Seven Links in the Data Value Chain

Over the years, I have worked with many teams to understand their data – what we need to run the business, what we can see about our value chain, what we can gather about our trading relationships. A pattern has emerged – the Data Value Chain – describing the multi-layered ecosystem of skills and experience required to get all the pieces right.

Think of each step in the Data Value Chain. What links do you go through to derive (and deliver) value from your data?

• Insight – That spark of imagination required when looking at a challenge or opportunity, figuring out what metrics or measurements might lead to real value, and determining how we might get at the data.
• Architect – Designing the infrastructure (databases, storage, communication, and access) to handle the flexibility, scale, and sustainability required – especially for new sources.
• Generate – The technical expertise (hardware and software) to pull data from existing collections, or read data from devices that have never been metered before.
• Store – Managing the “physicality” of the data – especially when Big Data is talking about terabytes and not just unstructured data.
• Process – Understanding, implementing, and improving the supporting processes that gather the data and keep it clean & complete – especially the master data and metadata that becomes the connecting tissue.
• Analyze – Similar to “Insight”, but instead of looking at a blank canvas, it’s the process of understanding the data model and the elements of information, and being able to define new and different ways to combine and interpret.
• Present – The “last mile”; how do I take a complex idea, expressed in data, and define reports and visualizations that communicate the hidden messages, and enable insights for the people that need to see?

Bigger Than I Thought

As you build your team with the Data Value Chain in mind, the most difficult skills to find (or learn) are the first (Insight) and the last (Present). These are themselves connecting points to reality – to the source and use of information – and they introduce the required non-IT, non-technical ways of thinking (Inspiration and Art). A non-IT background is helpful in these areas, yet performance here gets better with a decent appreciation for, and understanding of, what is going on in the other links.

It is essential to get comfortable and glib about all of the links in this Data Value Chain. If you understand the various pieces – enough to appreciate them, not necessarily to be a hands-on expert – it will inform how the links connect and depend on each other for maximum success.