Looking Inside the Box of Analytics and Business Intelligence Applications

To take technology and social process at face value is to risk failing to appreciate what they mean, do, and can do. Analytics and business intelligence applications or projects, in common with all technology supported innovations, are more likely to be successful if both technology and social spheres are better understood. I don’t mean to say that there is no room for intuition in such cases, rather that it is helpful to decide which aspects are best served by intuition or not and by whose intuition, if so. But how to do this?

Just looking can be a poor guide to understanding an existing application and just designing can be a poor approach to creating a new one. Some kind of method, some principles, some prompts or stimulus questions – I will use “framework” as an umbrella term – can all help to avoid a host of errors. Replication of existing approaches that may be obsolete or erroneous, falling into value or cognitive traps, failure to consider a wider range of possibilities, etc are errors we should try to avoid. There are, of course, many approaches to dealing with this problem other than a framework. Peer review and participative design have a clear role to play when adopting or implementing analytics and business intelligence but a framework can play a part alongside these social approaches as well as being useful to an individual sense-maker.

The culmination of my thinking about this kind of framework has just been published as the seventh paper in the CETIS Analytics Series, entitled “A Framework of Characteristics for Analytics“. This started out as a personal attempt to make sense of my own intuitive dissatisfaction with the traditions of business intelligence combined with concern that my discussions with colleagues about analytics were sometimes deeply at cross purposes or just unproductive because our mental models lacked sufficient detail and clarity to properly know what we were talking about or to really understand where our differences lay.

The following quotes from the paper.

A Framework of Characteristics for Analytics considers one way to explore similarities, differences, strengths, weaknesses, opportunities, etc of actual or proposed applications of analytics. It is a framework for asking questions about the high level decisions embedded within a given application of analytics and assessing the match to real world concerns. The Framework of Characteristics is not a technical framework.

This is not an introduction to analytics; rather it is aimed at strategists and innovators in post-compulsory education sector who have appreciated the potential for analytics in their organisation and who are considering commissioning or procuring an analytics service or system that is fit for their own context.

The framework is conceived for two kinds of use:

  1. Exploring the underlying features and generally-implicit assumptions in existing applications of analytics. In this case, the aim might be to better comprehend the state of the art in analytics and the relevance of analytics methods from other industries, or to inspect candidates for procurement with greater rigour.
  2. Considering how to make the transition from a desire to target an issue in a more analytical way to a high level description of a pilot to reach the target. In this case, the framework provides a starting-point template for the production of a design rationale in an analytics project, whether in-house or commissioned. Alternatively it might lead to a conclusion that significant problems might arise in targeting the issue with analytics.

In both of these cases, the framework is an aid to clarify or expose assumptions and so to help its user challenge or confirm them.

I look forward to any comments that might help to improve the framework.

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