My hack for getting started with data as a product

Progression from ad-hoc request to data product. **low cost as long as the tool has enough users to amortize the maintenance costs

Data products and marginal cost

The main value of treating data as a product is delivering tools that can be consumed over time instead of just delivering one-off insights (or worse, one-off raw data). If your culture is centered on Data as a Service (the service desk model), you are treating the data team like a low-leverage vocation where the members of the data team’s primary function is to be the human interface to the database. Data people are expensive and there is a much higher-leverage way to think about this team!

  1. Product skills and hiring
  2. Establish a product roadmap and lifecycle

1. Evaluate your organization

To get to data as a product in your culture, you’re going to have to convince your organization that it’s worth investing in reproducible tools and processes that deliver value over time. There is some up-front investment in these tools, but they pay dividends (like all software).

2. Product skills

Product skills and entrepreneurship can be learned and taught. While organizational buy-in is important, it’s also important to recognize that product skills across the data function are important for implementing Data as a Product. As an aside, I think you should hire for product skills from the beginning, and help mentor and teach this skill on your team.

3. Product lifecycle and the hack to get started with DaaP

Ad-hoc data requests are the perfect place to start the process of building a data product. My hack is to treat these ad-hoc requests like prototypes for a potential data product. Each of these ad-hoc requests are signal that your organization wants and needs data — and there might be an opportunity to productize the value generated from this request and get deeper into the business value by getting more context on the request.



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