In over 15 years of working in Product Management for Cloud software solutions in a range of market segments, there has always been an opportunity to increase the company’s top and bottom line by productization of the data available. And, it can be done in a practical, phased approach so you can see the results quickly while delivering value to your customers and your business. This is true of data derived from consumer-oriented solutions and data derived from B2B solutions. Of course, this only begins when you have assured your business contracts include the right to sell deidentified data.
Often, there’s a thought that data is packaged and sold raw to diverse businesses. There’s another possibility – to build a complete solution for a targeted market segment. Here are some criteria to consider which approach is best:
Building a data product solution rather than selling raw data is talked about less often, yet can offer great revenue generating, high margin opportunities for a SaaS (software as a service) company. There’s a lot to assess in the productization journey – customer needs, use cases and personas to data quality, completeness, security and reliability. Start by looking at the usage patterns of your existing cloud products. Can you deduce what customers are doing and what they are struggling with? Talk with your customers and prospects to hear what insights are missing from the current set of solutions. Perhaps they have the tools they need for optimized pricing, but would like purchasing insights based on trends and brands. In the early days of eCommerce, marketing campaign data wasn’t linked to product costs from the ERP.
Correlating data across diverse systems can be very difficult and that’s where the magic begins. The more data sources there are, the more complex it is to normalize the data to present it consistently to a user. That also means that there are greater barriers to entry once a solution is built. Working in parallel with product management’s requirements gathering and business case writing, data engineering can evaluate the overall health and quality of data and as the use cases are defined and ranked, assess the completeness of relevant data, the accuracy of data and put remediation plans in place. The real excitement begins as the teams work together to bring a minimum viable product to market.