Business Intelligence Consulting for Ohio Companies

What business intelligence actually looks like for Ohio companies

Ohio’s mid-market is not Silicon Valley. You’re not staffing a 12-person data team. You have an operations director who’s sharp, a sales manager who runs things in Salesforce, maybe a financial analyst who knows Excel better than almost anyone. And somewhere along the way, your company got big enough that the old reporting process — a few spreadsheets, a manual pull, an email chain — stopped being good enough.

Manufacturing and distribution companies here are dealing with inventory and margin data scattered across ERP systems, warehouse tools, and spreadsheets that haven’t been reconciled since Q3. Professional services firms have client utilization data that never makes it into a clean weekly report. Office equipment dealers have recurring revenue, service contracts, and hardware sales that should be tracked together but almost never are.

The tools exist. Power BI is already licensed. Salesforce is already running. The data is there. What’s missing is the layer between raw data and a report your VP can read on Monday morning without three caveats about why the numbers might be off. That’s what I build.

Five problems I find consistently in Ohio mid-market companies

• Manual reporting is eating analyst time Someone on your team spends significant hours every week pulling data from multiple systems, reformatting it, and assembling a report. That work is not analysis. It’s data transportation. It should be automated.
• Your KPIs aren’t agreed upon Sales thinks the number is one thing. Finance has a different spreadsheet. Operations has a third. Nobody’s wrong — they’re each pulling from a different source with different filters applied. Until there’s a single defined version of each metric, every Monday meeting starts with a 10-minute argument.
• Power BI (or Tableau) isn’t delivering The dashboards got built, but nobody looks at them. Or people look at them but don’t trust them. Or the person who built them left and now nobody knows how to update them. Tool investment without data model investment is a common and expensive mistake.
• Prospecting and pipeline data are still manual Sales teams in Ohio B2B companies are often still doing manual territory research, manual list building, and manual CRM hygiene. That’s fixable.
• Your close process takes too long Month-end closes that take five days when the data exists to do them same-day are a process problem, not a capacity problem.

None of these are failures of effort. Your team is working. The infrastructure underneath the work is the problem.

One operator. In your tools. Hands off when I leave.

I am a one-operator consultancy. You work directly with me — not a project manager who hands off to a junior analyst. I come into your environment, work in your tools, and build things your team can actually own when I’m done.

• Tool-agnostic. I work in Power BI, Tableau, Looker, Salesforce, HubSpot, dbt, SQL, Python, and Excel. I don’t have a vendor relationship that steers my recommendations.
• No production access required for assessment. I can conduct a full analytics assessment without needing credentials to your production systems. Deeper access comes only when it’s scoped and appropriate.
• Your team runs it when I’m done. Every engagement ends with documentation, a runbook, and enough hands-on time with your team that they own what we built.
• I come to you. I’m based in Ohio and available in-person in Columbus, Cincinnati, Dayton, and across the state.

Four ways to engage

Three things that matter here

• I understand the market. Ohio manufacturing, distribution, and professional services have distinct characteristics that national consultants coming in for a two-week engagement often miss.
• I’m here. In-person availability is real for me. When you need someone in the room for a workshop, a data review, or a stakeholder presentation, I can be there.
• I work at the right scale. My practice is built for 100–500 person companies. You’re not getting a Fortune 500 engagement model where the first three months are discovery and deliverables are frameworks and slide decks.

Real numbers

At Paytronix, I rebuilt the analytics reporting process for a recurring workflow that had been running on 14 analyst-hours per week. After the rebuild, the same output required 3 hours.

For a national restaurant group client, the monthly financial close process was taking five days. After rebuilding the pipeline and reporting structure, they closed same-day.

The pattern is consistent: the time and accuracy problems mid-market companies experience with their data are almost never caused by a lack of data. They’re caused by infrastructure that wasn’t designed to scale with the business.

Common questions