Six Cornerstones of Data Productization

In our previous blog we discussed the importance of understanding the value of data. Closely related is the concept of data productization enhancing the utility of data by providing structured, classified, and value-estimated data along with considerations for business, legal, and ethical elements. The drivers to examine impactful and safe data productization-based D&I assets utilisation are to

1. Comply with regulation,
2. Enable data sharing,
3. Manage the associated costs,
4. Mitigate risks,
5. Increase product, service and solution excellence and
6. Generate new, multi-purposed and reusable sellables.

Data productization plays a crucial role in creating measurable value for organizations and their customers, fostering innovation, and boosting competitive advantage. By packaging data and information wealth into standardized forms, data products enable organizations to streamline processes, mitigate risks, and capitalize on new value creation opportunities.

Data products are diverse, ranging from static commodities such as reports and documents to dynamic data products such as request-driven or subscription-based offerings. Each type serves a unique purpose and can be driven by different perspectives, including demand-driven, value-driven, and operations-driven approaches.

The importance of aligning data productization with real business needs is underscored, emphasizing the need for organizations to define data products clearly, consider multipurpose use and reuse, and deliver them in a consumable form with a defined purpose of use. Data products do not only bring about monetary value but have also strategic, secondary, and sustainability benefits. By following a customer-centric product management approach and incorporating data lifecycle management, organizations can effectively manage and derive value from their data and information assets.

Perceiving data as a product will require, for many organisations, a change of mindset – the idea of producing data with assetization in mind is not common practice yet. To successfully implement data productization, there is a need for a structured approach that is customized to the specific needs and objectives of the organizations. This approach involves several steps, such as identifying suitable methods and participants, defining criteria for data asset identification, conducting data inventory, assessing data quality dimensions, and designing an end-to-end data productization process. The latter requires identification of the need and purpose of the data product (i.e, the meaning of the data product), bundling resources into a consumable form (packaging the data with policies, Service Level Agreement, pricing and billing information, among others), and offering the data product for the data product consumers in a standardised form. Important is also to define the minimum components of a data product. Location, access, and logic for use are needed, consumption readiness, trustworthiness and adherence to certain quality and data sharing standards.

Identification of data assets that provide value and applying selected product management principles will empower the organisations to adapt data culture. By embracing a structured approach, organizations can harness the value of their data, drive innovation, and gain a competitive edge in today's data-driven landscape. In our next blog we will have a closer look what can be achieved with data productization as well as the status of standardisation to accelerate the business potential of data and information assets.