A key component of a data strategy is the creation and efficient management of the data value chain. The data value chain can be understood as the information flow within the organization and series of steps needed to generate value from that information. The value chain can be split into the inbound processing pattern and the outbound processing pattern.
In the former, the data supply chain takes data from acquisition to analytics and consumption. Data is received from a variety of sources, checked for relevancy, enriched, analyzed and put to use to gain useful insights. Inbound processing describes the transformation of inbound data into information. The data supply is a six-step process:
- Identify and acquire
- Catalog and cleanse
- Extract, Transform and Load (ETL)
- Prepare for end users
- Management information and reporting
- Advanced visualization, predictive and prescriptive analytics
This process can be envisioned as a ‘Fibonacci spiral’ funnel that moves inbound data from acquisition through to analytics. Once it comes in, data is assigned measurable values on an index that evaluates its relative importance. The valuable data identified is then moved for consumption.
Figure: Fibonacci Spiral – Data Value Chain
From the image above, the consumption phase is at the center of the spiral; this is where the data is readied for end users, the phase where data achieves its greatest value by getting transformed to value-generating information that improves decision quality. Organizations should focus a majority of their IT dollars and resources here.
A good tactic is to invest in types of consumption that are best at transforming data into usable information such as predictive and prescriptive analytics and visualizations. These tools do an excellent job of providing high-value insights and information because they make data easier to comprehend. They make the processing of the information intuitive by telling stories, illustrating through graphs or charts, and prompting quicker action.
When information is understood easily, it will be leveraged by users to make strategic decisions. When it is a confusing mess, people will likely not bother making sense of it. This is why person-centric analytics is so important to your data strategy. Give users a reason to embrace information and there is no reason why they wouldn’t – after all, they will benefit immensely from it. To enable this, organizations must create an economically optimized infrastructure that allows efficient ETL, and manage data well at every stage.