Building a million-dollar data product: A step-by-step guide 

So, you want to build an awesome data product, aka “the million-dollar data product,” but you lack a solid plan of execution to get it done.  

Building a million-dollar data product goes beyond traditional dashboards to creating dynamic solutions that your internal or external customers will be eager to pay for.  

If this sounds applicable to you, I implore you to read on for a step-by-step guide, derived from the experiences of having built many data products from start to finish. 

1. Define the problem and understand your users 

The first key is to treat a data product like a start-up. The journey begins with understanding the problem your data product will solve. Engage with potential users to gain insights into their pain points. Focus on the practical benefits to the end-user, not just interesting data visuals.  

For instance, think about how Amazon monetizes its data with sellers through Amazon Seller Central. Your goal should be to develop a product that solves real-world problems efficiently and effectively.  

2. Centralize your data 

Centralizing fragmented data sources is crucial. We’re not suggesting that everything must be in one table/schema or even database – but bring all your data ‘under one roof’.  

Later down the line, when we’re ready for more complex analyses – you want to be able to easily bring disparate data sources together which is why that data should already be in the same environment. This not only enhances the user experience by providing a single, comprehensive platform but also establishes your product as an industry benchmark. Invest in scalable infrastructure from the start.  

A robust data architecture will support your product’s growth and ensure seamless integration of various data sources. Platforms like Snowflake or Databricks are the perfect candidates for this phase. 

3. Ensure high data quality and integrity 

The credibility of your data product is as crucial as the insights it provides. Implement rigorous data quality checks to ensure reliability. Establish a data governance framework that includes data source management, quality checks, and security measures. High-quality data builds trust with your users, which is essential for the success of your product in the long run.  

It’s one thing indeed to create governance frameworks – but what use is a framework if it’s not rigorously and correctly applied? My best advice here is, don’t re-invent the wheel. There are lots of incredible software/frameworks/modules out there now which have components and modules built specifically to apply data quality measures, such as testing, reliably and at scale.  

Lean on these, utilize them and fold them into your product in line with the methodology/frameworks we’ve decided upon in the previous stages of the development cycle. 

4. Design with the user in mind 

The design phase involves creating a user-friendly interface that makes data insights easily accessible and actionable. This goes beyond mere visualization. Embedded analytics, for example, can empower your existing software applications with data-driven intelligence. By bringing existing applications that the user already knows, together with your newly created data product can tremendously drive user adoption as it lowers the need for adjusting their daily routines.  

Think about integrating predictive insights and recommendation systems into your existing CRM, CMS or other systems. Lastly, an intuitive design will drive user adoption and satisfaction even more. A pro tip here is to investigate visual design principles and apply them to end-user screens. This will help engage your users more quickly, directly and retain their attention to the things that are most important.  

5. Develop and test your product 

With a clear design and robust infrastructure in place, it’s time to develop your data product. Using agile development methodologies to iterate quickly and incorporate user feedback can help speed your development to a tangible product. Using agile also helps keep the end product closer to the expectations of users.  

Conduct thorough testing to ensure your product performs well under various conditions and scales efficiently. I’d highly recommend using a proof of concept (POC) as a low-risk way to test with customers or partners and gather valuable feedback.  

And, creating checkpoints such as ‘MVP’ or minimum viable product, which provides a superb opportunity to take stock of the state of development, check in with key stakeholders, and ensure the trajectory of development is correct. 

6. Launch and market your product 

A successful launch requires a well-thought-out marketing strategy, even for data products meant for internal use. Highlight your data product’s unique value propositions. Use case studies and success stories to demonstrate its effectiveness. For commercial data products, leverage social media, blogs, and webinars to reach a wider audience. Your marketing efforts should aim to educate potential users about how your product can solve their specific problems.  

One of the most useful strategies I have seen utilized here is trying to better understand your market by identifying and classifying your prospective users. Certain users or types of users can be more receptive to your offer, perhaps based on their role and its applicability to your product etc.  

These users can champion your product and help facilitate its uptake. In a perfect world, these champions are also your key decision makers – able to pull the trigger and sign the purchase order too. But even if that’s not the case, product champions may hold sway over decision-makers and help make your case ‘from the inside’.  

7. Monitor, maintain, and improve 

Post-launch, it’s essential to continuously monitor your data product’s performance and gather user feedback. Use analytics to track key metrics such as user engagement, retention rates, and error reports. Identify areas for improvement by analyzing usage patterns and addressing any issues promptly. Regularly update your product to add new features, fix bugs, and enhance overall performance. Implement a robust system for user support and feedback collection to ensure that user concerns are addressed swiftly.  

Engaging with your user community through regular updates, newsletters, and support channels will help build a loyal customer base. Additionally, consider conducting periodic reviews and audits to ensure your product remains secure and compliant with industry standards.  

By fostering an ongoing relationship with your users and continuously refining your product, you can sustain growth and drive long-term success. 

Conclusion 

Building a million-dollar data product is a dynamic process that goes beyond just displaying data. It’s about creating a platform where data is just the beginning.  

By focusing on end-user value, centralizing data, ensuring data quality, designing with users in mind, and continuously improving your product, you can develop a data solution that’s indispensable. Shift your perspective from merely showing data to constructing a platform that integrates actionable insights and solutions, making your product a must-have for your customers. 

Learn how you can unleash the potential of your data with our Data Fabric Studio – the creation engine for actionable insights and smarter decisions in a complex data ecosystem.