The More Data Hangover
Almost a decade ago I wrote a memo that said:
"The concept of Big Data creates an almost compulsive need to capture and store every piece of available data, based on the premise of unlocking vast untapped value. Yet the real challenge isn't just accumulating more data, it's extracting actionable insights and measuring the impact of those actions."
The premise was simple, gather everything, leverage things like machine learning and surely value will emerge.
Fast forward to today, and many of us are sitting in boardrooms asking the same question: "We've got petabytes of data. Now what?"
This question of deriving meaningful value remains just as relevant today, perhaps more so. We've managed to separate the hay from the needles, but we're still left wondering: "In this stack of needles, how do we find the really valuable ones?"
The Corporate Evolution of "Knowing Stuff"
At the risk of perhaps overstretching a point, I am sure even the Romans had a Business Intelligence department. A scroll based version of Crystal Reports telling them the Colosseum's P&L. But our journey from data to knowledge really has evolved considerably:
Data: "Here is what has happened" (The Romans had this)
Insights: "What's happened but with context" (We got here in the 90's)
Machine Learning & Prediction: "Here is what is likely to happen next" (2010's)
AI-Powered Knowledge: "Here's why it's happening and what you should do about it" (Now)
So if data is our "What" and activation is our "How," is AI finally our answer to "Why?"
An Ironic Silo Paradox: Fragmenting the "Why"
After spending years and millions breaking down data silos through data lakes, lake houses, customer data platforms and unified views of data, we're now inadvertently recreating silos with AI and in doing so, fragmenting our ability to answer the crucial "Why" questions.
How so?
Marketing deploys AI in their CRM to understand why customers churn. Finance embeds AI in their ERP to understand why costs fluctuate. Operations builds models in their supply chain systems to understand why delays occur. Each department gets a partial "Why" answer, but the complete picture remains elusive.
Before you know it, our organizations have scattered rather than centralized intelligence. We've solved the "What" question with centralized data, but our approach to the "Why" question remains fragmented.
Perhaps this better aligns with how we traditionally structure our businesses, vertically through departments and technologies. Yet we know our customers experience us horizontally across touchpoints. This fragmented, siloed approach creates challenges in coordination and achieving a true understanding of why things happen across the customer journey.
This doesn't need to be a binary decision. We can achieve business agility, leverage the capabilities of our leading SAAS solutions and maintain a centralized view of knowledge that finally answers the "Why" question, just as we have done with data to understand “What”
The Enterprise Knowledge Approach: Unifying the "Why"
To make this transition and keep our eye on the “Why” there are 4 things to consider:
Unified Knowledge Architecture - Create a foundation that connects AI capabilities across the enterprise, ensuring the "Why" insights from one department enhance understanding in another.
Cross-Functional Intelligence Teams - Move beyond isolated data science groups to build teams that span traditional boundaries, bringing together different perspectives on "Why" things happen.
Outcome-Based Measurement - Shift from technical metrics like model accuracy to business impact metrics that matter, measuring how well we're answering the "Why" questions that drive value.
Knowledge Democratization - Put AI capabilities directly in the hands of business users who make decisions, allowing them to explore "Why" questions without technical barriers.
Starting the Journey with JOURN3Y
At JOURN3Y, we help organizations navigate this transition with a balanced approach. We recognize that you can't boil the ocean - you need both the enterprise vision and practical, incremental execution to unlock comprehensive "Why" insights.
Our approach combines:
Creating the enterprise knowledge foundation that prevents new AI silos and integrates "Why" insights across the organization
Delivering quick-win implementations that demonstrate immediate value by answering specific "Why" questions
Building capabilities that allow organizations to identify which "needles" truly matter for understanding why things happen
The organizations winning today aren't necessarily those with the most data or the most sophisticated models. They're the ones with an enterprise approach to AI that transforms data into actionable knowledge by answering the "Why" questions at the decision points that matter most.