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For years, using more data to make better decisions has been the holy grail for global companies, and most of them aim to treat data as a strategic asset. But new research from the MIT Center for Information Systems Research (CISR) has found that future-ready companies have greater ambition regarding their data. These organizations strive to maximize their data monetization outcomes by pervasively improving processes to do things better, cheaper, and faster; wrapping products with analytics features and experiences; and selling new, innovative information solutions.1
To monetize data, companies must first transform it so that it can be reused and recombined to enable new value creation. The easier the reuse and recombination, the higher the data’s liquidity, which we define as “the ease of data asset reuse and recombination.”
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Preparing Strategic Data Assets for Reuse and Recombination
Data liquidity is a continuum, not a binary condition. It is a function of the ability to convert data for use, which means that a particular data asset may be more liquid or less liquid than another. Many companies’ data has low liquidity — it may be trapped in local business processes, locked in closed platforms, or replicated in multiple locations, for example — or it may be inaccessible simply because it’s incomplete, inaccurate, or poorly classified or defined.
Much managerial attention focuses on liberating data from silos and applying it to a new, specific use, such as calculating customer churn or spotting supply chain breaks. This is a good exercise, but not a strategic one. Sure, an initiative on customer churn or supply chain will realize new value for the company. But companies that continue to pursue only a linear value creation cycle are leaving money on the table.
It’s crucial to recognize that data does not have to be treated like traditional company assets. Heavy equipment, office furniture, land, and even cash will deteriorate or be depleted over time. Data is different and can be reused and recombined freely without degradation. Data assets are born to be liquid, but while data is inherently reusable and can be recombined, the organization must deliberately activate these characteristics.
There is, of course, a cost to liquidity, so the organization also must deliberately select which data assets to liquify.
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1. Companies can generate economic returns from their data by improving, wrapping, and selling it, as described in B.H. Wixom, “Data Monetization: Generating Financial Returns From Data and Analytics — Summary of Survey Findings,” working paper 437, MIT CISR, Cambridge, Massachusetts, April 19, 2019, https://cisr.mit.edu.
2. We explain the five data monetization capabilities in B.H. Wixom and K. Farrell, “Building Data Monetization Capabilities That Pay Off,” research briefing XIX-11, MIT CISR, Cambridge, Massachusetts, Nov. 21, 2019, https://cisr.mit.edu.
3. In 2020, MIT CISR researchers and collaborators conducted 73 interviews with data and analytics leaders at MIT CISR member organizations to understand emerging data-related strategic digital initiatives.
4. “ESG Scoring Framework,” BNP Paribas Asset Management, accessed July 22, 2021, www.bnpparibas-am.com.
5. I.A. Someh, B.H. Wixom, and R.W. Gregory, “The Australian Taxation Office: Creating Value With Advanced Analytics,” working paper 447, MIT CISR, Cambridge, Massachusetts, Nov. 10, 2020, https://cisr.mit.edu.
6. B.H. Wixom and C.M. Beath, “Pega Drives Customer Engagement Using AI-Enabled Decision-Making,” working paper 449, MIT CISR, Cambridge, Massachusetts, June 17, 2021, https://cisr.mit.edu.