Havish M Consulting
Customer Case Study · HR · People Analytics & Data Modelling
Power Transmission × Havish M Consulting

Twenty tables, zero VLOOKUPs, one click to any people answer

The power transmission company's HR numbers lived in separate extracts stitched together with VLOOKUPs. HMC connected them in one Power BI data model, moved every Excel calculation into Power Query (M) and DAX, and built a 12-page dashboard where every chart filters every other.

ClientPower Transmission FunctionHuman Resources ThemePeople Analytics · Power BI Data Modelling
Tables connected in one model
0tables
fact, dimensions, CTC and date tables
VLOOKUPs left in the working
0formulas
replaced by relationships, M and DAX
Report pages on one model
0pages
summary, people, attrition, cost, talent
Employees in the fact table
0active
every visual cross-filters the rest

Counts from the Power BI file shown below.

The Challenge

Disparate datasets held together by VLOOKUPs

Headcount, CTC, joining and relieving data came from different sources. Every monthly review meant rebuilding the joins in Excel, and every chart was a static picture of one cut.

Disparate datasets

HRMS extracts, CTC sheets, compa data and joining and relieving trackers, each with its own keys and layout.

VLOOKUP chains

Dozens of lookup columns copied down every row: slow to recalculate and broken by a single inserted column.

Static charts

Charts did not talk to each other. Answering "what about Finance, M4 and above?" meant a new pivot.

Repeated working

Age bands, tenure groups, fiscal months and attrition were recalculated by hand each cycle.

HMC Approach

Model first, visuals second

Built with the power transmission company's HR team, so the model matches how they already think about people.

Select a step to see what happened.

Discover · Map every lookup

Traced the Excel working end to end.

  • Listed each source file, its key and refresh cadence.
  • Catalogued every VLOOKUP and helper column.
  • Agreed the questions leadership asks every month.

Deliverable: Source and lookup inventory.

Design · A star schema

One fact table, many small dimensions.

  • Fact_Report at employee grain, active and exited.
  • Dimensions for age, grade, tenure, function, BU, location, CTC and attrition reason.
  • Sort-order columns so bands display in the right order.

Deliverable: Model diagram and relationship plan.

Build · M, DAX and visuals

Every Excel step rebuilt where it belongs.

  • Power Query (M) for cleaning, typing and derived columns.
  • DAX measures for headcount, diversity, attrition and cost.
  • A dynamic fiscal-month measure so YTD numbers annualise themselves.

Deliverable: 12-page Power BI report.

Adopt · Use it in the review

The dashboard replaced the Excel pack.

  • Landing page with tiles for each area.
  • Slicers for business unit, function and location.
  • HR team trained to refresh and extend it.

Deliverable: Refresh guide and walkthrough.

Scale · Add questions, not files

New analysis plugs into the same model.

  • New pages reuse the same measures.
  • New source columns flow through M automatically.
  • Talent management pages added on the same model.

Deliverable: Extensible model.

The Solution

One model under every chart

The original Power BI file first, then three working modules: explore the model, see the Excel working rebuilt in M and DAX, and use a cross-filtering dashboard built the same way.

01 · The original report

Pages, model and measures from the Power BI file

Shown as delivered. Click any screen to enlarge it.

Power BI landing page with six tiles
Landing page. Tiles for Summary, People Analytics, Attrition, Cost and Talent Management; 12 pages in all.
Summary page with KPI tiles and charts
Summary. Headcount 856, average age 36.48, diversity 12.15%, attrition 22.58%, with gender, age, location, joiners, grade and regrettable attrition.
Gender, grade, tenure and age analysis page
Gender, grade, tenure, age. 752 men and 104 women, grade spread, tenure bands and function headcount, all filtered by business unit.
Cost trend line chart with slicers
Cost. CTC by month for 2018, 2019 and 2020 with date, FY, function and business unit slicers; total 509.53.
Power BI model view with relationships
The model. Fact_Report at the centre, related to age, attrition reason, BU, CTC, division, grade, tenure, location and date tables.
DAX measure HMC_Dynamic Month Active Joining
DAX. HMC_Dynamic Month _ Active Joining finds the latest data month and converts it to a fiscal month number.
02 · Model explorer

From a web of VLOOKUPs to a star schema

Switch between the old Excel working and the Power BI model. Click any table to see its fields and relationship, then pick a value to watch the filter flow into Fact_Report.

Fact_Report

Fields

Filter by a value

Tables and fields from the model view; row counts from a synthetic workforce calibrated to the report's totals.

03 · Formula lab

Every Excel step, rebuilt in M and DAX

Pick a metric to compare the Excel formula with its DAX measure, step through the fiscal-month measure from the report, or watch Power Query shape a raw extract.

Before · Excel

After · Power BIcalculated once, filters everywhere

Result on the model
Data refreshed up to

    04 · Live people dashboard

    Click anything. Everything else follows.

    A working replica of the report on a synthetic workforce of 856 employees calibrated to the original totals. Click a bar, slice or icon to cross-filter every other visual; use the slicers or the filter chips to reset.

    Headcount by Gender
    Each figure = 100 employees
    Age Wise Distribution
    Location Wise Distribution
    New Joiners YTD
    FY 2020-21
    Grade Distribution
    Overall Regrettable Attrition
    FY 2020-21
    Gender Diversity
    Grade Wise Distribution
    Gender Wise Grade Grouping
    FemaleMale
    Tenure Wise Active Employees
    Years
    Age Wise Distribution
    Function Wise Head Count
    Exits by Month · Regrettable vs Other
    RegrettableNon-regrettable
    Exits by Reason
    Annualised Attrition by Tenure
    Annualised Attrition by Grade Group
    Cost by Year, Quarter and Month
    Note: Cost analysis is done for India-based employees only
    0
    Cost

    Synthetic employees; headline totals match the report (attrition within 0.05 pt). Cost series as read from the report's trend chart. Function names beyond the eight visible in the original, and exit reasons, are illustrative.

    Impact Dashboard

    Less stitching, more answering

    The monthly HR pack now refreshes from source, and follow-up questions are answered with a click instead of a new pivot.

    Hours per monthly HR review cycle

    By task, Excel working vs Power BI model

    Where the logic lives

    Calculation objects by type

    Figures indicative; validated metrics available on request.

    Before vs After

    How HR analytics works now

    DimensionBeforeAfter
    Joining dataVLOOKUP columns across sheetsRelationships in one star schema
    Derived fieldsHelper columns rebuilt each monthPower Query (M) steps that rerun on refresh
    MetricsCOUNTIFS and pivots per cutDAX measures that work for any cut
    YTD and fiscal monthsSet by hand each cycleDynamic fiscal-month measure
    ChartsStatic, one cut eachCross-filtering across 12 pages
    New questionsA new pivot and a new sheetA click, or a new measure on the same model
    Tools & Tech Used

    The Power BI stack

    Power BI Desktop 12-page report Star-schema modelling 20 tables, one fact Power Query (M) cleaning and derived columns DAX measures and dynamic fiscal month Excel source extracts only
    Key Takeaways

    What any HR team can borrow

    01

    Relate, don't look up

    A relationship replaces a VLOOKUP column on every row, and it never breaks when a column moves.

    02

    Put logic where it belongs

    Shape data in M, calculate in DAX, and leave Excel as a source, not a calculator.

    03

    Interactivity is a model feature

    Cross-filtering comes free once every chart sits on the same model.