Better data access improves business performance, but it also requires significant changes in our approach to data architecture. ‘Data-as-a-Product’ presents a paradigm shift in how organizations manage data to make data democratization a reality.
Put simply, Data-as-a-Product means delivering data in a way that allows your company’s users, at all skill levels, to get immediate value.
The concept is gaining traction with the emergence of defined approaches like the data mesh, pioneered by Zhamak Dehghani of ThoughtWorks, who proposed a methodology to:
Data-as-a-Product achieves these objectives by creating a customer-centric product mindset around organizational data.
Data as a Product
Making your data ‘product’ appeal to your customer audience
Successful product lines are tailored to specific customer needs. For data products the customer list includes anyone who needs data, ranging from data engineers, data scientists and analysts, to business users who lack technical expertise.
Let’s explore some critical elements that make data products suitable for this customer base:
Data-as-a-Product is Like Apps on a Smartphone
To better understand Data-as-a-Product, think about how its core principles mirror something we use daily–smartphone apps. It’s difficult to imagine life without our various ‘exchanges’, which deliver apps that:
These principles are in full force in the Data-as-a-Product paradigm. Let’s say you’re in the marketing department of a large enterprise and have a question that requires data. The traditional way might involve passing the question along to the central data team, who serve as a kind of customer support for data. From there it disappears into a black box and when the answer finally emerges it’s too late to be actionable.
Contrast this support ticket approach to the Data-as-a-Product way which, like finding a smartphone app, involves connecting to a central platform, conducting a Google-like search in natural language on your business problem, and getting:
The end result is a curated and prepped dataset, visualized through user-friendly tools that allow you to quickly dive in and get answers. This begs the question, who is responsible for getting data into this form?
The Ongoing Job of the Data Product Manager
With the old way of doing things each data dive is a one-off project. You find the data, get what you need, and move on. Data-as-a-Product, however, assumes that you’re not the first to ask a question like this, nor will you be the last. Under this approach a data product is ongoing, with continual improvements driven by a customer feedback loop.
Furthermore, the idea that data is an ongoing product implies a product owner with responsibility for overseeing its development. Consider, for example, a major financial services provider I recently spoke with that wants to implement a model with a “data product manager” for each business unit, who is empowered to:
Note that this person is not part of the organization’s central data team, but rather someone operating at the department level. For example, the data owner for marketing-related data products would be someone on the marketing team. The ability of someone who is not a data engineer or data scientist to take on data product ownership is made possible by some of the previously mentioned features of Data-as-a-Product, such as ease of finding data, and ease of building and managing data.
Delivering Speed, Productivity and Opportunities
With a Data-as-a-Product mindset and customer-ready data products in place, expect such improvements as:
In sum, Data-as-a-Product brings a simultaneous increase in ROI and lower TCO for analytics.
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