How to Unlock the Full Value of Data: Manage It Like a Product ????

In the age of digital transformation, data is often touted as the new oil. Yet, many organizations struggle to derive significant value from their data investments. Traditional strategies—be they grassroots or big-bang—often fall short. To truly unlock the potential of data, it's time to rethink our approach. The key lies in managing data like a consumer product, ensuring it is reusable, accessible, and aligned with business needs.

The Challenge of Current Data Strategies ??

Most organizations employ one of two predominant strategies to manage their data:

Grassroots Approach ??

In this decentralized strategy, individual teams are responsible for piecing together the data and technologies they need. While this method allows for tailored solutions, it often results in significant duplication of efforts and a tangled web of bespoke technology architectures. This approach is costly to build, manage, and maintain, leading to inefficiencies and inconsistencies.

Big-Bang Strategy ??

Conversely, the big-bang approach centralizes data management. A dedicated team extracts, cleanses, and aggregates data en masse. While this can reduce some redundancy, it often fails to align with specific business use cases. End users struggle to verify data governance and quality, leading to limited time savings and eventual reversion to grassroots methods for new use cases.

Both strategies fall short in creating a sustainable, value-generating data foundation for current and future needs.

A Better Approach: Managing Data Like a Product ????

By managing data like a consumer product—be it digital or physical—companies can realize immediate value from their data investments and set the stage for future gains. Here's how:

Data Products: A 360-Degree View ????

A data product delivers a high-quality, ready-to-use set of data that can be easily accessed and applied across various business challenges. For instance, a data product might provide a comprehensive view of customers, employees, product lines, or branches. Alternatively, it could offer a specific data capability, such as a digital twin that replicates real-world assets.

Standardized Consumption Archetypes ?????

Data products incorporate the necessary wiring for different business systems to consume the data. Each business system has unique requirements for data storage, processing, and management. These are known as “consumption archetypes.” Although organizations may have hundreds of use cases, they typically fit into one of five primary consumption archetypes. Data products designed to support these archetypes can be applied across multiple business applications, ensuring consistency and efficiency.

The Benefits of Data Products ????

Adopting a data product approach offers significant advantages:

- Speed and Efficiency: Teams can avoid the time-consuming tasks of searching for data, processing it into the correct format, and building bespoke data sets and pipelines. This standardization saves time and reduces architectural and governance challenges.

- Cost Savings: Total cost of ownership—including technology, development, and maintenance—can decline by up to 30%.

- Improved Governance: A structured approach reduces the risk and governance burden, ensuring data integrity and compliance.

Implementing a Data Product Strategy ?????

Success with data products requires a robust operating model, dedicated management, and continuous improvement. Here are the key components:

Dedicated Management and Funding ????

Each data product should have a product manager and a dedicated team of data engineers, architects, modelers, platform engineers, and reliability engineers. This team should be funded to build, improve, and enable new use cases. Positioned within a data utility group inside business units, these teams have access to necessary experts and user feedback, facilitating continuous improvement and compliance.

Standards and Best Practices ?????

Establishing organization-wide standards and best practices is crucial. This includes documenting data provenance, auditing data use, and measuring data quality. A data center of excellence can oversee this process, ensuring technologies fit together seamlessly for each consumption archetype and are reusable across all data products.

Performance Tracking ????

To ensure data products meet end-user needs and continuously improve, measure the value of data work. Key metrics include the number of monthly users, reuse frequency, user satisfaction scores, and return on investment from enabled use cases.

Quality Assurance ????

Maintaining high-quality data is essential to build trust and retain users. Data product teams must manage data definitions, availability, and access controls to meet governance standards. Close collaboration with data stewards ensures data integrity and alignment with source systems.

Conclusion ????

Managing data like a product unlocks significant value by making data more accessible, reusable, and aligned with business needs. By adopting this approach, organizations can transform their data management practices, ensuring they derive sustainable value from their data investments today and in the future.

Full McKinsey's article below ??

Manage data like a product to unlock full value | McKinsey

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