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How Gen AI Transforms the Data Value Chain

Summary:

An update to the original Seven Links in the Data Value Chain. Gen AI is a resource for acceleration and automation, augmenting the skills and work of smart people who are creative, curious, and willing to experiment with new tools as technology advances.

A few years ago, I defined the Seven Links in the Data Value Chain – a framework for understanding the unique skills required to get Information and Insights out of the Data your company is aggregating. The key message was that it takes a diverse set of skills to go from intriguing ideas to actionable insights – and it takes a well-rounded team to make that journey.

Fast-forward to today, and there is a new tool in the toolbox – Gen AI. Yes, statistical forecasting (“Predictive AI”) has been part of the data analyst’s arsenal for many years. But we are now in the era of Generative AI, where original (as it were) content is created from existing data sets.

Will Gen AI change the Data Value Chain? It won’t magically eliminate any of the Seven Links – but it may be able to accelerate and automate the work required to get things done.

The Seven Links Revisited

Let’s review the Data Value Chain – how might we apply Gen AI to augment our team’s skills and accelerate time to value? I captured my initial ideas, and then did some quick web searches to find examples “in the wild”.

  1. Insight – The 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.
  2. Architect – Designing the infrastructure (databases, storage, communication, and access) to handle the required flexibility, scale, and sustainability – especially for new data sources.
  3. Generate – The technical expertise (hardware and software) to pull data from existing collections or read data from devices that have historically have never been metered.
  4. Store – Managing the “physicality” of the data – especially when Big Data is talking about terabytes and not just unstructured data.
  5. Process – Understanding, implementing, and improving the supporting processes that gather the data and keep it clean and complete – especially the master data and metadata that becomes the connecting tissue.
  6. 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.
  7. 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?

Another Tool, Another Required Skill

Ultimately, AI will be a resource for acceleration and automation. This is a classic example of the power of AI to augment the skills and work of people – smart people who are creative, curious, and willing to experiment with new tools as technology advances.

How do you see these advancements impacting your business strategies? Join the conversation in the Comments below.

2 February, 2024

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