Research & Analytics

Data Analytics & Business Intelligence

Reporting the finance team will defend, built on numbers that tie back to the ledger.

We build the data model, the governed semantic layer and the dashboards on top of it, so that one number means one thing across the business. Every financial metric is reconciled to the trial balance before a dashboard is released.

Typical turnaround
A focused build over two to four clean source systems typically runs six to ten weeks from data access to user acceptance. Multi-entity groups, ERP sources requiring custom extraction, or environments needing a warehouse stood up first generally run twelve to twenty weeks. The pacing constraint is almost always upstream data quality and the speed of access provisioning, not development. Ongoing run and enhancement support is contracted separately on a monthly basis.
Service code
ART-DAB-009
Engagement models
Fixed fee build · Monthly retainer for run and enhancement · Time and materials for discovery phases
Delivery
Virtual, secure document exchange

Indicative fee from

₹1,75,000

Indicative fee for a foundational build across a small number of source systems with an initial dashboard suite. Final fee is confirmed after data profiling and depends on source count, data quality, entity structure and whether a warehouse layer is required. Ongoing run support is quoted separately.

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  • Engagement letter issued before work begins
  • Named engagement lead and defined reporting cadence
  • Confidentiality and access controls on all workpapers
  • Fee adjusted if the confirmed scope is smaller

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Description

The recognisable failure is not an absence of reports. It is three of them. Sales quotes one revenue figure from the CRM, operations quotes another from the billing system, finance quotes a third from the ledger, and the first twenty minutes of every review meeting is spent establishing which one is right instead of deciding what to do about it. Underneath that sits a familiar architecture: extracts pulled by hand, spreadsheets with formulas nobody living wrote, and a dashboard that was accurate on the day it was built and has been drifting ever since.

The cost is rarely a visible line item. It shows up as decisions taken late because the analysis took a week, as a month-end close extended by reconciliation of reports against each other, as an audit query that takes days to answer because nobody can trace a reported figure back to source, and as senior people doing data assembly rather than judgement. It also shows up as risk: reporting built on ungoverned personal extracts of customer and employee data sits badly against the obligations of the Digital Personal Data Protection Act 2023 and, for clients with EU exposure, the GDPR.

What the engagement covers

Work starts with the source systems as they actually are, not as the architecture diagram claims. Accounting platform, ERP, CRM, billing, payroll, marketplace and payment gateway feeds are inventoried, profiled for quality, and mapped. We then design a dimensional model, typically a star schema with conformed dimensions for customer, vendor, product, entity and calendar, and slowly changing dimension handling where history matters, so that the same customer means the same customer in every report. Pipelines are built to load it on a defined schedule, with row counts, control totals and referential integrity checked on every run rather than assumed.

The semantic layer is where trust is won

Above the model sits a governed semantic layer holding certified measure definitions in DAX, LookML or the equivalent for your platform. Gross margin, active customer, churn, DSO and recognised revenue are defined once, documented in plain language alongside the code, and reused everywhere. This is the difference between a dashboard estate that converges and one that fragments: without it, every new report re-implements the same metric slightly differently and the disagreement returns within two quarters. Financial measures are reconciled to the trial balance and to statutory filings before release, and the reconciliation is retained as a recurring control, not a one-time exercise.

How the virtual delivery model works here

Delivery is remote and incremental. We work in your tenancy wherever possible, so the Power BI, Looker Studio, Fabric or warehouse assets remain your property under your access controls from the first day. Connectivity is established through read-only service accounts scoped to the minimum required objects, personal data is masked or tokenised in non-production environments, and processing arrangements are documented to support your obligations as data fiduciary under the DPDP Act 2023. A working dashboard is put in front of users in the first few weeks rather than at the end, because requirements stated in a workshop and requirements revealed by use are seldom the same.

Who this is built for

Finance leaders who want a management pack that does not require three days of manual assembly. Founders and operators running on a stack that has outgrown spreadsheets. Multi-entity groups that need consolidated reporting across systems that do not talk to each other. Private equity portfolio companies asked for standardised monthly reporting by an investor who is not going to relax the deadline.

Engagement models

Fixed fee build · Monthly retainer for run and enhancement · Time and materials for discovery phases

Expected turnaround

A focused build over two to four clean source systems typically runs six to ten weeks from data access to user acceptance. Multi-entity groups, ERP sources requiring custom extraction, or environments needing a warehouse stood up first generally run twelve to twenty weeks. The pacing constraint is almost always upstream data quality and the speed of access provisioning, not development. Ongoing run and enhancement support is contracted separately on a monthly basis.

Scope of Work

  • Source system inventory and data profiling across accounting, ERP, CRM, billing, payroll, marketplace and payment gateway feeds, including completeness and cardinality checks
  • Reporting requirements definition by audience, mapping each proposed metric to the decision it supports and discarding those that support none
  • Dimensional model design using a star schema with conformed customer, vendor, product, entity and calendar dimensions and slowly changing dimension handling where history matters
  • ELT or ETL pipeline build with incremental loads, watermarking, restartability, and load logging that records row counts and control totals for every run
  • Semantic layer development holding certified measure definitions in DAX, LookML or platform equivalent, with plain-language documentation stored alongside the code
  • Reconciliation of all financial measures to the trial balance and, where relevant, to GST and statutory filings, retained as a recurring control rather than a one-time check
  • Data quality control framework covering null and duplicate detection, referential integrity, range and threshold tests, with defined owners and escalation on failure
  • Dashboard build for finance, operations, sales and leadership audiences, designed around exception and variance rather than undirected exploration
  • Row-level and object-level security configuration so entity, region and role-based access restrictions are enforced in the model rather than in the report layer
  • Personal data handling controls including masking in non-production environments, retention rules and access logging aligned to DPDP Act 2023 obligations
  • Refresh scheduling, failure alerting and monitoring, with a documented recovery runbook covering the common upstream breakage patterns
  • Data dictionary and metric catalogue publication, plus enablement sessions for the analysts who will own the estate after handover

Key Deliverables

Data Model and Source-to-Target Mapping

The dimensional model as built, with entity relationship documentation and a field-level mapping from each source system column to its destination, including every transformation rule applied.

Governed Semantic Layer

Certified measure definitions for every published metric, version-controlled, with plain-language descriptions so a business user and a developer read the same definition of gross margin.

Dashboard Suite

Role-specific dashboards for finance, operations and leadership, built with drill paths from summary to transaction and with security applied at the model layer.

Metric Dictionary

A published catalogue of every metric, its owner, its calculation in words, its source, its refresh frequency and its known limitations. The reference point when two teams disagree.

Data Quality Control Pack

The test suite covering completeness, uniqueness, integrity and threshold checks, with the reconciliation workings that tie financial measures back to the trial balance.

Operations Runbook

Refresh schedules, dependency map, alerting configuration, and documented resolution steps for the recurring failure modes, written so an internal analyst can operate the estate without us.

Enablement Sessions and Recordings

Working sessions for report consumers and for the technical owner, recorded and retained, covering navigation, metric meaning, and how to extend the model without breaking it.

How the Engagement Runs

Discovery and data profiling

We inventory source systems, profile the data for volume, quality and referential consistency, and interview the people who currently produce the reports. This phase closes with an honest assessment of what the data can and cannot support, which sometimes narrows the ambition of the brief before any build starts.

Metric definition and model design

Each proposed metric is tied to a decision and a named owner, and the dimensional model is designed to serve them. Finance signs off definitions for anything with a financial meaning. The phase closes on an agreed model design and a metric list that all stakeholders have seen in writing.

Pipeline and model build

Connections, transformations and the dimensional model are built in your tenancy, with load logging and quality tests included from the outset rather than added afterwards. Weekly demonstrations run against real data so drift between intent and build is caught early.

Semantic layer and dashboard development

Certified measures are implemented and documented, security is applied at the model layer, and dashboards are built by audience. Users are put in front of a working version early, and the feedback that follows first contact is treated as requirement, not scope creep.

Reconciliation and user acceptance

Financial measures are reconciled to the trial balance and filings, quality tests are run over a full period, and nominated users test against their own known cases. Sign-off requires the reconciliation to hold, not merely for the dashboards to load.

Handover and run support

The runbook, dictionary and model documentation are issued, enablement sessions are delivered and recorded, and monitoring is transferred. Where you want us to keep operating the estate, this converts into a defined monthly support arrangement with agreed response expectations.

What You Gain

One number, one meaning

Metrics are defined once in a governed layer and reused everywhere, which ends the standing argument about whose revenue figure is correct and returns review meetings to decisions.

Management pack without manual assembly

Recurring reporting that consumed days of analyst time each month is produced on a schedule, freeing capacity for analysis that actually requires human judgement.

Reported figures reconcile to books

Because financial measures are tied to the trial balance as a standing control, dashboards can be used in board and lender conversations without a parallel verification exercise.

Exceptions surface without being hunted

Threshold and variance logic pushes the outlier to the reader instead of waiting for someone to go looking, which shortens the gap between a problem occurring and being noticed.

Personal data handled defensibly

Access control, masking and retention are designed into the model rather than bolted on, which materially reduces exposure under the DPDP Act 2023 and comparable regimes.

An estate your team can own

Assets sit in your tenancy with documentation and a runbook, so continuity does not depend on the consultant or on the one analyst who understands the spreadsheet.

Industries We Serve With This Engagement

Financial Services and NBFCsManufacturing and DistributionRetail and E-commerceHealthcare and Life SciencesTechnology and SaaSLogistics and Supply ChainProfessional ServicesReal Estate and Infrastructure

Frequently Asked Questions

Most commonly Microsoft Power BI and Google Looker Studio, with warehouse layers on BigQuery, Snowflake, Azure SQL or Microsoft Fabric depending on where your data already lives. The choice should follow your existing stack, licensing and internal skills rather than our preference, because you will own the estate long after the build. Where a client already has Tableau or Qlik in production we build there. What does not change across tools is the discipline underneath: a proper dimensional model, a governed semantic layer, and reconciliation to the ledger. Tool selection is a smaller decision than it is usually treated as.

Direct connection is viable when you have one or two sources, modest volumes and no material history requirement, and it is the cheaper starting point. A warehouse becomes necessary once you are combining systems that disagree about customer identity, once you need history that source systems overwrite, once refresh performance degrades, or once direct querying starts affecting the operating system it queries. We are explicit about which situation you are in and will not recommend a warehouse you do not yet need. Many engagements begin with direct connection and add a warehouse layer at the point the constraint becomes real.

By reconciling it, and by continuing to reconcile it. Every financial measure is tied back to the trial balance during acceptance, and that reconciliation is retained as a scheduled control that runs each period rather than a one-time exercise at go-live. Where a dashboard figure legitimately differs from the ledger, for instance management revenue recognised before cut-off adjustments, the difference is documented in the metric dictionary rather than silently tolerated. A dashboard that cannot be reconciled to the books will not survive its first board meeting, and it should not.

You do. Wherever your licensing permits we build inside your tenancy from the first day, so workspaces, datasets, pipelines and reports are your assets under your access administration throughout the engagement. Model documentation, source-to-target mapping, the metric dictionary and the operations runbook are delivered as part of the closing package, and enablement sessions are recorded for staff who join later. The test of a genuine handover is whether an internal analyst can extend the model six months on without calling us, and that is what the documentation is written to enable.

You remain the data fiduciary. The obligations for lawful purpose, purpose limitation, retention and security stay with you, and a provider acting as data processor works under your instructions and a written arrangement. Practically, that shapes the build: personal data is masked or tokenised in development and test environments, access is granted through least-privilege service accounts, retention rules are configured rather than left indefinite, and access is logged. We can support your compliance posture with documented controls and processing terms, but this is not a legal opinion, and mapping your specific obligations should involve your counsel.

Yes, though the method differs. Cloud platforms such as Zoho Books, QuickBooks Online and NetSuite expose documented APIs and are straightforward. Tally is usually integrated through its XML or ODBC interface, or through scheduled structured exports where the deployment does not permit direct connection. SAP and other on-premise ERPs are extracted through views, data services or the client IT team depending on the landscape and on internal policy. Profiling in the discovery phase establishes exactly what is retrievable, at what granularity and at what refresh frequency, before anything is promised in a design.

Sprawl is a governance failure rather than a tooling failure. The controls that work are a governed semantic layer so metrics cannot be quietly redefined in a new report, a certification convention that distinguishes endorsed content from personal exploration, a named owner for every published metric, and a periodic review that retires reports nobody has opened. We put these in place during the build and document them in the runbook. They only hold if someone internally owns the estate, which is why we ask you to nominate that person during design rather than at handover.

Yes, under a separate monthly arrangement. Ongoing support typically covers refresh monitoring and failure resolution, changes driven by upstream system updates, new measures and reports as they are requested, periodic reconciliation review, and access administration. Clients who intend to run the estate internally often keep a lighter arrangement for the first two quarters while their team builds familiarity, then step down. We would rather you become self-sufficient than dependent, and the documentation is written accordingly.

Related engagements

Often scoped alongside this

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