
When we think of a smartphone, we instinctively understand its journey—from concept to customer. But what if we applied the same thinking to data? What if we treated data not as a byproduct, but as a product?
Imagine the lifecycle of a smartphone:
It begins with a spark of an idea, a recognition of a market need. Product managers analyze trends, forecast demand, and define the features that will make the next big hit. Similarly, in the world of data products, the journey starts with identifying a business need. A data product owner, who assumes the role of the product manager in the realm of data, is responsible for identifying areas where data can drive value, such as improving customer segmentation or enhancing operational efficiency. They also define success metrics and shape the vision for the data product.
Designing and assembling the data product
Once the vision is clear, the smartphone moves into the design and manufacturing phase. Engineers design the hardware and software, balancing performance, cost, and usability. Raw materials are sourced, components are manufactured, and the product is assembled and distributed. In the realm of data products, this phase is mirrored by data engineering and assembly.
Raw data is sourced from various systems, including CRM, ERP, and IoT devices, and transformed through pipelines. Data engineers and architects play the role of manufacturers, ensuring that the data is enriched, stored in accessible formats, and ready for use. Just as quality and consistency are paramount in manufacturing, they are equally critical in data engineering.
The launch moment: Driving adoption and access
With the smartphone assembled, it’s time for the big launch. Marketing campaigns drive awareness, sales channels distribute the product, and customers begin to interact with it. Feedback rolls in, and the product is refined based on user experience.
For data products, the launch phase involves making data discoverable through catalogs or APIs, which are platforms or interfaces that enable users to easily find and access the data they need. This data is then promoted through internal evangelism, and measures are taken to ensure that analysts, applications, or machine learning models can consume it. Adoption is the true test of a data product’s success, just as it is for a smartphone.
Iterate and improve: Keeping the data product relevant
But the journey doesn’t end at launch. A smartphone requires ongoing maintenance—software updates, bug fixes, and new features keep the product relevant. Similarly, a data product needs continuous monitoring, data quality checks, and iterative improvements. The lifecycle of a data product is a cycle of evolution, driven by feedback and changing business needs, engaging you in the process of continuous improvement.
Knowing when to let go: Sunsetting data products
Eventually, even the most successful smartphones will be replaced by newer models. It’s a natural part of the product lifecycle. Data products, too, may be deprecated, merged, or reimagined as business needs evolve. Sunsetting is part of responsible product stewardship, ensuring that resources are focused on delivering the most value and inspiring a sense of responsibility in your data strategy.
Conclusion
By thinking of data as a product, we shift from ad hoc reporting to intentional, scalable, and user-centric solutions. Just like a smartphone, a data product must be designed with purpose, built with quality, delivered with clarity, monitored for performance, and evolved with feedback.
Ultimately, whether it’s a smartphone or a data product, the goal is the same: to create something that delivers value, meets user needs, and stands the test of time.
This is where Mastech becomes important, as organizations require the right kind of partner to unlock the true potential of their data. With almost two decades of deep focus on data and a proven track record of making 100’s of clients successful in their respective data modernization initiatives, Mastech is a trusted name in this business.