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Data & analytics

Analytics Pipelines

Most companies do not need another generic dashboard. They need the right data connected, the definitions cleaned up, and reporting designed around the decisions they actually make. That is services work.

Client question

What questions should the business be able to answer every week without a manual spreadsheet project?

We work backwards from the business question, then identify the required data sources, clean the definitions, build the pipeline, and design the report or dashboard around the decision it supports.

Outcome

A working analytics pipeline: source data connected, definitions agreed, analysis built, and dashboards or reports delivered on a reliable cadence.

In practice

What this looks like for a real client.

A mid-size Pharma company has 1500+ distributors. Every month, each distributor sends a secondary sales running sheet — in their own format. Some use Tally exports, others send grouped CSVs or scanned PDFs. The finance team spends 250+ person-hours per month on copy-paste, VLOOKUP, and manual reconciliation. By the time the data is clean, the sales cycle for intervention has already passed. We built a pipeline that ingests files in 6 format families, uses AI to detect column layouts and extract transaction rows, matches 94%+ of product names to SKU codes automatically, and routes low-confidence records to human reviewers. What took 20 days now takes 2-4 hours.

How it works

We design around how the work actually moves.

01

Start with the business question

We clarify the decisions leadership, finance, operations, or procurement need to make. What does a useful answer look like? How often? Who sees it?

02

Map and clean the data

We identify where the data lives, how it should be joined, where definitions conflict, and what quality checks are needed before the numbers can be trusted.

03

Build the analytical layer

We create the transformations, metrics, SQL, matching logic, and validation needed to produce consistent, auditable numbers — not one-off spreadsheet analysis.

04

Design the reporting output

We turn the analysis into dashboards, scorecards, reports, or recurring briefings designed for the audience, cadence, and decision they support.

Role of AI

Not a generic chatbot. Specific techniques, applied to specific data.

Multi-format document parsing handles Excel, CSV, PDF, and image files — each with different column layouts, header depths, and naming conventions. Semantic product matching resolves "Naturolax 300gm," "Naturolax 300," and "NTLX 300" to the same SKU code. Brand-first fuzzy matching with pack-size tiebreakers handles combo products ("IPILL I-KNOW FREE") automatically. A 7-signal QC confidence score routes each file: high-confidence records auto-publish, low-confidence ones queue for human review.

Selected engagements

Not slides. Shipped work, running in production.

Deliverables

Shipped at the end of the engagement

  • Data source map and integration plan
  • Metric definitions and data quality rules
  • Custom analysis pipeline, queries, and validation checks
  • Dashboard, scorecard, report, or briefing designed for the target audience

What we need

What we need from your team to begin

  • Business questions and current reports
  • Source exports, API access, database access, or sample files
  • Owners for metric definitions and sign-off
  • Target audience and reporting cadence

Timeline

A practical delivery cadence, not an open-ended discovery.

Week 1

Questions, sources, and definitions

Decision mapping, data inventory, metric definitions, and access requirements. A working data model by Friday.

Weeks 2-4

Pipeline and analysis build

Data connection, transformations, analysis logic, matching engines, validation, and first report drafts.

Weeks 5-6

Dashboard and reporting rollout

Audience review, dashboard/report tuning, cadence setup, and handover with documentation.

Industries

Where we have delivered this service.

Pharma & Healthcare

Secondary sales consolidation across 1500+ distributors, multi-format data ingestion

Financial Services

Multi-source financial document reconciliation, risk analytics, project-level P&L

Manufacturing

OEE dashboards, supplier quality scorecards, production variance analysis

Cross-industry

Mystery shopping analytics, operational intelligence platforms, survey data pipelines

Good fit

When this service is the right answer

  • Company-specific dashboards and KPI reporting
  • Data trapped across ERP, spreadsheets, finance tools, and operational systems
  • Recurring analysis that currently depends on manual compilation

Honest call

When this is not the right choice

  • You already have a mature BI team and tooling — we accelerate, we don't replace
  • The data sources are two clean APIs with standard schemas — off-the-shelf ETL works
  • You need a one-off analysis, not a recurring pipeline

Start the engagement

Start with the question your team can't answer without a manual data pull.

We scope the first project around that problem, deliver a working result in weeks, and stay on as an operations partner — because the platform keeps getting sharper after go-live.