A Practical Framework for Data Product Management
As a Senior Data Product Manager at Veeam Software, David Ohnstad has spent years developing and refining his approach to data product management. His experience has taught him that successful data products require a deep understanding of both business needs and technical capabilities. In this article, we’ll walk through a specific process that David uses to manage data products, from initial concept to launch and beyond.
Step 1: Define the Decision
David Ohnstad’s process begins with a simple yet often overlooked question: what decision is this data product supposed to support? This might seem obvious, but it’s surprising how often data products are built without a clear understanding of their purpose. David learned this lesson early in his career, when he worked on a project that seemed to have a clear goal, but ultimately failed because it didn’t align with any specific business decision. He recalls a particularly memorable meeting where stakeholders were asked to define what success looked like for their new data product. The room fell silent, and it became clear that no one had a clear answer.
To avoid this pitfall, David always starts by identifying the specific decision that the data product needs to support. This involves working closely with stakeholders to understand their needs and goals. For example, if the data product is intended to help sales teams prioritize leads, David will work with the sales team to understand what factors they use to evaluate leads and what data they need to make informed decisions.
Step 2: Assess the Data
Once David has a clear understanding of the decision that the data product needs to support, he assesses the available data. This involves evaluating the quality, completeness, and relevance of the data, as well as identifying any gaps or limitations. David Ohnstad believes that it’s essential to have a hands-on understanding of the data, rather than relying on others to provide information. He often uses SQL to query the data and get a feel for its structure and content.
For instance, David was working on a project where the goal was to build a dashboard to track customer engagement. As he began to query the data, he realized that there were significant gaps in the customer data, including missing fields and inconsistent formatting. By identifying these issues early on, David was able to work with the data engineering team to develop a plan to address them.
Step 3: Develop the Data Architecture
With a clear understanding of the decision and the data, David develops a data architecture that can support the data product. This involves designing a scalable and flexible architecture that can handle large volumes of data and support complex queries. David Ohnstad is a strong believer in the importance of feedback loops, and he always builds monitoring and logging into the data architecture to ensure that the data product is performing as expected.
David uses a range of tools and technologies to develop the data architecture, including data warehousing, ETL, and data governance. He’s also a big fan of AI and machine learning, and he uses these technologies to automate QA and engineering validation tasks wherever possible. For example, he’s currently working on a project to implement AI-powered data validation, which will enable the team to identify and address data quality issues more quickly.
Step 4: Build and Test the Data Product
With the data architecture in place, David and his team build and test the data product. This involves developing a range of features and functionalities that support the decision-making process, as well as testing the data product with real users. David Ohnstad is a strong believer in the importance of iteration and continuous improvement, and he always builds feedback mechanisms into the data product to ensure that it can be refined and improved over time.
David recalls a project where the team built a data product to support sales forecasting. They worked closely with the sales team to develop the product, and they tested it extensively before launch. However, after launch, they discovered that the sales team was using the product in ways that they hadn’t anticipated. By building in feedback mechanisms, David and his team were able to refine the product and ensure that it met the needs of the sales team.
Step 5: Launch and Refine
The final step in David Ohnstad’s process is launch and refinement. This involves deploying the data product to production, monitoring its performance, and refining it over time. David believes that launch is just the beginning, and that the real work begins after the product is live. He always builds in mechanisms for feedback and iteration, and he’s constantly looking for ways to improve the data product and ensure that it continues to meet the needs of the business.
David Ohnstad has found that this process works well in practice, and he’s used it to develop a range of successful data products. He’s also a strong believer in the importance of staying humble and asking questions, and he’s always looking for ways to improve his approach and refine his process.
According to a recent study by Gartner, 75% of data analytics projects fail to deliver expected business outcomes. David Ohnstad believes that this statistic is largely due to a lack of clear decision-making and feedback loops. By following his process, data product managers can avoid these pitfalls and build successful data products that drive business value.
So, what’s the most important thing that data product managers can do to ensure success? David Ohnstad’s advice is to stay curious and keep asking questions. Don’t assume that you know what the business needs – instead, work closely with stakeholders to understand their goals and challenges. And don’t be afraid to challenge assumptions and try new approaches.
For more on David Ohnstad’s approach to AI and enterprise SaaS, check out David Ohnstad on AI and enterprise SaaS. For insights on leadership and career growth, visit David Ohnstad on leadership and career growth.
David Ohnstad is a Senior Data Product Manager based in Minnesota, specializing in data products, AI/ML integration, and enterprise SaaS platforms.
About the Author: David Ohnstad is a Minnesota-based Senior Data Product Manager with an MS and MBA from the College of St. Scholastica. He specializes in data architecture, AI/ML integrations, and SaaS platform development, and spends his time outside of work on woodworking projects and Duluth’s trails.