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Rafting Into the Business Intelligence Future, Part 2

  • Scott Humphrey, William McKnight
  • November 01 2004, 1:00am EST

William wishes to thank Scott Humphrey for his contribution to this month's column.

I continue to raft down the business intelligence (BI) river this month with the second part of my report on the Humphrey Strategic Communications' 3rd Annual Pacific Northwest Business Intelligence Summit. With the reach of BI extending back into the operational environment and the rage over corporate metrics, BI is becoming a form of HR in many environments, almost managing the people of the organization to standards. These standards are often also set by BI itself, paving the way for future systems featuring business actions conducted in the absence of human interference.

BPM (business performance management) is the latest buzzword manifestation of the business analytics craze. It is the modern take on KPIs, business analytics, executive dashboarding and the like. With this modernization comes a plethora of new rules and possibilities for business analytics. Real (or "right") time is a very real requirement for many business analytics today. Also, a fairly comprehensive set of potential analytic measures are interesting these days, as is comparison to peer companies. All of these BPM requirements have a huge data management component; however, it's not like the nightly batch data warehouse architectures. There could be pre data warehouse EAI (enterprise application integration), EII (enterprise information integration) or BAM (business activity monitoring) involved. Therefore, as long as you take a broad view of what BI is, BPM is very much a part of BI. It could be the focus of an iteration, or many iterations, of the BI program.

The best method to attain the right-time architecture is undetermined at this point. The overnight batch nature of most data warehouses simply does not lend itself easily to load frequency change. For those stuck in batch, there are BAM-oriented architectures that siphon data off of the operational environment, EAI and EII that integrate operational data on the fly, and combinations of BAM and EAI/EII that could find their way into BI environments of the future to meet these needs.

Companies are confused about the various value lines of these technologies. Regardless, there is no replacement for data warehousing out there, and these technologies and processes should be sold as such.

Compliance systems are typically closed systems. Compliance is a boost for BI; however, companies and the government are still trying to figure out what it means. While compliance issues are relevant to BI, they are not necessarily overly relevant to data warehousing, as data warehouses have typically failed to achieve the prominence necessary to serve such functions. However, it appears that compliance is more about processes, not data or financial numbers.

ROI and justification, as always, are hot topics for BI. Corporate executives are demanding ROI on BI but lack the investment required to create and measure the BI value chain. Usually there is a settling on a value proposition in the labor savings in alternative methods of extracting and cleansing data, which is really TCO (total cost of ownership) analysis, not ROI. ROI proper can be abused so the focus remains on soft ROI - employee retention, happier customers, etc.

Speaking of justification, there is a growing awareness that the BI aspects of new operational environments will be funded along with the environments themselves. BI is the ROI tipping point for operational systems. This is part of the growing symbiosis between BI and operational systems referred to in Part 1 of this column.

Some organizations have created a new role that recognizes the importance of data in the organization - the Data Czar (it's actually given that title in some organizations). In general, this has been a misguided attempt to formalize and raise consciousness of data in the organization. Often, the czar becomes a figurehead with no real responsibilities. One organization that could have used a Data Czar (but missed the boat) was the 9/11 Commission. Data integration is a core problem of intelligence, but there was no data expert on the commission.

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