Do You Need To Build A Data Product?
Look for these signs within your data teams
The data field has been maturing. Companies who used terms like “Big Data” and “Data Science” to describe their vision in 2014–2016 are now starting to create division and structure around their data-related teams (BI, data science, analytics, etc.). This structure unlocks new opportunities for product managers to develop complex data products to serve the organization’s needs (e.g. dashboards, APIs, data lakes, reporting interfaces, and machine learning algorithms). Many companies already do basic reporting (descriptive analytics), and they want to start developing more robust capabilities that will allow them to tell what happened in the past (diagnostic analytics) and even start thinking about what might happen in the future (predictive analytics).
A lot of organizations ask themselves: “How do I know if I need to build a data product?” It’s not always clear when (costly) cross-functional expertise from PMs, software developers, data scientists, and others should converge on internal tools.
The signs within your organization can be related to the complexity of business requests, your team’s performance, or both.