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BI Best Practices: Operational BI Archives

BI Best Practices


Tuesday, 26 June 2007

Operational BI - Is it right for you?

When I was first exposed to the new wave of Real-Time Operational BI solutions coming online, my initial reaction was, "Oh man, here we go again! Fighting the BI vendors' 'sizzle sell' and the "EDW heresy' that comes along with them selling direct connects to the operational support systems and the propagation of independent Data Marts."

But, I owed it to myself to get a better appreciation for the solution. The Operational BI I'm referring to is the real-time connection to federated sources (Operational Support Systems) through web service agents and the direct updates of "twinkling" Operational Dashboard measures. The end-users have control over the setting of performance thresholds and alerting schemes, dynamically, as needed to quickly react to blips in the operational metrics. This is a nice fit for specialized applications such as a Command and Control Center.

What about the metrics that get accumulated throughout the day? Push & persist them to the Enterprise Data Warehouse. This provides a history of these R/T metrics enabling more strategic trending and data mining functions.

Operational BI has a home in the overall Enterprise Information Management architecture. But is your enterprise ready for r/t Operational BI? Is the business prepared to react and evolve in real-time mode in response to r/t changes? The technologies are ready to enable and the vendors are ready to sell.

It's a paradigm shift. But hey every paradigm needs a nudge once in awhile!

Thursday, 7 June 2007

Best Practice Ideas

It was exciting to see this year's candidates utilizing some new best practices along with some of the ones we have seen work successfully for many years. It is great to see these sound data warehouse techniques work on different projects in different industries - practices such as:

1 - Comprehensive Data Governance framework providing guidance in Data Classification, Information Security, and Industry Data Sharing policies
2 - Metrics Cascade and Cause and Effect metrics maps
3 - Collaborating with vendors and clients on the direction of the data warehouse
4 - Revenue-generating products developed from the data warehouse
5 - Preconditioned customer contact groups (selection groups)
6 - Use of a Data Derivation Engine by the business users without necessary IT involvement to enable daily updates of data to provide Contextual Customer Dialog
7 - Off-the-shelf (preconditioned) contact groups
8 - Data evaluated and standardized to the extent possible prior to project
9 - SMEs and specialists leveraged
10 - Business project sponsor involvement during project
11 - Data quality considered to be one of the major decision drivers

I look forward to seeing the innovative ideas in next year's competition!

Patty Haines
Data Warehouse Principal - Chimney Rock Information Solutions
303-697-7740 (Denver)
847-712-7740 (Cell)