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Introducing a comprehensive method to measure the value of data and create a standardized balance sheet for data assets


Digital transformation has changed the role of IT, now contributing to data-driven decision-making and direct value creation. It is leading to unique data assets that differentiate organizations from others. Yet IT is still financially managed as a generic cost which can lead to value destruction when budget reductions are applied. We advocate that finance needs to embrace a standardized balance sheet for data assets equivalent to balance sheets for the enterprise’s tangible assets.

IT is no longer a generic cost

Before the digital transformation, a generic overall cost center for IT made sense. Hardware, software, and support were linked to the organization’s headcount, and applications served a specific business line with a predictable cost pattern once implementation was completed. Nowadays, IT and, more specifically, data play a direct role in business lines’ value creation, ideally being shared across departments.

Data is acknowledged as critical for businesses to survive[i]. Building unique data assets consisting of models, data sets, ecosystems, and platform capabilities is essential. As a result, data-related IT investments, including software, analytics, and cloud spending, are growing yearly. Yet if you would ask what the value of the data assets is, organizations would often be unable to answer this question or even identify the data assets correctly. This is because although data exhibits all the characteristics of an asset,[ii]  accounting doesn’t recognize it as an asset.[iii] Therefore, it is difficult for the finance department to apply its standard accounting principles.

Yet financial controls and processes must evolve to track and manage its data assets’ cost and value creation. For example, benchmarking is common in determining whether reductions should be applied to an IT budget. Because of digital transformation, lowering an IT budget based on a benchmark could automatically lead to value destruction as you could shut down parts of your data assets that differentiate you from the competition, not only in cost but also in value.  Considering the significant investments in IT[iv], executives need to understand where IT spending is related to direct value creation and discuss critical questions such as:

  • What are the competitive advantages of applying data in our business processes?
  • Can we show the actuals of the business cases for our digital transformation program?
  • Can we quantify the business impact if we lowered Cloud operating costs by 20%?

Data is an asset that requires a balance sheet

Many organizations[v] struggle[vi] to answer these questions while the cost of IT continues to rise[vii]. We recommend introducing a data asset balance sheet as a second set of financial books[viii] to regain control over your IT spending. The data asset balance sheet provides an overview of the budget allocated to direct value creation, called data investments, demonstrating a profit and loss overview assigned per data asset.

For example, machine usage data would be a logical data asset if you were a machine manufacturer. It would consist of predictive maintenance models and value-added services built on usage data and embedded platform capabilities, which can be well measured and appreciated by lowered operations costs and additional revenue created. Once you understand how the value and cost creation behaves, the investment can be managed to look for reuse opportunities, protect intellectual property, and find cost reductions by eliminating money-losing functionalities.

The balance sheet is managed by a guardian recognized as an investment authority within the organization. A senior executive within the finance department is an excellent candidate[ix]. This could be the Chief Data Officer position as long as it is positioned to have the investment mandate. This guardian takes responsibility that:

  • Data investment decisions are accelerated and more transparent by adding a financial perspective to technical debates.
  • IT and business have a shared language of what value is created at what cost,[x] facilitating a more meaningful dialog when business case forecasts diverge.
  • Key data assets are identified, protected from vendor dependencies (a.k.a. “lock-in”), and used to a maximum throughout the organization, improving competitive advantage.

This requires the organization to develop new skills and tools to understand how the valuation of data assets works and how to deal with the characteristic of data assets[xi]. Once these are understood, existing financial and planning processes can apply.

Start with the budget request inflow

An excellent place to start is the budget request inflow. Decision-making regarding data-related investments is frustrating and fraught with constant miscommunication. Proposals are made in temporary PowerPoints and are often evaluated based on the wrong or partial KPIs.[xii] Forecasts are often multiyear hockey sticks based on waterfall-oriented financial models, eventually leading to unresolvable technical debates. This is where the guardian can immediately add value by helping with the following:

  1. Getting involved at the start to understand project drivers and build data asset balance sheets.
  2. Suggesting relief of requirements for data projects that don’t contribute to strategic data assets but improve the efficiency of a stand-alone business process.
  3. Proposing opportunities to reuse infrastructure data components to lower costs.

The guardian plays a direct, tangible role in collaborating with the proposal creator to create financial models estimating and managing risk; and quantify the proposal’s benefits in easy-to-understand metrics. Once the inflow of standardized investment proposals is supported, driving progress, managing risk, and creating a portfolio view follow naturally. The organization achieves a better understanding of its data assets with each managed investment, resulting in better project outcomes, more efficient decision-making, and happier employees.

About the author

For over a decade, Dennis Groot has been responsible for data-related investments in organizations of various sizes. With support from the principals at Klarrio, he has founded an advisory firm to support organizations in managing their data investments. He has combined his expertise with a unique proprietary framework and associated tools to standardize budget requests for data-related projects and help the proposal owner at every step. Standardization facilitates the application of best practices and prior learnings. Business cases are connected directly with performance data, resulting in an up-to-date data asset balance sheet. This allows your organization to identify opportunities to create value and manage risk and cost. Ultimately driving higher returns from your data-related IT investments.


[i] link “Why digital strategies fail.” McKinsey, 2018.

[ii] link “Seven Laws of Information – A Foundation for “Digital Wisdom.” WisdomJunkie, 2017.

[iii] link Douglas Laney. “Why Your Company Doesn’t Measure The Value Of Its Data Assets.” Forbes, 2021

[iv] link  “The global big data analytics market.” Fortune Business Insights, 2022.

[v] link “How to Measure Digital Transformation Progress.” Gartner, 2019.

[vi] link “Data management investments often stumble, survey finds.” Venturebeat, 2021.

[vii] link “IT Spending Forecast, 3Q22 Update: Is IT Spending Recession Proof?” Gartner, 2022.

[viii] link, “When Maintaining a “Second” Set of Financial “Books” is Worthwhile”, Gartner, 2016

[ix] link “Reducing data costs without jeopardizing growth.” McKinsey Digital, 2020.

[x] link Mark Schwarz. The Art of Business Value. IT Revolution Press, 2016.  [Not a direct quote]

[xi] link “Characteristics of data as an asset,” from “Data Valuation” Wikipedia.

[xii] link “How the Wrong KPIs Doom Digital Transformation.” MIT Sloan Management Review, 2022.

Also, highly recommend Douglas Laney’s Infonomics, an excellent piece of work and one of the first pleas to treat data as an asset.