brings us to deadly sin number three.  Complexity:BI is no rocket science but it is also not a trifle undertaking. BI has both a technological as well as a business component. This makes it neither business nor IT. This creates a possibility of increased complexity in the development of BI solutions but also in the final product itself. The result may be a BI environment that is not used. It is important to keep the BI solutions simple. By taking small and simple steps and making sure that business and IT keep aligned. We must also be aware that there are different types of users and that they use BI differently. Manager need dashboards and aggregated numbers while financial experts need detailed number in spreadsheet like form.  Technology:The fourth deadly sin is that BI is often also the number one deadly sin. In my experience it is one of the main reasons why so many BI projects fail. It has to do with the tendency to focus on tools and techniques. BI delivery is often within IT where the understanding of the business can be absent. Realization of the technical solution, like a data warehouse, is the objective instead of fixing the business need (where the data warehouse is just part of the solution). Let’s remember that IT is just an enabler for doing better business (intelligence). So why are 9 out of 10 BI blogs only about technology? Ignorance:Let’s say that we have found a company that believes in BI, focuses on ROI and with a not all too complex solution in mind. Even better they also focus on business results and not technology. Sounds like all ingredients for a successful BI solution are in place. So they start this great project to improve their call center effectiveness or increased sales force performance. And in the back of the room there is this little geek from IT that always talks about the importance of data quality and Master Data Management. Don’t you wish they would have listened to that guy then in stead off afterwards when their reports have the wrong numbers? Or when they spend days or even weeks discussing about a difference in definitions? The fifth deadly sin is that the most important fundaments of BI are often ignored as they are not sexy enough. MDM and DQ are essential for acceptance of BI.  Deviation:Sometimes a BI initiative just starts. Not because there is a business need. Not because somebody has a vision. Only because the company needs to have a BI system. There is no time spend on thinking which business problem is helped or solved with this solution. All other people have BI so we want it too. The sixth deadly sin is to build something without knowing where to use if for or having no sense of direction. Organizations should never develop a BI environment before they have a clear BI strategy. Halting:The seventh and final sin is that after a BI project is finished or a 1^st increment is delivered no follow up is given and BI is not integrated in daily business. The real business value in BI will only emerge if you keep on investing in your BI environment and adapt for changing circumstances. Something like a BICC should solve this. I am not sure if these above mentioned seven deadly sins are in fact the only ones or in the right order. But I do think they contain elements that hinder the proper use of the BI function within many companies today. I would really appreciate your feedback on this. Perhaps you have some deadly sins to add to this list. Please do not hesitate to react.

One definition of Business Intelligence is the collection and structuring of corporate data in order to gain information for further control and management of a process, target or strategy. The return on intelligence (ROI) is achieved by doing better business. BI is therefore often part of a project or sometimes even a project in its own right.  Many corporation start a project by putting together a business case. All possible costs and benefits are taken into calculation. Only the projects with a sound or positive business case get the green light. But the economic downfall has changed all this. Not only are we starting with less projects, even those that have a positive business case to begin with, many project have been cancelled, delayed or re-scoped. In order to make reasoned decisions an answer must be given to questions such as: How will this project contribute to our business objectives, are we doing the right projects, are there better ways of using our resources, is the business case still valid? Portfolio management gives corporations the change to have a clear understanding of the contribution of an existing or potential initiative. This way a corporation can make sure that it only invests in projects that add value. The corporation is doing the right things.  But just as important is Portfolio management information. Monitoring the current portfolio enables corporations to get an early warning on possible risks but also the possibility for action taking if the value of certain projects decrease or even get negative. The corporation is doing things right. 

I see to many companies limiting their selves by building one single business case only at the start of the project. This way they will not get the maximal return on their investments.

These corporations might be doing the right things, but they are not doing things right.

“But it wasn’t because I didn’t know enough. I just knew too much”. Gnarls Barkley – Crazy

The market is volatile. Your customers are volatile. Maybe if you look in the mirror you will see a volatile person. But volatility is something we – in general - tend to dislike. Therefore we strive to regain control. Control of our finances, of our customer even of our future. Management Information seems to help us with this. But the way we use management information or business intelligence is not always up to par. Most of the time we use historical data or performance to make decisions. But many answers, smart moves, right decisions are hidden in the near future. That’s why we want to be proactive. Beat the other guys. Advanced Analytics seems to work. It combines techniques like datamining and statistics with business knowledge to create predictive models. But even in advanced analytics we seems to focus too much on using our own data. Some argue that the most valuable information is not found inside your company but outside. If this is true I don’t know. What I do know is that external data can help you improve your performance. But how do you regain control of external – often unstructured – data. And even better. How can you make this process repeatable? The most obvious three problems that arise are: [1] too much data, [2] data too late and [3] poor data quality.

The problem with having too much data is that you need to come to a selection. The data in the world keeps growing and growing in mind boggling amounts. But the majority of this increase is not relevant. Pictures on flickr are great but aren’t much help when figuring out your supply chain strategy for 2010. But it is not so much the amount of data that is the problem. It is the structure. If you find a way to effectively structure any increase can be handled without too much effort. Therefore I would suggest to start structuring your data for relevance.

Another problem is that the relevant structured data is often received too late. After the fact has already happened. This means that no proactive behavior but reactive behavior. So how can we improve on that? One solution could be to set up early warning signs. Maybe there are some early indicators that can warn us about things to come. For more information on this I would like to suggest the work of Ben Gilad (http://knol.google.com/k/ben-gilad/competitive-intelligence/1o41pnd9hgmyg/2#). Please make sure you know what early warning systems are crucial for your organization.

Finally, good old data quality. In our pursuit for perfect data we tend to forget the reason why? Do we really need perfect flawless data ? If we have one piece of the puzzle that is wrong it might causes a problem. But if we have 1000 pieces of the puzzle – some right some wrong – we have enough information to make our decisions. So forget about data quality and start focusing on relationships between the data elements that make for the big picture.

If business intelligence can improve by adding external data, why is it that a lot of companies keep looking inward for their management information? One explanation could be that compliance is often the reason why they have a BI environment anyway. For some reason they have to report their performance to stakeholders. This mandatory reporting is often focus on (financial) internal data. Or  somebody started with improving a process by making it measurable with the use of BI. Or even more simplw. It is easier to start with your own system, company or IT department.

In conclusion I would like to argue that we need to structure and combine internal and external data in order to really gain any competitive value.

On a Dutch website

( http://www.computable.nl/artikel/ict_topics/business_intelligence/2844877/1277145/bi-betaalt-zich-bijna-nooit-terug.html#2853949)

there has been some discussion about the return on investement for BI. I reacted (in Dutch). Follow the link for the reaction from myself and others.

Earlier this week I was driving home from work listening to the radio. It was around 6 P.M. so I was waiting for the six o’clock news while absently listening to the commercials in the background. One of the commercials caught my ear. The narrator asked us the listeners the following question: How is the performance of your network?  What would your first reaction be to such a question? I bet you were thinking about some kind of telecommunications service product with circuits and packets. Is this where we separate the geeks from the rest? The first thing I think about when I hear network is People. Or better even stakeholders (mind you that is much broader term than shareholders).  So this question about network performance made me wonder.  How can my stakeholders help me improve my performance?  The answer of course would be by sharing relevant information. This raises a bunch of other questions, such as:How would they know what is relevant to me?How can I even know what is relevant to me?Why would they share information in the first place (what’s in it for them)? If you look at the traditional knowledge pyramid – data, information, knowledge, action – one of the answers could be just to share data and let the recipient transform this further. Let them filter for relevance. This would allow for a SOA like architecture where data is provided in the form of messages and the BI solution just ‘mashes this up’. The benefit would be that the senders would expect the same in return. This is a relatively straightforward business model that has been around since EDI. At the same time we see a major trend which I labeled “increase”. We see an increase in (volume of) data, an increase in the speed we want to consume this data (even real time) and an increase in the impact of business decisions. We want smart and relevant decisions now! Not after we have loaded the data in our datawarehouses or datamarts. Not after we viewed it using reports or cubes. Not after we have come to some decision based on our implicit knowledge. This would allow for a architecture where no longer the data is provided but where decision is provided. The relevancy is determined by smart algorithms,  business rules or some other smart solution. Obviously this is more difficult but it would help me improve my performance. But what would be the benefit for the sender? Probably they will charge me money for adding analysis and actions on top of this data. So what do you think? Are we going towards a business model where information brokers are collecting data, making analyses and supplying decisions and actions to their clients (in fact take BI outside your company) or do we stick to the traditional model (where BI resides inside your company)?

Today we started with the keynote from Ben Klein. Some people might remember him as the teacher from Ferris Buelers Day off (Great movie btw). His speech was funny but very American. His basic theme was that the American Youth is getting more stupid by the hour. The focus is on material things, money i.s.o. taking care of eachother. This was folowed by a plantinum sponsor panel discussion with guys from accenture, Dell, HP, Hitachi and profit base. First take aways are. BI in the next 10 years is going to be more about social networking, instant information, even more data, integration, visibility of data, 90% on 3 or 4 platforms, role driven and less adhoc. There will be a gap between BI solutions and its users due to cultural differences, skills and so on. Perhaps this will create a new role of the information architect (iso the data architect) with more focus on business than on SQL. Understanding then underlying model is key for enduser buy in. There will be an ERP world (with focus on capturing the information) and an IRP world (with focus on delivering the information). Right now they are going into commercials so it is time for a cup of coffee.

Everybody deals with data all day long, from the board room down to the order entry department. Most of the time we do this without even thinking about the quality of the data. But when we do think about Data Quality (DQ) it often already has become an issue. Therefore our approach towards DQ is often negative (Garbage In, Garbage out). And also our approach is often technical (Field X in Interface Y is alphanumeric with 9 positions instead of numeric 8). So what happens? The IT department hurries towards the business owner with their negative technical problem often followed with an even more technical solution. Nine out of ten times this involves a tool from some vendor that promises a magic data laundry machine that cleans your dirty data. Let me tell you, there are not many business users that become enthusiastic after a negative technical speech. A more smart approach would be to make a simple business case. Bad DQ costs money, therefore good DQ makes money (or at least saves you some). That should be simple enough for any business user to understand.  As BI becomes more operational (do I dare to say real time) the chance that the data is polluted (or not yet cleaned in the ETL process) increases. Another trend is the combination of structured and unstructured data. Combination means coupling of two or more entities, also know as data integration. This is again an area where the DQ is crucial. In order to deal with DQ under these circumstances there are two options. Option 1: increase the performance by adding more of everything (CPU, memory, tuning IO and so on). Option 2: increase the performance by doing things smarter. This means creating some kind of solution that involves metadata or business rules. Also using a profiler tool to filter out the usual suspects is a smart way to go. Whatever you do, try to take a proactive approach instead of solving it after the ‘laundry is already dirty’. What can be the result of bad DQ? It can lead toward making the wrong decision (when based on incorrect data), it can cost money from an operational point of view (double mailings) or even lead to damage to the corporate image. It is safe to conclude that DQ is important for everyone. But by making it every ones responsibility it at the same time becomes no ones responsibility. There is always someone else that owns the problem, needs to fund the solution and so on. An at the end of the day still nothing happens to solve the DQ issues, leading to wrong decisions and more waste of money.  Let me explain what I mean using a real example. Department A has a customer database where customer A is known as Mr. A. Department B also has a customer database where customer A is know as Mr. B. During the data integration process Mr.A. and Mr.B. are seen as two different persons while in fact they are not. Should department A fix this? Why should they as this registration in their database of Mr.A. suits the purpose. The same applies for department B. Should the IT department fix it during data integration? If so, do they choose Mr.A. or Mr.B. in their Master Data approach? And by the way, who is going to fund this? It is not a problem for IT so they are not going to open their wallet. It is not a problem for department A or department B so they are not going to fund it. We have talked about the data delivery and the data integration, now enter the data subscriber. Department C uses the central data warehouse for their management decisions, corporate mailings and so on. They have problem with this whole Mr.A. and Mr.B. stuff but do not consider it their fault. So why should they pay for fixing this? This is what I mean. The whole DQ (and metadata issue for that matter) problem keeps being pushed from one desk to another and no one is going to solve it.  The central questions are: 1. Who owns the problem? 2. Who is going to solve it? 3. Who is going to pay for it? All the IT department can do is to identify the problem and facilitate the solution.  I would love to hear what you think about this. Please let me know and write your comments. Perhaps together we can find a suitable solution. In the meantime we keep our fingers crossed and hope for the magic laundry machine.

There is much talk about the impact of the (feared) recession on Business Intelligence. Most analysts are expecting a slowdown in customer spending and vendor revenues. I see their point and I am somewhat inclined to agree but there is another side to this. There is an expression that goes something like: you have to repair the roof when it’s not raining. In other words IT spending is obviously up during financial upturns. BI tends to benefit from this as well. But as the economic weather gets rough there is even more need for information. As the budget is cut back people are looking at smart things to save costs, recognize trends early, and identify the most valuable customers and so on. This is exactly what BI enables you to do. So my expectations are that an economic downturn will hit the general IT market much harder than it will impact BI.

I was asked by an IT magazine in the Netherlands to come up with a couple of practical tips on BI service delivery. In my opinion the most important element in service delivery is creating a optimal mix of all crucial elements. My submitted three practical tips cover design, build and implementation. However it is just a start. Please join me to create a top 10 in BI service delivery.  [1] When designing a Business Intelligence environment always consider target groups and type of usage.Target groups: Some users such as managers often have the need for a high level dashboards with little detail. However other users like analysts need lot of detail and powerful analysis tools. Every target group needs their own environment in form, function and frequency (of data refresh). Usage: There should be a mix of fast (ad hoc) and structural (more long term) BI solutions and products for the end user of a BI environment. [2] Excellent BI specialists are a scarce resource but are crucial for the success of the BI environment. Do not cut back on quality. Make sure you have a proper mix of ‘hardcore’ geeks as well as business architects or BI visionaries. In order to make the costs controllable consider outsourcing parts of your BI environment. A good starting point would be technical realization (build) and application maintenance.  [3] Don’t stop when it’s finished. After the technical realization of the BI environment the real work just starts. A critical success factor for this is the BI Competence Center. This center should mix IT and business components to ensure the optimal use of BI within the organization. It should be the BICC that pro actively supports the users in their daily work. In this phase it is no longer about high level sponsorship but more about working floor thought leadership.

Times they are changing. Last year has been tumultuous for BI at best. We have seen the BI market consolidate into a few remaining mega vendors. But there was also room for niche players especially the ones focusing on speed, visualization or collaboration. So what will 2008 bring? To quote from Star Wars: “Impossible to see the future is”. But let’s give a try anyway. There are five mega trends that I foresee. Some have been around for quite some time and some are new. The first one is called: (1) Increase: Business Intelligence must be able to cope with considerable increase in both data (size) and speed (real time). The second one is (2) Integration: Business Intelligence will continue to integrate with operational processes but will also be more aligned with strategy. The integration of (un)structured data will increase. Finally, Business Intelligence will integrate with other business applications or ERP systems. Third, and my personal favorite, is (3) Insight: Business Intelligence will continue to become more intuitive (less end user involvement) showing an increase in advanced visualizations but also more analytic (more end user involvement).The fourth is (4) Interactive: Business Intelligence will be more reciprocal by nature; creating a dialogue between system and end users as well as sharing information and collaborating within performance networks. Finally, number five will be (5) Industrialization: Business Intelligence development will be characterized by a more industrialized approach using standard development frameworks opening up the possibility to outsource.  Increase will be mostly characterized by the need for real time information (partly due to the fact that BI becomes more operational). This will be supported by tools like Netezza and Teradata. They can also support the growing process of data retrieval from vast amounts of data. Integration of unstructured data (text, search, documents, agents) for example in a document warehouse (using tags) sitting next to the data warehouse will be the wave of the future. I expect Google or Attunity to take the lead here but the original BI vendors (Cognos, BusinessObjects) have also been investing in this. Although the market is more or less divided among a few remaining vendors it is still possible that we will see some more integration. This will be partly triggered by the fact that BI will be used on all levels: strategic, tactical and operational. This means BI tooling must support strategy development as well as pervasive BI (it’s everywhere). The renewed interest in open source Business Intelligence (Pentaho, Jaspersoft) is a clear counter reaction against the market domination due to vendor consolidation. Creating Insight today will be the one mega trend where I expect the most innovation to come from. It is a hot topic and fun to watch evolve. Here you will find things like intuitive intelligence (Strategy companion) or decision engines supporting your daily work (Fair Isaac). Also advanced visualization will help the end user get more grip (fractal edge) on things. Triggered by the success of “competing on analytics” or “super crunchers” we will see an rise in the use of analytics, strongly combining informatics, statistics and business knowledge. Perhaps here lies the opportunity for the long awaited promise of data mining? A clear and much appraised trend is the sharing of information with other people either inside or outside your own company to create mutual benefit. But it is more than just sharing the information it is also about sharing the reasoning behind it. How have we come to this conclusion and do you agree? It is asking feedback or contributions to your problem solving by your stake holders. All the characteristics of the web 2.0 can also apply here. This can also be done by using a trusted third party to solve your BI wishes by providing BI as a service. As BI is getting more mature by the minute I expect that we will see a standardization of best practices resulting in an industrialized approach in (standard) BI development. BI factories, often based in typical outsourcing countries, can provide high quality, low cost ready-to-use BI solutions. Some people say that after all the consolidating this will be a boring year for Business Intelligence. But if I look at these five mega trends I can’t wait. Happy new Year.   

Detailed insight into crucial data and events is a necessity for organizations that want to navigate a constantly changing, information-rich environment. Intelligent organizations are constantly reading, analyzing and reacting to information inside and far outside the companies’ boundaries. Information thus becomes a corporate asset, which infuses itself into all strategic and operational parts of the business. Traditionally BI enables business decisions through information access, by delivering the right information, at the right time, in the right way. However nowadays BI does much more than then decision making. It also monitors and supports, among other things, crucial business processes and is much more then ever embedded into daily business. Therefore it can be said that BI is crucial for the strategic, tactical but also the operational needs of an organization. Many companies have service level agreements (SLA) between (mostly internal) parties to formalize the process of data delivery or extraction from data sources and/or the publication of information to business stakeholders. With a traditional focus on decision making or decision support only those SLA have been created with just that scope in mind. This has resulted into a situation where a problem with data delivery or information publication had to be resolved within for example a timeframe of 24 hours or even more. In other words, due to it’s reactive or even reflective nature, the repair of the BI problems have not been the highest priority for many organizations. This makes sense if you compare this to critical business applications such as cash registers or ATM for banking. They support the crucial daily operation and should get top priority. But with the changing nature of BI, becoming more operational and supporting real time business processes, it is time to redraw the SLA and giving (almost) top priority to BI systems as well.

Detailed insight into crucial data and events is a necessity for organizations that want to navigate a constantly changing, information-rich environment. Organizations that know how to connect the use of data to their strategic objectives are more intelligent: they become ‘Intelligent enterprises’, constantly reading, analyzing and reacting to information inside and far outside the companies’ boundaries. Information thus becomes a corporate asset, which infuses itself into all strategic and operational parts of the business. As a result it’s only natural that more and more organizations want to start a Business Intelligence initiative.  However BI initiatives cost money. This is often an inhibiting factor for many companies. At the one hand they need BI to face the business challenges of today’s market but at the other hand they need some kind of justification before they can start. This justification can take two shapes or forms.  The first and most well known is the business case. Basically this is a quantitative analysis of all expected costs versus the expected yield or profit resulting into a return-on-investment. The business case involves calculations on lower costs, more effective processes or less manpower. Those are all very tangible and well defined attributes. Business cases are often made by business departments in order to obtain a budget.   The other is less known - probably far more difficult - but with a higher yield. It involves a non quantitative analysis based on vision. Driven by thought leadership an ideal or goal is formed. Unlike the business case there are no tangible attributes. The advantages or benefits can be things like qualitative better decisions or accessing unknown data sources. Often these initiatives are considered to be vague or soft. There is a promise of profit not a proof of profit or like I call it: return-on-intelligence. This vision based justification is often made on an organizational level in stead of departmental. In my opinion, in this day and age a company can not allow it self to remain without business intelligence and survive the increased competition. A BI environment is a necessity just as much as a LAN or transactional business applications. Therefore they need BI initiatives even without a cold business case. I encourage all companies to take this vision based approach. Not only will it lead to more commitment from your employees, it will also support the execution of your strategy. And in the long run – there is proof enough for that – it will also pay back on its investment.

This is the second part of my posts about the ‘universal truths in business intelligence’. Check out what Frank Buytendijk thinks at: http://blogs.oracle.com/frankbuytendijk/2007/12/04#a95 4. BI projects need high level sponsorship. The reason for this is that we want to introduce Business Intelligence in the organization. It is all about creating awareness and enthusiasm. So do we really need a high level sponsor or manager for this? Perhaps in a very rigid hierarchic organization you do. But we Dutch do not deal well with a top down approach. The real innovation comes from the work floor, we think. I think organization can benefit much more from a highly committed thought leader.  3. BI development should be done incrementally. What would be the opposite of that? Probably some kind of big bang approach where all the functionality can be delivered in one single release. That sounds nice but often this is not possible for BI. To start with I find that when users start to interact with the new BI system they come up with new insights followed by change requests. A successful incremental approach focuses on the identification and prioritization of the most beneficial increments or slices. This should be based on the priority of the objective (is this a key process, is it in line with our strategy?). Other qualifying criteria can be availability and quality of the data. Each slice should be a complete solution. BI projects – just as in real life – should have a first things first approach. Delivering incrementally – keeping the long term view in mind – allows for faster speed to value. So I agree with this truth.  2. BI projects require a business driven approach. Any BI project or program should have business value. Creating a business intelligence environment without a goal is a mission impossible. We do not need data warehouse projects. We need project that can create more sales or reduce churn. A data warehouse or business intelligence project can support this goal. However I do not think that it is should necessarily be the business user who initiates such a project. IT can play an important role in taking the lead by showing what is possible. Especially by creating a prototype. See also my other post on IT push or pull (http://www.biguru-online.com/2007/12/04/active-push-or-passive-pull-role-for-it/).   1. BI needs one version of the truth? Has anyone of you ever played bullshit bingo? It was one of the sources of inspiration for this post. You take 10 of the most used words and cross them out during a meeting when used. Single version would be on the top of that list. I hope that from this day forward no one will ever use this expression again. This was born out of fear for multiple version in spreadsheets across organization. And frankly, I cannot stand the expression anymore. Also I totally disagree with it. Understanding is always determined by its context! And all communication should differ for each target group. Creating a single version – let alone the office politics involved in that – is an utopia. Instead let us focus on a single version of the facts (data quality, lineage and so on). To me the focus should be on the definitions or meta data. As long as the definition is clear you can have different version per context. So are there any ‘truths’ that we missed in our top 10? We would appreciate your suggestions. Also if you disagree with what I said (or agree for that matter) please post a comment. Remember this blog is all about the possibility to be an online BI guru yourself. It is a platform for an open discussion about BI – not just a floorshow for my thoughts.

I get most of my creative ideas when I have the time to contemplate. Often this happens under the shower or in the gym. I always thought the main reason for this was that since I had nowhere to go physically my mind could wander instead. I turns out that I am right, but only partially.  Recent studies show that the emotional state of people highly influence the way they process information. If you feel happy you are more receptive for outside stimuli. You start to see the beauty in things. Also there is an increase in creativity when you are in a good mood. The other way around, depressed people tend to focus on themselves and their problems. So now you know: I like taking showers.  Design can make a product more attractive. Attractive things create positive energy and state of mind. When asked to choose between products that basically do the same - have the same performance, price and so on – people tend to choose the attractive one. The Apple I phone is probably one of the best examples. So if attractive things work better (or at least create the feeling that they do) and stimulate creativity why is it that a lot of Business Intelligence solutions still look like spreadsheets, matrices or boring lists? I always encourage my colleagues to make their solutions super fancy sexy. Meaning that it should not only hold the correct data but at the same time it has to look good too. By presenting the information in a creative, nice looking way you increase the acceptance and usage of the product.  For Business Intelligence solutions the presentation and visualization are just as important as the content. Do you agree?

Ask anyone in this industry for his or hers definition of Business Intelligence and you get a thousand different answers. Is that a bad thing? Not necessarily, but it does imply that we still have not agreed upon some of the basics. It is an interesting paradox that although we are apparently not 100% sure about what Business Intelligence is we do have an opinion on how it should be or where it is going.  Over the last couple of years a common understanding of Business Intelligence has been created holding some universal truths firm in its grasp. Frank Buytendijk and I came up with a Top 10 of those universal (so called) ‘truths’. (Frank is Vice President of EPM Strategy for Oracle’s Business Intelligence and Performance Management Global Business Unit. You can find his blog at: http://blogs.oracle.com/frankbuytendijk/). We decided - just for fun – to turn the BI world as we know it around and see what comes up if we take the opposite view. Here is our Top 10:

1. We need one version of the truth.
2. BI projects require a business driven approach.
3. BI development should be done incrementally.
4. BI projects needs high level sponsorship.
5. BI = decision making process.
6. BI is going to be real time (BI 2.0).
7. Information democracy: BI for everyone.
8. BI cannot be outsourced.
9. Merging : BI, CI, EI, EPM, CPM, BIM…
10. BI is the answer to everything. 

I will start with the bottom five (saving the top 5 for a future blog). Frank will react to this on his own blog. This way we hope to create a dialogue or diablogue (dialogue + blog). Please feel free to join the discussion and share your opinions with us and all other readers.    10. BI is the answer to everything. No, almost every answer creates a new question. Data is often limited, not captured or of low quality. Therefore one can say that BI is NOT the answer to everything. In the best case scenario it is helpful.   9. Merging: BI, CI, EI, EPM, CPM, BIM. There are so many acronyms and abbreviations that I am not even sure that we can agree on all the two- or three letter words at the beginning. And even if we agree that BI stands for Business Intelligence - I doubt that we can agree on a common definition - let alone talk about merging. To my opinion it is not a merger but a different manifestation where the core or main point may differ per area (focus on strategy, or client, or process…). In each case: it should be information centric.  8. BI cannot be outsourced. A lot of BI professionals tell me that BI cannot be outsourced because of its specific nature (mutual development with end users, incremental approach, and so on). One of our clients has already outsourced its BI environment as long as 8 years ago. The proof of the eating is in the pudding.  7. Information democracy: BI for everyone. Information is critical for almost each person, process or technology. We in the western world produce more data than products. But in order to transform the data into information, knowledge and action you need to add your personal experience, time and effort to the mix. Some people will and will benefit from this. Some people will not and can live without it. BI is not for everyone but at least for the smart people.  6. BI is going to be real time (BI 2.0). If you view BI as strategic there is no need for real time data. But if you view BI as more operational than it will come in handy. In my view BI supports better decisions by making a strategy, process or objective - accountable, adjustable or adaptive. For accountability you do not need real time, for the other two you do. Therefore my answer would be: it depends.  5.BI = decision making process. Ouch, tough one. As mentioned previously I think that BI supports better decisions. It is up to the end user if they make use of it. They can make a decision that is in full conflict with the number crunching. Therefore BI is not the same as the decision making process it is just a supporting tool. I look forward to reactions from Frank and all the other readers.

“Who loves not wine, women and song; remains a fool whole life long”. This is an old German expression which means having fun. I wrote about “weib” yesterday (Cybersex and Business Intelligence). So today I will focus on the “weib und gesang”.  Regular readers of my blog know that I am very enthusiastic about the upcoming trend in decision making to rely more on statistical analysis. The umbrella term for this is called analytics. Recent books about this topic are: Competing on analytics by Thomas Davenport and Super Crunchers by Ian Ayres (www.supercrunchers.com). You are no longer a nerd if you think that regression is cool again. For the non mathematical persons: Regression is a statistical procedure that takes raw data and estimates how various causal factors influence a single variable of interest. Ayres describes in Super Crunchers how analyzing massive databases can provide greater insights in human behavior. One of his case studies involves the work of Oren Ashenfelter. Oren can assess the quality of Bordeaux wines based upon regression (basic variables are: rainfall & temperature). There is much skepticism about his work among wine connoisseurs. Robert Parker – probably one of the world most influential wine writers - said: “it is really a Neanderthal way of looking at wine”. However it turns about that Ashenfelter predictions are much more reliable than the old fashioned way. The week from 5-12 October was democracy week. Over forty broadcasting companies worldwide paid attention to this on radio and TV. Here in the Netherlands we had a mock competition for a new national anthem. The current one which is called The Williams song – or Wilhelmus – is considered to be the oldest one in the world. You can hear the competing songs on this website: http://www.wijzijndebaas.nl/page/liederen. As a follow up to this program there was a segment on a company called Platinum Blue. They have come up with a tool that is called Music X-Ray. This is a complex computer program that breaks songs down into 30 or so component parts’ including rhythm, melody, harmony, beat, cadence, timbre, pitch and gives each a number. All hit songs fit the same pattern. If your song matches the pattern this increases the change of success dramatically. They claim that human decision making (does it sound like a hit, can we promote it like a hit) delivers a 10% success rate. However if you add a third factor – statistical analysis – this score goes up to 75%. You can see their presentation on youtube: http://www.youtube.com/watch?v=8lUewWlLgLo. Just the same as with the wine there is a lot of criticism. The X-factor judge Louis Walsh said: “The only technology I would use to pick a hit is my ears. I would not listen to a computer,” The pattern is the same in both cases. The traditional or old fashioned way of doing business is challenged by a new approach based upon statistical analysis leading to much skepticism. In my view this discussion is too much binary digit, true or false (I wanted to say black-and-white but I am still a technology consultant). In both of the above mentioned cases it is just a tool or technique - a guide to making the final decision, which always comes down to a human being. It is not about replacing the human decision making process with statistical analysis. But on the other hand there is enough evidence to support the fact that adding analytics to the decision making process leads to increased results. This can be done by creating a dialogue between Business Intelligence tools and their users, instead of a one-sided approach of data delivery only, leaving the decision making process to the end user. This going back and forth between user and tool can lead to something that I call intuitive intelligence: leading to insight without (much) thinking.

Kaplan & Me [Click to open]

Yesterday was the launch of Microsoft Performance Point Server in the Netherlands. Keynote speaker was Robert Kaplan (Business Balanced Score Card, Strategy Maps). In the afternoon we did a workshop with Kaplan and some of our clients. This gave me the opportunity to ask him some BI questions.

The first question was what role BI can play for the BSC? He answered that BI is very important. Not only for the reporting framework but especially when it comes down to analytics. (See also: http://www.randomhouse.com/bantamdell/supercrunchers/ and http://www.amazon.com/Competing-Analytics-New-Science-Winning/dp/1422103323). According to Kaplan analytics are crucial during the review of the strategy. Domain experts can crunch the numbers to come up with new and perhaps more effective measures to drive performance. In his opinion all organizations should have an analytics group.

I also asked him what he thinks about two trends in BI: real time (or 2.0) and performance networks or ecosystem BI  (sharing your information with your stakeholders).  Kaplan mentioned that real time BI often is only considered for operational dashboards. However if these operational dashboards are alligned with the strategy they can also be your early warning system for more strategic decisions.

He also strongly believes in sharing information to improve performance, but he underlined that a feedback loop is crucial for this. This must start within your own company, allowing your own employees to react (for example by e-mail) if they find deviations. He gave an example of a large petrol company. Their strategy was to improve the convenience for the customer. The truck drivers, delivering gas to the stations, were very sceptical: “guys who do strategy don’t drive trucks”. But after a while they started calling the head office informing them about rude personel, unclean rest rooms and so on. Sharing the strategy was enough to start a change even before an improvement program was set up.

In summary:

1. Analytics are crucial for reviewing strategy (regression is cool again).

2. Real time BI can be an early warning system for strategic decision making.

3. Information sharing implies a feedback loop.

4. Communicating about strategy is almost as important as excuting the strategy.