Monetizing Data: Converting Data From a Cost Center to a Profit Center

Hear from Senior Research Analyst, Paige Bartley, at 451 Research as she discusses converting data from a cost center to a profit center in this brief video.

What steps can convert data from a cost center to a profit center? 

Enterprise data is often thought of as an internal resource for business insights. It also has an enormous potential for packaging and resale to other parties for profit in the long run. According to current 451 research data, one third of enterprise survey respondents in our research report that developing new data monetization services is an expected benefit from becoming more data-driven as an organization.

This is an enormous area of potential growth. The effort to monetize data, doesn’t happen overnight. Organizations need to prototype, launch, and then scale these efforts. Identifying datasets that are of high value to potential customers is a critical first step to monetization efforts. Ensuring the ongoing management and curation of this data, as well as its availability and accessibility to potential customers in an efficient way, facilitates a monetization model in the long run. Scale and consistency are both key for sustainable. Customers will need catered data products to meet their needs, but customization methods should be repeatable and not cause undue overhead or effort within the organization.

How can an organization maintain the integrity of data as a product? 

With data as a product, ongoing management and integrity of the data becomes paramount. Customer organizations depend on the quality, consistency and representativeness of datasets for their specific use cases. Data cannot be curated and delivered as an afterthought in a data monetization model. Attention needs to be paid to the entire stack of supporting technologies that generate and manage data throughout its life cycle. This includes everything from databases to metadata management systems, all the way to SAS application.

So for an organization looking to monetize data resources, having a consistent and complete understanding of the existing data quality issues is important. And according to 451 research’s surveys, data quality today ranks as the top challenge in modern enterprise analytics initiatives. In seeking or shopping for datasets, customer organizations in a data monetization model really need mechanisms to evaluate that data relevancy and integrity.

What are risks and rewards of using data to create new revenue streams? 

Most potential rewards come with potential risks. In data monetization efforts, the biggest risks are often associated with personal data sources and their associated regulations. Data privacy and data protection regulations around the world are evolving rapidly. In some cases, placing limitations on the third-party sharing and sale of that personal data and some cases, even the transfer of that personal data. In a data monetization effort, the selling organization needs to have a firm understanding of what data or datasets may contain potentially sensitive or personal information.

Personal data is often defined differently by different regulations. Organizations need to be aware of different jurisdictional requirements. Personal data needs to be identified so the anonymization techniques can be applied as a best practice prior to any monetization effort. In terms of rewards, data that may not be particularly valuable to the business itself could actually be very valuable to potential clients and business partners.

Historical data, especially in longstanding businesses, can be especially valuable. Consider business data sources that are generated, but not actively used for insight or decision-making. The so-called dark data might actually be overlooked internally, but have value for resale. So appropriate curation and resale of enterprise data can potentially generate revenue from sources of information that were previously underutilized or even incurring costs via storage and ongoing management.

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