The same mistake companies made with digital transformation - treating it as IT's problem - is happening again with AI. But every department already runs on information and technology. AI readiness is an organizational capability, not a technology initiative.
The Data Value Chain was built for structured data. But 80% of what your organization knows is unstructured - and AI just cracked it open. This is the knowledge management breakthrough we've chased for 30 years.
AI compressed the technical middle of the Data Value Chain, shifting the bottleneck to the human bookends - Insight and Present. Most organizations haven't noticed.
A seven-step framework for turning raw data into business decisions. The Data Value Chain maps the distinct skills required at each stage - and explains why you need a team, not a unicorn.
Every technology project comes down to two things: getting information into systems or getting information out. Understanding this pattern changes how you approach digital transformation - and determines whether AI creates value or just complexity.
We've got the activity captured, and now we have "effectiveness" ratings for the people in the group - how will we put them together and see a relationship? Mechanically, this is a simple XY scatter graph - a standard chart type in both Excel and Google Docs. Let's take a look at a sample data set that illustrates the idea: This is the picture I expected to see: the more you use the system (the X axis), the more results…
For our back-of-the-napkin experiment in tracking the useful value produced by enterprise systems - our next step will move away from the technical tools for a bit, and take a look at results. Specifically, we want to understand who is "delivering the goods" - and some sense of their success relative to others. At first, the question sounds simple - how do you measure success? Metrics are a common element of any business activity, and people are often paid, and…
We're trying use metrics to visualize the correlation between system use and business results - any system, regardless of the platform / technology - and we'd like to draw the same picture of input vs. results for any system. I'm stealing some inspiration from Tufte here - let's keep the story simple, clean, and consistent, so my audience can focus on the question (WIIFM). And it's a meant to be a universal question - doesn't matter what system we are…
Deriving information out of raw data involves seven separate, dependent processes - the Data Value Chain. Your team needs to have a multi-layered ecosystem of skills and experience required to effectively extract the most value.
During a process review session this week, I got involved in a terrific conversation about Smart Part Numbers. You know what I'm talking about - those cryptic SKU identifiers so popular with the folks in the warehouse, on the shop floor, in production planning. There is logic and structure to the characters and the numbers, structure that codes identification and classification details onto those bin labels. We are having problems with item WC1-644-A019910 Oh, that's because the trim press is…