How To Ensure Your Data Projects Don't Fail: Essential Tips For Data PMs

Here's why everyone is talking about effective data project management...

2 min read
engineering-management

Over the past 5 years, I have delivered many data tools, dashboards, and data models to solve business problems.

A lesson learned:

Product discovery phase is lacking in how data teams build solutions.

Unfortunately, many data teams do not succeed in building the right data product in an efficient manner. Let's talk about why and how to prevent it.

Building sustainable data products are hard.

My top reasons why delivering long-lasting data products and tools is challenging are:

  • Describing data does not elicit the right response from stakeholders. Data is tactile in nature.
  • It is hard to get requirements for solutions that are supposed to be insightful or exploratory in nature
  • No one in the team knows the solution
  • The modern data platforms enable us to build shiny and expensive solutions with ease

As more and more teams get to experience delivering dashboards and datasets, we are improving our ways of working to be leaner and more efficient.

What’s the fastest way to avoid Failure?

Spend time in product discovery to optimize for learning fast.

Success here 100% comes down to:

  • Verify that stakeholders will engage with the solution
  • Validating the quality of data required
  • Alignment of definitions

Build prototypes before real solutions.

People who succeed iterate quickly. They create sample data or dashboards to represent their understanding of the business context and assumptions.

So, here are a few easy ways you can do that.

  • Sketch dashboards to depict metrics and aggregations
  • Generate fake data with sufficient variety and volume
  • Create a decision-making deck with data to convince the stakeholders

Prototypes do not need real data.