VP of Data & Analytics
Build and manage the analytics and BI function
What You Do Today
Deliver business intelligence to every department — dashboards, reports, ad-hoc analysis, and self-service capabilities. Manage the analytics team that translates data into actionable insights.
AI That Applies
AI-powered analytics platforms that auto-generate insights, detect anomalies, and answer natural language queries against business data.
Technologies
How It Works
For build and manage the analytics and bi function, the system draws on the relevant operational data and applies the appropriate analytical models. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.
What Changes
Business users can ask questions of their data in plain English. Many routine reporting requests get self-served, freeing analysts for complex analysis.
What Stays
Translating data into business narrative, identifying the analysis that will actually change a decision, and building trust in data across the organization — those require human expertise.
What To Do Next
This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.
Establish Your Baseline
Know where you are before you move
Before adopting AI tools for build and manage the analytics and bi function, understand your current state.
Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.
Define Your Measures
What to track and how to calculate it
Time per cycle
How to calculate
Measure how long build and manage the analytics and bi function takes end-to-end today, then after AI adoption.
Why it matters
The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.
Quality of output
How to calculate
Track error rates, rework frequency, or stakeholder satisfaction scores before and after.
Why it matters
Speed without quality is just faster mistakes. Measure both.
Start These Conversations
Who to talk to and what to ask
your board chair or lead independent director
“Which of our current reports are manually assembled, and how much time does that take each cycle?”
They shape expectations for how AI appears in governance
your CTO or CIO
“What questions do stakeholders actually ask that our current reporting doesn't answer?”
They own the technology infrastructure that enables AI adoption
Check Your Prerequisites
Confirm readiness before you invest
Check items as you confirm them.