Is Your Data Monetizable?
If you want to monetize your data, you need to start thinking like a product manager.
- By Troy Hiltbrand
- November 15, 2021
For years, data and analytics teams have been focused on data-creation activities. Their value-add role in the organization was targeted at moving data from one place to another and cleaning and curating it in the process. This very important operational task supports multiple downstream business processes, but today the stakes are higher, and data and analytics teams are being called on to do more. Companies are looking to figure out the next step for generating value from that data and profiting from data monetization.
This increased scope requires that data and analytics teams change their way of thinking and start viewing their work in terms of product management, with its associated life cycle. This requires they not only shift how they work but also how they approach their end deliverable.
Whereas traditional data and analytics efforts focused on data creation, more companies are starting to draw boundaries around what they view as a data product and are changing their focus to who is consuming this product and how. They are identifying who their end-consumers are, what features are important to them, and what will increase their utilization of the product.
With this change, teams start to look not only at the costs of running their organization but also at the revenues generated by their product to ensure profitability. They must transition from a cost center to a profit center. This requires a different manner of thinking and strategizing. It requires a higher level analysis of risk and reward as they take bold, innovative steps that could result in higher revenues but could also destabilize their existing product features.
One of the first questions they must answer as they start to focus on profits is what type of data product will they provide. Data products frequently fall into one of three categories: an API that provides data access, a data feed, or the licensing of a data set.
Data APIs can focus on providing real-time access to data or they can focus on data enrichment or enhancement. A data API is generally a pull-based architecture where the end-consumer determines the frequency of the interaction. This requires infrastructure to handle varied loads with peaks and valleys in consumption.
Data feeds include a real-time push delivery of incremental information. Data feeds have an event-based architecture where incremental data is pushed to the consumer. Teams plan their infrastructure based on the rate of change associated with the source data and plan loads based on event frequency.
Data set licensing gives access to the full data set and lets the end consumer interact with the data more holistically. Along with understanding load-based capacity, infrastructure is needed for larger data pulls that usually occur less frequently than through an API.
In addition to product definition and infrastructure planning, teams must strategize about what pricing model they will use and how they remain competitive with their offering. Data product pricing can include up-front fees, annual fees, data limits based on the number of inbound or outbound transactions, or a combination of all three. Teams must make decisions about whether to utilize a pay-to-play model of pricing or leverage a freemium model where the base product is free but is complemented with a scale-centric subscription model to satisfy higher-use professional and enterprise clients.
Project Management Versus Product Management
The shift in the definition of the goal changes the way that your team does business. Instead of focusing on projects and project management (where the goal is milestone delivery), the emphasis turns to product management. Product management includes tasks such as product financing, designing the customer experience, feature road mapping, release management, maintenance and operations, and product marketing.
You will still have projects that support your product strategy, but the project work becomes secondary to the product's ability to deliver value. Many data and analytics organizations that move into the product management space start to employ agile project methodologies to ensure they are delivering frequent, incremental value from their product. This work shift often includes practices such as backlog management and refinement, sprint planning and execution, and team retrospectives to ensure continuous process improvement.
When the data becomes the product itself, the operations and maintenance of that data and the services surrounding it become a fundamental necessity that leads to end-user customer satisfaction. Data products are often coupled with defined service-level agreements (SLAs) that dictate terms such as availability, confidentiality, and data integrity. Meeting these obligations becomes a critical aspect of your product's success.
Complete Delivery Versus Continuous Delivery
Teams focused on transitioning their data into a product will often leverage DevOps practices to enable continuous integration and delivery (CI/CD) instead of waiting until a defined state of "done" before delivering. The utilization of DevOps practices in the data and analytics space combined with agile and statistical control processes is commonly referred to as DataOps. These DevOps practices include frequent builds of the data product code, running automated testing against it to ensure both functionality and data quality, and incrementally deploying the code to a production environment.
Requirements Versus Empathy
When your data and analytics team starts working with external consumers (or even redefining the relationship with internal customers) as they start to view their role as product providers instead of service providers, the way that work scope is defined and vetted begins to morph as well. Instead of approaching the business to gather requirements, the team has a product owner who represents the demand side of what the product needs to accomplish. The team works jointly with that product owner to ensure that the product is delivering ongoing value and is continually improved.
The team must develop empathy for who their consumers are and what they are trying to accomplish with the data product. Data teams must understand the consumer journey, including how their consumers work, what their target objectives are, and how the data product enables them to succeed. They start to view their efforts in terms of how the data product refinements reduce their pain points along the way and lead to an overall enjoyable experience.
Moving your team from being data-processing focused to data-product focused will not happen overnight, but if you can achieve the mindset shift, the results can be game-changing. As you transition, you can change your role from a cost center to a revenue center and have the potential to change how your data and analytics organization is viewed as a key component of business success.
Troy Hiltbrand is the chief digital officer at Kyäni where he is responsible for digital strategy and transformation. You can reach the author at email@example.com.