Data doesn't run HR. People do. Data just tells them the truth faster.
OlaDigitalHub was built on one observation: most HR teams have more data than they use and fewer answers than they need. The gap isn't a tool problem. It's a translation problem.
Why "Fractional People Analytics Partner"
Companies between 50 and 2,000 employees are usually too small to justify a full-time Head of People Analytics, and too data-heavy to run HR on instinct. OlaDigitalHub sits in that gap: a part-time analytics partner who builds the dashboard, reads it every month, and tells leadership what it means.
That's a different job from "someone who builds dashboards." A dashboard is a deliverable. A partner is accountable for the answer.
How we work
Every engagement starts the same way: one HR question the business can't currently answer with data. From there we build backwards — the data model, the dashboard, and the monthly report exist to answer that question, not to look impressive in a demo.
Tools are Power BI and Excel by default, because that's what most finance and leadership teams already trust. SQL and Python come in when the question needs them, not by default.
Olajumoke (Ola) Medunoye
OlaDigitalHub is one practitioner, not an agency bench. Ola is an HR and People Analytics professional based in Leeds, UK, holding an MSc in Human Resource Management, Associate membership of the CIPD, and the Master HR Analytics Professional accreditation.
The methods behind this practice are documented publicly, project by project — attrition modelling, recruitment analytics, capacity planning and operational performance reporting, each with the data sources and approach set out in full.
Three rules OlaDigitalHub doesn't break
01 — Start with the question
No dashboard gets built until we can name the decision it's meant to inform. "It would be nice to know" is not a brief.
02 — Numbers need a narrative
A chart without a sentence next to it gets ignored by boards. Every report ships with the "so what," not just the "what."
03 — Anonymize by default
Case studies, samples and templates use anonymized or synthetic data. Client data stays client data — always.