The « Data Maturity Model » – Part 4 [Data Neutrality]

The Data Maturity Model – Data Neutrality

Data Neutrality is an important concept for the evolution of an enterprise towards a fully data-driven organisation. It is necessary to focus on this concept in a separate and entire page

Data Neutrality is a pragmatic measure of good usage of data in an organization [project or enterprise] measured over a complete lifecycle, that prepares the organization in its endeavor towards Data Maturity.

This measure helps us to address the following aspects of data within an organization:

  • Within a lifecyle, are we producing more data than we consume?
    Even if we do so, is it really worthwhile?
  • How does data contribute to value creation / value-add?

In other words, how do we justify the usage of data in the lifecycle and are there means of improving it?

In first principles:

As can be seen, it is a fractional value which can also be expressed as a percentage. Interpretation of the positive or negative nature of Data Neutrality will be discussed in detail in later sections of this article and the following.

Definition of Net Value

There are different ways of measuring the Net Value of Data. Here are two principal ways:

Raw Measure

This is the simplest measure of Net Value. We measure simply the total volume of data consumed and produced in Bytes [e.g. MB or GB as the case may be].


  • Simple to calculate: just the sum of data consumed / produced.
  • Highly tangible: can be monetized in terms of cost of storage [e.g. $/GB of storage in AWS EBS or Google Cloud]


  • Does not take into account the intrinsic business value of data specific to the context of the organization [project or organization]

Earned Business Value

This is an improved measure based on the intrinsic business value of the data consumed or produced.


  • Reflects the true business context of the project or enterprise.
  • Better measure for evaluating the true status of an organization in its evolution towards a Data-driven stage.


  • Difficult to calibrate and calculate for each project.
  • Contextual interpretation: The interpretation of the value can be highly contextual. As a consequence, it may cause controversies, especially in a Data Audit scenario.
    Nevertheless, as in EBV calculations, several methods can be adopted to eliminate the contextual nature in net value calculations.

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Next page: « Data Maturity Model » – Part 5 [Positioning]

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