Your team has stopped trusting the numbers. Let's fix that.
We help small to medium manufacturers turn scattered data into working dashboards their plant actually runs on. Built on the tools you already pay for, designed for the way your team already works.
Book a Briefing CallYour morning meetings actually ended with decisions?
Your team would walk in with numbers they could defend. The yield report and the floor counts would agree. Capacity wouldn't be a Friday-afternoon guess. The systems you bought would actually be doing what you bought them for. We help small manufacturers get there.
Your data is creating questions, not answering them.
Reports get rebuilt every Monday because the system data doesn't match what's on the floor. Inventory counts diverge from what's actually in the warehouse. Capacity is whoever made the last guess. Half your team runs on Excel because nobody trusts the ERP. Decisions don't get made, or they get made on gut feel, and your operation pays for it whether you can see the cost or not.
The data has to be right first.
If your team doesn't trust the numbers feeding your dashboards today, they won't trust what an AI model gives them tomorrow. AI doesn't fix a data trust problem. It amplifies it at speed. Fix the foundation before you build on top of it.
What data failure looks like on a P&L.
Gartner's measure of what data quality failures cost a typical organization every year. Rework, errors, missed decisions, manual reconciliation, and unreliable reporting that no one can act on. It rarely shows up as a line item, which is why most leadership teams underestimate it until they look at the math.
McKinsey's measure of what happens when teams stop trusting the data they're given. Productivity drops roughly 20% as people verify before deciding and rebuild reports in Excel. Costs climb roughly 30% as rework compounds across functions and decisions stall.
We help small to medium manufacturers get their numbers right.
Using the tools you already pay for. Designed for the way your team already works. Built so you can buy what you need, when you need it.
DRIVE Index Audit
We dig into one of your operations, find every place your data is wrong, and rank what to fix first. You walk out with documented evidence and a clear order of work.
DRIVE Build
We build the dashboards your plant runs on, using the tools you already pay for - Power BI, Tableau, or Looker. Your team gets one place to look for the numbers.
DRIVE Watch
We stay on month to month, fixing what breaks, adding views you need, and catching new problems before they spread. Without putting another full-time salary on payroll.
Fixed scope. Tangible deliverables. No theater.
Every engagement starts with a 30-minute Briefing Call. No prep, no sales pitch - just a conversation about what's happening in your plant. If we move forward, the work has a fixed price and a fixed timeline. The deliverable is something tangible you own and can act on, whether you continue working with us or not. The methodology is grounded in the same engineering discipline your quality team applies on the floor, standards lineage, not just consulting opinion.
What it looks like when somebody actually does the work.
At one manufacturer, a single team did the foundational data work the rest of the building skipped. They validated every measurement input against the system of record. Traced every variance back to its source. Built risk-scoring on top of data they could actually trust.
The result was a monthly decision cadence where every number had a defensible source. Plant managers became accountable to one version of the data. Internal audits started running cleaner because the evidence was already documented. Decisions that used to take three days started happening the same morning.
Every other department in the same facility skipped the foundation. They assumed the data was reliable. The team had quietly stopped trusting it. Nobody said it out loud because acting on bad data had become the norm.
The methodology is the easy part. Doing the work is the difference.
Stephen Gnidovec
Founder, Great Lakes Analytics
Great Lakes Analytics exists to close the gap between data investments and the decisions they were supposed to enable. The DRIVE Index is the methodology Stephen built from running this work directly inside multi-plant operations, not from a consulting playbook.
He is the author of The Data Culture Handbook and teaches data analytics at Elmhurst University and Southern New Hampshire University.
The methodology lives in a book.
The Data Culture Handbook documents the frameworks every engagement runs on. Read it before a Briefing Call if you want to know what you're walking into. Read it after if you want to apply what we delivered.
Your team has stopped trusting the numbers. Let's fix that.
Book a 30-minute Briefing Call. Tell us what's happening in your plant. Walk away with a working hypothesis on where the leaks probably are and what an Audit would scope.
Book a Briefing Call