“Does Business Intelligence Require Intelligent Business?” by George M. Tomko

CIO Rant George M Tomko

Introduction

George Tomko’s CIO Rant has been on my list of recommended sites for quite some time. I also follow George on twitter.com (http://twitter.com/gmtomko) and have always found his perspective on business and technology matters to be extremely interesting and informative.

George’s latest blog post is is on a subject that is clearly close to my heart and is entitled Does Business Intelligence Require Intelligent Business? I should also thank him for quoting my earlier artcile, Data – Information – Knowledge – Wisdom, in this. Being mentioned in the same breath as Einstein is always gratifying as well!

George acknowledges that this is something of a “What comes first – the chicken or the egg?” situation. He starts out by building on an article by Gerry Davis at Heidrick & Struggles to state:

  1. collecting [information about customers] is “easy”
  2. analyzing it is hard
  3. disseminating it is very hard

Kudos to the first reader to correctly identify the mountain

Both George and Gerry agreed that the mountains of data that many organisations compile are not always very effectively leveraged to yield information, let alone knowledge or wisdom. Gerry proposes:

identifying and appointing the right executive — someone with superb business acumen combined with a sound technical understanding — and tasking them with delivering real business intelligence

George assesses this approach through the prism of the the three points listed above and touches on the ever present challenges of business silos; agreeing that the type of executive that Gerry recommends appointing could be effective in acting across these. However he introduces a note of caution, suggesting that it may be more difficult than ever to kick-off cross-silo initiatives in today’s turbulent times.

I tend to agree with George on this point. Crises may deliver the spark necessary for corporate revolution and unblock previously sclerotic bureaucracies. However, they can equally yield a fortress mentality where views become more entrenched and any form or risk taking or change is frowned upon. The alternative is incrementalism, but as George points out, this is not likely to lead to a major improvement in the “IQ” of organisations (this is an area that I cover in more detail in Holistic vs Incremental approaches to BI).
 
 
The causality dilemma

Which came first?

Returning to George’s chicken and egg question, do intelligent enterprises build good business intelligence, or does good business intelligence lead to more intelligent enterprises? Any answer here is going to vary according to the organisations involved, their cultures, their appetites for change and the environmental challenges and evolutionary pressures that they face.

Having stated this caveat, my own experience is of an organisation that was smart enough to realise that it needed to take better decisions, but maybe not aware that business intelligence was a way to potentially address this. I spoke about this as one of three sceanrios in my recent artcile, “Why Business Intelligence projects fail”. Part of my role in this organisation (as well as building a BI team from scratch and developing a word-class information architecture) was to act as evangelist the benefits of BI.

The work that my team did in collaboration with a wide range of senior business people, helped an organisation to whole-heartedly embrace business intelligence as a vehicle to increasing its corporate “IQ”. Rather than having this outcome as a sole objective, this cultural transfomation had the significant practical impact of strongly contributing to a major business turn-around from record losses over four years, to record profits sustained over six. This is precisely the sort of result that well-designed, well-managed BI that addresses important business questions can (and indeed should) deliver.
 
 
Another sporting analogy

I suppose that it can be argued that only someone with a strong natural aptitude for a sport can become a true athlete. Regardless of their dedication and the amount of training they undertake, the best that lesser mortals can aspire to is plain proficiency. However, an alternative perspective is that it is easy enough to catalogue sportsmen and women who have failed to live up to their boundless potential, where perhaps less able contemporaries have succeeded through application and sheer bloody-minded determination.

I think the same can be said of the prerequisites for BI success and the benefits of successful BI. Organisations with a functioning structure, excellent people at all levels, good channels of communication and a clear sense of purpose are set up better to succeed in BI than their less exemplary competitors (for the same reason that they are set up better to do most things). However, with sufficient will-power (which may initially be centred in a very small group of people, hopefully expanding over time), I think that it is entirely possible for any organisation to improve what it knows about its business and the quality of the decisions it takes.

Good Business Intelligence is not necessarily the preserve of elite organisations – it is within the reach of all organisations who possess the minimum requirements of the vision to aspire to it and the determination to see things through.
 


 
George M. Tomko is CEO and Executive Consultant for Tomko Tek LLC, a company he founded in 2006. With over 30 years of professional experience in technology and business, at the practitioner and executive levels, Mr. Tomko’s goal is to bring game-changing knowledge and experience to client organizations from medium-size businesses to the multidivisional global enterprise.

Mr. Tomko and his networked associates specialize in transformational analysis and decision-making; planning and execution of enterprise-wide initiatives; outsourcing; strategic cost management; service-oriented business process management; virtualization; cloud computing; asset management; and technology investment assessment.

He can be reached at gtomko@tomkotek.com
 

“Why Business Intelligence projects fail”

Introduction

James Anderson bowls Sachin Tendulkar for 1 - England v India, 3rd Test, The Oval, August 12, 2007

In this blog, I have generally tried to focus on success factors for Business Intelligence programmes. I suppose this reflects my general character as something of an optimist. Of course failure can also be instructive; as the saying goes “we learn more from our mistakes than from our successes.” Given this, and indeed the Internet’s obsession with “x reasons why y fails”, I have also written on the subject of how Business Intelligence projects can go wrong a few times.

My first foray into this area was in response to coverage of the Gartner Business Intelligence Summit in Washington D.C. by Intelligent Enterprise. The article was entitled, “Gartner sees a big discrepancy between BI expectations and realities” – Intelligent Enterprise; I have always had a way with words!

Rather than simply picking holes in other people’s ideas on this topic, I then penned a more general piece, Some reasons why IT projects fail, which did what it said on the can. Incidentally I was clearly in the middle of a purple patch with respect to article headlines back then, I am trying to recapture some of these former titular glories in this piece.

So what has moved me to put fingertip to keyboard this time? Well my inspiration (if it can be so described) was, as has often been the case recently, some comments made on a LinkedIn.com forum. In breech of my customary practice, I am not going to identify the group, the discussion thread or the author of these comments, which were from a seasoned BI professional and were as follows:

Most BI projects fail because:

a) the business didn’t support it properly or
b) the business didn’t actually know what they wanted

I realise that I should be more emotionally mature about such matters, but comments such as these are rather like a red rag to a bull for me. Having allowed a few days for my blood pressure to return to normal, I’ll try to offer a dispassionate deconstruction of these suggestions, which I believe are not just incorrect, but dangerously wrong-headed. If the attitude of a BI professional is accurately reflected by the above quote, then I think that we need look no further for why some BI projects fail. Let’s look at both assertions in a little more detail.
 
 
What does “the business didn’t properly support [BI]” actually mean?

The British and Irish Lions scrum in South Africa - 1997

For a change I am going to put my habitual frustration at unhelpful distinctions between “the business” and “IT” to one side (if you want to read about my thoughts on this matter, then please take a look at the Business / IT Alignment and IT Strategy section of my Keynote Articles page).

To me there are three possible explanations here:

  1. The business did not need or want better information
  2. The business needed or wanted better information, initially supported the concept of BI delivering this, but their enthusiasm for this approach waned over time
  3. The business needed or wanted better information, but didn’t think that BI offered the way to deliver this

maybe there are some other possibilities, but hopefully the above covers all the bases. Here are some thoughts on each scenario:
 
 
1. The business did not need or want better information

So the rationale for starting a BI project was… ?

On a more serious note, there could be some valid reasons why a BI project would still make sense. First of all, it might be that a BI system could deliver the same information that is presently provided more cheaply. This could be the case where there are multiple different reporting systems, each needing IT care and support; where the technology that existing systems are based on is going out of support; or where BI is part of a general technology refresh or retrenchment (for example replacing fat client reporting tools with web-based ones, thereby enabling the retirement of multiple distributed servers and saving on the cost of people to maintain them). Of course it may well be IT that highlights the needs in these circumstances, but there should surely be a business case developed with some form or payback analysis or return on investment calculation. If these stand up, then an IT-centric BI project may be justifiable. While I would argue that such an approach is a wasted opportunity, if such an initiative is done properly (i.e. competently managed), then the the lack of business people driving the project should not be an obstacle to success.

Second, it could be that the business saw no need for better information, but IT (or possibly IT in conjunction with a numerate department such as Finance, Change or Strategy) does see one. While this observation is perhaps tinged with a little arrogance, one of IT’s roles should be to act as an educator, highlighting the potential benefits of technology and where they may add business advantage. Here my advice is not to start a BI project and hope that “if we build it, they will come”, but instead to engage with selected senior business people to explain what is possible and explore whether a technology such as BI can be of assistance. If this approach generates interest, then this can hopefully lead to enthusiasm with appropriate nurturing. Of course if the answer is still that the current information is perfectly adequate, then IT has no business trying to kick off a BI project by itself. If it does so, then accountability for its likely failure will be squarely (and fairly) laid at IT’s door.
 
 
2. The business needed or wanted better information, initially supported the concept of BI delivering this, but their enthusiasm for this approach waned over time

To me this sounds like a great opportunity that the BI team have failed to capitalise on. There is a pressing business need. There is a realisation that BI can meet this. Funding is allocated. A project is initiated. However, some where along the line, the BI team have lost their way.

Why would business enthusiasm wane? Most likely because delivery was delayed, no concrete results were seen for a long time, costs ballooned, the system didn’t live up to expectations, or something else happened that moved executive focus from this area. In the final case, then any responsible manager should be prepared to cut their coat according to their cloth. The BI team may feel that their project is all-important, but it is not inconceivable that another project would take precedence, for example integrating a merger.

Assuming that external events are not the reason for business disenchantment, then all of the other reasons are 100% the responsibility of the BI team themselves. BI projects are difficult to estimate accurately as I described in The importance of feasibility studies in business intelligence, but – as the same article explains – this is not an excuse for drastically inaccurate project plans or major cost overruns. Also the BI team should work hard at the beginning of the project to appropriately set expectations. As with any relationship, business or personal, the key to success is frequent and open communication.

Equally, BI projects often require a substantial amount of time to do well (anyone who tells you the opposite has never been involved with one or is trying to sell you something). This does not mean that the BI team should disappear for months (or years) on end. It is important to have a parallel stream of interim releases to address urgent business needs, provide evidence of progress and burnish the team’s credibility (I explore this area further in Holistic vs Incremental approaches to BI).

If the BI system delivered does not live up to expectations then there are two questions to be answered. In what way does it not meet expectations? and Why did it take until implementation to determine this? It could be that the functionality of the BI tool does not meet what is necessary, but most of these have a wide range of functionality and are at least reasonably intuitive to use. More likely the issue is in the information presented in the tool (which is not judged to be useful) or in an inadequate approach to implementation. The way to address both of these potential problems from the very start of the project is to follow the four-pillared approach that I recommend in many places on this blog; notably in one of the middle sections of Is outsourcing business intelligence a good idea?.

So rather than blaming the business for losing interest in BI, the BI team needs to consider where its own inadequacies have led to this problem. It is sometimes tempting to dwell on how no one really appreciates all of the hard work that us IT types do, but it is much more productive to try to figure out why this is and take steps to address the problem.
 
 
3.The business needed or wanted better information, but didn’t think that BI offered the way to deliver this

While I recognise some aspects of the first scenario above, this one is something that I am more intimately familiar with. Back in 2000, I was charged with improving the management information of a large organisation, in response to profitability issues that they were experiencing. No one mentioned data warehouses, or OLAP, or analytics. A business intelligence implementation was my response to the strategic business challenges that the organisation was facing. However I initially faced some scepticism. It was suggested that maybe I was over-engineering my approach (the phrase “we need a diesel submarine, not a nuclear one” being mentioned) when all that was required was a few tweaks to existing reports and writing some new ones.

First of all, a BI professional should welcome such challenges. Indeed they should continually ask the same questions of themselves and their team. If your proposed approach does not stand up to basic scrutiny with respect to cost effectiveness and timeliness, then you are doing a poor job. However good BI people will be able to answer such questions positively, having devised programmes and architectures that are appropriate for the challenges that they seek to meet. A BI solution should clearly not be more expensive than is needed, but equally it should not be cheaper, lest it fails to deliver anything.

A skill that is required in a situation such as the one I found myself in back in 2000 is to be able to lay out your vision and proposals in a way that is logical, compelling, attractive and succinct enough to engage business enthusiasm and engender the confidence of your potential stakeholders. In short you need to be able to sell. Maybe this is not a skill that all IT people have acquired over the years, but it is invaluable in establishing and maintaining project momentum (I cover the latter aspect of this area in three articles starting with Marketing Change).

Again, if the lead of the BI team is not able to properly explain why BI is the best way to meet the information needs of the organisation, then this is essentially the fault of the BI lead and not the business.
 
 
So far, the main conclusion that I have drawn is the same as in my earlier piece about the failure of BI projects. I closed this by stating:

I firmly believe that BI done well is both the easiest of IT systems to sell to people and has one of the highest paybacks of any IT initiative. BI done badly (at the design, development, implementation or follow-up stages) will fail.

The issue is basically a simple one: just how good is your BI team? If a BI implementation fails to deliver significant business value, then instead of looking for scape-goats, the BI team should purchase a mirror and start using it.

Let’s see if an exploration of the second suggested reason for problems with BI projects changes my stance at all.
 
 
What does “the business didn’t know what they wanted [from BI]” actually mean?

What do I need

Business intelligence, when implemented correctly, helps organisations to be more successful by offering a way to understand the dynamics of their operations and markets and facilitating better business decisions. So, almost by definition, good BI has to be a sort of model of the key things that happen in an company. This is not easy to achieve.

Again I will come back to my four-pillared approach and emphasise the first pillar. There is an imperative to:

Form a deep understanding of the key business questions that need to be answered.

In my opinion, it is the difficulty in managing this process that plays into the assertion that “the business didn’t know what they wanted.” Putting it another way, the BI team were not skilful enough, engaging enough, or business-savvy enough to help the business to articulate what they wanted and to translate this into a formal set of definitions that could then form the basis of IT work. This process can indeed lengthy, tough and difficult to get right because:

  1. Businesses are often complex, with many moving parts and many things that need to be measured
  2. Different business people may have different visions of what is important – each of these may have validity, depending on context
  3. Both IT and business people may be unaccustomed to talking about business phenomena in the required way (one that is self-consistent and exhaustive)
  4. IT may not have a proper understanding of business strategy, business terminology and business transactions
  5. There is often a desire to start with the current state and adapt / add to this, rather than take the more arduous (but more profitable) approach of working out what is necessary and desireable
  6. The process is typically iterative and requires an ongoing commitment to the details, sapping reserves of perseverance

I have written about the level of commitment that can be required in defining BI business requirements in a couple of articles: Scaling-up Performance Management and Developing an international BI strategy. Please take a look at these if you are interested in delving further into this area. For now it is enough to state that you should probably allow a number of months for this work in your BI project plan; more if your objective is to deliver an all-pervasive BI system (as it was in the work I describe in the articles). It is also helpful to realise that you are never “done” with requirements in BI, they will evolve based on actual use of the system and changing business needs. You will end up living this cycle, so it makes sense to get good at it.

Sometimes it may be tempting for either IT or the business people involved to short-cut the process, or to give up on it entirely. This is s sure recipe for disaster. It is difficult to make establishing requirements a fun exercise for all those involved, but it is important the the BI team continually tries to keep energy levels high. This can be done in a number of ways: by reminding everyone about the importance of their work for the organisation as a whole; by trying to use prototypes to make discussions more concrete; and – probably most importantly – by building and maintaining personal relationships with their business counterparts. If you are going to work for a long time with people on something that is hard to do, then it makes sense to at least try to get along. It is on apparently small things such as these essentially human interactions that the success or failure of multi-million dollar projects can hinge.

Helping the business to articulate what they want from BI is extremely important and equally easy to get wrong. Mistakes made at this stage can indeed derail the whole project. However, this is precisely what the BI team should be good at; it should be their core competency. If this work is not done well, then again it is primarily the responsibility of the professionals involved. A statement such as “the business didn’t know what they wanted” simply reflects that the BI team were not very good at running this phase of a BI project.
 
 
Conclusion

The case against the BI team

I find myself back at my previous position. Of course the idea of finding scape-goats for the failure of a BI project can be very tempting for the members of the team that has failed. However, this is an essentially futile process and one that proves that the adage about learning from your mistakes does not always apply.

To make things personal. Suppose that I am responsible for leading project in which it is obvious up-front that extensive buy-in and collaboration will be required from a group of people. If the project fails because neither of these things was obtained, then surely that’s my fault and not theirs, isn’t it?
 


 
For some further thoughts on this issue, take a look at an article by Ferenc Mantfeld entitled: Top 10 reasons why Business Intelligence Projects fail.
 

“Big vs. Small BI” by Ann All at IT Business Edge

Introduction

  Ann All IT Business Edge  

Back in February, Dorothy Miller wrote a piece at IT Business Edge entitled, Measuring the Return on Investment for Business Intelligence. I wrote a comment on this, which I subsequently expanded to create my article, Measuring the benefits of Business Intelligence.

This particular wheel has now come full circle with Ann All from the same web site recently interviewing me and several BI industry leaders about our thoughts on the best ways to generate returns from business intelligence projects. This new article is called, Big vs. Small BI: Which Set of Returns Is Right for Your Company? In it Ann weaves together an interesting range of (sometimes divergent) opinions about which BI model is most likely to lead to success. I would recommend you read her work.

The other people that Ann quotes are:

John Colbert Vice president of research and analytics for consulting company BPM Partners.
Dorothy Miller Founder of consulting company BI Metrics (and author of the article I mention above).
Michael Corcoran Chief marketing officer for Information Builders, a provider of BI solutions.
Nigel Pendse Industry analyst and author of the annual BI Survey.

 
Some differences of opinion

As might be deduced from the title of Ann’s piece the opinions of the different interviewees were not 100% harmonious with each other. There was however a degree of alignment between a few people. As Ann says:

Corcoran, Colbert and Thomas believe pervasive use of BI yields the greatest benefits.

On this topic she quoted me as follows (I have slightly rearranged the text in order to shorten the quote):

If BI can trace all the way from the beginning of a sales process to how much money it made the company, and do it in a way that focuses on questions that matter at the different decision points, that’s where I’ve seen it be most effective.

By way of contrast Pendse favours:

smaller and more tactical BI projects, largely due to what his surveys show are a short life for BI applications at many companies. “The median age of all of the apps we looked at is less than 2.5 years. For one reason or another, within five years the typical BI app is no longer in use. The problem’s gone away, or people are unhappy with the vendor, or the users changed their minds, or you got acquired and the new owner wants you to do something different,” he says. “It’s not like an ERP system, where you really would expect to use it for many years. The whole idea here is go for quick, simple wins and quick payback. If you’re lucky, it’ll last for a long time. If you’re not lucky, at least you’ve got your payback.”

I’m sure that Nigel’s observations are accurate and his statistics impeccable. However I wonder whether what he is doing here is lumping bad BI projects with good ones. For a BI project a lifetime of 2.5 years seems extraordinarily short, given the time and effort that needs to be devoted to delivering good BI. For some projects the useful lifetime must be shorter than the development period!

Of course it may be that Nigel’s survey does not discriminate between tiny, tactical BI initiatives, failed larger ones and successful enterprise BI implementations. If this is the case, then I would not surprised if the first two categories drag down the median. Though you do occasionally hear horror stories of bad BI projects running for multiple years, consuming millions of dollars and not delivering, most bad BI projects will be killed off fairly soon. Equally, presumably tactical BI projects are intended to have a short lifetime. If both of these types of projects are included in Pendse’s calculations, then maybe the the 2.5 years statistic is more understandable. However, if my assumptions about the survey are indeed correct, then I think that this figure is rather misleading and I would hesitate to draw any major conclusions from it.

In order that I am not accused of hidden bias, I should state unequivocally that I am a strong proponent of Enterprise BI (or all-pervasive BI, call it what you will), indeed I have won an award for an Enterprise BI implementation. I should also stress that I have been responsible for developing BI tools that have been in continuous use (and continuously adding value) for in excess of six years. My opinions on Enterprise BI are firmly based in my experiences of successfully implementing it and seeing the value generated.

With that bit of disclosure out of the way, let’s return to the basis of Nigel’s recommendations by way of a sporting analogy (I have developed quite a taste for these, having recently penned artciles relating both rock climbing and mountain biking to themes in business, technology and change).
 
 
A case study

Manchester United versus Liverpool

The [English] Premier League is the world’s most watched Association Football (Soccer) league and the most lucrative, attracting the top players from all over the globe. It has become evident in recent seasons that the demands for club success have become greater than ever. The owners of clubs (be those rich individuals or shareholders of publicly quoted companies) have accordingly become far less tolerant of failure by those primarily charged with bringing about such success; the club managers. This observation was supported by a recent study[1] that found that the average tenure of a dismissed Premier League manager had declined from a historical average of over 3 years to 1.38 years in 2008.

As an aside, the demands for business intelligence to deliver have undeniably increased in recent years; maybe BI managers are not quite paid the same as Football managers, but some of the pressures are the same. Both Football managers and BI managers need to weave together a cohesive unit from disparate parts (the Football manager creating a team from players with different skills, the BI manager creating a system from different data sources). So given, these parallels, I suggest that my analogy is not unreasonable.

Returning to the remarkable statistic of the average tenure of a departing Premier League manger being only 1.38 years and applying Pendse’s logic we reach an interesting conclusion. Football clubs should be striving to have their managers in place for less than twelve months as they can then be booted out before they are obsolete. If this seems totally counter-intutitive, then maybe we could look at things the other way round. Maybe unsuccessful Football managers don’t last long and maybe neither do unsuccessful BI projects. By way of corollary, maybe there are a lot of unsuccessful BI projects out there – something that I would not dispute.

By way of an example that perhaps bears out this second way of thinking about things, the longest serving Premier League manager, Alex Ferguson of Manchester United, is also the most successful. Manchester United have just won their third successive Premier League and have a realistic chance of becoming the first team ever to retain the UEFA Champions League.

Similarly, I submit that the median age of successful BI projects is most likely significantly more than 2.5 years.
 
 
Final thoughts

I am not a slavish adherent to an inflexible credo of big BI; for me what counts is what works. Tactical BI initiatives can be very beneficial in their own right, as well as being indispensible to the successful conduct of larger BI projects; something that I refer to in my earlier article, Tactical Meandering. However, as explained in the same article, it is my firm belief that tactical BI works best when it is part of a strategic framework.

In closing, there may be some very valid reasons why a quick and tactical approach to BI is a good idea in some circumstances. Nevertheless, even if we accept that the median useful lifetime of a BI system is only 2.5 years, I do not believe that this is grounds for focusing on the tactical to the exclusion of the strategic. In my opinion, a balanced tactical / strategic approach that can be adapted to changing circumstances is more likely to yield sustained benefits than Nigel Pendse’s tactical recipe for BI success.
 


 
Nigel Pendse and I also found ourselves on different sides of a BI debate in: Short-term “Trouble for Big Business Intelligence Vendors” may lead to longer-term advantage.
 
[1] Dr Susan Bridgewater of Warwick Business School quoted in The Independent 2008
 

Automating the business intelligence process?

Balanced Insight Merv Adrian - IT Market Strategies for Suppliers
Balanced Insight Merv Adrian

 
 
Introduction

I enjoy reading the thoughts of vastly experienced industry analyst Merv Adrian on his blog, Market Strategies for IT Suppliers, and also on twitter via @merv. Merv covers industry trends and a wide variety of emerging and established technologies and companies. I would encourage you to subscribe to his RSS feed.

In a recent artcile, Balanced Insight – Automating BI Design to Deployment, Merv reviews the Consensus tool and approach developed by Ohio-based outfit Balanced Insight. I suggest that you read Merv’s thoughts first as I won’t unnecessarily repeat a lot of what he says here. His article also has links to a couple of presentations featuring the use of Consensus to build both Cognos 8 and Proclarity prototypes, which are interesting viewing.
 
 
An overview of Balanced Insight

Disclaimer: I haven’t been the beneficiary of a briefing from Balanced Insight, and so my thoughts are based solely on watching their demos, some information from their site and – of course – Merv’s helpful article.

The company certainly sets expectations high with the strap line of their web site:

Agile & Aligned Business Intelligence - With Balanced Insight Consensus® deliver in half the time without compromising cross project alignment.

Promising to “deliver in half the time without compromising cross project alignment” is a major claim and something that I will try to pay close attention to later.

The presentations / demonstrations start with a set-up of a fictional company (different ones in different demos) who want to find out more about issues in their business: outstanding receivables, or profit margins [Disclosure: the fact that the second demo included margins on mountain bikes initially endeared me to the company]. In considering these challenges, Balanced Insight offers the following slide contrasting IT’s typical response with the, presumably superior, one taken by them:

IT's approach to information problems vs Balanced Insight's
IT's approach to information problems vs Balanced Insight's

I agree with Balanced Insight’s recommendation, but rather take issue with the assumption that IT always starts by looking exclusively at data when asked to partake in information-based initiatives. I have outlined what I see as the four main pillars of a business intelligence project at many places on this blog, most recently in the middle of my piece on Business Intelligence Competency Centres. While of course it is imperative to understand the available data (what would be the alternative?), the first step in any BI project is to understand the business issues and, in particular, the questions that the business wants an answer to. If you search the web for BI case studies or methodologies, I can’t imagine many of these suggesting anything other than Balanced Insight’s recommended approach.

Moving on, the next stage of both the demos introduces the company’s “information packages”. These are panes holding business entities and have two parts; the upper half contains “Topics and Categories” (things such as date or product), the bottom half contains measurements. The “Topics and Categories” can be organised into hierarchies, for example: day is within week, which is within month, quarter and year. At this point most BI professionals will realise that “Topics and Categories” are what we all call “Dimensions” – but maybe Balanced Insight have a point picking a less technical-sounding name. So what the “information package” consists of is a list of measures and dimensions pertaining to a particular subject area – it is essentially a loose specification for a data mart.

The interesting point is what happens next, the Consensus Integrator uses the “information package” to generate what the vendor claims is an optimised star-schema database (in a variety of databases). It then creates a pre-built prototype that references the schema; this can be in a selection of different BI tools. From what I can tell from the demos, the second stage appears to consist of creating an XML file that is then read by the BI tool. In the first example, the “Topics and Categories” become dimensions in Cognos AnalysisStudio and the measures remain measures. In both demos sample data is initially used, but in the ProClarity one a version with full data is also shown – it is unclear whether this was populated via Consensus or not. The “information package” can also be exported to data modelling tools such as ERwin.

One of the Balanced Insight presentations then mentions that “all that’s left to do is then to develop your ETL”. I appreciate that it is difficult to go into everything in detail in a short presentation, but this does rather seem to be glossing over a major area, indeed one of my four pillars of BI projects referred to above. Such rather off-hand comments do not exactly engender confidence. If there is a better story to tell here, then Balanced Insight’s presentations should try to tell it.
 
 
The main themes

There are a few ideas operating here. First that Balanced Insight’s tools can support a process which will promote best practice in defining and documenting the requirements of a BI project and allow a strong degree of user interaction. Second that the same tools can quickly and easily produce functioning prototypes that can be used to refine these same requirements and also make discussions with business stakeholders more concrete. Finally that the prototypes can employ a variety of database and BI tools – so maybe you prototype on a cheap / free database and BI tool, then implement on a more expensive, and industrial strength, combination later.

Balanced Insight suggest that their product helps to address “the communication gap between IT and the business”. I think it is interesting using the “information package” as a document repository, which may be helpful at other stages of the project. But there are other ways of achieving this as well. How business friendly these are probably depends on how the BI team set them up. I have seen Excel and small Access databases work well without even buying a specific tool. Also I think that if a BI team needs a tool to ensure it sticks to a good process, then there is probably a bigger problem to worry about.

Of course, the production of regular prototypes is a key technique to employ in any BI project and it seems that Balanced Insight may be on to something here, particularly if the way that their “information package” presents subject areas makes it easier for the BI team and business people to discuss things. However, it is not that arduous to develop prototypes directly in most BI tools. To put this in a context drawn from my own experience, building Cognos cubes to illustrate the latest iteration of business requirement gathering was often a matter of minutes, compared to business analysts putting in many days of hard work before this stage.

Having decided to use Consensus to capture information about measures and dimensions, the ability to then transfer these to a range of BI tools in interesting. This may offer the opportunity to change tools during the initial stages of the project and to try out different tools with the same schema and data to assess their effectiveness. This may also be something that is a useful tool when negotiating with BI vendors. However, again I am not sure exactly how big of a deal this is. I would be interested in better understanding how users have taken advantage of this feature.
 
 
A potential fly in the ointment

It would be easy to offer a couple of other criticisms of the approach laid out in the demos; namely that it seems to be targeted at developing point solutions rather than a pervasive BI architecture and that (presumably related to this) the examples shown are very basic. However, I’m willing to given them the benefit of the doubt, a sales pitch is probably not the place for a lengthy exploration of broad and complex issues. So I think my overall response to Balanced Insight’s Consensus product could be summed up as guardedly positive.

Nevertheless, there is one thing that rather worries me and this can best be seen by looking at the picture below. [As per the disclaimer above, the following diagram is based on my own understanding of the product and has not been provided by Balanced Insight.]

My perception of how Balanced Insight addresses needs for information
My perception of how Balanced Insight addresses needs for information

I think I understand the single black arrow on the right of the diagram, I’m struggling to work out what Consensus offers (aside from documentation) for the two black arrows on the left hand side. Despite the fact that Balanced Insight disparaged the approach of looking at available data in their presentation, there is no escaping the fact that some one will have to do this at some point. Connections will then have to be made between the available data and the business questions that need answering.

In both demos Consensus is pre-populated with dimensions, measures and linkages of these to sample data. How this happens is not covered, but this is a key area for any BI project. Unless Balanced Insight have some deus ex machina that helps to cut the length of this stage, then I begin to become a little sceptical about their claim to halve the duration of BI work.

Of course my concerns could be unfounded. It will be interesting to see how things develop for the company and whether their bold claims stand the test of time.
 

“Why taking a few punches on the financial crisis just might save IT” by Patrick Gray on TechRepublic

linkedin TechRepublic

Back on April 27th I wrote an article, The scope of IT’s responsibility when businesses go bad, that was inspired by a thread that Patrick Gray had started on the LinkedIn.com Chief Information Officer (CIO) Network group. This was entitled Is IT partially to blame for the financial crisis? (as ever you need to be a member of LinkedIn.com and the group to view these links).

Since then, there have been nearly 80 comments made by a wide variety of people, with an equally wide range of opinions. As can often happen in on-line discussions, positions were taken, attitudes were hardened and eventually some sort of stalemate was reached; probably as the protagonists were too weary to fight any more. In this respect seasoned IT professionals can be no different to teenagers discussing the merits of different genres of music! I certainly employed my method acting approach at a new level on this thread.

As a result of the overall feedback, Patrick has now composed a blog article on TechRepublic.com, an outlet that has also featured one of my favourite technology writers, Ilya Bogorad (see this earlier blog post). Patrick’s piece is titled Why taking a few punches on the financial crisis just might save IT and takes a thought-provoking stance with respect to the comments that his thread engendered. Here are the introductory remarks:

Patrick Gray believes that IT leaders still looking to find a seat at the C-level table might gain that influential position by taking a share of the responsibility for the failures that led to financial crisis.

It is certainly worth reading this article, but I recommend that you do so with an open mind.
 


 
Patrick Gray is the founder and president of Prevoyance Group, and author of Breakthrough IT: Supercharging Organizational Value through Technology. Prevoyance Group provides strategic IT consulting services to Fortune 500 and 1000 companies. Patrick can be reached at patrick.gray@prevoyancegroup.com.
 

Maureen Clarry stresses the need for change skills in business intelligence on BeyeNetwork

The article

beyenetwork2

Maureen Clarry begins her latest BeyeNETWORK article, Leading Change in Business Intelligence, by stating:

If there was a standard list of core competencies for leaders of business intelligence (BI) initiatives, the ability to manage complex change should be near the top of the list.

I strongly concur with Maureen’s observation and indeed the confluence of BI and change management is a major theme of this blog; as well as the title of one of my articles on the subject. Maureen clearly makes the case that “business intelligence is central to supporting […] organizational changes” and then spends some time on Prosci’s ADKAR model for leading change; bringing this deftly back into the BI sphere. Her closing thoughts are that such a framework can help a lot in driving the success of a BI project.
 
 
My reflections

I find it immensely encouraging that an increasing number of BI professionals and consultants are acknowledging the major role that change plays in our industry and in the success of our projects. In fact it is hard to find some one who has run a truly successful BI project without paying a lot of attention to how better information will drive different behaviour – if it fails to do this, then “why bother?” as Maureen succinctly puts it.

Without describing it as anything so grand as a framework, I have put together a trilogy of articles on the subject of driving cultural transformation via BI. These are as follows:

Marketing Change
Education and cultural transformation
Sustaining Cultural Change

However the good news about many BI professionals and consultants embracing change management as a necessary discipline does not seem to have filtered through to all quarters of the IT world. Many people in senior roles still seem to see BI as just another technology area. This observation is born out of the multitude of BI management roles that request an intimate knowledge of specific technology stacks. These tend to make only a passing reference to experience of the industry in question and only very infrequently mention the change management aspects of BI at all.

Of course there are counterexamples, but the main exceptions to this trend seem to be where BI is part of a more business focused area, maybe Strategic Change, or the Change Management Office. Here it would be surprising if change management skills were not stressed. When BI is part of IT it seems that the list of requirements tends to be very technology focussed.

In an earlier article, BI implementations are like icebergs I argued that, in BI projects, the technology – at least in the shape of front-end slice-and-dice tools – is not nearly as important as understanding the key business questions that need to be answered and the data available to answer them with. In “All that glisters is not gold” – some thoughts on dashboards, I made similar points about this aspect of BI technology.

I am not alone in holding these opinions, many of the BI consultants and experienced BI managers that I speak to feel the same way. Given this, why is there the disconnect that I refer to above? It is a reasonable assumption that when a company is looking to set up a new BI department within IT, it is the CIO who sets the tone. Does this lead us inescapably to the the conclusion that many CIOs just don’t get BI?

I hope that this is not the case, but I see increasing evidence that there may be a problem. I suppose the sliver lining to this cloud is that, while such attitudes exist, they will lead to opportunities for more enlightened outfits, such as the one fronted by Maureen Clarry. However it would be even better to see the ideas that Maureen espouses moving into the mainstream thinking of corporate IT.
 


 
Maureen Clarry is the Founder and President/CEO of CONNECT: The Knowledge Network, a consulting firm that specializes in helping IT people and organizations to achieve their strategic potential in business. CONNECT was recognized as the 2000 South Metro Denver Small Business of the Year and has been listed in the Top 25 Women-Owned Businesses and the Top 150 Privately Owned Businesses in Colorado. Maureen also participates on the Data Warehousing Advisory Board for The Daniels College of Business at the University of Denver and was recognized by the Denver Business Journal as one of Denver’s Top Women Business Leaders in 2004. She has been on the faculty of The Data Warehousing Institute since 1997, has spoken at numerous other seminars, and has published several articles and white papers. Maureen regularly consults and teaches on organizational and leadership issues related to information technology, business intelligence and business.
 

Mountain Biking and Systems Integration

Introduction

Mountain Biking

This is another in my occasional series in which I draw conclusions about business, technology and change from my experiences in various of the sports that I enjoy. The first article in this series was Perseverance, which compared the degree of effort required to succeed in rock climbing with that necessary in change management. Here I am going to focus on another of my pastimes, mountain biking.
 
 
Some background on Mountain Biking

Mountain biking (invariably abbreviated to MTBing, with the bikes themselves called MTBs) is something that I have got into more recently than rock climbing, but I suppose there are some parallels between the two. My path into rock climbing started with loving walking in the outdoors, which became hill-walking, which became scrambling (hill-walking where you have to use your hands to get through steeper sections), which eventually became rock climbing. Starting at the same place, MTBing is another logical progression from general and hill-walking. While rock climbing was about increasing the angle at which I moved (to vertical and then over-hanging), MTBing was about increasing my speed.

Another thing that rock climbing and MTBing have in common is their adherents’ obsession with equipment. With rock climbing this ranges from the shoes you use, to bouldering mats, to ropes, to the various pieces of equipment that you use to protect yourself. With MTBing, the focus is mostly on the bike itself.

There are two main types of mountain bikes: hard tails and full suspension bikes. Hard tails have a shock absorber at the front, but the rear is essentially the same as in a road bike. An example of two hard tails appears below – these are actually my bike and my partner’s, both rather muddy from a day out.

Two hard tail mountain bikes
Two hard tail mountain bikes

Full suspension bikes (as the name suggests) have suspension at both front and rear. The fact that you also need to drive the suspended rear wheel with the pedals makes these types of bikes much more complicated from an engineering perspective (and inevitably more expensive). While some zealots aver that all you ever really need is a hard tail, regardless of the conditions, the majority accept that a full suspension MTB will allow you to take on more challenging terrain with greater comfort and security. It is full suspension bikes that I will devote the rest of this article to.
 
 
The anatomy of a full suspension mountain bike

The following is a diagram showing the general anatomy of a full suspension bike; the UK version of a Specialized Stumpjumper FSR Elite (the US one currently being black). The image is copyright Specialized, one of the most well-know bike manufacturers, but the annotations are mine.

Full suspension mountain bike. Image © Specialized Bicycle Components
Full suspension mountain bike. Photo © Specialized Bicycle Components

As you might expect, one of the major factors influencing the performance of a MTB is the frame – this is the main structure of the bike, generally made from steel or alloy tubing, or sometimes carbon fibre in more expensive models. Helpfully in the diagram, the frame is essentially all the white bits. As we are talking about a full suspension bike, the back of the frame needs to flex in order to allow the rear wheel to move up and down.

The above design is for a cross country bike. This is the least aggressive type of MTBing and consequently places least stress on the frame. In this bike, the seat and chain stays (the triangular white bits at the back extending back to meet at the axle of the rear wheel) are both pivoted and their movement is damped by the rear shock absorber. In more full-on types of mountain biking (all mountain, free ride and down hill) the bikes can begin to resemble tanks – think of a 4×4 (SUV) compared to a regular car. Designs with a single, beefy swing arm at the rear are more common in these types of bikes.

However it is not the frame that I really want to focus on here, but the other components that go to make up the bike. Most of these are labelled in the diagram. Sometimes a bike comes complete with all of these and ready to ride. Other times you buy the frame only (or maybe the frame and shocks) and then add the other components that you would like to have. In this second approach the idea is to tailor the components to the type of riding that you want to do and of course to your wallet.

Often, even with a ready-to-ride bike, the owner might decide to upgrade some components, for example by purchasing a better front fork. Some of the manufacturers will sell you a ready to ride bike that has essentially all of their own components, but even then it is highly likely that the equipment for pedalling, changing gears and braking will be made by another organisation. On the bike in the diagram above, a lot of the components are from manufacturers other than Specialized. The range of other companies involved in the above bike includes:

  • Fox – the front and rear shock absorbers
  • SRAM – the chain
  • Shimano – the crank arm, chain rings, pedals, front and rear derailleurs, gear shifters and rear sprocket
  • Avid – the brake assemblies and levers (Avid are now part of SRAM)
  • DT Swiss – the rear hub (the front one being by Specialized)

It is also worth noting that Specialized, more than most other frame manufacturers, are known for producing their own components – they even make the shock absorbers for many of their models. On a bike from another company, the seat post, saddle, head stem, handle bars and tyres would all also be from another organisation; often different ones to those listed above.
 
 
The connection with Systems Integration

While, as mentioned above, the frame plays a very important role in determining how well a bike performs (and it is generally the most expensive part of the bike), what really leads to a great bike is how all of the different components are blended together. While Component A may be great on Frame X, working with Component B, it will be a much less good choice for Frame Y, working with Component C. The other factor that comes into this is of course cost and this makes a balance even harder to find.

It is in the above area that I see some similarities with systems integration. Here too the aim is to make products from different organisations work in harmony in order to deliver the greatest level of effectiveness at the lowest cost. Here too trade-offs will be necessary to meet the often incompatible goals of performance/functionality and cost. In systems integration, as in MTBing above, Product A may work well with Product B in Industry X, but be a bad option to run with Product C in Industry Y.
 
 
The most expensive is not necessarily the best

An interesting observation in MTBing is that simply picking the most expensive component in each category will not necessarily lead to the best performing bike. Some components suit the geometry of some bikes and work well in conjunction with other components regardless of cost. Here is just one example from a review of a bike in a magazine I subscribe to, Mountain Bike Rider (or MBR). The bike in question is a Giant Trance X5 and it just came first in MBR’s review of full suspension bikes under £1,000 ($1,500 – though it is probably cheaper than that in the US). This bike received a rating of 9/10 from MBR. Here is a picture of it:

Giant X5 full suspension mountain bike. Image © Giant U.K. Ltd
Giant X5 full suspension mountain bike. © Giant U.K. Ltd

This bike features an OEM rear shock as below (image copyright MBR):

Giant rear shock. © MBR magazine
Giant rear shock. © MBR magazine

Here is what MBR had to say about it (note that Fox – whose shocks were featured on the Specialized Stumpjumper above – produce both the most respected and most expensive shocks in the industry):

We found this presumably cheap own-brand unit to be the best we’ve ridden on a Maestro linkage [the type of rear pivot arrangement featured on the bike], whether by chance or design. It felt like it had less compression damping and married with the linkage better than any Fox unit we’ve tried. It was a lot more active, allowing the Maestro design to track the terrain better on this bike than on any other Giant Trance we’ve ridden previously.

It is worth noting that MBR terminology can be just as confusing as IT jargon until you get used to it. The above quote is actually relatively light on terminology.
 
 
Closing thoughts

While it is dangerous to extend some analogies too far, I think that there is something to be learnt here about how to run systems integration projects. It is the systems that work best with the other parts of the design that should be selected, not those that have the best features when considered in isolation, or those that come from the most prestigious companies.

It used to be said that buying the products of some IT vendors was a sure way to avoid getting fired (the vendors mentioned have varied over time). The above insight from the world of mountain biking suggests that looking beyond the obvious (and often more expensive) products may sometimes yield significant benefits.
 

 

Business Intelligence Competency Centres

Introduction

The subject of this article ought to be reasonably evident from its title. However there is perhaps some room for misinterpretation around even this. Despite the recent furore about definitions, most reasonable people should be comfortable with a definition of business intelligence. My take on this is that BI is simply using information to drive better business decisions. In this definition, the active verb “drive” and the subject “business decisions” are the key elements; something that is often forgotten in a rush for technological fripperies.
 
 
The central issue

Having hopefully addressed of the “BI” piece of the BICC acronym, let’s focus on the “CC” part. I’ll do this in reverse order, first of all considering what is meant by “centre”. As ever I will first refer to my trusted Oxford English Dictionary for help. In a discipline, such as IT, which is often accused of mangling language and even occasionally using it to obscure more than to clarify, a back-to-basics approach to words can sometimes yield unexpected insights.

  centre / séntər / n. & v. (US center) 3 a a place or group of buildings forming a central point in a district, city, etc., or a main area for an activity (shopping centre, town centre).
(O.E.D.)
 

Ignoring the rather inexcusable use of the derived adjective “central” in the definition of the noun “centre”, then it is probably the “main area for an activity” sense that is meant to be conveyed in the final “C” of BICC. However, there is also perhaps some illumination to be had in considering another meaning of the word:

Centre of a Sphere

  n. 1 a the middle point, esp. of a line, circle or sphere, equidistant from the ends, or from any point on the circumference or surface.
(O.E.D.)
 

As well as appealing to the mathematician in me, this meaning gives the sense that a BICC is physically central geographically, or metaphorically central with respect to business units. Of course this doesn’t meant than a BICC needs to be at the precise centre of gravity of an organisation, with each branch contributing a “weight” calculated by its number of staff, or revenue; but it does suggest that the competency centre is located at a specific point, not dispersed through the organisation.

Of course, not all organisations have multiple locations. The simplest may not have multiple business units either. However, there is a sense by which “centre” means that a BICC should straddle whatever diversity there is an organisation. If it is in multiple countries, then the BICC will be located in one of these, but serve the needs of the others. If a company has several different divisions, or business units, or product streams; then again the BICC should be a discrete area that supports all of them. Often what will make most sense is for the BICC to be located within an organisation’s Head Office function. There are a number of reasons for this:

  1. Head Office similarly straddles geographies and business units and so is presumably located in a place that makes sense to do this from (maybe in an organisation’s major market, certainly close to a transport hub if the organisation is multinational, and so on).
  2. If a BICC is to properly fulfil the first two letters of its abbreviation, then it will help if it is collocated with business decision-makers. Head Office is one place than many of these are found, including generally the CEO, the CFO, the Head of Marketing and Business Unit Managers. Of course key decision makers will also be spread throughout the organisation (think of Regional and Country Managers), but it is not possible to physically collocate with all of these.
  3. Another key manager who is hopefully located in Head Office is the CIO (though this is dispiritingly not always the case, with some CIOs confined to IT ghettos, far from the rest of the executive team and with a corresponding level of influence). Whilst business issues are pre-eminent in BI, of course there is a major technological dimension and a need to collaborate closely with those charged with running the organisation’s IT infrastructure and those responsible for care and feeding of source data systems.
  4. If a BI system is to truly achieve its potential, then it must become all pervasive; including a wide range of information from profitability, to sales, to human resources statistics, to expense numbers. This means that it needs to sit at the centre of a web of different systems: ERP, CRM, line of business systems, HR systems etc. Often the most convenient place to do this from will be Head Office.

Thusfar, I haven’t commented on the business benefits of a BICC. Instead I have confined myself to explaining what people mean by the second “C” in the name and why this might be convenient. Rather than making this an even longer piece, I am going to cover both the benefits and disadvantages of a BICC in a follow-on article. Instead let’s now move on to considering the first “C”: Competency.
 
 
Compos centris

Returning to our initial theme of generating insights via an examination of the meaning of words in a non-IT context, let’s start with another dictionary definition:

Motar board

  competence /kómpit’nss/ n. (also competency /kómpitənsi/) 1 (often foll. by for, or to + infin.) ability; the state of being competent.

and given the recursive reliance of the above on the definition of competent…
  competent /kómpit’nt/ adj. 1 a (usu. foll. by to + infin.) properly qualified or skilled (not competent to drive); adequately capable, satisfactory. b effective (a competent bastman*).
(O.E.D.)
 

* People who are not fully conversant with the mysteries of cricket may substitute “batter” here.

To me the important thing to highlight here is that, while it is to be hoped that a BICC will continue to become more competent once it is up and running, in order to successfully establish such a centre, a high degree of existing competence is a prerequisite. It is not enough to simply designate some floor space and allocate a number of people to your BICC, what you need is at least a core of seasoned professionals who have experience of delivering transformational information and know how to set about doing it.

There are many skills that will be necessary in such a group. These match the four main pillars of a BI implementation (I cover these in more depth in several places on the blog, including BI implementations are like icebergs and the middle section of Is outsourcing business intelligence a good idea?):

  1. Understand the important business decisions and what figures are necessary to support these.
  2. Understand the data available in the organisation, how it relates to other data and to business decisions.
  3. Transform the data to provide information answering business questions.
  4. Focus on embedding the use of information in the corporate DNA.

So a successful BICC must include: people with strong analytical skills and an understanding of general business practices; high-calibre designers; reliable and conscientious ETL and general programmers; experts in the care, feeding and design of databases; excellent quality assurance professionals; resource conversant with both whatever front-end tools you are using to deliver information and general web programming; staff with skills in technical project management; people who can both design and deliver training programmes; help desk personnel; and last, but by no means least, change managers.

Of course if your BI project is big enough, then you may be able to afford to have people dedicated to each of these roles. If resources are tighter (and where is this not the case nowadays?) then it is better to have people who can wear more than one hat: business analysts who can also design; BI programmers who will also take support calls; project managers who will also run training classes; and so on. This approach saves money and also helps to deal with the inevitable peaks and troughs of resource requirements at different stages in a project. I would recommend setting things up this way (or looking to stretch your people’s abilities into new areas) even if you have the luxury of a budget that would allow a more discrete approach. The challenge of course is going to be finding and retaining such multi-faceted staff.

Also, it hopefully goes without saying that BI is a very business-focussed area and some BICCs will explicitly include business people in them. Even if you do not go this far, then the BICC will have to form a strong partnership with key business stakeholders, often spread across multiple territories. The skill to manage this effectively is in itself a major requirement of the leading personnel of the centre.

Given all of the above, the best way to staff a BICC is with members of a team who have already been successful with a BI project within your organisation; maybe one that was confined to a given geographic region or business unit. If you have no such team, then starting with a BICC is probably a bridge too far. Instead my recommendation would be to build up some competency via a smaller BI project. Alternatively, if you have more than one successful BI team (and, despite the manifold difficulties in getting BI right, such things are not entirely unheard of) then maybe blending these together makes sense. This is unless there is some overriding reason not to (e.g. vastly different team cultures or methodologies. In this case, picking a “winner” may be a better course of action.

Such a team will already have the skills outlined above in abundance (else they could never have been successful). It is also likely that whatever information was needed in their region or business unit will be at least part of what is needed at the broader level of a BICC. Given that there are many examples of BI projects not delivering or consuming vastly more resource than anticipated, then leveraging those exceptional people who have managed to swim against this tide is eminently sensible. Such battle-hardened professionals will know what pitfalls to avoid, which areas are most important to concentrate on and can use their existing products to advertise the benefits of a wider system. If you have such people at the core of your BICC, then it will be easier to integrate new joiners and quickly shepherd them up the learning curve (something that can be particularly long in BI due to the many different aspects of the work).

Of course having been successful in one business unit or region is not enough to guarantee success on a larger scale. I spoke about some of the challenges of doing this in an earlier article, Developing an international BI strategy. Another issue that is likely to raise its head is the political dimension, in particular where different business units or regions already have a management information strategy at some stage of development. This is another area that I will also cover in more detail in a forthcoming piece.
 
 
Conclusions

It seems that simply musing on the normal meanings of the words “competency” and “centre” has led us into some useful discussions. As mentioned above, at least two other blog postings will expand upon areas that have been highlighted in this piece. For now what I believe we have learned so far is:

  • BICCs should (by definition) straddle multiple geographies and/or business units.
  • There are sound reasons for collocating the BICC with Head Office.
  • There is need for a wide range of skills in your BICC, both business-focussed and technical.
  • At least the core of your BICC should be made up of competent (and experienced) BI professionals .

More thoughts on the benefits and disadvantages of business intelligence competency centres and also the politcs that they have to negotiate will appear on this blog in future weeks.
 

The importance of feasibility studies in business intelligence

Introduction

Feasibility Study

In a previous article, A more appropriate metaphor for business intelligence projects, I explained one complication of business intelligence projects. This is that the frequently applied IT metaphor of building is not very applicable to BI. Instead I suggested that BI projects had more in common with archaeological digs. I’m not going to revisit the reasons for the suitability of looking at BI this way here, take a look at the earlier piece if you need convincing, instead I’ll focus on what this means for project estimation.

When you are building up, estimation is easier because each new tier is dependent mostly on completion of the one below, something that the construction team has control over (note: for the sake of simplicity I’m going to ignore the general need to dig foundations for buildings). In this scenario, the initial design will take into account of facts such as the first tier needing to support all of the rest of the floors and that central shafts will be needed to provide access and deliver essential services such as water, electricity and of course network cables. A reductionist approach can be taken, with work broken into discrete tasks, each of which can be estimated with a certain degree of accuracy. The sum of each of these, plus some contingency, hopefully gives you a good feel for the overall project. It is however perhaps salutary to note that even when building up (both in construction and in IT) estimation can still sometimes go spectacularly awry.

When you are digging down, your speed is dependent on what you find. Your progress is dictated by things that are essentially hidden before work starts. If your path ahead (or downwards) is obscured until your have cleared enough earth to uncover the next layer, then each section may hold unexpected surprises and lead to unanticipated delays. While it may be possible to say things like, “well we need to dig down 20m and each metre should take us 10 days”, any given metre might actually take 20 days, or more. There are two issues here; first it is difficult to reduce the overall work into tasks, second it is harder to estimate each task accurately. The further below ground a phase of the dig is, the harder it will be to predict what will happen before ground is broken. Even with exploratory digs, or the use of scanning equipment, this can be very difficult to assess in advance. However it is to the concept of exploratory digs that this article is devoted.
 
 
Why a feasibility study is invaluable

At any point in the economic cycle, even more so in today’s circumstances, it is not ideal to tell your executive team that you have no idea how long a project will take, nor how much it might cost. Even with the most attractive of benefits to be potentially seized (and it is my firm belief that BI projects have a greater payback than many other types of IT projects), unless there is some overriding reason that work must commence, then your project is unlikely to gain a lot of support if it is thus characterised. So how to square the circle of providing estimates for BI projects that are accurate enough to present to project sponsors and will not subsequently leave you embarrassed by massive overruns?

It is in addressing this issue that BI feasibility studies have their greatest value. These can be thought of as analogous to the exploratory digs referred to above. Of course there are some questions to be answered here. By definition, a feasibility study cannot cover all of the ground that the real project needs to cover, choices will need to be made. For example, if there are likely to be 10 different data sources for your eventual warehouse, then should you pick one and look at it in some depth, or should you fleetingly examine all 10 areas? Extending our archaeological metaphor, should your exploratory dig be shallow and wide, or a deep and narrow borehole?
 
 
A centre-centric approach

In answering this question, it is probably worth considering the fact that not all data sources are alike. There is probably a hierarchy to them, both in terms of importance and in terms of architecture. No two organisations will be the same, but the following diagram may capture some of what I mean here:

Two ways of looking at a systems' hierarchy
Two ways of looking at a systems' hierarchy

The figure shows a couple of ways of looking at your data sources / systems. The one of the left is rather ERP-centric, the one on the right gives greater prominence to front-end systems supporting different lines of business, but wrapped by a common CRM system. There are many different diagrams that could be drawn in many different ways of course. My reason for using concentric circles is to stress that there is often a sense in which information flows from the outside systems (ones primarily focussed on customer interactions and capturing business transactions) to internal systems (focussed on either external or internal reporting, monitoring the effectiveness of processes, or delivering controls).

There may be several layers through which information percolates to the centre; indeed the bands of systems and databases might be as numerous as rings in an onion. The point is that there generally is such a logical centre. Data is often lost on its journey to this centre by either aggregation, or by elements simply not being transferred (e.g. the name of a salesperson is not often recorded on revenue entries in a General Ledger). Nevertheless the innermost segment of the onion is often the most complex, with sometimes arcane rules governing how data is consolidated and transformed on its way to its final destination.

The centre in both of the above diagrams is financial and this is not atypical if what we are considering is an all-pervasive BI system aimed at measuring most, if not all, elements of an organisation’s activity (the most valuable type of BI system in my opinion). Even if your BI project is not all-pervasive (or at least the first phase is more specific), then the argument that there is a centre will probably still hold, however the centre may not be financial in this case.

My suggestion is that this central source of data (of course there may be more than one) is what should be the greatest focus of your feasibility study. There are several reasons for this, some technical, some project marketing-related:

  1. As mentioned above, the centre is often the toughest nut to crack. If you can gain at least some appreciation of how it works and how it may be related to other, more peripheral systems, then this is a big advance for the project. Many of the archaeological uncertainties referred to above will be located in the central data store. Other data sources are likely to be simpler and thus you can be more confident about approaching these and estimating the work required.
  2. A partial understanding of the centre is often going to be totally insufficient. This is because your central analyses will often have to reconcile precisely to other reports, such as those generated by your ERP system. As managers are often measured by these financial scorecards, if you BI system does not give the same total, it will have no credibility and will not be used by these people.
  3. Because of its very nature, an understanding of the centre will require at least passing acquaintance with the other systems that feed data to it. While you will not want to spend as much time on analysing these other systems during the feasibility study, working out some elements of how they interact will be helpful for the main project.
  4. One output from your feasibility study should be a prototype. While this will not be very close to the finished article and may contain data that is both unreconciled and partial (e.g. for just one country or line of business), it should give project sponsors some idea of what they can expect from the eventual system. If this prototype deals with data from the centre then it is likely to be of pertinence to a wide range of managers.
  5. Strongly related to the last point, and in particular if the centre consists of financial data, then providing tools to analyse this is likely to be something that you will want to do early on in the main project. This is both because this is likely to offer a lot of business value and because, if done well, this will be a great advert for the rest of your project. If this is a key project deliverable, then learning as much as possible about the centre during the feasibility study is very important.
  6. Finally what you are looking to build with your BI system is an information architecture. If you are doing this, then it makes sense to start in the middle and work your way outwards. This will offer a framework off of which other elements of your BI system can be hung. The danger with starting on the outside and working inwards is that you can end up with the situation illustrated below.
A possible result of building from the outside in to the center
A possible result of building from the outside in to the centre
 
Recommendations

So my recommendation is that your feasibility study is mostly a narrow, deep dig, focussed on the central data source. If time allows it would be beneficial to supplement this with a more cursory examination of some of the data sources that feed the centre, particularly as this may be necessary to better understand the centre and because it will help you to get a better idea about your overall information architecture. You do not need to figure out every single thing about the central data source, but whatever you can find out will improve the accuracy of your estimate and save you time later. If you include other data sources in a deep / wide hybrid, then these can initially be studied in much less detail as they are often simpler and the assumption is that they will support later deliveries.

The idea of a prototype was mentioned above. This is something that is very important to produce in a feasibility study. Even if we take to one side the undeniable PR value of a prototype, producing one will allow you to go through the entire build process. Even if you do this with hand-crafted transformation of data (rather than ETL) and only a simplistic and incomplete approach to the measures and dimensions you support, you will at least have gone through each of the technical stages required in the eventual live system. This will help to shake out any issues, highlight areas that will require further attention and assist in sizing databases. A prototype can also be used to begin to investigate system and network performance, things that will influence your system topology and thereby project costs. A better appreciation of all of these areas will help you greatly when it comes to making good estimates.

Having understood quite a lot about your most complex data source and a little about other ones and produced a prototype both as a sales tool and to get experience of the whole build process, you should have all the main ingredients for making a credible presentation to your project sponsors. In this it is very important to stress the uncertainties inherent in BI and manage expectations around these. However you should also be very confident in stating that you have done all that can be done to mitigate the impact of these. This approach, of course supported by a compelling business case, will position you very well to pitch your overall BI project.
 

Two pictures paint a thousand words…

IT / Business Alignment
IT / Business Alignment

versus

IT / Business Integration
IT / Business Integration

Which is more likely to lead to sustained success?
 


 
See also: Business is from Mars and IT is from Venus and The scope of IT’s responsibility when businesses go bad.
 
Note: I have just had it pointed out to me that I missed out HR from the above diagrams. I hope that none of the HR professionals who read this blog will be too offended by my oversight.