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Data analytics services

Data analytics services for mid-market leaders: six connected capabilities, from strategy and BI to integration, governance, automation and decision intelligence, each with a defined deliverable.

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Our data analytics services help leaders answer the questions behind revenue, margin and cash, with data they trust.

Your company probably has the data already. It sits in the ERP, the CRM, accounting software and spreadsheets. It is just scattered and inconsistent, so every report starts with manual work.

We advise first: we define the KPIs and confirm where we can genuinely help. Where your team needs new skills, we train it. When you are ready, we connect the systems, clean and govern the data, and deliver dashboards and automations people use. Each service adds to one reliable data stack, not another disconnected tool.

Engagements usually start with data advisory or an AI and data readiness assessment. Training, a strategy and implementation project or a fractional or embedded data team follow when they fit. We serve clients worldwide from our Dubai headquarters, with delivery across US and UAE time zones.

What it is

Our data analytics services cover everything between raw data and a decision

Data and analytics services cover the full path from raw data to a business decision. Our work spans six areas.

Where the pain is decides where the work startsDecision tree: leaders arguing about numbers start with data governance and business intelligence; reports that take days start with data integration and automation; unclear priorities start with data strategy. All three lead to faster decisions on trusted data.Where the pain is decides where the work startsCommon symptom, the services to start with, and the goal they shareSymptomStart withShared goalLeaders argueabout numbersData governance +business intelligenceReports take daysData integration +process automationPriorities areunclearData strategyand roadmapDecisions madefaster, on dataeveryone trustsMost engagements combine two or three services; each can start small and growWhere the pain is decides where the work startsMobile view. Decision tree: leaders arguing about numbers start with data governance and business intelligence; reports that take days start with data integration and automation; unclear priorities start with data strategy. All three lead to faster decisions on trusted data.Where the pain is decideswhere the work startsCommon symptom, the services to startwith, and the goal they shareLeaders argue about numbersData governance + business intelligenceReports take daysData integration + process automationPriorities are unclearData strategy and roadmapDecisions made faster, on trusted dataMost engagements combine two or threeservices; each can start small

Data strategy sets priorities and the roadmap. Business intelligence turns data into dashboards, reports and data visualization. Data integration, data warehouse and data management work bring sources together in one place. Data governance and CRM data cleansing keep records accurate. Process and AI automation remove manual steps. Decision intelligence links each metric to the choice a leader makes.

The insight is that these services only work together. A dashboard is only as good as the integration and governance underneath it. Automation fails on messy data. That is why an engagement often combines two or three services.

We focus on the analytics leaders use every week. What happened? Why did it happen? What should we do next?

We work across Power BI, HubSpot, Salesforce, GA4, NetSuite, Odoo, Zoho, QuickBooks, Airtable, Zapier, Make and n8n. Each service can start small and grow as the value becomes clear.

Start where the pain is greatest. If leaders argue about numbers, begin with governance and business intelligence. If reports take days, begin with integration and automation. If priorities are unclear, begin with strategy. Each path leads to the same goal: decisions made faster, on data everyone trusts.

The rest of this page shows how.

Each step adds value.

Where to begin

If you are unsure which area to start with, the process section below explains how a short assessment decides it, and what you receive at the end of each step.

Benefits

One connected data stack means less time on reports and more on decisions

This is what changes when the services are delivered as one connected stack, so leaders make informed decisions faster. Each outcome supports a specific decision your leadership team makes every month.

Before: reports take days and nobody agrees on the numbers. After: one governed source, refreshed automatically, that every leader uses.

Month-end reporting moves from manual to automatedIllustrative bar chart: manual month-end hours fall from 64 in month 1 to 9 in month 6.Month-end reporting moves from manual to automatedManual hours per month-end close, months 1 to 6 of engagementDemo data60h40h20h64hMonth 158hMonth 241hMonth 326hMonth 414hMonth 59hMonth 6Illustrative exampleMonth-end reporting moves from manual to automatedMobile view. Illustrative bar chart: manual month-end hours fall from 64 in month 1 to 9 in month 6 (demo data).Month-end reporting movesfrom manual to automatedManual hours per month-end close,months 1 to 6 of engagementDemo data20h40h60h64hM158hM241hM326hM414hM59hM6M = month of engagementIllustrative example

Each benefit supports one decision, and each is measured after launch.

Results are reviewed with you.

  • One version of the numbers

    Shared KPI definitions end debates about whose figures are right. Meetings move on.

  • Faster reporting

    Automated data flows replace manual exports, so finance closes and reports sooner. Fewer manual steps.

  • Clear drivers behind results

    Drill-down shows why a number moved, not only what it is. Causes become visible.

  • Records you can trust

    Governance and cleansing fix duplicates and gaps at the source. Fewer errors reach reports.

  • Hours back every week

    Process and AI automation remove recurring, low-value tasks. Teams refocus.

  • Skills without hiring

    A fractional data team adds capacity that scales up or down. Capacity flexes.

Services

Start with the service that fixes your most urgent data problem

Each service solves one problem and connects to the others as your data stack matures. An engagement can combine two or three. Pick the card that matches the decision you cannot make today.

How the services fit together: integration brings the data into one place. Governance keeps it accurate. Business intelligence makes it visible. Automation removes the manual steps around it. Decision intelligence ties it to actions. Strategy decides the order. You do not need all six at once. You need the right first one, and a plan for the rest.

A simple rule helps you choose. If your problem is that data is missing or scattered, start with integration. If it is that numbers conflict, start with governance. If it is that leaders cannot see performance, start with business intelligence. If it is that people waste hours on manual steps, start with automation. If it is unclear where to begin at all, start with strategy.

We help you choose in one call. A common starting point is one service, adding others later.

Data strategyBusiness intelligenceData integrationData governanceBusiness process automationDecision intelligence
Service finder

Which data analytics service should you start with?

2 questions. We suggest one service. Nothing is sent anywhere.

Illustrative
1. Which statement sounds most like your team today?
2. What would you most like to have first?
0 of 2 answered
How we work

We advise first, train where needed and build only when you are ready

Every engagement follows the same order, with a review after each step. Depth varies with scope, but the order does not. We advise first, train where your team needs new skills, and propose implementation only when the scope is agreed and your organization is ready to adopt the result.

Advice first, implementation when you are ready

Select a stage, use Next or the arrow keys

Advise. A clear view of what works. Advise and assess: interviews, a map of systems and data flows, and a review of current reports. We confirm that we can genuinely help. Owners are named early.

Step 1 of 5

You see a defined deliverable at the end of every step. Nothing is built until leadership signs off on the KPIs and the plan, and until the fit and readiness check confirms that a build will pay back. If it shows that implementation is premature, or that another provider would serve you better, we say so and continue with advisory or training.

Why this order? Because starting with tools is how data projects go wrong. A dashboard built before KPIs are agreed shows the same disagreements faster. Integration built without governance moves bad data faster. Our sequence puts decisions first, skills second and tools third, so each step builds on solid ground. We measure our work by the results you achieve, not by the size of the engagement.

After the first project, a named team stays accountable through adoption, and you can keep us on as a fractional data team so the work keeps improving instead of decaying.

Each step ends with a review meeting where you decide whether to continue, adjust or stop. You stay in control of scope and spend at every point.

Next step: a consultation maps your current state in one conversation and shows which step comes first. As a guide to time to value (typical, depends on scope): an AI and data readiness assessment runs two to three weeks, and the first release follows on the phased plan agreed in the design step.

Use cases

Each leader's first business question decides where the work starts

Data volume matters less than the question. Whether you run a few systems or many sources, we start from the decision each leader must make.

GoalProfitable, predictable growth
  1. CFO: protect marginReview first
    • Gross margin
    • Budget variance
    • Cash forecast vs actual
  2. COO: fix bottlenecks
    • Cycle time
    • On-time delivery
    • Manual handoffs per process
  3. CMO: fund channels that pay
    • Marketing-sourced pipeline
    • Cost per acquisition
    • Return by channel
  4. RevOps: trust the forecast
    • Pipeline coverage
    • Win rate
    • Forecast accuracy
Goal, drivers, metrics. The highlighted driver is the one to review first.
  1. CFO

    ResultDecide where to protect margin

    What we build

    Financial dashboards, budget variance analysis, cash forecasting and automated monthly reporting on ERP data. Margin leaks get found.

  2. COO

    ResultDecide where to fix bottlenecks

    What we build

    Operational dashboards on ERP and service data, plus automation that removes manual handoffs. Delays get traced.

  3. CMO

    ResultDecide which channels get budget

    What we build

    Marketing analytics linking GA4, the CRM and finance, with tracking that captures conversions consistently. Budget follows revenue.

  4. RevOps

    ResultDecide which deals to trust

    What we build

    CRM data cleansing, governed pipeline definitions and revenue dashboards in HubSpot, Salesforce or Power BI. Forecasts get easier to trust.

These are typical questions by role, and what answers them.

Each question links to one decision and one measurable outcome. That is how we scope work: start from the decision, then build only the data, dashboards and automation needed to support it. Nothing is built for its own sake.

Your leadership team will recognize these questions. The aim is to answer them every week, without a special report.

Each answer becomes a dashboard tile.

Your questions may differ.

Sample dashboards

Sample dashboards on demo data show our work before you commit

Instead of logos and testimonials, we show our work. Each sample dashboard runs on demo data and lists the KPIs it tracks. In discovery we walk through them with your team, then adapt them to your systems. You judge the quality of the work before any project starts.

Sample dashboards on demo data, one decision each

Demo data
Revenue vs target$24.1M▼ -3%
Gross margin42.5%▲ +0.6 pts
Cash$6.8M▲ +$0.5M
Pipeline coverage2.1x▼ -0.6x

Pipeline covers only 2.1x of next quarter's target

Pipeline coverage of next quarter's target, last 6 months

1.5x2x2.5x3x3.5xJanFebMarAprMayJunCoverageTarget2.1x in Jun: Coverage below the 3x target1.5x2x2.5x3x3.5xJanMarMay2.1x in Jun: Coverage below the 3x target
CoverageTarget

So what: Revenue is 3% behind target this quarter, and next quarter needs more pipeline now.

MeasureActualTargetStatus
Revenue$24.1M$24.8MBehind
Gross margin42.5%42.0%On track
Cash$6.8M$6.0MAhead
Pipeline coverage2.1x3.0xBehind
View 1

Decide if the quarter is on track

Executive overview: revenue, gross margin, cash and pipeline against target.

View 2

Decide where costs need attention

Financial performance: budget vs actual, operating expenses and profitability by segment.

View 3

Decide which forecast to trust

Revenue and pipeline: coverage, win rate, deal velocity and forecast accuracy.

Every chart title states its insight. Each view pairs headline KPIs with one annotated chart and a detail table.

Ask to see any of them.

A 30-minute call shows which service to start with

Not sure which service fits first? Book a consultation with a data analytics lead. We review your systems, reports and priorities. Then we recommend whether to start with strategy, business intelligence, integration, governance or automation.

You leave with a clear first step and the engagement model that fits it: a project, advisory, a fractional data team, training or a readiness assessment. There is no obligation to continue, and if implementation is premature, we say so.

Bring one report you do not trust. We will look at why.

No preparation is needed.

Calls run virtually.

See how we work
Frequently asked questions

Data analytics services: questions buyers ask first

Data analytics services help a company collect, connect, clean and analyze its data so leaders can make better decisions. They usually include data strategy, business intelligence and dashboards, data integration and warehousing, data governance and process automation. We deliver them as one connected stack rather than separate projects.

Business intelligence focuses on reports and dashboards that show what is happening. Data analytics is broader. It includes the strategy, integration, governance and analysis that make those dashboards reliable and explain why results change. Business intelligence is one part of what we deliver.

An in-house team makes sense when you need full-time capacity across many skills. Hiring a data engineer, BI developer and analyst at once is a large commitment for a mid-market company. A fractional or embedded data team gives you those skills on demand, and can help you hire and hand over later.

Yes. We work with the systems you already run, including Power BI, HubSpot, Salesforce, GA4, NetSuite, Odoo, Zoho, QuickBooks and Airtable. We usually improve integration, data quality and reporting on your existing tools before recommending any new software or licenses.

Cost depends on scope, data sources and the engagement model: project, advisory, fractional team, training or readiness assessment. We quote after a discovery call. On AI, we deliver AI automation and AI and data readiness work built on governed data, so AI tools draw on numbers your team already trusts.

Related resources

Read how data strategy and decision intelligence connect analytics to the choices your leadership team makes every quarter, and where your team should begin.