In my earlier post, I covered Product Management and different types of Product Managers.
You can read it here.
In this post, I want to cover one specific type of Product Manager. Before we jump into it, let’s build some context around the role of Data Product Manager and how it came into being.
Contrary to the popular belief in the Tech Industry, Product Managers didn’t just spawn out of thin air and began to dictate how product needs to be built. However, there is no smoke without fire, so make of that what you may as well will and the white smoke from the start-up does not indicate appointing a new PM.
Onto the main story point, let’s discuss the popular data-driven approach of organizations.
What does Data-Driven mean for an Organization?
Being data-driven is not a novel concept. Successful organizations have been doing it for a long time. Today, we hear the term more and more due to the sheer amount of data collected by organizations in the name of analytics.
Nothing wrong with measuring performance and having healthy KPIs.
During and post-pandemic, the organizations realized how important it is to rely on data and facts to not only predict the interruptions to the health of the product and organizations but also react accordingly to major disruptions.
Organizations need to make provisions to collect data not just for long-term solutions but also for the short-term. This gives immediate reason to have a robust data strategy and thus a chief data officer to overlook the said data strategy.
Product Managers working within a product organization rely on this data as well. It’s essential to collect relevant data.
Here is a great reference article on data-driven organizations.
The Role of Analytics In Data Building vs Ready-Made Data
Decision-making is crucial and nerve-wracking for the sheer ability of the impact it may have on a product or an organization.
Why not have data available at hand to understand the impact and probably ease the nerves?
Data & Analytics can help organizations in planning, strategy, and innovations. It also helps in building and refining processes for smoother execution.
On a broader scale, two types of analytics can help organizations to be data-driven
- The Analytics from internal tools and product metrics
- The analysis from the market and competitors is typically acquired from outside/other sources.
In both of these cases, the important factor is data literacy which drives the governance of the data to maintain data quality to be effective for the relative purpose.
Here is a great article that explains the Key Metrics in Data Strategy & Analytics.
As mentioned above, the Chief Data Officer can play a vital role in all this but is it only one person who needs to maintain all this?
We already have Data Scientists and Data Analysts looking into the tooling and maintenance of data.
There is a gap in the process here for someone to take ownership of the efficient implementation of data strategy.
Who are Data Product Managers?
Data Product Managers, unlike their product counterparts, do not delve into the Product Management jargon.
They are the experts in understanding the data at hand, capturing new data, understanding the requirements from stakeholders, and making them clearer for data scientists and analysts to create a viable data product.
A good Data Product Manager understands the business & stakeholder demands and creates an effective plan to implement the data strategy in coordination with the Chief Data Officer.
Just to grab your attention with the most popular word in the Tech industry, Data Product Managers can help organizations enable & drive their AI strategy.
Yes, Data Product Managers help organizations to be AI-driven.
Data Product Managers & Effective AI Strategy
Organizations have rapidly moved from Digital Transformation to Automation to the logical next step of integrating AI into the organizations’ process and even in Product.
AI is only as effective as the data used for its training.
How many times have we seen in the news that companies like Adobe, Udio & Suno are being sued for their use of AI?
The latest to get into hot water is Figma using Generative AI.
Data Product Managers, to an extent, can help organizations effectively understand the implications of using third-party AI tools as well as using internal data to train the AI for efficient use within the product.
Conclusion
Product Managers in general get a lot of flack for being non-technical and/or lacking a basic understanding of the implementation cycle of a product. That is changing rapidly, as the role is evolving and diversifying into every aspect of product development.
Data Product Managers are not new additions but they are just getting the limelight they truly deserve within the long line of Product Managers that are driving organizations and products to rapid growth and success.