Rich data, poor data.


Is data an asset?” I recently asked, in introduction to a French government initiative to apply a tax to data collection.

This simple question generated a lot of debate, as can be seen here.  The general consensus, with a few exceptions, was that trying to convince accountants that data is a balance sheet asset is a difficult, if not futile, exercise.

The challenge is that data volumes and complexities prohibit a “fix everything” approach. Many data management exercises run into difficulty due to the volume of data and the unrealistic management overheads that trying to address all perceived issues can create.  Clearly, the ability to prioritise and focus data management efforts on more valuable data is important for success.

The way to do this is to align data management efforts to support business goals and objectives.

A simple definition of an asset is “any item of economic value owned by a corporation, especially that which could be converted to cash.”  Data does not easily fit this simple view, particularly as it is easier to account for it as a cost.

We can all agree, I hope, that it costs something to capture, store and maintain data.  This cost can be measured and, divided by the total number of records, will give us a tangible cost per record.  This cost can be relatively easily calculated, however, this cost is no reflection of the true cost of managing each individual record. Records that are captured right, first time, cost less to manage than those that are captured with errors that must subsequently be corrected.

Similarly, however, not all records have the same value.

 A more complete definition of an asset is “anything tangible or intangible that is capable of being owned or controlled to produce value.”  Robert Kiyosaki’s book, Rich Dad, Poor Dad, uses this definition to describe an approach to financial independence.

In the book, Kiyosaki’s rich dad (a family friend) invests in assets that generate income –such as a business. 

Similarly, rich data enables the business to meet its financial goals – whether to cut costs, increase revenue or reduce risk. Few would argue that business can operate without data – therefore business profitability is directly dependent on the supporting data.

Poor quality data, on the other hand, is consistently associated with higher operating costs, increased risk and a failure to meet revenue growth targets. The costs of poor quality data are estimated to run into billions of dollars a year.

The challenge of data management is to shift poor data to rich data where possible. – in other words to take data that cannot drive revenue, due to poor quality, and improve its quality so that it does.

Understanding the causal link between data and value also allows us to identify specific records that are more valuable, and is a primary objective of the data excellence framework 

For example, two client records may share common data quality issue – the lack of a delivery address – that must be addressed.  One customer places orders worth millions; the other has never placed an order.  Customer one can be viewed as an asset due to the ability to generate revenue, while customer two (at this point) is not.

In real environments data quality issues may impact hundreds of thousands, or even millions, of records, overwhelming operational staff responsible for addressing issues. If we focus on those records that would drive revenue then we can reduce complexity to manageable levels.

Data may never be recognised as a balance sheet item but good quality data is definitely an asset that drives revenue.

Both good and poor quality data have both value and impact on the financial returns of any organisation that can be quantified and managed by value. The data management organisation needs to align to the business to be taken seriously – this is one way to do it.

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