Operational BI From the Trenches

February 19, 2009
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Buzzword_box2

 Operational BI is getting a lot of attention.  The idea is a reasonable one – using recent data to make timely decisions.  However as with any other current buzzword, the world seems to be piling on and the meaning of operational BI seems to be is evolving (or eroding).

BI has been around a while now.  The idea is to leverage technology to allow a business person to utilize detailed data to answer timely business questions.  The most well known BI tools come from established vendors: IBM, Microsoft, Business Objects, Microstrategy.  Most tools use relational databases and rely on the SQL language to navigate and manipulate the data.   Most data warehouses that provide data to BI tools have been built to support query flexibility, performance, and maintain a large volume of history data.  The trade-off is often that there are delays in getting data loaded.  Most high-value data warehouses rely on regular monthly, weekly, or daily updates.   They were never built to support “operational” functionality.

The fuzzy part is what we mean by “operational.”  Rather than engaging in a semantic debate, I thought I’d share what we see at clients as the three common requirements where for truly

Buzzword_box2

 Operational BI is getting a lot of attention.  The idea is a reasonable one – using recent data to make timely decisions.  However as with any other current buzzword, the world seems to be piling on and the meaning of operational BI seems to be is evolving (or eroding).

BI has been around a while now.  The idea is to leverage technology to allow a business person to utilize detailed data to answer timely business questions.  The most well known BI tools come from established vendors: IBM, Microsoft, Business Objects, Microstrategy.  Most tools use relational databases and rely on the SQL language to navigate and manipulate the data.   Most data warehouses that provide data to BI tools have been built to support query flexibility, performance, and maintain a large volume of history data.  The trade-off is often that there are delays in getting data loaded.  Most high-value data warehouses rely on regular monthly, weekly, or daily updates.   They were never built to support “operational” functionality.

The fuzzy part is what we mean by “operational.”  Rather than engaging in a semantic debate, I thought I’d share what we see at clients as the three common requirements where for truly operational BI:

  1. Load the data fast – usually right after it’s created.
  2. Run a query fast. For instance, look up the customer’s billing history while he’s waiting on the phone.
  3. Identify a specific business circumstance when it happens. For instance, tell the customer when she’s exhausted her cell phone minutes.

As you can imagine, any one of these individual capabilities is likely to require specialized development work .  When you combine these functions, it becomes pretty clear that traditional data warehouses or business intelligence tools  can struggle to support Operational BI.  When a legitimate need for Operational BI arises, most IT departments simply build a separate reporting data mart or a reporting platform.  Why? Because the timeliness of loading and query processing makes it impractical to add on to an existing platform—unless of course they happen to have  a large-scale data warehouse with unused processing capacity just laying around.

The truth is, you may not need to limit your operational BI solution to relational database, or even to a BI tool! (I made this point on a recent broadcast of DM Radio and it invited a lot of post-show dialog.) The fact is that that relational databases and SQL aren’t the best (or even the most efficient) technologies to support operational BI.   Indeed, there are other technologies that can support some of the Operational BI activities in a simpler and more efficient manner. We’ll talk about those in another blog posting, after you’ve had a chance to consider this one.

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