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.
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. (UScenter) 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).
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:
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.
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:
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).
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.
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.
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.
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:
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*).
* 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.
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.
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.
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.
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:
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:
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.
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.
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.
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.
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.
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.
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.
Information is one of the key components of any IT organization (I would personally argue it’s more important than the technology aspect). Two facts disturb me when one looks at IT’s role in the financial crisis:
1) We in IT have been pushing data warehouse and business intelligence technology for years, saying these technologies should allow for “proactive” decision making at all levels of an organization, and an ability to spot trends and changes in a business’ underlying financial health.
2) The finance industry is usually spends more on IT than any other industry.
This being the case, if BI actually does what we’ve pitched it to do, shouldn’t one of these fancy analytical tools spotted the underlying roots of the financial crisis in at least one major bank? Is IT partially culpable for either not looking at the right data, or selling a bill of goods in terms of the “intelligence” aspect of BI?
I have written elsewhere on LinkedIn.com about business intelligence’s role in the financial crisis. My general take is that if the people who were committing organisations to collateralised debt obligations and other even more esoteric assent-backed securities were unable (or unwilling) to understand precisely the nature of the exposure that they were taking on, then how could this be reflected in BI systems. Good BI systems reflect business realities and risk is one of those realities. However if risk is as ill-understood as it appears to have been in many financial organisations, then it is difficult to see how BI (or indeed it’s sister area of business analytics) could have shed light where the layers of cobwebs were so dense.
So far, so orthodox, but Patrick’s question got me thinking along a different line, one that is more closely related to the ideas that I propounded in Business is from Mars and IT is from Venus last year. I started wondering, ‘is it just too easy for IT to say, “the business people did not understand the risks, so how were we expected to?”?’ (I think I have that punctuation right, but would welcome corrections from any experts reading this). This rather amorphous feeling was given some substance when I read some of the other responses.
However, I don’t want to focus too much on any one comment. My approach will be instead to take a more personal angle and describe some of the thoughts that the comments provoked in me (I am using “provoked” here in a positive sense, maybe “inspired” would have been a better choice of word). If you want to read my comments with the full context, then please click on the link above. What I am going to do here is to present some excerpts from each of my two lengthier contributions. The first of these is as follows (please note that I have also corrected a couple of typos and grammatical infelicities):
Rather than being defensive, and as a BI professional I would probably have every right to be so, I think that Patrick has at least half a point. If some organisations had avoided problems (or mitigated their impact) through the use of good BI (note the adjective) in the current climate, then BI people (me included) would rush to say how much we had contributed. I have certainly done this when the BI systems that I have implemented helped an organisation to swing from record losses to record profits.
Well if we are happy to do this, then we have to take some responsibility when things don’t go so well. It worries me when IT people say that non-IT managers are accountable for the business and IT is just accountable for IT. Surely in a well-functioning organisation, IT is one department that shares responsibility for business success with all the other front-line and service departments.
I have seen it argued with respect to failed financial institutions that IT can only provide information and that other executives take decisions. Well if this is the case, then I question how well the information has been designed to meet business needs and to drive decisions. To me this is evidence of bad BI (note the adjective again).
There are some specific mitigating factors for IT within the current climate, including poor internal (non-IT) governance and the fact that even the people who were writing some financial instruments did not understand the potential liabilities that the we taking on. If this is the case, then how can such risk be rolled up meaningfully? However these factors do not fully exculpate IT in my opinion. I am not suggesting for a second that IT take prime responsibility, but to claim no responsibility whatsoever is invidious.
So yes either poor information, or a lack of information (both of which are IT’s fault – as well as that of non-IT business folk) are a contributory factors to the current problems.
Also, while IT managers see themselves as responsible only for some collateral department, semi-detached from the rest of the business, we will see poor IT and poor information continuing to contribute to business failure.
This is the second passage:
I just wonder how it is that IT people at such firms can say that any failures are 100% nothing to do with them, as opposed to say 1% responsibility, or something of that nature.
Part of the role of professionals working in BI is to change the organisation so that numerical decision making (backed up of course by many other things, including experience and judgement) becomes part of the DNA. We are to blame for this not being the case in many organisations and can’t simply throw our hands up and say “wasn’t me”.
I will freely admit that there was a large dose of Devil’s Advocate in my two responses. As I have stated at the beginning of this piece, I am not so masochistic to believe that IT caused the current financial crisis, however I do not think that IT can be fully absolved of all blame.
My concerns about IT’s role relate to the situation that I see in some companies where IT is a department set apart, rather than being a central part of the overall business. In this type of circumstance (which is perhaps more common than anyone would like to think), the success of the IT and the non-IT parts of the business are decoupled.
Under these arrangements, it would be feasible for IT to be successful and the business to suffer major losses, or for the business to post record profits while IT fails to deliver projects. Of couse such decoupling can happen in other areas; for example Product A could have a stellar year, while Product B fails miserably – the same could happen with countries or regions. However there is something else here, a sense that IT can sometimes be an organisation within an organisation, in a way that other service departments generally are not.
Rather than expanding further on this concept here, I recommend you read Jim Anderson’s excellent article Here’s What’s Really Wrong With IT And How To Fix It on his blog, The Business of IT. I think that there is a good deal of alignment between Jim and I on this issue; indeed I was very encouraged to find his blog and see that his views were not a million miles from my own.
I would also like to thank Patrick for posting his initial question. It’s good when on-line forums lead you to take an alternative perspective on things.
Gavin quotes Steve Ballmer, Microsoft CEO, as saying that his corporation will be “sticking to the knitting” in response to Oracle‘s swoop on Sun. He goes on to cover some aspects of the Oracle / Sun link-up; specifically referring to the idea of “BI in a box” that seems to be gaining credence as one rationale for the deal. In his words, this trend is about:
storing, serving, and understanding information […]: the trend for getting fast access to huge quantities of data on massive networks and making sense of it.
However mention is then made of co-offerings that Oracle and HP have teamed up to make in this space – surely something that would be potentially jeopardised by the Sun acquisition:
Oracle last year announced the HP Oracle Exadata Storage Server and HP Oracle Database Machine, a box from Hewlett-Packard featuring a stack of pre-configured Exadata Storage Servers all running Oracle’s database and its Enterprise Linux.
Returning to Microsoft’s response, the article stresses their modus operandi of focussing on software components and then collaborating with others on hardware. Refernce is also made to Kilimanjaro, Microsoft’s forthcoming SQL Server version that will further emphasise business intelligence capabilities.
In closing Gavin states that:
Acquisition of a hardware company would break the DNA sequence and fundamentally change Microsoft in the way that owning Sun’s hardware business will change Oracle.
It’s tempting to note that DNA is broken (and then recombined) millions of times by RNA Polymerase, that is after all how proteins are synthesised in cells; one characteristic of Microsoft’s success (notwithstanding its recent announcement of its first ever dip in sales) has been a willingness to reinvent parts of its business (else where did the XBox come from), while relying on a steady income stream from others. When it comes to the idea of Microsoft acquiring a major hardware vendor, I agree it seems far-fetched at present, but never say never.
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One area that people seem agreed upon is the importance of Java to Oracle’s application strategy, so it makes sense – as a defensive move if nothing else – for them to seek to prevent influence over its future direction falling into the hands of a competitor (which in turn raises the question of when exactly Oracle and Sun started talking and how much overlap there was with the IBM negotiations).
The future of MySQL seems less clear. Some commentators feel that Oracle will support it and allow it to continue to thrive as one of their products. At the other extreme, I have seen suggestions that it will be killed off. Of course as an open source database, this might be easier said than done. There seems to have been a steady trickle of MySQL people out of Sun, pre-acquisition and I would have thought that there is enough expertise and ownership outside of Oracle/Sun for MySQL to have some sort of future regardless of Oracle’s strategy for it.
A bit of a dark horse is OpenOffice.org. A lot of commentary has focused on Oracle positioning themselves to compete with IBM via the acquisition. Perhaps OpenOffice.org offers Larry Ellison another chance to cross swords with his old adversaries at Microsoft.
Moving from software to operating systems, Sun’s Solaris has probably suffered more than most from the rise of Linux, but there have been rumours about Solaris offering Oracle a better route to the current technology Nirvana of cloud computing. Whether this is really the case, I’ll leave to more technically competent authorities to discuss.
But beneath Solaris beats the SPARC chips and other components of Sun’s hardware. Is Oracle’s real aim to offer a complete solution: ERP, CRM, BI and DW in a box? Sun’s hardware has not exactly been flying off the shelf in recent months, but perhaps the sales team at Oracle have other ideas. Maybe their feeling is that all that Sun’s boxes need is to be part of a more alluring overall package. Leveraging Sun’s hardware and operating system is what many people assume is behind Oracle’s strategy. This is certainly the path that would lead to challenging IBM as a company that can meet many of an organisation’s needs as a one-stop-shop.
However, this segues into another observation. If Oracle really has IBM in its sights, then it lacks one crucial piece of ammunition, a global services organisation; the sort of outfit that IBM acquired from the hiving off of PwC’s consulting arm. Maybe now is a good time to but stock in CSC?
But to return to some of the points I made earlier, there is a further possibility. Perhaps Oracle don’t want to move into the fiercely competitive and low-margin arena of hardware sales after all. Perhaps it was Sun’s software assets that were the real goal. Does Oracle really want to position itself as a hardware vendor, no doubt poisoning strong relationships with people such as HP in the process? Maybe not. If this is indeed the case then maybe there will be a spin-off of Sun’s hardware assets, or indeed a sale to someone like HP – assuming that they wanted them.
One of the most intriguing aspects of Oracle’s proposed acquisition of Sun is just how many balls have been thrown up into the air by it. It will be really interesting to see how they fall over the next few months.
Some of the blogs that I have read on this subject are acknowledged at the end of my earlier article.