This week, by way of variation, I present an article on TechRepublic that has led to heated debate on the LinkedIn.com Organizational Change Practitioners group. Today’s featured article is by one of my favourite bloggers, Ilya Bogorad and is entitled, Lessons in Leadership: How to instigate and manage change.
The importance of change management in business intelligence projects and both IT and non-IT projects in general is of course a particular hobby-horse of mine and a subject I have written on extensively (a list of some of my more substantial change-related articles can be viewed here). I have been enormously encouraged by the number of influential IT bloggers who have made this very same connection in the last few months. Two examples are Maureen Clarry writing about BI and change on BeyeNetwork recently (my article about her piece can be read here) and Neil Raden (again on BeyeNetwork) who states:
[…] technology is never a solution to social problems, and interactions between human beings are inherently social. This is why performance management is a very complex discipline, not just the implementation of dashboard or scorecard technology. Luckily, the business community seems to be plugged into this concept in a way they never were in the old context of business intelligence. In this new context, organizations understand that measurement tools only imply remediation and that business intelligence is most often applied merely to inform people, not to catalyze change. In practice, such undertakings almost always lack a change management methodology or portfolio.
You can both read my reflections on Neil’s article and link to it here.
Ilya’s piece is about change in general, but clearly he brings both an IT and business sensibility to his writing. He identifies five main areas to consider:
Do change for a good reason
Set clear goals
Use the right leverage
Measure and adjust
There are enormous volumes of literature about change management available, some academic, some based on practical experience, the best combining elements of both. However it is sometimes useful to distil things down to some easily digestible and memorable elements. In his article, Ilya is effectively playing the role of a University professor teaching a first year class. Of course he pitches his messages at a level appropriate for the audience, but (as may be gauged from his other writings) Ilya’s insights are clearly based on a more substantial foundation of personal knowledge.
When I posted a link to Ilya’s article on the LinkedIn.com Organizational Change Practitioners group, it certainly elicited a large number of interesting responses (74 at the time of publishing this article). These came from a wide range of change professionals who are members. It would not be an overstatement to say that debate became somewhat heated at times. Ilya himself also made an appearance later on in the discussions.
Some of the opinions expressed on this discussion thread are well-aligned with my own experiences in successfully driving change; others were very much at variance to this. What is beyond doubt are two things: more and more people are paying very close attention to change management and realising the pivotal role it has to play in business projects; there is also a rapidly growing body of theory about the subject (some of it informed by practical experience) which will hopefully eventually mature to the degree that parts of it can be useful to a broader audience change practitioners grappling with real business problems.
I have featured Neil Raden’s thoughts quite a few times on this blog. It is always valuable to learn from the perspectives and insights of people like Neil who have been in the industry a long time and to whom there is little new under the sun.
In his latest post, IBM System S: Not for Everyone (which appears on his Intelligent Enterprise blog), Neil raises concerns about some commentators’ expectations of this technology. If business intelligence is seen as having democratised information, then some people appear to feel that System S will do the same for real-time analysis of massive data sets.
While intrigued by the technology and particular opportunities that System S may open up, Neil is sceptical about some of the more eye-catching claims. One of these, quoted in The New York Times, relates to real-time analysis in a hospital context, with IBM’s wizardry potentially alerting medical staff to problems before they get out of hand and maybe even playing a role in diagnosis. On the prospects for this universal panacea becoming reality, Neil adroitly observes:
How many organizations have both the skill and organizational alignment to implement something so complex and controversial?
Neil says that he is less fond of sporting analogies than many bloggers (having recently posted articles relating to cricket, football [soccer], mountain biking and rock climbing, I find myself blushing somewhat at this point), but nevertheless goes on to make a very apposite comparison between professional sportsmen and women and carrying out real-time analysis professionally. Every day sports fans can appreciate the skill, commitment and talent of the professionals, but these people operate on a different plane from mere mortals. With System S Neil suggests that:
The vendor projects the image of Tiger Woods to a bunch of duffers.
I think once again we arrive at the verity that there is no silver bullet in any element of information generation (see my earlier article, Automating the business intelligence process?). Many aspects of the technology used in business intelligence are improving every year and I am sure that there are many wonderful aspects to System S. However, this doubting Thomas is as sceptical as Neil about certain of the suggested benefits of this technology. Hopefully some concrete and useful examples of its benefits will soon replace the current hype and provide bloggers with some more tangible fare to write about.
Neil Raden is founder of Hired Brains, a consulting firm specializing in analytics, business Intelligence and decision management. He is also the co-author of the book “a consulting firm specializing in analytics, business Intelligence and decision management. He is also the co-author of the book Smart (Enough) Systems.
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:
Vice president of research and analytics for consulting company BPM Partners.
Founder of consulting company BI Metrics (and author of the article I mention above).
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
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 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.
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.
On further reflection about this earlier article, I realised that I missed out one important point. This was perhaps implicit in the diagram that I posted (and which I repeat below), but I think that it makes sense for me to make things explicit.
The point is that in this architecture with different BI tools in different layers, it remains paramount to have consistency in terminology and behaviour for dimensions and measures. So “Country” and “Profit” must mean the same things in your dashboard as it does in your OLAP cubes. The way that I have achieved this before is to have virtually all of the logic defined in the warehouse itself. Of course some things may need to be calculated “on-the-fly” within the BI tool, in this case care needs to be paid to ensuring consistency.
It has been pointed out that the approach of using the warehouse to drive consistency may circumscribe your ability to fully exploit the functionality of some BI tools. While this is sometimes true, I think it is not just a price worth paying, but a price that it is mandatory to pay. Inconsistency of any kind is the enemy of all BI implementations. If your systems do not have credibility with your users, then all is already lost and no amount of flashy functionality will save you.
This post follows on from a question that was asked on the LinkedIn.com Data Warehousing Institute (TDWI™) 2.0 group. Unfortunately the original thread is no longer available for whatever reason, but the gist of the question was whether anyone had experience with using a number of BI tools to cover different functions within an implementation. So the scenario might be: Tool A for dashboards, Tool B for OLAP, Tool C for Analytics, Tool D for formatted reports and even Tool E for visualisation.
In my initial response I admitted that I had not faced precisely this situation, but that I had worked with the set-up shown in the following diagram, which I felt was not that dissimilar:
Here there is no analytics tool (in the statistical modelling sense – Excel played that role) and no true visualisation (unless you count graphs in PowerPlay that is), but each of dashboards, OLAP cubes, formatted reports and simple list reports are present. The reason that this arrangement might not at first sight appear pertinent to the question asked on LinkedIn.com is that two of the layers (and three of the report technologies) are from one vendor; Cognos at the time, IBM-Cognos now. The reason that I felt that there was some relevance was that the Cognos products were from different major releases. The dashboard tool being from their Version 8 architecture and the OLAP cubes and formatted reports from their Version 7 architecture.
A little history
Maybe a note of explanation is necessary as clearly we did not plan to have this slight mismatch of technologies. We initially built out our BI infrastructure without a dashboard layer. Partly this was because dashboards weren’t as much of a hot topic for CEOs when we started. However, I also think it also makes sense to overlay dashboards on an established information architecture (something I cover in my earlier article, “All that glisters is not gold” – some thoughts on dashboards, which is also pertinent to these discussions).
When we started to think about adding icing to our BI cake, ReportStudio in Cognos 8 had just come out and we thought that it made sense to look at this; both to deliver dashboards and to assess its potential future role in our BI implementation. At that point, the initial Cognos 8 version of Analysis Studio wasn’t an attractive upgrade path for existing PowerPlay users and so we wanted to stay on PowerPlay 7.3 for a while longer.
The other thing that I should mention is that we had integrated an in-house developed web-based reporting tool with PowerPlay as the drill down tool. The reasons for this were a) we had already trained 750 users in this tool and it seemed sensible to leverage it and b) employing it meant that we didn’t have to buy an additional Cognos 7 product, such as Impromptu, to support this need. This hopefully explains the mild heterogeneity of our set up. I should probably also say that users could directly access any one of the BI tools to get at information and that they could navigate between them as shown by the arrows in the diagram.
I am sure that things have improved immensely in the Cognos toolset since back then, but at the time there was no truly seamless integration between ReportStudio and PowerPlay as they were on different architectures. This meant that we had to code the passing of parameters between the ReportStudio dashboard and PowerPlay cubes ourselves. Although there were some similarities between the two products, there were also some differences at the time and these, plus the custom integration we had to develop, meant that you could also view the two Cognos products as essentially separate tools. Add in here the additional custom integration of our in-house reporting application with PowerPlay and maybe you can begin to see why I felt that there were some similarities between our implementation and one using different vendors for each tool.
I am going to speak a bit about the benefits and disadvantages of having a single vendor approach later, but for now an obvious question is “did our set-up work?” The answer to this was a resounding yes. Though the IT work behind the scenes was maybe not the most elegant (though everything was eminently supportable), from the users’ perspective things were effectively seamless. To slightly pre-empt a later point, I think that the user experience is what really matters, more than what happens on the IT side of the house. Nevertheless let’s move on from some specifics to some general comments.
The advantages of a single vendor approach to BI
I think that it makes sense if I lay my cards on the table up-front. I am a paid up member of the BI standardisation club. I think that you only release the true potential of BI when you take a broad based approach and bring as many areas as you can into your warehouse (see my earlier article, Holistic vs Incremental approaches to BI, for my reasons for believing this).
Within the warehouse itself there should be a standardised approach to dimensions (business entities and the hierarchies they are built into should be the same everywhere – I’m sure this will please all my MDM friends out there) and to measures (what is the point if profitability is defined different ways in different reports?). It is almost clichéd nowadays to speak about “the single version of the truth”, but I have always been a proponent of this approach.
I also think that you should have the minimum number of BI tools. Here however the minimum is not necessarily always one. To misquote one of Württemberg’s most famous sons:
Everything should be made as simple as possible, but no simpler.
What he actually said was:
It can scarcely be denied that the supreme goal of all theory is to make the irreducible basic elements as simple and as few as possible without having to surrender the adequate representation of a single datum of experience.
but maybe the common rendition is itself paying tribute to the principle that he propounded. Let me pause to cover what are the main reasons quoted for adopting a single vendor approach in BI:
Consistent look-and-feel: The tools will have a common look-and-feel, making it easier for people to use them and simplifying training.
Better interoperability: Interoperability between the tools is out-of-the-box, saving on time and effort in developing and maintaining integration.
Clarity in problem resolution: If something goes wrong with your implementation, you don’t get different vendors blaming each other for the problem.
Simpler upgrades: You future proof your architecture, when one element has a new release, it is the vendor’s job to ensure it works with everything else, not yours.
Less people needed: You don’t need to hire an expert for each different vendor tool, thereby reducing the size and cost of your BI team.
Cheaper licensing: It should be cheaper to buy a bundled solution from one vendor and ongoing maintenance fees should also be less.
This all seems to make perfect sense and each of the above points can be seen to be reducing the complexity and cost of your BI solution. Surely it is a no-brainer to adopt this approach? Well maybe. Let me offer some alternative perspectives on each item – none of these wholly negates the point, but I think it is nevertheless worth considering a different perspective before deciding what is best for your organisation.
Consistent look-and-feel: It is not always 100% true that different tools from the same vendor have the same look-and-feel. This might be down to quality control at the vendor, it might be because the vendor has recently acquired part of their product set and not fully integrated it as yet, or – even more basically – it may be because different tools are intended to do different things. To pick one example from outside of BI that has frustrated me endlessly over the years: PowerPoint and Word seem to have very little in common, even in Office 2007. Hopefully different tools from the same vendor will be able to share the same metadata, but this is not always the case. Some research is probably required here before assuming this point is true. Also, picking up on the Bauhaus ethos of form dictating function, you probably don’t want to have your dashboard looking exactly like your OLAP cubes – it wouldn’t be a dashboard then would it? Additional user training will generally be required for each tier in your BI architecture and a single-vendor approach will at best reduce this somewhat.
Better interoperability: I mention an problem with interoperability of the Cognos toolset above. This is is hopefully now a historical oddity, but I would be amazed if similar issues do not arise at least from time to time with most BI vendors. Cognos itself has now been acquired by IBM and I am sure everyone in the new organisation is doing a fine job of consolidating the product lines, but it would be incredible if there were not some mismatches that occur in the process. Even without acquisitions it is likely that elements of a vendor’s product set get slightly out of alignment from time to time.
Clarity in problem resolution: This is hopefully a valid point, however it probably won’t stop your BI tool vendor from suggesting that it is your web-server software, or network topology, or database version that is causing the issue. Call me cynical if you wish, I prefer to think of myself as a seasoned IT professional!
Simpler upgrades: Again this is also most likely to be a plus point, but problems can occur when only parts of a product set have upgrades. Also you may need to upgrade Tool A to the latest version to address a bug or to deliver desired functionality, but have equally valid reasons for keeping Tool B at the previous release. This can cause problems in a single supplier scenario precisely because the elements are likely to be more tightly coupled with each other, something that you may have a chance of being insulated against if you use tools from different vendors.
Less people needed: While there might be half a point here, I think that this is mostly fallacious. The skills required to build an easy-to-use and impactful dashboard are not the same as building OLAP cubes. It may be that you have flexible and creative people who can do both (I have been thus blessed myself in the past in projects I ran), but this type of person would most likely be equally adept whatever tool they were using. Again there may be some efficiencies in sharing metadata, but it is important not to over-state these. You may well still need a dashboard person and an OLAP person, if you don’t then the person who can do both with probably not care about which vendor provides the tools.
Cheaper licensing: Let’s think about this. How many vendors give you Tool B free when you purchase Tool A? Not many is the answer in my experience, they are commercial entities after all. It may be more economical to purchase bundles of products from a vendor, but also having more than one in the game may be an even better way of ensuring that cost are kept down. This is another area that requires further close examination before deciding what to do.
A more important consideration
Overall it is still likely that a single-vendor solution is cheaper than a multi-vendor one, but I hope that I have raised enough points to make you think that this is not guaranteed. Also the cost differential may not be as substantial as might be thought initially. You should certainly explore both approaches and figure out what works best for you. However there is another overriding point to consider here, the one I alluded to earlier; your users. The most important thing is that your users have the best experience and that whatever tools you employ are the ones that will deliver this. If you can do this while sticking to a single vendor then great. However if your users will be better served by different tools in different tiers, then this should be your approach, regardless of whether it makes things a bit more complicated for your team.
Of course there may be some additional costs associated with such an approach, but I doubt that this issue is insuperable. One comparison that it may help to keep in mind is that the per user cost of many BI tools is similar to desktop productivity tools such as Office. The main expense of BI programmes is not the tools that you use to deliver information, but all the work that goes on behind the scenes to ensure that it is the right information, at the right time and with the appropriate degree of accuracy. The big chunks of BI project costs are located in the four pillars that I consistently refer to:
Understand the important business decisions and what figures are necessary to support these.
Understand the data available in the organisation, how it relates to other data and to business decisions.
Transform the data to provide information answering business questions.
Focus on embedding the use of information in the corporate DNA.
The cost of the BI tools themselves are only a minor part of the above (see also, BI implementations are like icebergs). Of course any savings made on tools may make funds available for other parts of the project. It is however important not to cut your nose off to spite your face here. Picking right tools for the job, be they from one vendor or two (or even three at a push) will be much more important to the overall payback of your project than saving a few nickels and dimes by sticking to a one-vendor strategy just for the sake of it.
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
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:
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:
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.]
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.
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.
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:
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.
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.