Data as a Product (DaaP): Treating Data as a Strategic Asset

The World Economic Forum predicted that by 2025, businesses globally will generate a whopping 463 exabytes of data every day, or the equivalent of about 212.8 million DVDs! Coping with this flood requires a new way of thinking. The role of data in modern enterprises is changing from a mere byproduct, often scattered and difficult to harness, to a strategic asset.

Data is No Longer a Byproduct

The good old days of just building a one-off report or custom data extract are fading. The new paradigm of using data, also known as ‘Data as a Product’ (DaaP), treats data assets as curated, governed entities designed for direct business use. This model makes data a strategic asset that has its own identity — it has clear ownership, is subject to version control, has defined SLAs and can even be monetised internally.

This marks a shift in how organisations use, govern and analyse their most vital digital asset. Finally, with a DaaP model, businesses can truly achieve democratisation of data access. End-users can spend a lot less time wrangling raw data into actionable, high-quality, dependable and contextual insights.

Pipelines to Products: A Mindset Shift in Design

A Fraunhofer survey found that 54% of respondents agreed that the gap between business strategies and the implemented analytics infrastructure is a leading factor for failure of data-driven projects.

Most enterprises have complex pipelines for transferring data from systems like CRMs and ERPs to analytics platforms. These pipelines don’t inherently add value to the data. When data is treated as a product, the focus changes from just transferring data to making it into a product that’s useful and creates ‘value’ for the end user.

Consider, for instance, a marketing department requesting churn data. A pipeline would just shoot out raw transaction logs in rows, but a DaaP model would yield an enriched data set saying which customers are likely to churn, along with contextualization — their purchase history, engagement scores and other background.

A well-designed data product is straightforward, reusable and purpose-built. It acts as a service—with inputs (like source systems), outputs (enriched tables or dashboards) and SLAs (update frequency and accuracy). Users know what to expect, how to use it, and when it will be updated.

Embedded Intelligence as the Differentiator

To stand out, the DaaP model needs to offer more than just raw data — it needs to offer context and intelligence. The use of AI for smart alerts, forecasting models, anomaly detection, and natural language processing turns passive data sets into active tools for a business’s decision-making process.

This intelligence lifts the cognitive burden and delivers relevant insights to end-users, enabling appropriate and timely responses. For instance, it automatically identifies sales anomalies or forecasts supply chain risks to optimise operations without human interference.

Governance Must Travel with the Data

A business’s data flows across different environments, systems and users. Governance can’t be an afterthought — it must be a characteristic of the DaaP model. Such security requirements include not only access controls but also sensitivity classification, data lineage and auditability.

Without strong governance, risks escalate. IBM reports that the global average cost of a data breach reached USD 4.88 million in 2024, highlighting the high stakes of mishandled data. With DaaP, governance is not centralised and static, but it’s federated and dynamic. It runs with the data and changes according to the circumstances. This makes sure data is kept secure, compliant and trusted across BI solutions, APIs and multi-cloud environments.

The Marketplace Model Is Here

The evolution of internal data catalogues into full-fledged data marketplaces is transforming data discovery. In a DaaP marketplace, consumers can browse certified data products, view usage metrics, trace data lineage and submit enhancement requests.

This consumer-like experience fosters transparency and accelerates collaboration. Departments don’t work separately anymore, hoarding datasets. Instead, they contribute to a shared inventory of reliable products. This boosts visibility, reduces redundancy and empowers faster experimentation.

Cloud-Agnostic and Infrastructure-Resilient

Modern enterprise ecosystems are spread across multi-cloud, on-premises and hybrid environments. A true DaaP model must be infrastructure-resilient—deployable anywhere, without any dips in performance and accessibility.

This level of flexibility means BI and analytics tools scale without locking into a single cloud vendor, even as storage, compute and geographic requirements change or evolve.

Ownership = Accountability = Trust

According to Integrate research, out of the 3.64M B2B leads examined for their report, 45% of leads were filtered out due to duplicated data, invalid formatting, failed email validation or missing fields. According to Gartner, bad data quality is the cause of an average of USD 15 million per year in losses, and that number is a stark reminder why design and stewardship are so critically important.

The DaaP models help solve this issue. Ownership is at the heart of DaaP. Every data product should have an explicit owner to manage its uptime, accuracy and user experience. This chain of accountability cuts through the ambiguity to quicken the resolution and build trust with users.

Trust is especially important in high-stakes decision environments. With defined ownership, teams know where to go for support, updates or questions—eliminating the confusion that so often derails data initiatives. Moreover, ownership creates pride and investment in quality, leading to better outcomes and stronger data cultures.

Authored by Anurag Sanghai, Principal Solutions Architect at Intellicus Technologies