Why Business Intelligence needs a specific approach to data governance
During my talk at the Data Governance Conference, at least one of my audience was paying attention and asked me a pertinent question. “Why should you need a separate approach for data governance in Business Intelligence?”
My first reaction was “’Oops, I’ve skipped a few stadia in my introduction…” So here’s an opportunity to set things right.
Some theory, from the presentation
At the conference, I took some time to explain the matrix below.
|Data portfolio management as presented at the 2016 data governance conference in London|
If you analyse the nature of the data present in any organisation, you can discern four major types.
Let’s take a walk through the matrix in the form of an ice cream producer.
Strategic Data: this is critical to future strategy development; both forming and executing strategy are supported by the data. By definition almost, strategic data are not in your process data or at best are integrated data objects from process data and/or external data. A simple example: (internal) ice cream consumption per vending machine matched with (external) weather data and an (external) count of competing vending machines and other competing outlets create a market penetration index which in its turn has a predictive value for future trends.
Turnaround Data: critical to future business success as today’s operations are not supported, new operations will be needed to execute. E.g.: new isolation methods and materials make ice cream fit for e-commerce. The company needs to assess the potential of this new channel as well as the potential cannibalizing effect of the substitute product. In case the company decides not to compete in this segment, what are the countermeasures to ward off the competition? Market research will produce the qualitative and quantitative data that need to be mapped on the existing customer base and the present outlets.
Factory Data: this is critical to existing business operations. Think of the classical reports, dashboards and scorecards. For example: sales per outlet type in value and volume, inventory turnover… all sorts of KPIs marketing, operations and finance want every week on their desk.
Support Data: these data are valuable but not critical to success. For instance reference data for vending locations, ice cream types and packaging types for logistics and any other attribute that may cause a nuisance if it’s not well managed.
If you look at the process data as the object of study in data governance, they fall entirely in the last two quadrants.
They contribute to decision making in operational, tactical and strategic areas but they do not deliver the complete picture as the examples clearly illustrate. There are a few other reasons why data governance in BI needs special attention, If you need to discuss this further, drop me a line via the Lingua Franca contact form.