Data-Enabled Business Models: Data Monetization

There is rarely a strong concurrence in regard to the monetary value of a company’s data. Sellers and buyers perceive its worth very differently, with key variables including the scarcity of a particular data type and what is intended to be done with the data.

Data by itself has little value; it’s the insight it generates once processed that makes it valuable. But after decades as a “secondary asset,” data is moving into a primary asset position for many businesses as data science models progress and provide opportunities to monetize data sources. A combination of completeness, accessibility, accuracy, and availability drives the value of data; data sellers have to be able to build trust with data buyers to maintain its value.

Business and IT teams need to work together in the journey to overcome data-related challenges. The IT function has long been the steward of data for the enterprise. But as the strategic importance of data has grown and the scale of the data management effort has risen, many organizations have found that the workload and expertise required necessitates new stand-alone functional capability. Data management organizations and a new executive leadership role, the chief data officer, is a widespread organizational development.

These new organizational capabilities equip businesses to manage the increasing regulation of privacy. GPDR and HIPAA as well as a host of state-level laws concerning consumer data mandate the sanitization of personal identifiable information and other consumer protections. Monetizing data presents a host of legal and ethical issues with which these data teams need to grapple.

Unlocking Value from Data at Scale: Analytics and Digital

An understanding of the “4 Vs” of big data—volume (the scale of data), velocity (analysis of streaming data), variety (the different forms of data), and veracity (uncertainty of data)—is essential to grappling with data and analytics. For those who are beginning the journey, discovery-focused workshops offer a starting point for developing an understanding of the possibilities and to brainstorm ideas as input toward identifying analytics use cases.

While “cool” projects may briefly dazzle decision-makers, data projects need to demonstrate a clear path to value through quick wins. A solution-first approach around seemingly basic questions helps to ensure that projects deliver value. Smallish pilot efforts can demonstrate value within a relatively brief two- to three-month time window to help build enthusiasm and momentum for analytics efforts.

For advanced analytics to scale, information consumers inside the business (and outside) need to understand what is inside the “black box”—in other words, the algorithms that generate the insights on the raw data. Without this understanding, faith in the results and the recommendations that follow will not be achieved. Regardless of their complexity and arcaneness, the human desire to understand trumps everything else. This requires providers to reduce the model complexity or make the models easier to understand for users.