Before going further, it's important to be clear that IT departments are not villains in this tale. They have sound reasons to be cautious in accepting any new technology, because they will be held responsible if it fails. Systems that promise to give more control to end users are legitimately worrisome because end users may undertake projects they do not realize are beyond their true competence and will make mistakes that IT professionals would avoid. IT departments make mistakes too, including some that end users would not. This is why the groups must work together and find the right balance of tasks. New technologies call for an adjustment of this balance, which is precisely the challenge posed by new analytical systems.
Enough philosophizing. Companies don't assess technologies in terms of the proper balance between end users and IT. Most have a much simpler approach that boils down to assessing benefits versus costs.
In the case of BI systems, the benefits are often vague (things like better decisions) and are particularly difficult to quantify in advance. When weighing one BI technology against another, companies can easily assume that the outputs, and therefore the benefits, will be the same regardless of which technology is chosen. Even though they may recognize this is not completely true, it's a helpful simplifying assumption because it lets them focus solely on comparing the costs of the competing solutions. One approach to promoting a new analytical system, therefore, is showing that it has a substantially lower total cost of ownership.
As with any TCO calculation, the trick is in knowing which elements to include. The main benefit of TCO is that it helps buyers look beyond the out-of-pocket investment (typically the license fee in the case of software) to include the cost of services and ongoing support. But this definition itself is still too narrow, because it doesn't capture the time end users spend working with the system. One of the main benefits of new analytical technologies is giving end users a greater ability to do things for themselves. This might seem to increase the amount of time required of them. However, in practice, it eliminates the effort devoted to explaining their needs to technical specialists, reviewing what those specialists produce and then revising the requirements based on the returns. The result may well be a net decrease in the time that businesspeople spend to complete a given project.
There is also an intangible but real benefit that arises from letting businesspeople work directly with the data. Doing this provides an opportunity to gain insights that they miss if they only review the results of someone else's labor. Even though the value may be hard to calculate, it's intuitively clear that an hour spent analyzing data is a more valuable use of a business analyst's time than an hour spent explaining his specifications to IT.
In fact, it's possible to argue that a business analyst generates value only while doing analysis and generates cost while doing anything else. By this logic, a system that lets analysts spend more time on analysis and less on meetings simultaneously increases value and reduces cost. However, that's a rather extreme position related to the analytical systems because much of the newly gained analysis time is really spent doing system development. Still, most analysts probably think that working through development issues is more productive, in terms of learning new information, than writing up specs and explaining them.
Finally, although it should be obvious that shifting work from IT to business users will reduce IT costs, a conventional TCO analysis may not show this. It's quite easy, and in many ways only fair, to prepare an apples-to-apples comparison that assumes IT will continue to provide the services it does today. But an experienced BI specialist can often perform a given task almost as quickly with existing systems as with new ones. Comparing those times ignores the fact that with the new system, the task would be performed by a business user instead. In short, to properly capture the benefits of new analytical systems, traditional TCO analysis must be extended to include the costs of end-user time and to capture the shift in labor to end users from IT.
Conventional TCO analyses also ignore the cost of waiting for an answer. In cases like inventory and price optimization, a company can calculate the precise penalty for every second of delay. Most BI applications are less time-sensitive, but there is a real difference between getting an answer today and getting that same answer one month from now. In a follow-up online article, I will look at ways to factor this time element into the system evaluation. Watch for it on info-mgmt.com in December.
David M. Raab is a Principal at Raab Associates Inc., a consultancy specializing in marketing technology and analytics. Raab is also the author of the book The Marketing Performance Measurement Toolkit (Racom Communications). He can be reached at draab@raabassociates.com or via his blog at http://customerexperiencematrix.blogspot.com/.
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