“See your data as a corporate asset.”

This is the long-standing war cry of the proponents of data value management.

Just like any asset you should attach cost and value to your data. You should make the necessary investments to ensure you can manage the desired yield from it.  Some analysts propose that the data asset should appear on an organisation’s balance sheet.

How many organisations are actually doing any of this?  A small minority of market leaders.

But, the majority only consider data in this way when a specific requirement rears its head.  Often this will be a regulatory or technology driven change.

This is understandable.  The cost of entry into the data value management club can be high.  There are software costs, management consulting costs and technical implementation costs. The promise of return on investment from data governance needs to be cast iron.

The temptation to wait until a project demands better data management is commonplace.  But project-thinking can mean data governance and lifecycle management processes happen in a ‘siloed’ fashion.

There is another problem too.  It is a hackneyed question but apposite in this context; “What does good look like?”  The response is typically difficult to define.  What is good for a pharmaceutical company may be quite different to what is good for a retailer.

The question also exposes a failing in traditional approaches to data value management projects.  Common wisdom would ask an organisation to consider, say, people process and technology.

Have you identified all the stakeholders, the steering committee and nominated the data stewards?

Have you defined data related rules and processes?  Have you implemented data quality related processes and assigned decision rights and accountabilities?

Have you standardised data models, database designs and leveraged service oriented architecture?

Important though these considerations are, the missing question is, “where’s the value?”

This is where two key factors come into their own.

The first is measurement.  Don’t they say if you want to improve something you must measure it first?  Build a data governance scorecard.  But, build it with someone with experience in your industry.  That way you can benchmark your organisation and see if your strategy is working over time.  It will also help focus minds on how the team’s efforts are having an effect and on what matters most.  Measure visible success not simply the work done.  Measuring in this way will also help to secure the requisite business buy-in.  You are measuring business value, not some abstruse data task.  It also helps to establish a common language when discussing data between the business and IT.

The second factor is experience – for which there is no substitute. Here, that means choosing people with experience in delivering a true value-driven approach.  Preferably in your industry. People who understand what value means to you and have the experience to deliver the results as business value.

Finally, remember data value management for your glittering new data lake too.  Assuming that the data lake will deliver value straight off the bat may be wishful thinking. Technologies like Hadoop, machine learning and graph databases will take you so far.  A data value management approach will help to measure the value and govern the data.  As a result it could prevent investments that don’t drive core business value.  In short, it will stop you creating a costly data cesspit!

Remember none of this needs to be scary.  Talk to us about our approach to data value management and how quickly we can get you to the value.

Written by Dominic Bridgman


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