information strategy

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In his article Analytic Databases Power BI Boom, James Kobielus notes that analytic databases have been largely ignored in the BI industry merger spree of the past few years. He predicts that analytic database vendors will develop or partner to offer BI appliances with built-in analytic applications, and that these BI appliances will be particularly attractive to mid-market customers. I think he’s right - DW vendors will need to do something to differentiate.

This begs the question, though, if it’s the applications that mid-market customers will buy, why will they care what database is powering the application, and doesn’t this still sound a lot like plumbing? Furthermore, if database vendors or partners begin offering DW SaaS, customers should not need to know or care what powers the service. Isn’t that one of the main ideas behind SOA?

So, I say, let the best BI apps win. I think that BI applications or SaaS are most likely to succeed where the data sources are themselves packaged suites or SaaS. That’s because, having built my share of successful business intelligence systems, I can confirm that it’s the “gezintgas and gezoutgas” (inputs and outputs) where the lion’s share of the development and maintenance cost is consumed. Only when the sources and uses of the data are reasonably standardized, can we get enough repeatability.

Not to say that more granular SaaS can’t work. In Persistent Storage for Amazon EC2, Werner Vogel from my alma mater, Amazon.com, announced that beta EC2 customers can stand up raw storage in sizes ranging from 1 GB to 1 TB. EC2 also provides the capability to copy a snapshot of the data into their S3 distributed store. It would be interesting to see how this would work for building OLAP cubes or other specialized analytic data structures using these capabilities.

So database vendors may try to differentiate by building analytic appliances, but if cloud computing services like EC2 keep advancing, cloud computing may eventually give traditional vendors a run for their money.

Ideally, data across the enterprise should conform to a common information model - a model that supports current and planned business goals, and with the ability to gracefully adapt to meet future needs. In reality, the typical enterprise contains scores of information silos, independently built to support individual lines of business or business regions.

As long as the business units can thrive more or less independently, there may be no real business need to integrate. However, growth (whether the growth occurs organically or by merger and acquisition) brings opportunities to leverage economies of scale. Large enterprises want to be able to pool purchasing, marketing and other resources to lower costs. Large enterprises also want to create a consistent customer experience across lines of business amd channels to improve the customer experience, increase loyalty and value. The same silo systems that enabled independent growth can become inhibitors to growth.

Enterprise information strategy tries to find an optimal balance between the need for line of business autonomy and enterprise economies of scale. The right information strategy bridges gaps between business and IT.

The optimal information strategy:

• Generates strong business sponsorship by enabling new business models and revenue opportunities
• Overcomes process, data and technology gaps to build world class information services
• Builds a platform that scales to support growth and future needs

Important aspects of a successful information strategy include:

• Vision
• Alignment
• Phasing
• Socialization

In the context of an information strategy, vision is about creating a conceptual future state of the information systems that is innovative and achievable.

Alignment is absolutely essential to a successful strategy (and architecture). Alignment should not be confused with consensus, which is an element of socialization. In an information strategy, alignment is about demonstrating clear linkage between business goals and the information systems capabilities that are both necessary and sufficient to attain the goals.

An information strategy may take several years to execute. Phasing is the art of finding a way to build out a platform by making a series of incremental investments with measurable returns.

Socialization involves communication and consensus. Socialization also helps identify enablers - other programs across the enterprise that will either benefit from or contribute to the information strategy.

Information architecture should be founded on basic principles. These principles can also be used to evaluate the “goodness” of a given architecture. A good discussion of the characteristics and components of architecture principles can be found in the TOGAF chapter on Architecture Principles.

I think that principles, in turn, should be founded on a set of core values. I’ve distilled some some of the core architecture values that I’ve used in the following table:

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In the article The origins of today’s OLAP products, Nigel Pendse chronicles the history of OLAP (On-line Analytical Processing), tracing the ancestry of the technology as far back as 1962.

Personally, my introduction to BI (Business Intelligence) technologies doesn’t go back quite as far. In the late 1980’s, I worked for a systems integrator, developing and supporting COBOL applications software. Around this time, we noticed that our customers were increasingly spending time and money on reporting tools and report development.

At the time, we used report writer (designer/generator) tools. Sophisticated users generated report specifications using the designer; the generator tools translated the report specifications into COBOL that could be compiled and executed to produce reports. These tools seemed to work pretty well, and they certainly were a big improvement over hand coding. The main problem, as we soon learned, was that report writers generated reports - and lots of them. So it wasn’t long before I found myself standing before an customer’s executive committee to explain why they had four “green bar” sales reports for February in hand, none of them matching, and all of them “wrong”!

Upon investigation, I came to suspect that the reports didn’t jive because:

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