Viewing data as just a commodity leads to congestion and overwhelming amounts of data. The key transformation in today's data-driven businesses is turning data from a passive output to a carefully organized, controlled asset.
The difference between a resource and a product lies in their purpose. A resource is extracted, while a product is designed for consumer use.
Data is seen as 'exhaust' produced by operational applications, remaining dormant in silos, data lakes, or warehouses until it is utilized to create value.
Data is viewed as a valuable asset that is carefully managed, contextualized, and delivered with SLAs to address targeted consumer needs.
Shifting to a Data Product mindset addresses key issues that hinder organizations from fully scaling their analytics and AI capabilities.
As data becomes a valuable asset, teams line up to rely on the central IT team to create a custom pipeline. By transferring ownership of data products to decentralized domains, the bottleneck in central engineering is removed, resulting in a significant increase in delivery speed.
Resources are naturally chaotic and unreliable. Products are backed by warranties (Data Contracts) and service levels (SLAs). Users can develop essential ML models and operational workflows with confidence that the foundational data will remain stable.
Instead of creating 10 individual pipelines for calculating 'Customer LTV' for various departments, you can create one 'Customer LTV Data Product' that is Discoverable and Addressable. This certified asset can be utilized across the entire organization.
Moving towards a Data Product operating model necessitates an initial investment in tools, culture, and architecture (the Data Mesh). The payoff for this investment is determined by increased speed, reduced costs, and growth in revenue.
Organizations that do not transition will see their data scientists dedicating 80% of their time to cleaning 'resources' instead of creating value.
Analysts can find and access data products quickly and easily thanks to their discoverability and self-describing nature, saving them days of time that would have otherwise been spent searching and understanding the data.
Reusability reduces the need for repetitive pipeline development. Standardized infrastructure lowers the expenses of compute and storage expansion.
By consuming trusted, SLA-backed features from input ports, data scientists can significantly cut down on model training and deployment times.
When data is transformed into a top-notch product within the organization, it becomes much simpler to share those output ports with external partners or customers to generate revenue.
Present a compelling argument to your leadership team in favor of transitioning from a centralized resource model to a decentralized product model, initiating the strategic shift for the business.