Rethinking business intelligence

Breaking Down BI

At its simplest, business intelligence analyzes data derived from the business itself (as opposed to such external data as market information); that analysis arrives in the form of answers to questions, either canned or ad hoc. Within that broad range you'll find these subcategories of solutions from over 300 companies.

Business intelligence platform

Business intelligence software

Retrospective BI Semistructured and Specialty tools & add-ons
bQuerying, reporting, unstructured analytics bGeospacial analytics
and analysis tools bSearch and presentation
bDynamic bNatural language query
Operational BI summarization bContent analysis of
bIn-process and speech and freeform text
transaction analysis, Predictive analytics
alerting, and bModeling and
reporting (including analysis tools
dashboards and KPIs)

Data management platform Data sources

bData warehouse, data marts, and
other data repositories
bData sources
bData models

Data management
bETL
bData cleansing
bData integration mechanisms

 

sistent. “Data quality and data integrity are not going away. There’s no easy way to solve them,” says Betsy Burton, a Gartner vice president.

Forrester’s Evelson agrees. Before launching a BI initiative, he says, “I would have a data governance effort — and drop everything else.”

BI vendors have tried to address data quality and integration issues with MDM (master data management) solutions, but efforts to govern, cleanse, and reconcile data go beyond BI to affect every corner of the organization. In many instances, BI stakeholders have lacked the clout to drive enterprisewide MDM, yielding frustration when business execs want to scale BI beyond the original requirements that drove adoption.

Until a company cleans up its data act globally — a long-running project if there ever was one — the best strategy is to reduce the data sources to those that serve well-defined business objectives. “You’ve got no business putting in BI unless you’ve whittled down those core systems,” Martens says. That can eliminate conflicting sources and yield manageable data integration and cleansing. Keeping data close to home also keeps it closer to its context and metadata, something that can get lost when data is transformed for

storage in a data warehouse. “ETL [extract, transform, load] will cost you hugely,” Martens adds, referring to the common method of pulling huge chunks of static data from legacy systems.

Reducing the number of data sources helps avoid grunt work, but data quality must still be up to par. Some data will always be dirty, perhaps because it comes from outside sources or perhaps because you’re seeking something difficult to extract. One common example is getting birth dates of customers, who see no reason to share their age, notes Anne Milley, director of technology product marketing at SAS Institute, so you get false data, such as the easy-to-en-ter 11/11/11, or no information at all.

In such cases, thought should be given to whether you really need that information for your analysis and, if so, how your analysis will account for the missing data so results remain meaningful, she says. This kind of thinking should be done before you deploy data collection, transformation, mining, analysis, or reporting systems, she adds.

Fleet management services provider PHH Arval provides a simple example of how such compromises can be reached. The company tracks odometer readings when truckers refuel to aid customer analyses of vehicle efficiency, delivery costs, and conformance to safety standards. But many drivers don’t take the time to transcribe odometer readings and instead enter guesstimates at the fuel terminals where this data is collected. To adjust analyses appropriately, PHH Arval created a statistical processing model that took this data weakness into account, says Greg Corrigan, the company’s vice president of BI.

Downsizing solutions

Simplification should go beyond data, says Kirk Hewitt, director of reporting and finance at Valero Energy, an oil refiner. Consolidate your BI tools as well. After a decade of acquisitions, Valero found itself with five BI tools in use. The company had already simplified its data environment through the adoption of a common ERP system, common financial management artifacts (such as chart of accounts and management software), and unified databases such as those for customer or refinery information. “We are really a big believer in master data management and in cleaning