Is data your most valuable asset?

We recently set to investigate the drivers and types of data valuation methods, as well as their suitability according to the purpose of the valuation. 

The relevance of research on value of data is evident as data holds significant economic value, not only for individual organizations but also for the broader social welfare and economy. Data valuation is essential to harness the technological advancements for what it’s really for; to create business value, to enable transformation, to differentiate, mitigate risks, reduce costs, create efficiencies and new revenue streams. Also estimates of the value of data are crucial for example to determine appropriate levels of investment, for safe data sharing and to gain deeper understanding of how data products can contribute to margins and growth.

“Organisations who are top performers in data monetization outcomes have capabilities that are about 1,5 times stronger than those of bottom performers, and the top performers’ outcomes are 2,5 times better than the bottom performers”, writes Barbara H. Wixom from MIT Sloan Center for Information Systems Research in “Data monetization: Generating financial returns…”.

We discovered that the data monetization examples have a wide reach and impact varying from improving existing products and services to efficiencies gained by process automation, fraud detection in finance, new applications and other data intensive, data enabled digital services, transparency for sustainability improvements - which today influences most actions that any business strategy sets as objectives and targets. 

The current state of organisations abilities to monetise data and information assets is quite bleak. For public and private organizations to have capabilities to evaluate that their data investments generate value, the starting point is to understand a) data and information as an asset and b) how the value can be measured. Organizations should generate more monetary value from their data and information assets than they invest in producing and managing them. The net-value, where all related costs have been deducted, is likely to indicate and recommend feasibility of much lesser volumes of digitalisation. 

Evaluating the possible value created by data or data sharing can be difficult and expensive in the real world. Simulations can provide a tool for estimating value in situations where it might not be possible due to variety of reasons. As part of our research, we conducted simulation of smart-Agri value creation harvesting which utilized a data-space ecosystem. A simple scenario compared a) No data is shared: stakeholders optimize their operations on limited knowledge, and b) Data is shared using a data space ecosystem: stakeholder can optimize operations based on relevant data.

One interesting concept is the Data Valuation Chain which assumes that data's value increases as it moves through the data valuation chain, and valuation depends where the data lies on the chain. Data, that serves a specific purpose or contributes to understanding a particular cause for the organisation has greater value. In other words, ultimately, the value of data to businesses will depend on how and where in the business value chain data is put to use. 

The complex nature of data - influenced by factors such as the data lifecycle management, affected stakeholders, contextual value, and stage in the data value chain - illustrates why many factors influence how the value of data can be measured. There is a wealth of viable if yet emerging methods to assess data’s worth. And since the value of data is inherently tied to its relation to the organization, valuation methods ought to be tailored based on targeted impact to clarify and quantify that impact and thus determine the value of the data. 

As we continue to advance the systemic change, those who understand the value of data will be best positioned to succeed.