The Rise of Data Productization: A New Way of Thinking?
Businesses are getting creative about data. Around 147 million zettabytes of the stuff will be generated this year (402 million daily terabytes), giving rise to new roles, new ways of working, and ultimately, new products.
Given the exponential growth of data and the slew of high-precision tools on the market, productization is a natural step. Data products and Data as a product (DaaP), while related, have a few unique distinctions:
Data products typically refer to any product or service that uses data to generate insights, automate processes, or deliver value to customers through analysis, prediction, and visualisation, like Netflix’s recommendation engine.
DaaP usually refers to the approach taken to data management. Raw data will be treated as the product itself, and consequently, data sets are designed with the end-user in mind. For example, financial insights reports.
As enterprises race to adopt product thinking in the data space, engineers lean on increasingly sophisticated tools to ensure this data is scalable, accessible, secure, and reliable for the consumer.
From the recruitment process to the designation of role responsibilities, this emergent approach to data is reshaping the way businesses build teams. What does it mean for the talent market?
New(ish) Roles
It’s not a completely new concept, but looking at data through the product lens has gained traction in recent years, characterised by a shift in the talent market. The growing number of vacancies for Data Product Owners is a prime example, a role that aims to bridge gaps between technical and business teams.
How does it differ from traditional roles? The data product owner typically defines the vision and roadmap for data products while aligning them with business goals to ensure that data is used to arrive at strategic decisions – it’s reflective of the increased demand for outcome-focused data management in a space where data is being treated as a high-value asset with clear ownership.
Alongside this, we’re seeing a cultural change in the way data science teams operate, shifting from the lone-operator style of work to a more team-centric setup. It’s largely driven by the uptick in cross-functional input and continuous development as businesses double down on agile development.
What this means for data professionals:
A change of expectations as the focus moves away from pure technical execution toward a more business-oriented strategy and consumer needs
A product-focused mindset will become more valuable as DaaP involves lifecycle management and stakeholder alignment
Change! The market is evolving quickly, and so too are data roles. Those who can draw influence from a product mindset will likely have a range of new career development avenues to explore.
Data governance and ethics are rising to the top of the priority list (certainly on the consumer’s side), and when combined with heightened regulatory pressures, businesses must ensure that their data teams can advocate for responsible data use.
Data Mesh
Data mesh is a decentralised data architecture that treats data as a product across separate domains – for example, instead of a business relying on a single platform or lake to serve the organisation, each function (marketing, finance, sales, etc) takes individual ownership of their data.
This decentralised approach sees individual domains take ownership of their data, which in theory, can drive greater accountability for data quality.
This can enhance architecture scalability, increase flexibility, and improve data-led decision-making in a more transparent environment, provided teams can manage the cultural shift.
DaaP is a fundamental concept in data mesh. This architectural realignment encourages teams to think beyond storing and generating data – they need to get proactive about how it will be consumed by others, the context behind the data, and the model used to ensure access and explainability.
Despite the speed of its post-pandemic emergence, it’s still fairly uncommon to see organisation-wide data mesh. We expect this to change as larger businesses continue to reframe the way they approach data, particularly in Germany, whose markets are still in a state of ‘digital lag’ compared to their Eurozone counterparts.
Plugging the Gaps
As always, access to skilled and adaptable data professionals will be a key differentiator in the race to make the most of data. This race will only heat up as data-intensive areas gain traction elsewhere, particularly in finance and healthcare.
How is your role evolving? Whether you’re a hiring manager or a data specialist navigating the ebb and flow of today’s market, I’d love to hear from you. As a specialist data recruiter, I’m always interested in hearing your market insights. Drop me a message: Francis Alexander.
