3 data monetization paths to extract value

Bobby Fisher and Matt Patterman

3 data monetization paths to extract value

Data is the “oil” of the digital economy. It is quickly becoming the world’s most valuable resource in the digital world, and it is an immensely untapped, valuable asset.

If data is the new oil, then data monetization is the process of extracting, refining, and using that raw material to create a product that can be monetized. At Credera, we’ve enabled our clients to monetize this data through creating a revenue-generating offering based on their data. We have combined our strategic advisory services with our data expertise to uncover insights for our clients on the data monetization strategies available to them.

Here we’ll outline three potential data monetization strategies we’ve found useful when partnering with clients.

Client problem statement

Recently Credera partnered with a financial services client to create a data monetization strategy. Our client’s business model relied on the manual review and verification of documentation. This documentation was submitted to the business through a variety of intake pipelines including email, a secure file transfer portal (SFTP), and an online user platform. The paperwork was managed by a large workforce responsible for identifying, verifying, and validating that key information is consistent across a variety of documents. As business grew, the demands of document processing became untenable for the existing workforce.

After implementing Credera’s strategy, our client accelerated its document processing with the aid of data-driven optimizations and machine learning models. The result was a dramatic increase in the number of transactions processed per person, even full automation of processes for a portion of the business. In this way we enabled our client to support their exploding business without growing their workforce at the same rate.

Monetization options

In crafting our client’s data monetization strategy, we considered three different paths to monetization—improving internal efficiency, enriching existing offerings, and direct monetization. We believe these can be helpful paths for many organizations interested in data monetization.

1. Improving internal efficiency

Improving internal efficiency entails leveraging data to ensure the workforce is focused on the tasks that matter. This is about getting the right data to the right person, at the right moment in time. When done well it reduces mistakes, increases productivity, and allows the business to scale non-linearly to your staff size.

Client application

Originally, our client relied on the manual processing of paperwork to complete business transactions. The business’ rate of growth resulted in an accelerated load on the workforce and without a change in approach, would have required a substantial hiring boost to meet the uptick in demand.

To start, Credera reviewed the most repetitive tasks for personnel to identify which could be codified and automated. We then leveraged historical data for how documents had been classified to develop machine learning models capable of performing documentation reviews mimicking what the personnel used to do manually.

To maintain quality control of the document processing, we implemented check points along the process that would kick documents out of the automated process for manual review. If a set of documentation did not pass standard automatic verification, it would then be placed into a queue for manual verification, which displayed the reasons for failure that personnel could easily review. By flagging only problematic documentation and surfacing the key reasons for failure, we dramatically accelerated the rate individuals could process transactions.

2. Enriching existing offerings

The second method is focused on enriching existing product offerings. By leveraging insights from data on processes or offerings that can be improved, organizations can gain a competitive advantage through building better products cheaper and faster while targeting more intelligently.

Client application

Credera first centralized our client’s data in one storage location so it could be easily queried, graphed, and analyzed. Centralizing the data enabled us to explore relationships in the data and identify several areas where the business’s processes or offerings could be improved. In our client’s case, we used real-time feedback from production processes as training data within a MLOps framework. This enabled the data science team to be highly responsive to deviations in expected model performance to ensure the integrity of core business processes.

Additionally, as the team developed and released new features, they used a data-driven approach to ensure they were achieving the expected goals. By engineering a few key performance metrics, our client quickly iterated through new variations of an existing business flow until they found their most optimal configuration.

These enhancements to existing processes ultimately resulted in several new potential revenue streams and invaluable improvements to the automation and machine learning operations for the client. The net effect was more intelligent models driving business decisions while aggressively reducing cloud expense.

3. Exploring direct monetization

Finally, organizations can monetize data by licensing or selling the data. At Credera, we find that raw data is rarely valuable to sell on its own. Rather, it is more valuable to build and sell outcomes and insights based on the data.

Client application

Our client’s data represented a significant share of all data in their industry. As such, we processed this data into a variety of useful insights that could lend a further competitive advantage for their business. For example, the wealth of data we had accumulated allowed for precise forecasting of a few key performance indices specific to our client’s industry. These forecasts would be valuable to competitors as well as providing insight into the industry’s risk and future market conditions. These insights could be sold directly or used internally to drive revenue by better assessing and hedging risk.

Our structured approach to data exploration

At Credera, we take a three-step approach to determine how organizations may benefit from data monetization:

We start with an interactive workshop that uses various design thinking methods to uncover ideas specific to the organization and explore the avenues for monetization.
We then utilize design sprint methodology to prove value early with prototypes and proof of concepts.
Once an idea has proven value, we’ll work to institutionalize data science models that can generate insights to the organization into how to improve operations or offerings that will ultimately drive value for the organization.

If you want to learn more or you need help identifying all the ways to monetize your most valuable asset, contact Credera at findoutmore@credera.com.