Click to learn more about author Chandra Shekhar.

Facebook and Cambridge Analytica are very hot in news nowadays and have created a worldwide storm – they are accused of many wrong doings. At the core of this is nothing but wrong and unethical (possibly illegal) practices of Data Monetization. It is a great lesson for businesses and data practitioners on “how not to monetize data”.

A separate case study on the practice of Data Monetization by these two companies may be reasonable as it has created ripples in the political and social spheres. Accusations of data stealing, using it for the wrong purposes, and the unauthorized selling by Cambridge Analytica are all important. So is the fact that Facebook said sorry for the mess of unauthorized data selling and their inability to secure customer data; they now have very serious dents to their reputations. All businesses need to be very careful, but it is quite likely that businesses will become very sensitive to practice of Data Monetization itself, and it is unfortunate.

Data Monetization, conceptually, is applicable to both businesses and individuals. Individuals are monetizing through posting data to portals like YouTube (number of views and subscribers) and Twitter (from followers to TLs) accounts. Innovative business models have emerged out of portals and social media platforms. All may have opportunities for monetization, but the scope of this discussion is limited to sizable and conventional businesses that accumulate data and wish to monetize it.

From literature and referring to data professionals, Data Monetization in a nutshell, may be summarized in few points, among other things:

1. Creating or preparing data products as instruments of advisory and sellable products.
2. Ability to show data, as assets of a business, to reflect on financial statements.
3. Efforts to demonstrate the internal use of data to improve performance of process and operations, and savings in expenses, as Data Monetization.

Here is a high-level analysis of these points if organizations are in a position to practice Data Monetization.

Data Monetization Through Data Products

Selling data is a lucrative business in digital age. Enterprises like Lexis Nexus, Bloomberg, Dun & Bradstreet, Thompson Reuters, credit rating and monitoring agencies like Experian, TransUnion, Equifax etc. sell exclusive ‘data’ as their business products and are well known for it.

Another category of “data as product sellers” are search engines, social media platforms, portals and websites who sell “marketing” focused data. There are examples like USPS that use data for marketing business and monetizing data. The accumulation of data over a long period, by portals and search engines, which is data hoarding, or digital hoarding/e-hoarding, is a common practice of search engines and social media platforms for Data Monetization. For this and other reasons (such as data leaks or stealing from Facebook) there is now a much larger debate on Data Monetization.

Every digitized business is accumulating a large variety and sizable volumes of data depending on the nature of their domains, scale they operate at and business models. Businesses can monetize data, beyond their primary business, through ideas, approaches to competency and practices to sell data without compromising privacy and information security regulations.

It should be noted that “value addition” is key for Data Monetization. Business and operational process architectures need to be modified and aligned. When these items are matured enough, along with certainty to revenue generation, a new business model may emerge.

From spinoffs like Broadridge from ADP, Optimum Insight from United Healthcare, even educational institutions like the University of Maryland University College when it created its Office of Analytics into a new company, Helio Campus, it is understandable that the development of data products from datasets, already available within an organization, is a good business case for Data Monetization.

Listing Data as an Asset on the Balance Sheet

There are organizations relatively effective and efficient in managing data. Yet, the lack of a standard accounting model, to value data as an asset, is a limiting factor to list on balance sheets. Also, the decades old debate needs to be settled whether data is tangible or intangible property. If it is tangible, what class of asset is it? It’s historical cost, future cost, and determining rate of depreciation etc. all matter.

It is quite likely that data assets are inaccurately estimated because of no “readily available market” to compare. For operational purposes, within retention periods, valuation may be relatively higher as compared to beyond retention periods. The same data asset used for analytical purposes (e.g. data mining) may have a different value for a specific dataset if a useful outcome emerges.

The real problem is because of many uncertainties around the valuation of data. The value of a dataset may also vaporize for not being a physical property. Due to such reasons, dealing with intangible assets, it is not only tricky, complicated, and confusing, but may also be inaccurate as it has been observed in the case of Goodwill and intellectual properties like brand names, trademark, copyrights and patents to be listed on balance sheets.

Data Monetization from the Use of Data within an Organization

There are many factors to be considered in order to understand Data Monetization from the use of data internally. Data is used in two ways, one for primary business processes, and to support functions like quality, process improvement, risk and compliance to name a few. Support functions rely on a variety of datasets and metrics about problems, incidents, issues and events in operations, enterprise and financial risks. Organizations are invested in these functions and to reduce costs through capital expenditure (capex) projects.

The following items cannot be ignored as expenses while using of data within an organization:

• Data is not a free resource, but businesses do invest and spend on creating and acquiring data.
• Stakeholders of data are support functions too. Should investment towards support functions be treated as contributors to data creation costs? It is suitable to consider it, but there are no organizational processes to recognize it from operational and accounting aspects.
• Organizations do invest and spend for Data Management for the lifecycle of data assets. So, unless the cost towards a data asset for its lifecycle is recognized and measured in dollar value, any savings or benefits and profits, are not true metrics of Data Monetization.

Costs Associated Activities in the Lifecycle of a Data Asset

Data Monetization may be a onetime activity or process for a business. To be profitable, in terms of costs and risks vs benefits, a repeating exercise makes sense. A data asset should be evaluated and determined for the value of a dataset as an asset, regularly and ad hoc, at any given point of time.

Expenses toward a data asset need to be understood for the entire process of data aging all along the retention period and beyond until safely disposed, if at all. Generally associated activities with data assets are:

• Understanding data assets of an enterprise in terms of the scope of the business case for Data Monetization.
• Identifying the use of data assets, possible partners and targetable consumers.
• Preparation of data, data products, updating and management of data assets.
• Cost of data storage for the entire lifecycle – during production, before and after the retention period.
• Cost of managing the Data Quality of a data asset.
• Cost towards managing data privacy and data security.
• Cost for risk and compliance management.
• Tools and techniques usable from creating data assets until Data Monetization.

Conclusion

Unless and until an organization puts together a business model, and identifies revenue sources it may be far from realizing Data Monetization. The risks weigh more than profits, as just happened for Facebook. The need for consistent and mutually acceptable processes like the valuation of data assets is also a big challenge. Data valuation may keep on varying at different junctures of applications, operations and aggregations, based on the interpretations of stakeholders. Along with the costs involved, for the lifecycle of a data asset, businesses must be careful about possible mistakes and regulatory violations that can be counterproductive and disastrous to the primary business.