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Understanding the cost and value of data

Micro Focus Frequent Contributor
Micro Focus Frequent Contributor
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It’s exciting to talk about data as “the new oil” but the truth is organizations need to treat data like other assets and understand the data lifecycle cost of ownership (DLCO) and the data lifecycle value (DLV) to extract the value in their data.  We need to develop objective and repeatable methods and tools to Data Lifecycle Management (DLM). In this post I will start to explore these topics.

We often hear “Big Data is the new Oil!”, it is certainly an attractive notion. I am not alone in thinking it is not entirely true, however[1]. Having these sorts of analogies is useful, but they don’t cover the true nature of data, its opportunities and challenges.  In this and subsequent posts, I want to focus on key aspects of big data. In this post I will introduce the concepts of Data Lifecycle Cost of Ownership and Data Value.  With these concepts, we can think of better ways to manage and use data in all kinds of ways; in all types of organizations.  In doing so it drives us towards ways of thinking and acting to generate maximum value from that data.euro-447209_1920.jpg

There have been a number of different data lifecycles produced over the years.  For the sake of this conversation I would propose the following data life cycle:



Data Creation

Original creation or capture of data objects

Data Control

Directing data objects

Filtering data objects

Data Storage

Placing the data object into one or more points of storage

Data Indexing

Data is indexed to be found or used immediately or in the future

Data Consumption

Usage of the data object, analysis and synthesis may result in the creation of new data objects

Data Disposition 

Disposal of every copy instance of the data object


For some data objects, this whole lifecycle occurs in milliseconds; for others it might take decades.

Considering data cost and the complete data lifecycle

Each step in the lifecycle has a related cost.  Without diving too deeply in economic theory and accounting practices, there are costs related, for example, with creating data.  Data Entry Operators have to be paid, they need premises and technologies.  If new data is created by a sensor, there is the cost of acquiring, locating, configuring and maintaining that sensor.  The cost of data is not limited to the Data Creation stage; cost is present for every stage of a data object’s life cycle. I can expand on this in a later post if there is interest.  This means that DLCO for any data object is the aggregate of each stage’s cost and, where appropriate, how often that stage is transacted. Additionally, it could be expressed by this formula:EQ1.png

 Where DLCO is the Data Lifecycle Cost of Ownership : DCr is the cost of data object creation : DCo is the cost of data object control : DS is the cost of data object and its index data storage for a defined period of time :  T is the number of periods of time in the data’s lifetime : DI is the cost of data object indexing : DCn is the cost of one retrieval of the data object for purposes of consumption : N is the number of times the data is consumed in its lifetime : DD is the cost of data object disposal : DTTi is any data transformation and transportation involved.

This cost model can easily be expanded to take into account other factors, but that’s not the point I’m trying to make here. The point is that it is possible to quantify and assign cost to any data object.

Data value

If cost can be ascribed to the data object – can economic value be ascribed to a data object? The answer is yes, but it is trickier because there may be different kinds of value depending on how the value is derived, so the formula looks more like:

 Where DLV is the Data Lifecycle Value : DVi is the value of each consumption of lifecycle data.

Clearly, therefore, if data is never consumed, it never generates value.  Since all created data has a cost, such a data object is a drag on the organization’s financial performance – to be avoided!

Cost and Value

This is really important for a number of reasons, namely:

  • Organizations which can ascribe cost and value to individual (or, more realistically, collections of) data objects can improve financial performance:
    • Cost avoidance opportunities
      • Ceasing the collection of non-value generating data
      • Early disposal of negative value data, i.e. DLCO > DLV
      • Managing the cost of data by breaking down its cost components
    • Value creation opportunities may exist by finding more data value opportunities for data objects, in particular those which must be retained, such as regulated data.
  • By identifying the total value of data objects (DLV – DLCO) over their lifetime, the organization can evaluate the value of investment decisions relating to any or all stages of the data object’s lifecycle.
  • This may also provide a basis for a more accurate and objective basis to value data as an asset held by organizations

DLCO and its related concepts offers organizations a way forward to quantify the cost and the value of data which will lead to better data usage and data project investment opportunities.

In my next post, I will look at this concept from an industry perspective and suggest how a new approach to data format standardization, combined with industry cooperation (or more accurately coopertition[2]), can lead to substantial cost reduction for all industry participants.

I am about to commence research into this area and would be very interested in doing so with other industry practitioners.  I will also be shortly publishing a papers on these topics.  If you would like to join me on that journey, or receive the white papers, please reach out to me at

[1] See Jer Thorpe, 2012, HBR,  “Big Data Is Not the New Oil”

[2] See:

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