Data & Insights - December 18 2025
Organizations are collecting more data than ever, investing heavily in analytics platforms, and accelerating the use of AI across every function. Yet many still struggle to translate all that activity into better decisions. The problem isn’t a lack of data, tools, or ambition. It’s a lack of clarity about how meaning is constructed from data and how judgment should be applied once insights appear.
This week’s Data & Insights explores that gap from multiple angles:
- the growing pressure CFOs feel to prove value from analytics investments
- the rise of new senior data leadership roles
- what effective data leadership actually looks like in practice
- what happens when analytics and AI are deployed without modeling intent, ambiguity, and context
- and how to fix the problems we're seeing across these scenarios
Together, these pieces reinforce a simple idea: analytics scales not when organizations measure more, but when they deliberately design how data informs decisions at every level.
5 Steps CFOs Can Take to Maximize ROI From AI Initiatives (Gartner)
Summary:
According to a recent research by Gartner, the “AI value gap” exists because organizations jump to tools before they clarify where AI matters most, who will use it, and how it will be governed. Finance leaders who close this gap do these 5 things:
- Start with high-value, context-specific use cases, not generic best practices
- Invest heavily in upskilling finance teams so AI becomes a shared capability (not a black box owned by vendors or data scientists)
- Use a hybrid build-and-buy strategy to balance speed with differentiation
- Actively manage AI costs and ROI as a portfolio, not as one-off projects
- Treat governance and risk as enablers of scale, not blockers of progress
AI succeeds in finance when it’s treated as an operating capability. AI as more than just another software purchase.
Insight: Make AI decisions grounded in people, process, and discipline
Why Your Company Needs a Chief Data, Analytics, and AI Officer (Harvard Business Review)
Summary:
The article argues that as data and artificial intelligence become critical drivers of competitive advantage, companies should unify leadership of data, analytics, and AI under a single senior executive, a Chief Data, Analytics, and AI Officer (CDAIO) rather than spreading these functions across multiple roles.
As data volumes grow and AI adoption accelerates, strategic leadership matters more than ever. A CDAIO helps companies unify fragmented practices, drive measurable ROI from data and AI investments, and navigate risks while leveraging these technologies for competitive advantage.
Making Data Indispensable: Frameworks for Effective Data Leadership (MIT Sloan)
Measurement isn't the same as insight.
- Just because we measure things doesn't mean we know what to do about it
Summary:
This 57 minute webinar is centered around that one theme. We are capturing more data, more IoT, more databases, more customer interactions than ever before. Measuring more, and drowning in data. We are missing the boat on understanding the data. And that's not something AI can necessarily fix. Good old fashioned data management techniques, simple math and statistics, and business analytics tools solve can this problem without AI.
Taco Bell’s Attempt to Replace Drive-Thru Employees With AI Is Not Going Well (Futurism.com)
Summary:
As I went through this article looking for the bright spot, for a lesson to apply to my life as an data insights architect with tools like nollejBase, I realized there's a common theme between Taco Bell's failed attempt documented in this article and with similar outcomes by McDonald's and Wendy's with their AI-powered drive thru botched projects.
Key Take-Away: Intelligence requires interpretation PLUS judgement.
All three are blaming AI, which is dependent on training data.
They measured and trained for:
- What was said
- What was ordered
- What went wrong
I suspect they didn’t model:
- What the customer meant
- When meaning was unclear
- When judgment should override automation
- When context mattered more than correctness
The systems knew what happened. It just didn’t know what to do. And more data does not mean better outcomes.
Breaking Away: The Secrets To Scaling Analytics (McKinsey)
Summary:
This article claims that only a handful of the world's companies have cracked the code on embedding analytics into every layer of the organization.
Their research suggests:
- Obtain strong, unified commitment from all levels of management
- Increase analtyics investments, and don't give up as you get close to a real roll-out
- Develop a clear data strategy with strong data governance
- Use sophisticated analytics methodologies (doesn't require AI)
- Build deep analytics expertise and hire wisely
- Create cross-functional and collaborative agile teams
- Prioritize top decision-making processes
- Establish clear decision-making accountability
- Empower EVERY employee to make analytics-driven decisions
A real leader with a seat in the C-Suite (A Chief Data or Analytics Officer) that levels up the entire organization in terms of data literacy and a culture of analytics is required to make this work.