Thoughts on the confluence of business, change and technology. Areas covered include business intelligence, cultural transformation, business and IT alignment, business and IT strategy, project execution and social media.
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.
An example of a multi-tier BI architecture with different tools
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:
An example of a multi-tier BI architecture with different tools
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
London Bridge circa 1600
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.
Yesterday I was tweeting quotes from Poe and blogging lines attributed to Heraclitus. Today I’m moving on to Shakespeare. Kudos to anyone posting a comment pointing out the second quote that appears later in the text.
Dashboards are all the rage at present. The basic idea is that they provide a way to quickly see what is happening, without getting lost in a sea of numbers. There are lots of different technologies out there that can help with dashboards. These range from parts of the product suites of all the main BI vendors, through boutique products dedicated to the area, all the way to simply using Java to write your own.
A lot of effort needs to go into how a dashboard is presented. The information really does need to leap off the screen, it is important that it looks professional. People are used to seeing well-designed sites on the web and if your corporate dashboard looks like it is only one step removed from Excel charts, you may have a problem. While engaging a design firm to help craft a dashboard might be overkill, it helps to get some graphic design input. I have been lucky enough over the years to have had people on my teams with experience in this area. They have mostly been hobbyists, but they had enough flair and enough of an aesthetic taste to make a difference.
However, echoing my comments on BI tools in general, I think an attractive looking dashboard is really only the icing on the cake. The cake itself has two main other ingredients:
The actual figures that it presents (and how well they have been chosen) and
The Information Architecture that underpins them
I’ll now consider the importance of these two areas.
Choosing the KPIs
The acronym KPI is bandied about with enormous vigour in the BI community. Sometimes what the ‘K’ stands for can get a bit lost in the cacophony. Stepping back from dashboards for a few minutes, I want to focus on the measures that you have in your general business intelligence applications such as analysis cubes. Things like: sales revenue, units sold, growth, head count, profit and so on.
[Note: If you don't like BI buzzwords, please feel free to read "figures", or "numbers" where ever you see "measures". I may attempt to provide my own definitions of some of these terms in the future as the Wikipedia entries aren't always that illuminating.]
When you have built a Data Mart for a particular subject area and are looking to develop one or more cubes based on this, you may well have a myriad of measures to select from. In some of the earliest prototype cubes that my teams built, we made the mistake of having too many measures. The same observation equally applied to the number of dimensions (things that you want to slice and dice the measures by, e.g. geography, line of business, product, customer etc.). Having too many measures and dimensions led to a cube that was cumbersome, difficult to navigate and where the business purpose was less that crystal clear. These are all cardinal sins, but the last is the worst as I have referred to elsewhere. The clear objective is to cut down on both the figures and the business attributes that you want to look at them by. We set a rule (which we did break a couple of times for specialist applications) of generally having no more than ten measures and ten dimensions in a cube and ideally having less.
Well this all sounds great, the problem – and the reason for this diversion away from dashboards – is which measures do you keep and which do you drop. Here there is no real alternative to lots of discussions with business partners, building multiple prototypes to test out different combinations and, ultimately, accepting that you might make some mis-steps in your first release and need to revisit the area after it has been “shaken down” by real business use. I won’t delve into this particular process any deeper now. Suffice it to say that choosing which measures to include in a cube it is both an area that is important to get right and one in which it is all to easy to make mistakes.
So, retuning to our main discussion, if picking measures at the level of an analysis cube is hard, just how hard is it to pick KPIs for a dashboard. I recall a conversation with the CEO of a large organisation in which he basically told me to just pick the six most important figure and put them on a dashboard (with the clear implication that sooner would be rather better than later). After I had explained that the view of the CEO in this area was of paramount importance and that his input on which figures to use would be very valuable, we began to talk about what should be in and what should be out. After a period of going round in circles, I at least managed to convey the fact that this was not a trivial decision.
What you want with the KPIs on a dashboard is that they are genuinely key and that you can actually tell something from graphing them. The exercise in determining which figures to use and how to present them was a lengthy one, but very worthwhile. You need to rigorously apply the “so what?” test – what action will people take based on the trends and indicators that are presented to them. In the end we went for simplicity, with a focus on growth.
There was a map showing how each country was doing against plan; colour-coded red, amber and green according to their results. There were graphs comparing revenue to budget by month and the cumulative position and there was a break-down by business unit. The only to elements of interaction were to filter for a region or country and a business unit or line of business. Any further analysis required pulling up an underlying cube (actually we integrated the cube with the dashboard so that context was maintained moving from one to the other – this was not so easy as the dashboard and cube tools, while from the same vendor, were on two different major release numbers).
There were many iterations of the dashboard, but the one we eventually went live with received general acclaim. I’m not sure what we could have done differently to shorten the process.
Where does the data come from?
A dashboard without an underlying Information Architecture
The same range of dashboard tools that I mention in the introduction are of course mostly capable of sourcing their data from pretty much anywhere. If the goal is to build a dashboard, then maybe it is tempting to do this as quickly as possible, based on whatever data sources are to hand (as in the diagram above). This is probably the quickest way to produce a dashboard, but it is unlikely to produce something that is used much, tells people anything useful, or adds any value. Why do I say this?
Well the problem with this approach is that all you are doing is reflecting what is likely to be a somewhat fragmented (and maybe even chaotic) set of information tools. Out of your sources, is there a unique place to go to get a definitive value for measure A? Do the various different sources hold data in the same way and calculate values using the same formulae? Do sources overlap (either duplicating data, or function), if so, which ones do you use? Do different sources get refreshed with the same frequency and do they treat currency the same way? Are customers and products defined consistently everywhere?
A dashboad underpinned by a proper Information Architecture
Leaving issues like these unresolved is a sure way to perpetuate a poor state of information. They are best addressed by establishing a wider information architecture (a simplified diagram of which appears above). I am not going to go into all of the benefits of such an approach, if readers would like more information, then please browse through the rest of this blog and the links to other resources that it contains (maybe this post would be a good place to start). What I will state is that a dashboard will only add value if it is part of an overall consistent approach to information, something that best practice indicates requires an Information Architecture. Anything else is simply going to be a pretty picture, signifying nothing.
So my advice to those seeking to build their first dashboard has three parts. First of all, keep it simple and identify a small group of measures and dimensions, which are highly pertinent to the core of the business and susceptible to graphical presentation. Second, dashboards are not a short-cut to management information Nirvana, they only really work when they are the final layer in a proper approach to information that spans all areas of the organisation. Finally, and partly driven by the first two observations, if you are in charge of building a dashboard, make sure that the plans you draw up reflect the complexity of the task and that you manage expectations accordingly.