Skip to main content
INTELLIA

Data & Analytics

One version of the numbers. Every morning.

Data pipelines, warehouses and dashboards that turn ERP, CRM and operational data into decisions your managers trust.

Start a projectTalk to an expert

Data & Analytics at INTELLIA, in brief

INTELLIA's data and analytics service connects the systems a business already runs, such as ERP, CRM, POS, telephony and spreadsheets, into a single governed data store, then builds dashboards and reports on top. The goal is one agreed set of metrics, refreshed automatically, that managers can use without asking IT for exports.

Last updated

Reporting that takes days and still gets argued about

In most growing companies, the monthly report is assembled by hand from exports. Sales, finance and operations each have their own spreadsheet, the numbers rarely match, and by the time the report is ready the decision has already been made.

  • Data spread across ERP, CRM, POS, call center and spreadsheets
  • Manual exports and copy-paste that introduce errors
  • Different definitions of revenue, customer or active user
  • Dashboards nobody opens because nobody trusts them

Capabilities

What our Data & Analytics team delivers

  • Data audit and metric definitions

    We inventory sources and agree written definitions for the 20 to 40 metrics that matter, owned by named people.

  • Data pipelines and integration

    Automated extraction from databases, APIs and files, with scheduled refreshes, validation and alerting when a feed breaks.

  • Data warehouse and modeling

    A reporting database designed around your business entities, so new reports take hours instead of weeks.

  • Dashboards and self-service BI

    Executive, departmental and operational dashboards in Power BI, Apache Superset or embedded in your own apps.

  • Advanced analytics

    Forecasting, cohort and funnel analysis, customer segmentation and what-if models built on the same governed data.

  • Data governance

    Role-based access, data lineage, quality checks and retention rules that satisfy auditors and regulators.

Our approach

How a typical engagement runs

  1. 1

    Decide the questions first

    We start from the ten decisions leadership makes each month and work back to the data needed, not the other way around.

  2. 2

    Map sources and gaps

    We document every source, its owner, refresh frequency and quality issues, and agree what to fix at source.

  3. 3

    Build the foundation

    Pipelines, a modeled warehouse and automated tests for the core metrics.

  4. 4

    Ship dashboards in weekly increments

    One dashboard at a time, reviewed with the people who will use it, then refined.

  5. 5

    Train and hand over

    Training for analysts and managers, documentation of every metric, and a support plan for new sources.

What does a modern analytics setup look like for a mid-sized company?

For most organizations with 50 to 2,000 staff, a practical analytics setup has four layers: sources (ERP, CRM, POS, telephony, HR), pipelines that copy data on a schedule, a warehouse where data is cleaned and modeled, and dashboards for each audience. You do not need a data lake, streaming infrastructure or a large team to get value. You need agreed definitions and reliable refreshes.

Build in-house, buy a BI tool, or use a partner?

Option Works well when Watch out for
BI tool only Data already lives in one clean system Reports break when sources change; logic duplicated in every report
In-house data team You have budget for at least two specialists Hiring time; key-person risk
Partner-built foundation, in-house analysts You want speed now and ownership later Insist on documentation and handover

Most of our clients choose the third option: we build the pipelines and model, their analysts own the dashboards.

Which metrics should we start with?

Start with metrics tied to a recurring decision. Examples we often implement first:

  • Sales and finance: revenue by product and region, gross margin, receivables ageing.
  • Customer support: first response time, resolution time, backlog, satisfaction (see CX Hub).
  • Contact centers: service level, abandonment rate, average handle time (see CONVERSA).
  • Fleet and field operations: utilization, on-time delivery, fuel per kilometer (see NAVIX).
  • HR: headcount, attrition, overtime and leave liability (see RAINMAKER HRMS).

From dashboards to prediction

Once the data foundation is reliable, the same data supports forecasting and machine learning: demand forecasts, churn risk, fraud flags. That work is covered by our AI and machine learning service. For a step-by-step plan, read a data analytics roadmap for SMEs.

Technologies we use

  • PostgreSQL and SQL Server
  • Python and dbt-style SQL modeling
  • Power BI
  • Apache Superset
  • Azure Data Factory
  • Odoo, REST and webhook connectors

Related INTELLIA products

Related case studies

Pricing and engagement

How we work together

Data projects succeed when they deliver value in weeks, not after a year-long warehouse build. We scope the first release around a small number of high-value dashboards.

  • Analytics quick start

    Best for: Teams with no central reporting

    Fixed-scope project delivering a governed data store and the first three to five dashboards.

  • Data team as a service

    Best for: Ongoing reporting needs

    A part-time data engineer and analyst who add sources, build reports and answer ad-hoc questions monthly.

  • Embedded analytics

    Best for: Software products

    Analytics features built into your own application for your customers.

We do not publish fixed prices because scope drives cost. Every proposal states assumptions, inclusions and exclusions.

FAQ

Data & Analytics: common questions

What is the difference between a dashboard and a data warehouse?

A data warehouse is the organized store of data pulled from your systems; a dashboard is a visual view on top of it. Dashboards built directly on operational systems are slow and inconsistent, so we usually build a small warehouse first.

Do we need Power BI licenses?

Not necessarily. Power BI suits organizations already on Microsoft 365. Apache Superset is open source and has no per-user license cost. We recommend based on your users, budget and existing tools.

How long until we see the first dashboard?

Usually four to six weeks from kickoff for the first production dashboard, assuming we can access the source systems. Additional dashboards typically follow every one to two weeks.

Can you work with data from Odoo or our existing ERP?

Yes. We regularly extract data from Odoo, SQL Server and PostgreSQL based systems, REST APIs and spreadsheets. Legacy systems without APIs can be read through database replicas or scheduled exports.

Who owns the data and the dashboards?

You do. The warehouse runs in your cloud tenancy or on your servers, and all pipeline code, models and dashboard definitions are handed over.

How do you handle data quality problems?

Automated tests check every refresh for missing, duplicate or out-of-range values. Problems are flagged to the data owner and, where possible, fixed at the source system rather than patched in reports.

Further reading

Planning a Data & Analytics project?

Tell us what you are trying to achieve. A solution architect will reply within one business day with next steps — not a sales script.

Start a projectRequest a demo
CallWhatsAppGet a quote