By Paul Alexander, CEO, Beyond: Putting Data to Work
Are you au fait with Data as a Product (DaaP)? If not, read on because it’s about to rock 2025.
So, what is it?
It’s a transformative idea, championed by Zhamak Dehghani—a prominent technologist renowned for introducing the Data Mesh framework in 2018— and moves beyond traditional data management approaches, advocating for a product-centric mindset.
Essentially, treating data not as a byproduct of operations, but as a strategic asset with a lifecycle, owners, and measurable value.
The timing of this shift is no coincidence. The explosion of real-time data generation, fuelled by advancements in artificial intelligence (AI), machine learning (ML), and cloud computing, demands new models for managing and leveraging data.
At the same time, businesses face growing pressure to innovate, deliver personalised experiences, and drive operational efficiencies.
As Forrester highlights, companies adopting product-based data models see enhanced collaboration, agility, and faster decision-making, giving them a competitive edge.
Understanding Data as a Product
At its core, Data as a Product involves treating data with the same rigor and strategic importance as any other product offered by a company.
This paradigm shift encompasses the entire data lifecycle—from creation and maintenance to enhancement and evolution—ensuring it delivers continuous value to both internal stakeholders and external customers.
IBM articulates that DaaP requires a clear focus on managing data products iteratively, refining them based on emerging use cases and feedback.
The goal is to make data usable, accessible, and high-quality, enabling it to power everything from analytics to customer engagement and AI applications.
The Imperative for Businesses in 2025
As we approach 2025, several key factors underscore the importance of adopting a DaaP approach:
Accelerated Digital Transformation:
The COVID-19 pandemic fast-tracked digital initiatives, leading to an unprecedented surge in data generation. IOt goes without saying that organisations that effectively harness this data gain a significant competitive advantage.
Advancements in AI and Machine Learning:
AI and ML technologies rely on high-quality, accessible data, making Data as a Product (DaaP) essential for maximising their potential.
McKinsey & Company research highlights the transformative impact of these technologies on product development and time-to-market.
For instance organisations integrating generative AI into their workflows have accelerated product development timelines by approximately 5%, allowing faster delivery of innovations to market.
Additionally, product managers using AI tools have reported a 40% increase in productivity, streamlining operations and enabling teams to focus on strategic priorities.
The integration of AI has also led to a 100% uplift in employee experience, fostering more engaging and efficient workplaces.
Ultimately, however, these advancements underscore that data isn’t just a support mechanism for AI and ML—it is their backbone, driving innovation, operational efficiency, and competitive advantage.
Evolving Consumer Expectations:
In today's market, consumers increasingly demand hyper-personalised experiences, compelling businesses to deeply understand customer behaviours and preferences.
This understanding is contingent upon the effective utilisation of data as a product. Notably, a study by Epsilon indicates that 80% of consumers are more likely to make a purchase when brands offer personalised experiences.
Furthermore, research by Segment reveals that 71% of consumers feel frustrated when shopping experiences are impersonal. These insights underscore the necessity for businesses to leverage data effectively to meet evolving consumer expectations.
Strategic Implications of DaaP
Embracing Data as a Product offers a multitude of benefits:
Enhanced Decision-Making: High-quality, well-managed data ensures organisations can make informed decisions, driving better outcomes at all levels of the enterprise.
Revenue Generation: Data products can be monetised, creating new revenue streams. Financial institutions, for example, are exploring ways to monetise data through personalised offerings or anonymised insights sold to advertisers.
Operational Efficiency: A product-centric approach fosters continuous improvement and innovation, streamlining operations and reducing redundancies.
Implementing Data as a Product
To successfully adopt DaaP, organisations should take the following steps:
Establish Clear Ownership: Create dedicated teams or "squads" to manage data products, ensuring accountability for quality, performance, and evolution. McKinsey predicts that by 2025, most data assets will be managed as products with dedicated teams supporting their lifecycle.
Invest in Data Infrastructure: Build robust platforms that facilitate seamless access, integration, and analysis of data across the organisation. As part of the investment bring in experts that can help to unlock the value of the investment and bring tangible returns to the business.
Foster a Data-Driven Culture: Encourage collaboration between data scientists and business stakeholders to align data initiatives with the business goals. Recent research by SpringerLink shows that alignment is critical for leveraging data in innovation and performance.
Ensure Compliance and Ethics: Implement stringent data governance frameworks to uphold privacy, security, and ethical standards, building trust with customers and partners alike.
Don’t navigate the future, shape it.
This is a topic very close to my heart. Since being a young account exec in advertising, I have seen the strategic value of data.
So, older (and hopefully wiser), I put my money where my mouth was and founded Beyond.
We believe that adopting a Data as a Product approach unlocks new opportunities for innovation, operational excellence, and customer value.
While the journey may present challenges, the rewards—enhanced agility, competitiveness, and growth—make it a vital imperative for forward-thinking business in 2025 and beyond.
Put your data to work so that you don’t just navigate the future, but shape it.