If you’re running a data business, you’ll know that demand for data products isn’t the problem; discoverability is. Companies that understand this find themselves at the forefront of data commerce. And while they’re already seeing amazing results, the industry as a whole has a long way to go. So we set out to explore three of the most important concepts underpinning successful modern data businesses:
- Differentiation. How can you stand out from the crowd?
- Speed of innovation. Can you innovate — and bring to market — quickly and effectively to beat the competition?
- Unit economics. Will your data monetization strategy build in comfortable margins to ensure sustainable growth?
This blog serves as a brief introduction into these topics. If you want to learn how to develop more valuable data products, bring them to market faster, and improve your margins, we’ve put together a guide that you can read in about 8 minutes.
Data monetization through differentiation
A differentiated value proposition is essential for any business to rise above the competition. In the context of data monetization, this value proposition should include both the data products themselves and the experience customers have of acquiring and using these products. Maintaining a high-value, differentiated offering will be crucial to driving sales, protecting margins, and building profitable customer relationships. It also helps avoid the pitfalls of low-value data commoditization.
Established data businesses should be asking:
- How can we deliver the best customer experience?
- Does this get us closer to a strategic partnership model with our data customers?
- How can we maximize revenue opportunities?
- What is the quickest and most cost-effective route to market?
To see an example of a company that has successfully used differentiation to evolve their data business, check out our case study with Moody’s Analytics, whose DataHub platform is built on Harbr.
Speed of innovation
Established data companies should be well aware of the commercial value of certain data products. But how can this commercial value be proven early on, so that time and money isn’t wasted on ultimately unsuccessful data products?
A key concept can be borrowed from the world of Agile product development. The idea is to rapidly build prototypes of data products, which can then be tested on a limited number of key customers within a particular segment for suitability and value. While not every data product will end up being a winner, this process will help you discover products which can generate significant value for your customers — and in turn, generate high margins for your business.
Developing agile data products
Here’s a basic structure for agile data product development:
- Rapid iteration: Rapidly develop, test, and iterate data products to gather information from your target market and accelerate market readiness at the lowest cost.
- Secure Collaboration: Data is rarely a standalone product. The customer will likely need to bring their own data, models, and tools to test the value proposition.
- Feedback mechanisms: Gain feedback on the specification, use case, and commercial value of the product:
- Specification: Format, structure, frequency of update, accuracy, cleanliness, etc.
- Use Case: Desired outcome, integration point(s), theme
- Value: Economic impact of business outcome and associated variables
- Continuous Improvement: Observing ongoing usage patterns to enhance the product or identify opportunities for new products that meet adjacent use cases or markets.
Go-to-market strategy for data products
There are basically three ways of getting your data products into the market:
- Traditional sales channels
- Distribution through your own branded storefront
- Participation in a public data marketplace
Generally speaking, public marketplaces can be a quick way to launch a data product into the world. However, there are certainly some key drawbacks of public marketplaces — uncertainty around differentiation of data products, lack of control of the customer experience, risk of commoditization, and potentially being positioned (and compared unfavorably) alongside your competitors. This leads established data vendors to find more success with their own channel. To learn more about the decision to build, buy, or participate in a marketplace, take a look at our Data Marketplace Decision Guide.
The third key aspect of a data monetization strategy is unit economics. Put simply, are you making more money on a data product than it costs to produce, market, and distribute it? Let’s break out both sides of the equation: the revenue opportunities and costs.
The degree to which your data products can generate revenue will depend on whether your customers understand the value proposition. The challenge then becomes generating awareness and providing access to these products. Demand for data products isn’t the problem; discoverability is.
Data commerce platforms can drive additional revenue by providing value in four areas:
- Discoverability: Can your customers find data products that will appeal to them?
- Delivery: Does the speed and ease of delivery encourage customers to buy more data products?
- Trialability: Can customers easily sample a dataset or product, confirm the fit of the data product and the problem they are trying to solve, and thus prove to internal stakeholders that this data product will be a smart investment?
- Collaboration: Are you able to collaborate with customers in order to understand their use case, operationalize sales more efficiently, and generate additional value to them? Do you have access to a shared space that allows customers to combine their own data and models with your data products?
Naturally, higher value data products will generate higher margins for data vendors. On the other side of the transaction, data consumers will experience accelerated time to value, thanks to shorter sales cycles, instant discovery and access, and on-platform collaboration.
Data businesses can be hard (and expensive) to support operationally:
- Data needs to be regularly updated; products that require queries to be run on a scheduled or event-driven basis need to be automated, requiring expert input.
- Splicing data requires hands-on intervention and maintenance, which can become unmanageable.
- File transfers can require support from IT in order to execute.
- Sample data sets offer a fractured and incomplete view into the overall potential value of the data.
- Data providers need their sales teams to have a deep and exhaustive understanding of their data and their customers’/prospects’ use-case in order to sell the right data products to the right people.
Operators of traditional data businesses will be well aware of these challenges. The good news is that treating data as a product can decrease total ownership costs — development, maintenance, and technology — by up to 30%. (Harvard Business Review, July/August 2022)
Pulling ahead of the pack
Data vendors wouldn’t be in the business if they weren’t making money selling data. However, as innovators switch to new models of data commerce, sticking with the status quo becomes increasingly unwise. For a deeper dive into this topic, check out our free guide. To continue your data monetization journey, chat with one of our experts.