Lest it be thought that I am wholly obsessed by the Business Intelligence vs Business Analytics issue (and to be honest I have a whole lot of other ideas for articles that I would rather be working on), I should point out that this piece is not focussed on SAS. In my last correspondence with that organisation (which was in public and may be viewed here) I agreed with Gaurav Verma’s suggestion that SAS customers be left to make up their own minds about the issue.
However the ripples continue to spread from the rock that Jim Davis threw into the Business Intelligence pond. The latest mini-tsunami is in an article on CIO.com by Scott Staples, President and Co-CEO of IT Services at MindTree. [Incidentally, I’d love to tell you more about MindTree’s expertise in the area of Business Intelligence, but unfortunately I can’t get their web-site’s menu to work in either Chrome or IE8; I hope that you have better luck.]
Scott’s full article is entitled Analytics: Unlocking Value in Business Intelligence (BI) Initiatives. In this, amongst other claims, Scott states the following:
To turn data into information, companies need a three-step process:
- Data Warehouse (DW)—companies need a place for da…
Lest it be thought that I am wholly obsessed by the Business Intelligence vs Business Analytics issue (and to be honest I have a whole lot of other ideas for articles that I would rather be working on), I should point out that this piece is not focussed on SAS. In my last correspondence with that organisation (which was in public and may be viewed here) I agreed with Gaurav Verma’s suggestion that SAS customers be left to make up their own minds about the issue.
However the ripples continue to spread from the rock that Jim Davis threw into the Business Intelligence pond. The latest mini-tsunami is in an article on CIO.com by Scott Staples, President and Co-CEO of IT Services at MindTree. [Incidentally, I’d love to tell you more about MindTree’s expertise in the area of Business Intelligence, but unfortunately I can’t get their web-site’s menu to work in either Chrome or IE8; I hope that you have better luck.]
Scott’s full article is entitled Analytics: Unlocking Value in Business Intelligence (BI) Initiatives. In this, amongst other claims, Scott states the following:
To turn data into information, companies need a three-step process:
- Data Warehouse (DW)—companies need a place for data to reside and rules on how the data should be structured.
- Business Intelligence—companies need a way to slice and dice the data and generate reports.
- Analytics—companies need to extract the data, analyze trends, uncover opportunities, find new customer segments, and so forth.
Most companies fail to add the third step to their DW and BI initiatives and hence fall short on converting data into information.
He goes on to say:
[…] instead of companies just talking about their DW and BI strategies, they must now accept analytics as a core component of business intelligence. This change in mindset will solve the dilemma of data ≠ information:
Current Mindset: DW + BI = Data
Future Mindset: DW + (BI + Analytics) = Information
Now in many ways I agree with a lot of what Scott says, it is indeed mostly common sense. My quibble comes with his definitions of BI and Analytics above. To summarise, he essentially says “BI is about slicing and dicing data and generating reports” and “Analytics is about extracting data, analysing trends, uncovering opportunities and finding new customer segments”. To me Scott has really just described two aspects of exactly the same thing, namely Business Intelligence. What is slicing and dicing for if not to achieve the aims ascribed above to Analytics?
Let me again – and for the sake of this argument only – accept the assertion that Analytics is wholly separate from BI (rather than a subset). As I have stated before this is not entirely in accordance with my own views, but I am not religious about this issue of definition and can happily live with other people’s take on it. I suppose that one way of thinking about this separation is to call the bits of BI that are not Analytics by the older name of OLAP (possibly ignoring what the ‘A’ stands for, but I digress). However, even proponents of the essential separateness of BI and Analytics tend to adopt different definitions to Scott.
To me what differentiates Analytics from other parts of BI is statistics. Applying advanced (or indeed relatively simple) statistical methods to structured, reliable data (such as one would hope to find in data warehouses more often than not) would clearly be the province of Analytics. Thus seeking to find attributes of customers (e.g. how reliably they pay their bills, or what areas they live in) or events in their relationships with an organisation (e.g. whether a customer service problem arose and how it was dealt with) that are correlated with retention/repeat business would be Analytics.
Maybe discerning deeply hidden trends in data would also fall into this camp, but what about the rather simpler “analysing trends” that Scott ascribes to Analytics? Well isn’t that just another type of slice and dice that he firmly puts in the BI camp?
Trend analysis in a multidimensional environment is simply using time as one of the dimensions that you are slicing and dicing your measures by. If you want to extrapolate from data, albeit in a visual (and possibly non-rigorous manner) to estimate future figures, then often a simple graph will suffice (something that virtually all BI tools will provide). If you want to remove the impact of outlying values in order to establish a simple correlation, then most BI tools will let you filter, or apply bands (for example excluding large events that would otherwise skew results and mask underlying trends).
Of course it is maybe a little more difficult to do something like eliminating seasonality from figures in these tools, but then this is pretty straightforward to do in Excel if it is an occasional need (and most BI tools support one-click downloading to Excel). If such adjustments are a more regular requirement, then seasonally adjusted measures can be created in the Data Mart with little difficulty. Then pretty standard BI facilities can be used to do some basic analysis.
Of course paid-up statisticians may be crying foul at such loose analysis, of course correlation does not imply causation, but here we are talking about generally rather simple measures such as sales, not the life expectancy of a population, or the GDP of a country. We are also talking about trends that most business people will already have a good feeling for, not phenomena requiring the application of stochastic time series to model them.
So, unlike Scott, I would place “back-of-an-envelop” and graphical-based analysis of figures very firmly in the BI camp. To me proper Analytics is more about applying rigorous statistical methods to data in order to either generate hypotheses, or validate them. It tends to be the province of specialists, whereas BI (under the definition that I am currently using where it is synonymous with OLAP) is carried out profitably by a wider range of business managers.
So is an absence of Analytics – now using my statistically-based definition – a major problem in “converting data into information” as Scott claims? I would answer with a very firm “no”. If we take information as being that which is generated and consumed by a wide range of managers in an organisation, then if this is wrong then the problem is much earlier on and most likely centred on how the data warehousing and BI parts have been implemented (or indeed in a failure to manage the concomitant behavioural change). I covered what I believe are often the reasons that BI projects fail to live up to their promise in my response to a Gartner report. This earlier article may be viewed here.
In fact I think that what happens is that when broader BI projects fail in an organisation, people fall back on two things: a) their own data (Excel and Access) and b) the information developed by the same statistical experts who are the logical users of Analytic tools. The latter is characterised by a reliance on Finance, or Marketing reports produced by highly numerate people with Accounting qualifications or MBAs, but which are often unconnected to business manager’s day-to-day experiences. The phrase “democratisation of information” has been used in relation to BI. Where BI fails, or does not exist, then the situation I have just described is maybe instead the dictatorship of the analysts.
I have chosen the word “dictatorship” with all of its negative connotations advisedly. I do not think that the situations that I have described above is a great position for a company to be in. The solution is not more Analytics, which simply entrenches the position of the experts to the detriment of the wider business community, but getting the more mass-market disciplines of the BI (again as defined above) and data warehousing pieces right and then focussing on managing the related organisational change. In the world of business information, as in the broader context, more democracy is indeed the antidote to dictatorship.
I have penned some of my ideas about how to give your BI projects the greatest chance of success in many places on this blog. But for those interested, I suggest maybe starting with: Scaling-up Performance Management, “All that glisters is not gold” – some thoughts on dashboards, The confluence of BI and change management and indeed the other blog articles (both here and elsewhere) that these three pieces link to.
Also for those with less time available, and although the article is obviously focussed on a specific issue, the first few sections of Is outsourcing business intelligence a good idea? pull together many of these themes and may be a useful place to start.
If your organisation is serious about adding value via the better use of information, my recommendation is to think hard about these areas rather than leaping into Analytics just because it is the latest IT plat du jour.
