Wednesday, May 23, 2007

The Evil That is Master / Meta Data - Part 2 (or the one where Steve talks about Ireland..!)

In the last post on this subject I talked about some of the key attributes of master data and meta data management and how it is intrinsically synched with what I call the Information or Data lifecycle. Now I would like to elaborate on this and identify some of the advantages that investing in this kind of strategy can provide.

Embarking on corporate wide gathering of all things data requires investment at all levels. Time, effort, money and most importantly commitment are essential. But ensuring a business receives any kind of ROI (return of investment which in the early part of my career I thought meant Republic of Ireland, hence the title) before that kind of commitment takes place can prove daunting and a little difficult to say the least. Let's forget the cons for a moment and look at some examples of the key advantages a data and information strategy can provide.

  • Everyone on the same page.
  • Information means the same thing throughout the business.
  • Reduced cost of new reporting and / or analytical requirements.

This doesn't look like a very extensive list and to be honest if someone presented me with a pitch like this I would be showing them the exit pretty rapidly but when you further examine the nature of each of these bullets you see that they are rooted incredibly deeply in practically all business processes and systems in place within an organisation. Key to the whole concept is that meta data and master data are not only for use within reporting systems. It's just that any projects that tend to drive this kind of requirement are also implementing some kind of reporting mechanism.

Lets take a step back and look at a simplified implementation of a number of reports. First we gather the requirements for the reports which would be based upon an existing set of data, possibly sheets of paper possibly a database storing transactions. Then the nasty business of performing analysis on said data, conforming it into your existing dimensional structure or creating new dimensions from scratch. On defining the model from which you will base your reporting you can finally start building reports.

So how could we improve this process and reduce the time taken to turn around a reporting requirement. First having some degree of knowledge of the system prior to a reporting requirement coming along would be advantageous but that's not the way the world works. Looking at a single report as a deliverable we would need to understand where the constituent data is sourced from. The report, for example, has customers, geographical breakdown, product type, number of orders and order value. Very simple but already pulling data perhaps from CRM, product catalogue and ordering systems.

When building a picture of the data held within the company it is very important that ownership is established. Who owns the customer data? Who is responsible for maintaining the product catalogue? These are the people that own these data elements within the organisation and are therefore ensuring the quality of not only the data in their own systems but also the reporting that is based on this data.

The point to this is that data quality needs to come from the top down. BI projects are generally just the the catalyst for this but should also be used as means of improvement in the source systems. Too often has data cleansing been hooked on to the back of a BI project and weighed it down with responsibility that should lie elsewhere.

Ok enough of this business type talk of responsibility and stuff. Next time I'm going to go into what master data and meta data are actually made of.

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