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.
