Blurring the Line Between SOA and BI


photo by Siomuzzz

I recently read with interest an article in the Microsoft Architect Journal on so-called Service-Oriented Business Intelligence or, as the article’s authors call it, “SoBI.” The article was well-intentioned but confusing. What it confirmed to me is that plenty of experienced IT professionals are struggling to reconcile Service Oriented Architecture (SOA) concepts with business intelligence.

SOA is certainly a valuable tool in the architecture and development toolbox; however, I think it’s only fair to keep SOA in perspective.  It’s an evolutionary  technology  in IT that has numerous benefits to developer productivity and application connectivity.  I’m not sure that injecting SOA into a data warehouse environment or framework will do anything more than freshen a few low-level building blocks that have been neglected in some data warehouse environments.  I’m certainly not challenging the value of SOA; I’m just trying to put in perspective to those folks that are focused on data warehouse and business intelligence activities.

The idea around SOA is to create services (or functions, procedures, etc.) that can be used by other systems.  The idea is simple: build once, use many times.  This ensures that important (and possibly complicated) application processes can be used by numerous disparate applications. It’s like an application processing supply chain:  let the most efficient resource build a service and provide to everyone else for use.   SOA provides a framework for allowing multiple applications access to common, well-defined services.  These services can contain code and/or data.  

The question for most data warehouse environment’s isn’t whether SOA can improve (or benefit) the data warehouse; it’s understanding how SOA can benefit a data warehouse. 

We’ve got lots of clients leveraging SOA to support their data warehouse.  They’ve learned they can leverage SOA techniques and coding to deliver standardized data cleansing and data validation to a range of business applications.  They have also upgraded the operational system data extraction code to leverage SOA which allowed other application systems (or data marts) to reuse their code.

However, their use of the SOA hasn’t been focused on enhancing the data warehouse environment as much as has been focused on packaging their development efforts for others to use.  Most data warehouse developers invest heavily in navigating IT’s labyrinth of operational systems and application data in order to identify, cleanse, and load data into their warehouses.  What they’ve learned is that for every new ETL script, there are probably 20 other systems that have to custom developed their own data retrieval code and never documented it.  The value that many data warehouse developers find with SOA isn’t that they are improving their data warehouse;  they’re just addressing the limitations of the application systems.

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About Evan Levy

Evan Levy is management consultant and partner at IntegralData. In addition to his day-to-day job responsibilities, Evan speaks, writes, and blogs about the challenges of managing and using data to support business decision making.

One response to “Blurring the Line Between SOA and BI”

  1. Scott Davis says :

    Evan – Nice clarifications. IMO, the most valuable notion of SOA for BI is the idea of composite application development. This actually happens all the time at the grassroots level, but there is great potential for grown-up solutions.
    If we view the analytical process (the work flow that teams of departmental analysts go through) as sets of often-repeated “functions” which are assembled differently for different analytical circumstances, we can begin thinking about allowing analysts to create and share their statistical or charting or reporting or transformational modules (or even their data products!) via a Service-Oriented BI hub.
    In other words, SOA within BI need not be simply a technique for making IT engineers more productive. If we are to see major step-function improvements in BI agility and pervasiveness, it is likely to be via a move to user-created, user-assembled analytical components exposed via a services hub.

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