Data Monetization is a topic that reaches the desk of every business executive.  According to the research of consulting firm McKinsey in their 2017 report titled “Fueling Growth Through Data Monetization“, a key insight of their research concluded that “data monetization seems to correlate with industry leading performance”.   Without question, the extraction and application of data is key for the future of the success of all businesses — whether its for marketing, research, product development, finance, or other functions.

However, “data monetization” is still perceived as a vague term.  Does it mean that a company sells their data?  How can a business monetize data while protecting confidentiality and privacy? What data should companies monetize?  How much money can a company make selling data?  Who buys this data?  The list of questions goes on…

At Highland Math, we help companies build a Data Monetization capability, so we encounter these questions a lot.  In a series of posts, we’re going to address the different ways to think about data monetization at a business.

The first challenge is whether to monetize data internally or externally (or both).

Internal Data Monetization
When a company has data assets that they want to extract value from, but don’t want to share this data with outside parties, that is called “Internal Data Monetization”.  Often, this is a data science need, and the company doesn’t have internal data teams who can extract, transform, analyze, and load data into internal systems.  Use cases can include cross-divisional data applications (for example, connecting supply chain data with marketing tools to improve the efficiency of products promoted), or simply extracting raw data and packaging it into valuable use cases for an end user (for example, taking raw financial data around customer pricing and grouping them into cohorts for sharper pricing analysis). The monetization of data is simply the transformation of raw, previously neglected data streams into valuable insights that businesses can execute a decision against.  This decision can drive up revenue, reduce costs, improve customer experience or awareness, and more.  Overall, internal data monetization is increasing in scope and is widely practiced among leading organizations who make the investment in hiring data science and engineering teams that can operate cross-functional across an organization.

External Data Monetization
With the growing number of data exchanges and marketplaces, businesses are beginning to capitalize on “External Data Monetization”.  This is simply the exposure of business data out to a third-party for the benefit of the third-party.  The reasons businesses share their data out can range from generation of new revenue, to bartering data sets, to strengthening the business of a partner which will yield direct benefit back to the data owner (imagine retailers sharing sales data with product manufacturers to improve products, leading to increased sales for the retailer).  External data monetization generally requires a data scientist to export raw streams of data, package them into consumable, valuable data products, and deliver them directly to the end client either directly or via an integration with a data exchange like Snowflake Exchange or Amazon Data Marketplace.  While nascent, this practice of External Data Monetization is growing as businesses become more aware of the value of the data they sit on, and the ability to share data becomes easier and easier.

The first question to understan about your business is what your goals are with data monetization, and whether its an Internal Data Monetization product or an External Data Monetization product you want to develop.  Based on the direction you choose, the implementation of the data science and data distrubution processes will vary, and the required internal stakeholders will vary too.