Although every company has the potential to earn revenue from the information it generates, in a recent study of more than 400 companies in 34 countries, only 1 in 12 were monetizing their data to its fullest extent. Modern data monetization strategies can help you open up brand new revenue streams. Here are four tips to help you start your own data monetization journey.
- Understand your data offerings
What types of data or data services are you producing that would be valuable to other companies? Whether you are looking to monetize what is marketing data, operational data, commercial data, or behavioral data, the type of data offering may determine how you charge for it and how much to charge. Five of the most common types of data or data services you can monetize include:
- Raw data: Primary data that is collected from a source for later analysis
- Packaged data product: Ready-to-consume data that requires little or no analysis or transformation
- Data analysis or insights: Dashboards, metrics, and indices
- Data enhancement: A service that augments customer data with additional insights
- Data trade: Exchanging your data for access to data from another source
- Decide how to price data products
As a public or private company data provider, you can use different methodologies to price your data, each with its own benefits. Two of the most common ways of looking at how to price your data products to optimize data monetization are cost pricing and value pricing.
- Cost pricing involves adding a percentage to your actual costs for data collection, storage, preparation, and transformation
- Value pricing involves charging for the value your data will bring a customer
Determining costs and value helps you establish different pricing tiers or packages. You’ll also need to decide whether to sell data by the set or by subscription, perhaps monthly or annually, or if you want to charge based on usage of the data.
You could also consider a freemium structure featuring limited access as a teaser for new leads, charging nominal fees for standard access and premium fees for additional services. Snowflake offers a sample pricing and packaging matrix in our ebook, Modern Data Monetization Strategies.
- Enrich your data with additional data sets
As you identify the types of data you own, decide whether customers can derive value from the raw data, or if customers need the data to be combined or enhanced with additional data sets. For example, retailers’ customer data sets are valuable to suppliers, but suppliers might be willing to pay a premium if that data were combined with a demographics or weather data set.
- Avoid the pitfalls of traditional data sharing methods
Traditional data sharing and distribution methods often use technology such as FTP, cloud buckets, or APIs. These methods have the following disadvantages:
- Storage costs for both parties
- ETL costs and effort for both parties
- Security vulnerabilities
- Service and support costs
- Latency and potential errors leading to poor customer experience
If you are just beginning your data monetization journey, you might be tempted to develop APIs as a way of sharing data. Although APIs are a great way to connect different systems and automate processes, they have a series of additional challenges when they are used for data exchange, including:
- Requiring in-house expertise to develop and maintain them
- Limiting the volume of data available to access
- Requiring data consumers to learn to use the API
- Limiting the types of analyses the data buyer can perform
- Causing performance and quality issues that are difficult to resolve
Instead, consider modern data sharing methods such as the Snowflake Data Exchange, which allows companies to easily publish a variety of data sets that then become immediately available for use or purchase by other Snowflake users.
For more strategies on how to successfully create and monetize your own data products, download our ebook, Modern Data Monetization Strategies.