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The value of data isn't the insight. It's what the organization can do with it.

I work with organizations whose product is information — research and insights businesses, and the corporate teams that operate like them. When the analysis is sound and the business still runs the way it did last quarter, the problem is almost never the analysis. It's somewhere between the data, the process, and the people expected to act on it.

Sound familiar

The symptom is rarely where the problem lives.

Reporting

The dashboards ship on time. Decisions get made without them.

Process

A workflow nobody designed, which everyone has quietly built a workaround for.

Ownership

The problem sits between several teams and belongs to none of them.

Margin

Delivery costs keep climbing and no one can name the step that's eating them.

Growth

Volume arrived faster than the operating model, and it's showing at the seams.

AI

Someone senior has asked what it means for the business. Nobody's sure yet.

Why me

The problem doesn't stay in one department. Neither do I.

Analysts find the pattern. Process people find the bottleneck. Operators know what a team will actually do on Monday morning. Most problems worth paying to solve need all three — and most of the cost, delay and dilution in consulting comes from passing the problem between people who each hold one piece of it.

Twenty-five years of running operations means I can sit in the stakeholder interview, get into the data myself, walk the process, and still know what the team on the floor will make of the answer. I'm not a data scientist and I don't sell technology. I'm the person who works out which of those levers is actually the one that moves your problem.

DataDecisionsProcessExecutionResults

How engagements usually start

Four shapes, sized to the problem.

Operating diagnostic

A few weeks inside the operation: where the work actually stalls, what the rework is costing, and the fixes ranked by payback. Ends with a decision, not a deck.

Usually 4–6 weeks

Decision-to-execution rebuild

Connecting a reporting or insight capability to the decisions and processes it was supposed to drive — KPI structure, ownership, handoffs, and the operating rhythm around them.

Scoped per engagement

AI and automation reality check

An honest read on where these tools genuinely change your economics, where they're a distraction, and what has to be true about your data and process before either is worth attempting.

Short and specific

Interim operations leadership

Running the function while you fix, hire or integrate. Selective and deliberately short — I take these on when the problem is interesting and the mandate is real.

By arrangement

How this works

The same person, from the first call to the last.

You meet me. You're interviewed by me. The proposal is written by the person who sat in the room, not summarized for someone who didn't. And I'm still there when it goes live — because a recommendation nobody stays to implement is a document, not a result.

How far that goes: one engagement needed me in Denver, so I moved to Denver and stayed two years until the transition was done. That isn't every project. But it's the standard.

The honest trade-off: I take on a small number of engagements at a time, so occasionally the answer is that I can't start when you'd want me to. That's the cost of there being no bench, no handoff, and no one learning your business on your budget.

The principal

Joe Giacobbe

Twenty-five years running operations rather than advising them from the outside — large teams, cross-functional processes, analytics capabilities built and then actually used, and stakeholders from the front line to the boardroom.

More recently, hands-on with data science and applied AI. Not to build the models: to tell the difference between the ones that will change how a business runs and the ones that will produce a very good demo.

Selected results

+28%

YoY revenue growth at a research SaaS firm — Agile operating model, KPI framework and process redesign, delivered without adding headcount.

$200K+

Added monthly profit from an analytics delivery model that had been running at break-even. Operational redesign, training and servicing standards.

Integration

Directed the operational integration of a major acquisition — systems consolidated, KPIs aligned, client satisfaction held through the transition.

Migration

Led the cross-functional migration of community infrastructure and virtual payment systems, improving performance and scale across global operations.

Product

Conceived analytics products combining survey, behavioural and social data — shortening clients' insight-to-action cycles inside real privacy and governance constraints.

Client service

Awarded for client service excellence across Europe, South America and Japan — earned in part by getting on planes and solving problems in the room rather than over email.

A point of view

AI is repricing the insight business from both ends.

The cost of producing an answer is falling fast — and not only in research. Anywhere a business cleans, transforms, models and interprets data to reach a conclusion someone will act on, the economics are moving. So, quietly, is the price clients are willing to pay. Both ends are shifting at once, and the middle — the delivery model built for the old numbers — is where the squeeze lands.

The firms and teams that come out ahead won't be the ones with the best models. Those will be commodity soon enough. They'll be the ones whose operations can absorb the change: re-cut a delivery model, retrain a team, rebuild a process faster than the market moves around them.

Which makes this an operating problem wearing a technology costume. That's a distinction worth getting right before you spend on either.

Tell me where it's breaking.

A first conversation is a conversation, not a pitch. If I'm not the right person for the problem, I'll usually know inside half an hour — and I'll say so.