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Less guesswork. More evidence.

Data analytics services.
Turn raw data into daily decisions.

Most companies have more data than they use. Reports arrive late, numbers disagree between teams, and forecasting still depends on a spreadsheet. Surfytech builds the pipelines, models and dashboards that make your data answerable.

Start your analytics project Explore what we build
  • Strategy before tooling
  • Governed, trusted data
  • Dashboards people use
Why analytics projects disappoint

Dashboards are not the
hard part. Trust is.

A dashboard built on unclear definitions creates arguments instead of decisions. Every metric needs an owner, a source and a rule for how it is calculated.

We start with the decision you want to make, then work backwards to the data, models and refresh that decision needs — so the output is used, not abandoned.

Report or decision? If a dashboard has not changed a decision in months, it is documentation rather than analytics. We help retire low-value reports and focus effort where it changes outcomes.
  • 01
    One agreed number

    Shared definitions and a governed source for revenue, cost, margin and service metrics.

  • 02
    Faster answers

    Pipelines and models refreshed on a schedule that matches how you operate.

  • 03
    Earlier warnings

    Trends, thresholds and alerts that surface problems before month end.

  • 04
    Analyst time back

    Less manual extraction, so your team analyses instead of assembling spreadsheets.

What we deliver

From scattered sources to
decisions you can defend.

Each deliverable stands alone or combines into a wider analytics programme with clear review points.

Data strategy & consulting

Source review, metric definitions, roadmap and platform choices tied to business priorities.

Plan the roadmap

Data warehousing & ETL

Pipelines that extract, transform and load data from ERP, CRM, web and operational systems.

Discuss pipelines

Visualization & reporting

Dashboards and scheduled reporting designed around the decisions each team makes.

See capabilities

Predictive analytics & modelling

Forecasting, scoring and segmentation models validated against historic outcomes.

Explore AI services

Data governance & quality

Ownership, access rules, validation checks and reconciliation that keep numbers trustworthy.

Discuss governance
Reporting or analytics?

Let’s start where it matters.

Share the questions you cannot answer today. We’ll identify the fastest route to a reliable answer.

Talk through your data
Analytics capabilities

Every layer. One accountable team.

Data engineering, modelling, visualization and enablement planned together so insight reaches the people who decide.

Customer analytics

Cohorts, retention, lifetime value and churn signals built on governed customer records.

Marketing analytics

Channel performance, attribution and campaign reporting connected to revenue.

Financial analytics

Margin, cash flow, cost centre and budget-versus-actual reporting with defined rules.

Operational analytics

Throughput, quality, utilisation, downtime and service levels across operations.

Data mining & exploration

Pattern discovery, anomaly detection and root-cause analysis on historic data.

Data quality & reconciliation

Validation rules, exception handling and reconciliation between systems and reporting.

Dashboard delivery

Interactive dashboards, scheduled reports and embeddable views with role-based access.

Enablement & support

Training, documentation and iterative improvements as questions and data change.

Analytics depends on integration and clean systems. Explore system integration, ERP solutions and CRM data.
Our analytics process

Question first.
Then data, model and dashboard.

Discovery → Data review → Pipeline build → Modelling → Visualization → Enablement. Each stage ends with something you can inspect and challenge.

  1. Discovery

    Agree the decisions in scope, the metrics behind them and who owns each definition.

    Output: metric definitions & priorities

  2. Data review

    Map sources, quality, ownership and access limits. Identify gaps and manual steps.

    Output: source inventory & risks

  3. Pipeline build

    Build extraction, transformation and loading with validation and reconciliation checks.

    Output: refreshable data pipeline

  4. Modelling

    Design the data model and, where useful, forecasting or scoring models validated on history.

    Output: model & validation results

  5. Visualization

    Deliver dashboards and scheduled reports around how each team works and reviews.

    Output: dashboards & reports

  6. Enablement

    Train users, document definitions and refine the reporting as questions evolve.

    Output: training & improvement backlog
Platforms & tooling

The right platform for
the way you operate.

We work with the major warehouse, transformation and visualization platforms, and choose based on volume, latency, budget and team skills.

  • TableauInteractive business dashboards
  • Google LookerModelled, governed reporting
  • SisenseEmbedded analytics for products
  • Azure SynapseCloud data warehousing
  • BigQueryServerless analytics warehouse
  • Amazon RedshiftScaled analytic queries
  • DatabricksLakehouse engineering & ML
  • Apache SparkLarge-scale data processing
  • TalendETL & data integration
Already have dashboards? We often improve definitions and pipelines before adding new tools, which is usually faster and cheaper than a platform migration.
Industries

Analytics shaped by
how each sector measures.

The metrics that matter — and the questions an auditor will ask — differ by industry. We define them with your team during discovery.

Healthcare

Utilisation, waiting times and service reporting built with carefully defined access to patient data.

Healthcare technology

Finance

Portfolio, risk, reconciliation and regulatory reporting on traceable, auditable data.

Finance solutions

Retail

Basket, margin, stock cover and channel analytics across stores and online.

Retail solutions

Education

Admissions funnels, engagement, progression and outcome reporting for institutions.

Education solutions

Travel

Booking curves, seasonality, ancillary revenue and service quality analytics.

Travel solutions
Case studies & solution examples

See the problem.
Imagine the possibilities.

The scenarios below illustrate possible analytics outcomes—not published client case studies or measured results. Ask us about relevant experience for your data.

Illustrative · Metric alignment

Two teams, two versions of revenue

Challenge: Finance and sales report different numbers, so planning meetings stall on definitions.

Approach: Agree one governed definition, document the calculation and reconcile it across both source systems.

Discuss metric definitions →
Illustrative · Pipeline rebuild

Reports that need a manual export every month

Challenge: Analysts spend days assembling spreadsheets before every review, and errors slip through.

Approach: Replace manual extraction with validated pipelines, refresh schedules and exception alerts.

Discuss pipelines →
Illustrative · Demand forecasting

Stock decisions based on gut feel

Challenge: Seasonal peaks cause stockouts in some lines and excess inventory in others.

Approach: Build a validated forecast model with clear assumptions, review cadence and accuracy tracking.

Explore AI services →
Frequently asked questions

Good questions.
Clear starting points.

Here’s what to consider when starting with data analytics.

Ask about your data →
What do data analytics services include?

Analytics engagements usually combine data strategy, source and pipeline engineering, warehousing, modelling, dashboard delivery and enablement. Scope depends on which decisions are slow or unreliable today.

Our reports already exist. Why rebuild anything?

If definitions are unclear, refreshes are manual or numbers disagree between teams, the reporting layer is the symptom rather than the cause. Fixing sources, definitions and pipelines usually resolves many reports at once.

Which analytics platform should we choose?

It depends on data volume, latency expectations, budget, existing cloud commitments and team skills. We compare realistic options before recommending a platform rather than starting from a preferred tool.

Can you work with our existing ERP and CRM data?

Yes. ERP, CRM and operational systems are common sources. We review available APIs, exports and database access, then plan extraction with validation and reconciliation against the source systems.

How accurate are predictive models?

Accuracy depends on data quality, history and how stable the underlying behaviour is. We report validation results and expected error ranges against historic data, and avoid promising precision the data cannot support.

Who owns the pipelines and dashboards you build?

Deliverables, repository access, documentation and intellectual property are defined in the engagement agreement. Dashboards, models and pipelines are handed over with documentation so your team can extend them.

Start your data project

Let’s make your data
easier to act on.

Tell us which decisions are slow, which systems hold the data and who needs the answers. We’ll propose a practical starting point and a roadmap you can extend.

Get a free consultation Call +91 88821 12317

No complete specification needed. A clear business goal is a great place to start.

What happens next?

  1. Share the decisions, reports and data sources involved today.
  2. Discuss data quality, access, refresh needs and reporting tools.
  3. Agree a first analytics deliverable and the scope for a proposal.