Most companies recognize the power of data, but struggle to truly unlock its potential. Data initiatives often fail to deliver quick wins while laying the groundwork for future uses. Evolving technologies and ever-growing data volumes further complicate the picture.
Traditional data strategies, like the "big bang" approach or individual teams building custom pipelines, have limitations. The big bang approach, aiming to accommodate all needs from the outset, often overlooks critical architectural details and leaves many users behind. On the other hand, custom pipelines create an unmanageable mess, hindering reusability and slowing down digital transformation.
The solution lies in treating data like a product. Just like a commercial product, a data product should cater to various user needs, with a base version that can be customized for different use cases. Think of it like modular car components – a standard engine chassis can be equipped with different features for diverse customer preferences.
Benefits of treating data as a product:
Faster implementation: Reduce time to implement new use cases by up to 90%.
Reduced costs: Lower total ownership costs by up to 30%.
Improved efficiency: Increased production efficiency through data reuse and standardization.
Reduced risks: Lower risks and data governance burdens.
What is a data product?
A data product is a high-quality, readily-usable set of data accessible across the organization for various business challenges. It can be a 360-degree view of customers, employees, channels, or even virtual representations of real-world assets.
Data product consumers
Data products cater to different "consumption archetypes," essentially systems with varying data needs:
Digital applications: Real-time data feeds for personalized experiences.
Advanced analytics systems: Cleaned data for machine learning and AI processing.
Reporting systems: Highly governed data for aggregated reports and compliance.
Discovery sandboxes: Platforms for ad-hoc exploratory data analysis.
External data-sharing systems: Data sharing with specific policies and security measures.
Each archetype requires unique data storage, processing, and delivery technologies, forming an "architectural blueprint" for the data product. Think of it as Lego bricks – a data product built for one archetype can be easily snapped into various applications.
Examples of data product success:
A mining company's live GPS data product for ore trucks not only improved processing yields but also fueled truck routing improvements and safety solutions through employee initiative.
A national bank's customer data product supported fraud management and marketing, generating $60 million in incremental revenue and $40 million in reduced losses.
Building and managing data products:
Data product managers: Dedicated professionals responsible for building, supporting, and improving data products.
Data utility groups: Teams within business units to build and deploy data products.
Center of excellence: Centralized hub for setting standards, best practices, and supporting product teams.
Key considerations:
Identify high-value use cases: Assess feasibility and potential value of use cases across business domains.
Group use cases by data product and consumption archetype: This helps prioritize work and maximize ROI.
Start with an initial target product and archetype: Choose something with high impact, feasibility, and a potential pipeline of future applications.
By treating data like a product, companies can unlock its full potential, gaining a significant competitive edge through increased speed, flexibility, and innovation.
Remember, data is more than just numbers; it's a valuable asset waiting to be transformed into a powerful resource for your business.
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