CW reports that Forrester has released a report that managers believe they have too many BI tools in house. Although I have not read the full report yet, I agree with the basic claim. In my own experience, where I was heavily involved in analysis in a large enterprise, there were at least a half dozen tools that were commonly used. Plus just about every other tool was being used somewhere for something. It is true that this creates considerable chaos. Much of hidden deep in budgets and collaboration complexity. Duplication of training, interfaces, licenses and the understanding of the basic assumptions used by various tools is a costly mess for the enterprise.
So why is this the case? In part because there are no complete definitions for the meaning of BI. As a result many enterprise packages like SAP and Oracle come with their own BI capabilities. What were once purely statistical packages like SPSS and SAS that now have BI constructs. Even ubiquitous packages like Excel have become so feature rich that they too can be transformed into something that looks much like BI.
It is easy to convince management that any of the packages above have just that feature that will provide the key transformational analysis that will solve an important problem. IT may disagree, especially if the license fee is high, and may argue that the complexity added to the enterprise has its own cost, but in general they will lose the argument because they just do not understand the analytical side of the argument.
Finally BI tools proliferate because their best advocates are new-hires coming out of school have been trained in the use of new tools. It is something fresh, new and unique, like them. Which is why vendors provide free licenses to Universities. So the training of new people in the internal tool is a barrier. The overall costs are not thought through. Management usually does not understand the features and mathematics of tools already in use. Once a shiny new tool is brought in and is presented to the right management it is then very hard to dislodge.
I have seen this complexifying effect operate for many years, in fact for my entire career in the enterprise. At least now we rarely write our own tools, but the sheer number of options is numbing today.
So what to do about it? Have a core team that really understands the infrastructure, IT and mathematical capabilities of the tools in use. They also need to know how the problems have been solved in the past. The team needs to work with all the divisions: Engineering, R&D, HR and marketing etc .... It also needs to have some clout and reasonable veto power. They also need to make the overall solution more accessible and easier in most cases. Benchmark with similar firms.
Sure there may be a need for specialized vertical capabilities. But with only some work these can be kept at a minimum. Choosing a limited set of options also allows the construction of libraries of solutions that can be reapplied. Have analytical experts available that can evaluate the problem solutions. Connect all your BI users together with Web tools so they can collaborate, that is easy today.
Update: in the comments below, Colleague Stan Dyck provides some excellent additional insight into this topic.
1 comment:
Stan Dyck, a colleague, makes some excellent points:
I’d perhaps add:
1) Management/strategy setter should lead first with a definition of business critical metrics prioritized from highest to lowest and articulated within something like an OGSM (by business/brand/region/function)…this could take a few years of trial and error.
2) Go after the most important ones first. There are probably only a very few that really matter across the organization. There may be 2-3 others within each “function” that have smaller but local incremental value. These metrics should be based on proven ROI association or closest data-driven approximation to same.
3) Harmonization/convergence to the fewest possible # of tools must be weighed against the entrepreneurial spirit engaged by allowing local differences (functions, regions). The compensation system which rewards success incorporates cost/benefit ration anyways and should help drive efficiency to the enterprise. There needs to remain robust, thoughtful experimentation with “new” systems by knowledgeable, continuous community of practice which knows what is in place across the enterprise (part of core team’s work above). It is critical to get the highest level metrics right so that the lower down and localized ones support it whatever the degree of experimentation.
4) The core team has to be business driven so needs to esp. include groups leading metric tracking of the business organized by 4C’s or something similar. Increasingly this includes sales/bus dev (customer), market research (consumer), finance and whoever owns cost of goods where that is a big piece (product supply, operations, purchasing), competition (CI) and other companies (benchmarking)
5) There is huge organizational failure repeatedly occurring when novel systems are brought in…implementation, marketing, training is severely under-emphasized. The last system I put cost $5K/month, cost $15K upfront and took 25% of my time for 3 months to get up and running – resulted in rapid uptake by 250 people globally and fortunately had very granular user statistics that helped with targeting, encouraging the right people. Critical to success are early, rapid wins. Need to capture these learnings.
6) It’s much easier to get a knowledgeable business person and experienced decision maker (in the process) to implement BI than to let IT do so. It’s a much smaller investment (if the pay & progression system will allow it!) to let these people learn a little about IT and comparative algorithms, than it is to teach IT folks about the business or decision process (at least in the culture you and I worked in)
7) It is also critical in evaluating BI not to think of it in terms of simply cost savings but to look for overall enablement of the organization. This requires some judgement of experienced and often senior managers. IT is often hung up on cost esp. in organizations where there key mission in the organization is adding efficiency and reducing cost. They are not very good at the worker bee level in identifying “opportunity”. If a new BI system offers benefits such as: more rapid response to a business threat, accelerating community-wide identification and agreement of a business opportunity, enhancement of likelihood of innovation through more real-time (at time of specific problem solving) assessment of business potential and ease of connection with external world/SMEs, etc. then some judgement and experience are required. Can elaborate further if you like.
8) The fundamental structure of the Core team specifically takes into account the fact that in a competitive organization (like P&G) everyone thinks they, own, control, best understand BI. The Core team has to be chosen so as to include participation of representatives from each of the strongest advocates in this space. (for me it was R&D, PDD, IT, CMK, F&A, CI, BIS and also important for them to have corporate functional perspective/networks…e.g. know within IT what is happening by SBU and also globally)
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