How Gen AI Transforms the Data Value Chain

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.
   • AI Impact: Gen AI might help your team brainstorm ideas and flesh things out. But I still believe that most insights come from a solid understanding of your particular business, customers, products, and markets. Few organizations have enough tacit knowledge stored digitally to feed your own custom GPT.
   • In the Wild: Leveraging ChatGPT for Qualitative Analysis: Exploring the Power of Generative AI (ethosapp.com)
2. Architect – Designing the infrastructure (databases, storage, communication, and access) to handle the required flexibility, scale, and sustainability – especially for new data sources.
   • AI Impact: Leveraging the code-generation capabilities of Gen AI could accelerate the design process and increase the level of consistency and completeness in the schema.
   • In the Wild: How ChatGPT Can Help Design System Architecture for Your Applications👷‍♂️ – DEV Community
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.
   • AI Impact: Again, code-generating AI can speed up the process of creating data extraction and transformation routines for esoteric data sources.
   • In the Wild: 10 powerful ChatGPT prompts for Data Extraction – HogoNext
4. Store – Managing the “physicality” of the data – especially when Big Data is talking about terabytes and not just unstructured data.
   • AI Impact: I can see AI used to optimize data storage, but that is something your database vendor would probably add and not your team.
   • In the Wild: 10 powerful ChatGPT prompts for Data Center Architecture – HogoNext
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.
   • AI Impact: This would be a terrific use case for Gen AI. Technical documentation and automated consistency checks would be easier to create using Gen AI tools.
   • In the Wild: Data Wrangling with ChatGPT: A Practical Example (capellasolutions.com)
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.
   • AI Impact: Similar to the first step (Insight), I think that for most companies, there will need to be a deep understanding of the particular nuances of your business (Customers, Operations, and Products).
   • In the Wild:  How to Use ChatGPT Advanced Data Analysis | Codecademy
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?
   • AI Impact: It would be very interesting to ask ChatGPT to design visuals from data sets. There has to be a combination of understanding the underlying truth to be told and “presentation as art”.
   • In the Wild: 5 Ways to Use ChatGPT for Data Visualization (luzmo.com)

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.