01
Cut
Low‑value software vendors, redundant reports, unread dashboards, or unvouched AI experiments.
Analytics and AI Spend Review
Cut the analytics and AI spend that isn't earning its keep.
A fixed‑scope forensic review for companies investing heavily in data infrastructure, software tools, and AI pipelines but losing confidence in the actual return. I find the shelfware, eliminate duplicate data platforms, and show you exactly how to trim the fat without putting your operational decisions at risk.
- The Investment: From $25,000 Fixed Fee
- The Framework: One clear roadmap: what to cut, keep, rebuild, automate, govern, or stop.
- The Stand: 100% Independent — No vendor kickbacks, no cloud reseller incentives.
Do not cut analytics blindly.
Cut what no longer improves decisions.
This review is a full, independent look at your data architecture, software tools, cloud integration layers, and AI configurations.
- Expose exactly where reporting metrics and executive dashboards have quietly multiplied.
- Verify whether your high‑cost AI pilots actually changed any real‑world decisions.
- Deliver a single, definitive operational roadmap: cut, keep, rebuild, automate, govern, or stop.
Best‑Fit Triggers
- New Leadership: An incoming CFO inherits an unmapped, completely opaque web of organizational reporting.
- Cost Pressure: Direct board or investor pressure to optimize skyrocketing cloud compute and business intelligence costs.
- Contract Expirations: Too many active data vendors, or an enterprise infrastructure renewal coming up in the next 90 days.
- Labor Drain: Highly paid analysts spending more hours on manual data assembly than actual market strategy.
- Operating Discipline: A private equity owner pushing for immediate margin discipline across a newly acquired portfolio company.
Required inputs
What we need before the review
To perform a Spend Review, FIP needs a clear understanding of your analytics and AI cost drivers.
- A summary of your data infrastructure, cloud compute, and vendor spend broken down by category.
- A comprehensive inventory of your current analytics software tools and active license allocations.
- A high‑level map of your primary reporting workflows, highlighting manual Excel or sheet dependencies.
- An inventory of your active AI pilots and what each configuration was originally intended to solve.
Review lens
Every line of spend is judged against decision quality.
The review does not reward shiny tools. It rewards reliable numbers, defensible workflows, clear ownership, and executive usefulness.
02
Keep
What is actively useful, verified, trusted, and easy for your teams to defend.
03
Rebuild
High‑priority components currently built on flawed data logic or weak pipeline architecture.
04
Automate
Intense manual reporting that is worth keeping but shouldn't be executed by hand.
05
Govern
Useful numbers, internal metrics, or AI outputs that currently lack security and compliance oversight.
06
Stop
Anything introducing unnecessary infrastructure cost, platform confusion, or false executive confidence.
Deliverables
An executive roadmap, not a tool inventory.
The output is designed for leadership decisions about spend, operating model, reporting quality, AI governance, and next-step funding.
- The Waste Index: A plain‑English breakdown pinpointing duplicate reporting tools, idle data assets, and bloated cloud pipelines.
- The Core Roadmap: A definitive, prioritized cut, keep, rebuild, automate, govern, or stop master plan.
- Vulnerability Mapping: A summary of exactly where your organization's decision quality is structurally exposed.
- Vendor Consolidation: A direct layout of which tools and platform vendors to consolidate or drop entirely to maximize renewal leverage.
- AI Audit & Ingest: A brutal technical review of your automated AI workflows, token prompts, and pilot allocations.
- Executive Readout: A direct briefing for the C‑suite and board detailing the exact path forward.
Scope guardrail
This is independent diagnosis, not implementation labor.
The review can identify what should be fixed, cut, governed, automated, or rebuilt. It does not include dashboard rebuilds, vendor management, data pipeline development, or unlimited stakeholder interviews.
Start smaller when
If the buyer has one suspicious dashboard, metric, forecast, workflow, or AI output, start with the Analytics Debt Ledger instead of a full Spend Review.
Anonymized outcomes
Cost rationalization in action
These examples show how rationalizing analytics and AI spend improves decisions and reduces waste.
- Enterprise Waste Elimination: Identified more than $20M in redundant analytics and storage spend for a large‑scale digital services enterprise by systematically cutting unused tooling and duplicate warehouse architectures.
- Capital Realignment: Across a mid‑market operational framework, redirected budget out of low‑value, unread legacy reports and into the core predictive analytics infrastructure that leadership actually used to hit targets.
- Vendor Consolidation: Streamlined overlapping business intelligence and visualization platforms for a PE‑backed B2B company, cutting annual platform spend by over 30% with zero loss in reporting fidelity.
Why Stephen
Absolute analytical independence. No implementation bias.
I have spent more than 20 years designing enterprise data pipelines, advanced forecasting frameworks, and local AI infrastructure for organizations like Netflix, Yahoo, SAS, and Oracle. I am the technical author of multiple editions of core data textbooks on SAS, Tableau, and The Accidental Analyst.
When I enter your environment to find where your data cash is leaking, I bring zero implementation alignment. I do not sell software licenses, I do not take referral kickbacks from major cloud providers, and I am not angling to land an army of junior consultants in your building for a year. I hand your executive team the exact playbook to stop the bleeding, protect your data privacy, and keep your numbers completely defensible.
Next step
Make your analytics and AI spend completely defensible.
Let’s clean up your data spend, tool sprawl, and unmonitored AI configurations before your next quarterly budget review or board presentation.