Data Analytics Development Services

Data analytics: from spreadsheets to decisions

Most companies already have the data they need to run better: sales in the CRM, orders in the store, costs in accounting, activity in their product. The problem is that it lives in separate systems, uses different definitions and only comes together in spreadsheets that someone rebuilds every week.

Data analytics development connects those sources, cleans and models the data once, and presents it in dashboards and reports that update automatically. Done well, it gives everyone — from the CEO to a team lead — the same numbers, with the ability to drill into the details behind them.

Problems we solve

• Weekly reports assembled by hand from several exports
• Different teams reporting different numbers for the same metric
• Dashboards that exist but nobody trusts or uses
• No visibility of margins, customer profitability or cohort behaviour
• Analysts spending most of their time cleaning data rather than analysing it

What’s included

How we deliver analytics projects

We start with decisions, not charts. In workshops we list the decisions each team makes regularly and the metrics behind them, then agree exact definitions. Only then do we connect sources, build the data model and design dashboards. Each dashboard is reviewed with its users and refined until it answers their questions quickly.

• Sources connected through APIs and connectors, refreshed on a schedule
• Transformations version-controlled (often with dbt) and tested
• Row-level security so people see only the data they should
• Documentation of every metric, visible next to the dashboard

Choosing a BI tool

We recommend the tool your team will actually adopt, considering existing licences and skills.

Timeline and budget

Typical ranges to help you plan. Your written estimate comes after a free discovery call.

Defining metrics people trust

Dashboards lose credibility the moment two reports show different revenue figures. The fix is a shared definition layer — often called a semantic layer or metrics layer — where each business measure is defined once, documented and reused everywhere. We run short workshops with finance, sales and operations to agree definitions such as “active customer”, “gross margin” or “on-time delivery” before any chart is built.

• Each metric has an owner, a plain-language definition and the exact calculation
• Filters such as refunds, test accounts and internal users are applied consistently
• Time zones and fiscal calendars are handled in one place
• Changes to definitions are versioned and announced

Designing dashboards that get used

• Start from decisions — each dashboard answers specific questions for a specific audience, such as a weekly sales review.
• Few headline numbers — three to six key figures at the top, with trends and comparisons to target or last period.
• Drill-down, not clutter — detail lives one click away instead of crowding the first view.
• Performance — pre-aggregated tables keep dashboards loading in seconds, which matters more than visual polish.
• Adoption tracking — usage statistics show which reports are valuable and which can be retired.

Self-service without chaos

Giving every employee a BI licence and raw tables rarely works. A better pattern is governed self-service: curated, documented datasets that business users can explore safely, with certified dashboards marked as the official source. Analysts keep control of definitions while teams answer their own everyday questions.

A typical analytics architecture

Behind every reliable dashboard sits a simple, layered architecture. Keeping these layers separate means a change to a source system does not break every report, and business logic is written once instead of in dozens of spreadsheets.

Embedded analytics inside your product

For SaaS and platform businesses, analytics is often a feature customers pay for. Embedded analytics shows each customer their own data inside your application — usage, performance, savings or outcomes — with strict tenant isolation. Options range from embedding a BI tool with row-level security to building native charts in your front end on top of pre-aggregated tables. We help you choose based on cost per customer, design control and how interactive the analytics need to be.

• Tenant isolation enforced in the data layer, not only the UI
• Pre-aggregated tables for fast loading at scale
• Consistent styling with your product
• Export to CSV or PDF where customers need it
• Licensing cost modelled at your expected customer count

Measuring whether analytics is working

Dashboards are a means, not an end. We agree with you how success will be judged: fewer hours spent assembling reports, faster month-end, decisions made on the same numbers across teams, or measurable improvements in the metrics the dashboards track. Usage monitoring shows which reports are opened and which can be retired, keeping the estate small and trusted.

Common analytics mistakes

• Building dashboards before agreeing metric definitions
• Connecting BI tools directly to production databases
• Dozens of near-duplicate reports nobody maintains
• No owner for data quality issues
• Treating the first version as finished rather than iterating with users

Analytics depends on good pipelines — see data engineering — and is often the first step towards machine learning.

Questions to ask before choosing an analytics partner

• Will we own the warehouse, models and dashboards in our own accounts?
• How are metric definitions documented and changed?
• How will our team learn to build and maintain reports themselves?
• What does the platform cost per month at our data volume and user count?
• How are data access and row-level security handled for sensitive information?

Clear answers to these questions protect you from a reporting setup that only one outside person understands.

Related services

Analytics depends on solid data engineering. It is part of our wider AI & data services.

This page is part of AI Development & Data Engineering Services — see the full overview for related services, approach and pricing.