Collecting and monetizing data is a goal that many organizations now have. Setting out a goal, however, isn’t the same as actually employing data monetization strategies.
When we think about data monetization strategies, they can be broadly put into two main camps. These are strategies that are meant to be:
- Cost-saving measures
- Revenue-generating ones
Similarly, strategies tend to be either internally or externally facing. Let’s take a look at how each approach works and how they might fit your operation’s needs.
Cost-Saving Data Monetization Strategies
Oftentimes, the simplest data monetization strategy is the one that leverages something a company already has. For example, a large home nursing agency has lots of data about the appointments it makes. That data can be leveraged to make determinations about when to schedule employees, how to handle travel and even what order clients should be visited in.
A cost-saving data monetization strategy is typically internally facing. This is because the easiest data to get your hands on is what your company has.
There are, however, businesses that have emerged that provide cost-saving data monetization strategies to others. Many consulting firms now help other companies make the most of their existing data in order to:
- Improve processes
- Spot fraud
- Create better information-driven products
- Develop better compliance measures
- Anchor analysis
If a problem can be identified, there’s a good chance it can be monetized if you can find and exploit a data pool related to it.
Revenue-Generating Data Monetization Strategies
Streamlining a business is one thing, but at the end of the day, your organization needs to turn a profit. The best way to accomplish that sometimes is to employ a revenue-generating data monetization strategy.
A simple version of this approach is assembling collected data into products that can be sold to other parties. This is an especially good plan if you’re trying to monetize what is fundamentally dead data. For example, a healthcare company might not have much use for decades-old epidemiological data. Plenty of researchers, though, would pay to get their hands on that data. Bear in mind, however, that anonymization is often necessary when selling data products.
Turning information into a new product is another way to generate revenue. Your company might compile loads of data on trends in your industry, for example. Converting such information into reports that are sold to outside parties is one of the most time-honored data monetization strategies.
Creating whole new opportunities is another approach. A company might focus on culling existing data to determine where there are new markets to enter. For example, your organization might examine international sales and see that loads of folks in Australia are ordering your products online. This may suggest an opportunity to open up new stores in the country.
Inward vs. Outward
Another question is just how inward- or outward-facing you want your data monetization strategies to be. Naturally, some organizations are reluctant to put their data in places where competitors might take advantage of it. There also may be concerns about regulations limiting the transfer of data.
In some cases, an outward trajectory is the only viable approach. In the previous example involving dead data, there simply may be no other way to extract any more value from the data as a product.
The question of inward versus outward monetization sometimes hinges on building a business model. Inward-facing models generally are more sustainable because they don’t depend on outside parties continuing to pay for reports or subscriptions. Conversely, an inward-facing model frequently has an installed limit on how much value it can generate because its audience is capped.
Preparing a Data Monetization Strategy
Having a strategy isn’t enough. To put one to work, you need to prepare the data itself and to be prepared for several potential issues.
If your operation doesn’t already have a large data infrastructure and a data-centric culture, you’ll need to put that in place. This entails:
- Developing a business case for the strategy
- Establishing processes and a compliance structure
- Hiring professionals who can handle data
- Building up servers and networks for storage and processing
- Refining processes
- Maintaining the strategy long-term
In many instances, the strategy you choose will guide the decision-making process. For example, a company that’s building a model for selling anonymized customer data to third parties will need to build its processes around privacy and consent concerns. A solution will need to be in place for customers to deny the use of their information, and an audit system will need to be designed to ensure privacy concerns are addressed.
Data monetization is a huge opportunity, especially for organizations that are already accumulating loads of information. It does require a cultural commitment, though, to handling the data itself and treating it with an appropriate level of care. In time, your company can realize major savings and turn its data into a profit generator.
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