Workforce & HR · HR analytics dashboards
Your HR data exists. The dashboard that uses it doesn't
Most HR teams have three to five systems — an ATS, an HRMS, a payroll tool, a learning platform — and no single place to read them together. The result is a monthly report built manually in a spreadsheet, handed up two days after anyone could act on it.
Banao builds HR analytics dashboards that connect your actual systems, update without manual pulls, and give HR, finance, and line managers the numbers they ask for without a request to the data team.
The first call is free · 45 minutes · no obligation
What we build
What a Banao HR analytics build delivers
A dashboard that nobody opens is an analytics project, not an analytics product. Every item below is designed to be used by the people who need it — not just viewed in a demo.
Unified HR data layer
ATS, HRMS, payroll, and attendance feeds joined into a single warehouse with a consistent schema — so every dashboard draws from one source of truth instead of separate extracts.
Live headcount and attrition tracking
Current headcount, department-level attrition rate, and 30/60/90-day early-warning signals — updated daily, not after the quarter ends. Line managers see their own numbers without filing a request.
Hiring funnel analytics
Stage-by-stage conversion by role, source, and month — so you know whether a long time-to-fill is a sourcing problem, a screening bottleneck, or a panelist scheduling backlog.
Compensation equity analysis
Pay bands, outliers, and pay-gap flags surfaced before the annual audit, not during it. Anomalies are flagged with the data behind them so a comp team can investigate without running their own query.
Workforce planning view
Actual headcount against plan by function and quarter, open roles, and projected capacity gaps — so HR and finance are reading the same table in the same meeting.
Scheduled HR reporting
Weekly and monthly reports generated and distributed on schedule — the data team stops pulling them by hand, and stakeholders stop waiting on a request queue.
Receipts
Where this pattern is running
Metrics shown dotted (··) are being finalised in our case-study metrics pack — published only once verified.
Multi-system HR data joined for workforce planning
HR data was split across three platforms with no shared key between them. Banao built an ingestion layer, a unified schema, and a dashboard set used by HR operations and finance planning teams each month.
Attrition early-warning built from HRMS and payroll signals
The company learned about attrition spikes in retrospect, after headcount reports were pulled and reviewed. Banao built a weekly signal model on top of existing HRMS and payroll data — early indicators fed directly into the team lead's dashboard.
Dogfooding
We run HR analytics on our own 300-person team
Banao's own recruiting pipeline generates data we analyse the same way we build for clients. InterviewGod tracks every hiring stage — application volume, AI screen pass rates, panel outcomes — and that data feeds into our own workforce planning dashboard.
We are not describing what HR analytics could look like for someone else. We are reading the same dashboards on our own team every week, which means every edge case in data joins, permission scoping, and daily refresh cadence has already been a real problem we had to fix.
Generates hiring-funnel data for Banao's own analytics stack each week.
Feeds demand-gen signal back into workforce capacity planning.
The honest version
When an analytics dashboard is the wrong first step
We will say this before you spend on a dashboard build:
- Broken source data: if the HRMS has five years of inconsistent department codes, a dashboard just displays the noise at scale. A data-quality sprint comes first.
- No dashboard consumer: if the intended audience is a CHRO who reads one annual deck, a live dashboard won't get opened. We'd ask who will actually look at it each week before scoping anything.
- Wrong tool for the question: some workforce questions are better answered by a one-time analysis than a maintained dashboard. We'll say if that's the case.
How we start
How we start — from your data before we build anything
We look at what your HR systems actually export before we scope a line of code.
- 01
AI Discovery Sprint
2 weeks · fixed price
We audit your HR data sources — HRMS, ATS, payroll, attendance — map the joins, identify quality gaps, and hand back a dashboard spec with the metrics that are actually computable from your data. If you proceed, the Sprint fee credits against the build.
- 02
Build
Ingestion layer, unified warehouse schema, and the dashboard set — built in your preferred stack (Looker, Metabase, Power BI, or a Next.js embedded layer). Access control and data refresh cadence are part of the deliverable.
- 03
Handover and scheduled reporting
Documentation, scheduled report distribution, and a 90-day support window. Your data team owns the warehouse schema from day one — we don't hold the keys.
FAQ
Frequently asked questions
Which HR systems can you connect to?
We connect to any system with an API or export — Workday, BambooHR, SAP SuccessFactors, Darwinbox, GreytHR, Keka, and most ATS platforms. Where no API exists, we build on scheduled CSV exports. The Discovery Sprint maps what is actually available.
How long does it take to go from four disconnected systems to a live dashboard?
The Discovery Sprint takes two weeks. A full build — ingestion, warehouse, and the first dashboard set — typically runs six to ten weeks depending on how many source systems need joins and how consistent the underlying data is.
What BI tool do you build on?
We work in Looker, Metabase, Power BI, or a Next.js embedded layer — whichever fits your existing stack and license situation. We don't mandate a platform; we build to what your team will actually open.
Can line managers see their own team data without seeing everyone else's?
Yes. Row-level security is part of every build — managers see their direct reports, HRBPs see their business units, and central HR sees the full picture. Permissions are defined by your HRMS hierarchy, not maintained manually.
What if our HR data has quality issues?
Most HR data has quality issues — inconsistent job codes, duplicate employee IDs, missing fields. The Discovery Sprint surfaces what is there and what needs fixing before a dashboard build starts. We will tell you if the data needs a clean-up pass before it is worth visualising.
Get started
Find out which HR metrics you can actually see today
In 45 minutes we will audit what your HR systems export, tell you which key metrics are already computable, and show you what a dashboard built from your real data would look like.
Book a Discovery Sprint