The Data Warehousing Institute (TDWI) conference is known for training IT workers to navigate schemas, optimization, metadata, and queries.
Last month, IT managers also gathered to become conversant in terms such as discount rate, net present value, and internal rate of return. Like it or not, Finance 101 is rapidly becoming a prerequisite for pushing through data warehousing and analytic technology projects, and vendors that help IT organizations quantify, predict and manage costs and returns stand to be amply rewarded for providing expertise in this area.
The increasing use of return on investment (ROI) calculations in technology is not new. However, although financial metrics were introduced into IT as a way to evaluate new projects, more and more technologists are seeing them as necessary for justifying long-term ongoing programs -- and, in some cases, their very jobs -- as they compete with other business functions for budget dollars.
As time goes on, expected ROI time frames are getting shorter, metrics are viewed with growing skepticism, and the perceived value of "strategic advantage" -- an IT organization's usual fallback when it is unable to quantify benefits -- is rapidly approaching zero.
In addition to these broad concerns, the data warehousing and analytics disciplines are facing some domain-specific problems that are threatening to derail their progress:
- To justify analytics, you need analytics. For example, a real return on better data can be an
increase in the number or value of customers, but without knowledge of lifetime customer value --
provided by analytics -- this ROI calculation cannot be performed. Aberdeen research has shown a
strong correlation between existing metrics programs and ROI.
- A data warehouse or broad analytics platform supports many functions, all of which must be
modeled (with the business user's cooperation) to project expected returns. Often those users are
interested in downplaying the role of the technology and claim that performance improvements should
mostly be credited to the business unit, not to IT.
- Successful data warehouses are multistep programs, not one-off projects. By tackling critical problems first, they provide high returns -- and set high expectations. A 50% ROI looks good, but not when it was 95% last year, making the program a victim of its success.
In light of these concerns, the sellout of the featured presentation at TDWI -- which focused on getting businesspeople involved and believing in data warehousing -- makes perfect sense. And the significant attendance at a subsequent ROI metrics class only reinforces the message: to build an analytics program, you must first build an ROI model.
Although attending a show like TDWI may be a first step, it is hardly sufficient. With the complexity of industry- and company-specific revenue and cost metrics, no amount of training will turn a data warehousing genius into a financial guru. A portion of the need may be filled by professional services organizations (it is no accident that the TDWI courses on ROI were presented by implementation consultancies), but budgeting for ROI calculations on the user side can be as difficult as budgeting for the project itself.
Technology users may be flummoxed by the need to provide financial metrics, but their suppliers should not be. Indeed, many large players have already created cost and benefit analysis programs that employ seasoned financial professionals with dozens of ROI calculation engagements under their belts.
Two notable examples in the analytics world are Teradata and the WebTrends division of NetIQ. Teradata's business impact modeling (BIM) program not only employs full-time financial analysts to discover hidden costs and benefits of, for example, consolidating multiple data marts but also collaborates with the Kellogg School of Management on publishable research.
With more than 100 assessments under its belt, the BIM team has seen 95% of its ROI estimates met or exceeded by clients. NetIQ uses its perch as a Web analytics application service provider (ASP) to calculate its precise costs at any level of service and use them to fine-tune the profitability of its service offerings.
These financial analysis forays foreshadow the increasing use of ROI calculation as a value-added offering by technology vendors. Those that can lend a financial analyst to the IT department during budget battles will be remembered when those hard-won dollars are put to use.
This type of offering will require not just financial expertise, but also domain-specific ROI models. To identify the most compelling case for technologies such as data warehouses or analytic platforms, vendor services organizations will need to know what revenue and cost models work for the vertical industry, company size, region, and business model to which they are applied.
The net result: the rich will get richer, and the poor will get poorer. The already consolidated analytics market stands to increase the gap between the haves and the have-nots as the former leverage their experience in a broad range of industries to create rapid and precise domain-specific ROI models.
In short, ROI calculations will become a key differentiator in the analytics market. With IT organizations struggling to justify further technology investments, ROI analysis help from a vendor will be an invaluable -- and well remunerated -- value-added service.
Alex Veytsel is a research analyst for corporate performance management at Aberdeen Group, a Boston-based IT market research and consulting firm.</<I>
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