Data is everything in the modern business world, and it isn’t just used for internal business insights. Companies are now monetizing data.
So, what is data monetization? Some data monetization companies are selling direct access to the data they have collected. Depending on the type of data, third parties can buy access to data in its raw form or in a form that’s been processed into insights.
Some data monetization examples from third parties include processing data for data providers and sending back insights in return. For instance, a third-party provider could process customer data for a retailer then return insights on different purchasing behaviors. A third-party consultant could also use data to uncover new business opportunities or potential new markets for products.
What Industries Are Monetizing Data?
The monetization of data is more prevalent in certain industries compared to others. According to a recent survey from McKinsey, industries where monetization is prevalent include financial services, healthcare, energy, and technology.
The survey also found that these efforts have become a differentiating factor. In some industries, a data monetization strategy is strongly linked with industry-leading performance. Survey respondents in high-performing companies were more likely than other respondents to say their organization is currently monetizing data. These same respondents were also more likely to say their organization has multiple data monetization strategies. These data monetization strategies included adding new products or services, designing new business models, and partnering with other data monetization companies to share data.
In general, survey respondents said a data monetization strategy has led to key changes in the way they do business. Respondents from companies in energy or technology reported the highest number of business functions being affected by data.
In addition to changing individual companies, data monetization has also been found to transform the nature of entire industries. In the survey, 70 percent of executives said data monetization tools have led to moderate changes in their industry as a whole, with the most commonly reported change being the creation of companies that specifically offer data monetization services.
What Is a Data Monetization Framework?
Whether it’s for internal user data monetization or external data monetization, a data monetization platform should be based on an established framework.
Embedded analytics is a framework that tends to provide the most return on investment. With this approach, data monetization tools are used to extract business insights and add analytics features to existing business intelligence software. These features can include data visualization and dashboard reporting. Development teams can also create standalone analytics apps that integrate with other applications. Embedded analytics can unlock new revenue streams and translate into or competitive advantage.
Data-as-a-service is another framework and perhaps the most direct approach to data monetization. This approach involves selling data access directly to customers or third-party organizations. If data does not contain sensitive information, it can be sold in its raw form. If data does contain sensitive information, such as personally identifiable information, privacy must be preserved to ensure compliance with privacy regulations such as Health Insurance Portability and Accountability Act (HIPPA) and General Data Protection Regulation (GDPR) before it can be sold. With this approach, the buyer uses the data for whatever purpose they see fit.
Insight-as-a-service is a framework that involves the use of data monetization tools to generate insights. The resulting insights could then be sold or used to create analytics-based applications or other products.
What are Common Privacy Issues with Data Monetization?
Whether your organization is looking to start monetizing data, or it already is, it is critical to prioritize the protection of data, especially for any data that must be kept private.
According to the Federal Trade Commission, businesses should base their data protection plan on five main stages: taking stock, scaling down, locking data up, disposing of unneeded data and planning ahead.
The first step is to take stock of all the data within company computers and files. This should include taking an inventory of all computers, mobile devices, and other equipment where data may be stored. Documented information should be categorized based on location and type of data.
After stock has been taken, it is important to eliminate all unnecessary data and unnecessary data collecting processes. If the inventory step turns up data that is not connected to a business purpose, it should not be kept. Additionally, if data has a limited window of use, it should be marked for deletion after that window has closed.
The next step is to establish a system for locking down both sensitive data-at-rest and data-in-use. The type of protection should be based on the type of information and how it’s being stored or used. The typical data protection plan is based on four elements: employee training, physical security, electronic security, and ensuring safe practices from third-party providers. A system for locking down sensitive data could include business associate agreements (BAAs) or secure enclaves; however, these are not airtight approaches to protecting data.
After a data protection plan is in place, it is important to have a plan for disposing of data that no longer has a business purpose. Unnecessarily holding on to data means taking on unnecessary risk, as this data could potentially be hacked. Therefore, sensitive information that doesn’t need to be kept must be promptly disposed of in a way that it can no longer be read or recovered.
Finally, it is important to create a response plan for security incidents. Unfortunately, security breaches can happen. It is important to have a response plan in place that covers how to investigate security incidents and informs the steps to address exposed vulnerabilities. A response plan should also cover the persons or organizations that will be notified if a security incident should occur.
How Can TripleBlind Help?
Many companies have found they are able to safely and securely extract more intellectual property value value from their data using TripleBlind’s data privacy solution.
With our solution, data partners can collaborate on sensitive data without exposing private information or proprietary insight-generating algorithms, as our novel one-way encryption technology allows both sensitive data and algorithms to remain behind protective firewalls. This approach simplifies compliance issues and avoids issues related to digital rights.
With TripleBlind’s innovative Solution, your data instantly gains more value and utility. Contact us today to find out how we can unlock the intellectual property value of your data while preserving privacy and enforcing compliance.