How to Build Data Products that Solve Real-World Problems

Greetings, my fellow data enthusiasts!

Today, we're going to talk about how to build data products that don't just look pretty on a dashboard but actually solve real-world problems. As a product leader at Holmusk and a builder of 0-1 health data products, I've had my fair share of successes and failures in this space.

“Build it and they will come” right?!- hate to break it to you but this ain’t no field of dreams. It has to be one of realities.

Ask the right questions, put together the right team, build with the right thinking, experiment and iterate with the right vision. With that, you'll get somewhere great.

So, let's dive into the steps you need to take to create data products that make a difference.

Step 1: Define the problem

Before you even think about gathering data or building a model, you need to define the problem you're trying to solve. What's the pain point for your target audience? What's the impact of not solving this problem? Who are the stakeholders involved? You need to have a clear understanding of the problem you're solving before you can even start thinking about how to solve it with data.

Step 2: Gather the right data

Now that you've defined the problem, it's time to gather the data that will help you solve it. You need to gather data that's relevant to the problem you're trying to solve. This can come from a variety of sources, including public datasets, internal databases, and user-generated data. You also need to ensure that the data you're gathering is of high quality, accurate, and complete. Garbage in, garbage out, as they say.

Step 3: Clean and prepare the data

Now that you have the data, it's time to clean and prepare it. This can be a time-consuming process, but it's essential if you want your data product to be accurate and reliable. ETL is critical for lineage, documentation, and merging and all of the other governance components needed. A skilled data engineer is worth their weight in gold here.

Step 4: Analyze the data

Once you've cleaned and prepared the data, it's time to analyze it. This is where you'll start building your models and algorithms to help solve the problem you've defined. You need to choose the right analytical techniques for the problem you're solving, and you need to ensure that your models are accurate and reliable.

Step 5: Communicate the insights

Now that you have your insights, it's time to communicate them to your stakeholders. This can be a challenging step, as not everyone is familiar with data and analytics. You need to present your insights in a way that's easy to understand and actionable. This could mean creating visualizations, dashboards, or reports that are tailored to your audience. Build with the end (and ideal customer profile (ICP)) in mind.

Step 6: Test and iterate

Building a data product is not a one-and-done process. You need to test your product and iterate based on feedback from your stakeholders. This means going back to step 1 and refining your problem definition, gathering more data, or changing your analytical techniques. The key is to stay agile and be willing to pivot when needed.

Step 7: Monitor and maintain

Once you've built your data product, it's essential to monitor and maintain it. This means ensuring that the data you're using is up-to-date and accurate. You also need to monitor your models and algorithms to ensure that they're still providing the insights you need. Finally, you need to ensure that your product is secure and compliant with any regulatory requirements.

The same but different

Before we wrap up, let's talk about how data product management differs from traditional software product management. While there are many similarities, such as defining the problem and iterating based on user feedback, there are also some key differences.

First and foremost, data products are inherently more complex than traditional software products. They require a deep understanding of data science, machine learning, and statistical analysis. This means that data product managers need to have a strong technical background and be able to communicate complex concepts to both technical and non-technical stakeholders.

Secondly, data products are often more experimental than traditional software products. You're dealing with data that's constantly changing and evolving, and you need to be able to adapt your models and algorithms quickly. This means that data product managers need to be comfortable with ambiguity and be able to pivot quickly when needed.

Finally, data products require a different kind of team than traditional software products. You need data engineers, data scientists, and analysts who can work together to build, analyze, and communicate insights from the data. This requires a strong focus on collaboration and cross-functional teamwork.

So, if you're building a data product, make sure you have the right team in place. This means hiring people with a strong technical background in data science and analytics, as well as people with strong communication and collaboration skills.

Conclusion

In conclusion, building data products that solve real-world problems is a challenging but rewarding process. By following the steps outlined above and building the right team, you can create data products that make a difference in people's lives. So, go forth and build great data products!