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Data Analytics Services

Data Analytics Services - from raw data to decisions.

We build data pipelines, dashboards, data warehouses, and predictive models - the full modern data stack that turns scattered data across your CRM, database, and SaaS tools into KPI dashboards your team actually uses.

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Trusted by businesses worldwide
$83.79BGlobal analytics market 2026
79%of CIOs increasing analytics budget
4 typesDescriptive · Diagnostic · Predictive · Prescriptive
50% TCOSavings: cloud warehouse vs on-premise
Overview

Data that moves from silos to decisions.

Most businesses collect data. Few use it well. The gap shows up when finance runs a number in Excel that contradicts the CRM, when marketing reports a CAC that differs from what the CEO sees, or when the growth team waits 3 days for an analyst to build a retention report. That's a data infrastructure problem, not a people problem.

We build the modern data stack that eliminates these gaps: pipelines that move data from every source (Stripe, Salesforce, PostgreSQL, Google Analytics, Shopify) into a centralised warehouse (Snowflake, BigQuery, or Redshift), transformed with dbt into clean, tested models, and served to BI dashboards in Power BI, Looker, or Tableau that every team can use without writing SQL.

We also build predictive analytics - churn models, demand forecasts, customer lifetime value predictions, and anomaly detection - on top of the data foundation, giving your team forward-looking signals rather than just historical reports.

01
Data engineering
Pipelines, ETL/ELT, warehouses, and data quality testing.
02
BI dashboards
Power BI, Looker, and Tableau dashboards with governed KPIs.
03
Predictive analytics
Churn prediction, forecasting, and segmentation models.
04
Data strategy
Architecture design, tool selection, and governance frameworks.

4 types of analytics we deliver.

Each answers a different question - we build all four, starting from the foundation.

Foundation · Start Here

Descriptive Analytics

Answers: what happened? It summarises historical data into metrics, trends, and KPI dashboards - revenue by channel, churn over time, top products by margin. This is the foundation every other type builds on: without clean descriptive analytics, predictive models have nothing reliable to train on. We build this layer first - warehouse, ETL, dbt models, and BI dashboards - before advancing to predictive work.

KPI dashboardsdbt modelsSnowflake / BigQueryPower BI / LookerData quality tests
Root Cause Analysis

Diagnostic Analytics

Answers: why did it happen? When revenue drops in a region, diagnostic analytics finds root causes through drill-down, cohort comparisons, and correlation analysis. It requires clean, granular data, quality gaps produce misleading conclusions. We design data models with diagnostic use cases in mind: event-level granularity, proper date dimensions, and fact-dimension schemas that support any drill-down a business analyst needs.

Drill-down analysisCohort analysisCorrelation analysisFunnel analysisSegment comparison
Forward-Looking

Predictive Analytics

Answers: what will happen? Predictive models use machine learning trained on historical data to forecast outcomes - churn probability, next-month revenue, demand by SKU, credit risk. Accuracy depends directly on data quality: a model trained on inconsistent data predicts poorly regardless of algorithm. We build these after a clean foundation exists, with proper train/test splits, cross-validation, and FastAPI deployment with scheduled retraining.

Churn predictionDemand forecastingLTV modellingAnomaly detectionXGBoost / scikit-learn
Predictive Analytics
Decision Support

Prescriptive Analytics

Answers: what should we do? It takes prediction further - using optimisation engines, recommendation systems, and decision frameworks to recommend specific actions. Examples: dynamic pricing based on demand elasticity, marketing budget allocation to maximise ROAS, and next-best-action models for sales teams. Prescriptive systems require both a clean foundation and reliable predictive models feeding the recommendation engine.

Dynamic pricingBudget optimisationRecommendation enginesNext-best-actionSimulation models

Data, specialised.

Four deeper services under this area, each covering a different layer of the reporting stack.

01

Dashboard Development

Working dashboards connected to your real databases, APIs, and third-party tools, with live updates and role-based access.

02

Business Intelligence Solutions

A data warehouse and semantic layer so every dashboard pulls the same metric definition, and teams stop arguing about whose number is right.

03

Data Visualization

Custom D3.js charts, maps, and interactive visuals matched to the actual relationship in your data, colorblind-safe by default.

04

Data Reporting

Automated reports generated and delivered on schedule to email, Slack, or a client portal, with no manual assembly.

9 data analytics services we deliver.

Every layer of the modern data stack, from ingestion to insight.

Data engineering & pipeline development

ELT pipelines from every source - Stripe, Salesforce, HubSpot, PostgreSQL, Google Analytics, Shopify - into your warehouse via Fivetran or Airbyte, orchestrated with Apache Airflow and transformed in dbt.

Data warehouse setup & modelling

Snowflake, BigQuery, or Redshift setup with Medallion Architecture (Bronze/Silver/Gold), Star Schema or Data Vault modelling, and a semantic layer so every KPI has one consistent definition across teams.

BI dashboard development

Interactive dashboards in Power BI, Looker Studio, or Tableau - revenue, product, marketing, and operational KPIs. Self-service setup so business users slice data without waiting for an analyst to write SQL.

Predictive analytics & ML models

Churn prediction, demand forecasting, LTV modelling, lead scoring, and anomaly detection - trained on your warehouse data, evaluated on held-out test sets, and deployed as APIs for operational use.

Real-time & streaming analytics

Event-driven pipelines with Apache Kafka or AWS Kinesis for real-time dashboards, fraud detection, IoT sensor analytics, and live operational monitoring, where batch ETL latency is too slow.

Data governance & quality

dbt tests on every model (not null, unique, referential integrity), data lineage tracking, column-level security, GDPR and CCPA data classification, and a data catalogue with metadata documentation.

dbt modelling & transformation

Full dbt project setup - staging, intermediate, and mart layers - with version-controlled SQL models, automated testing, documentation generation, and incremental materialisation that keeps query costs low.

Analytics reporting & KPI strategy

Define the 8–12 metrics that actually drive your business, standardise KPI definitions across finance, marketing, and product, and build executive reporting that gives leadership decision-quality views without manual compilation.

Data strategy & stack audit

A technical audit of your existing data infrastructure - pipeline reliability, model quality, dashboard usage, cost efficiency - followed by a prioritised roadmap to the architecture your business actually needs, not the most expensive one.

The modern data stack - layer by layer.

5 layers that every production analytics system runs on, and what we put in each one.

01 · Ingestion

ELT connectors pull data from source systems into the warehouse on scheduled cadences - hourly, daily, or real-time via CDC (Change Data Capture). 200+ pre-built connectors cover every SaaS tool, database, and API without custom code.

FivetranAirbyteStitchAWS GlueKafkaKinesis
02 · Storage

Cloud data warehouses and lakehouses store and compute data at scale - separating storage from compute so you only pay for queries you run, not idle capacity. The right choice between Snowflake, BigQuery, and Databricks depends on your cloud, data volume, and whether you need ML workloads alongside BI.

SnowflakeGoogle BigQueryAmazon RedshiftDatabricksDelta Lake
03 · Transform

dbt transforms raw data into clean, tested, documented models using version-controlled SQL, treating data transformation with the same engineering standards as application code. dbt runs tests on every model before results reach dashboards, preventing bad data from reaching decisions.

dbt Core / CloudMedallion ArchitectureStar SchemaData VaultApache Spark
04 · Semantic Layer

The semantic layer defines metrics once - churn, MRR, CAC, ROAS - so every dashboard uses the same calculation. Without it, finance calculates MRR one way, marketing another, and leadership sees three different numbers. dbt Semantic Layer, Cube, or LookML defines the single source of truth for every KPI.

dbt Semantic LayerCubeLookMLMetricFlowAtScale
05 · BI & Visualisation

BI tools serve dashboards and reports to non-technical stakeholders without requiring SQL. Self-service analytics lets product managers, marketers, and executives build their own views from governed data. The right tool depends on your team's technical maturity and ecosystem alignment.

Power BILookerTableauLooker StudioMetabaseRedash

Proof, not promises.

A complete platform rebuild with automated data pipelines replacing manual catalogue management.

All Case Studies
  • Data Engineering
  • Automated Pipelines
  • FastAPI
  • Multi-vendor Marketplace
Client
BeesApp
Industry
Retail Tech
Location
Saudi Arabia
Timeline
6 Months

40%

Lower Server Costs

BeesApp managed a multi-vendor marketplace in Saudi Arabia with entirely manual catalogue and inventory management, an operational bottleneck that didn't scale. We rebuilt the platform with automated inventory sync pipelines, real-time product data processing across vendors, and an admin analytics dashboard giving the operations team live visibility into orders, inventory, and vendor performance. Server costs dropped 40%, uptime reached 99.9%, and manual data entry was eliminated from the core workflow.

40%

Lower server costs

99.9%

Platform uptime

74%

Faster data load

BeesApp platform rebuild
Read Full Case Study
Why Build With Hoop

Data built for decisions, not reports.

Most data projects produce dashboards that look good in demos and get ignored within 30 days. We build data systems around the 8–12 decisions your team makes weekly, not every metric your platform can surface.

  • 01

    Data quality before dashboards

    Every dbt model ships with automated tests - not null, unique key, referential integrity, accepted values. Bad data reaching a dashboard is worse than no dashboard. We run quality gates before every model promotion to production.

  • 02

    KPI definitions locked before development

    MRR, CAC, churn, ROAS, we define each metric precisely with business stakeholders before writing SQL. One definition, one calculation, one number across every dashboard. No more "which MRR is right?"

  • 03

    Engineering standards applied to data

    dbt projects with version control, code review, automated testing, and documentation, the same practices applied to application code. Data models don't break silently when someone changes an upstream table.

  • 04

    Data and product under one roof

    Our software team builds the product; our data team builds the analytics layer. No coordination overhead between separate agencies, no schema mismatches, no data-engineering gaps that block product insights.

How we build your data stack.

A 5-phase process from data audit to live dashboards your team trusts.

01

Data audit & KPI definition

Map all data sources, audit quality and completeness, define the 8–12 KPIs that drive decisions, and choose the right warehouse and BI tool for your team.

KPIs set first
02

Pipeline & warehouse build

ELT connectors, warehouse setup, Medallion Architecture staging layers, and dbt transformation models with automated quality tests at every layer.

Quality tested daily
03

Dashboard development

Power BI, Looker, or Tableau dashboards built on dbt marts - interactive, self-service, and built around how each team actually makes decisions.

Team-specific views
04

Predictive models

Churn, LTV, and forecasting models trained on your warehouse data - with proper train/test evaluation, production deployment, and scheduled retraining.

Evaluated before production
05

Governance & handover

Data catalogue, lineage documentation, access controls, dbt model docs, and team training so your analysts own the system after handover, not just the agency.

Your team owns it
Our Stack

Tools we build data systems with.

Every ingestion, warehouse, transformation, and visualisation tool in our production data stack.

Ingestion / ELT
FivetranAirbyteStitchAWS GlueApache AirflowPrefect
Warehouses
SnowflakeGoogle BigQueryAmazon RedshiftDatabricksPostgreSQLDuckDB
Transformation
dbt Core & CloudApache SparkGoogle DataformSQLPython (Pandas)
BI & Visualisation
Power BILooker / Looker StudioTableauMetabaseSupersetRedash
Streaming & Real-time
Apache KafkaAWS KinesisGoogle Pub/SubClickHouseFlink
ML & Predictive
Python (scikit-learn)XGBoostPyTorchMLflowWeights & BiasesFastAPI

Ways to work with us.

4 engagement structures that fit your data maturity and project scope.

Data foundation build

Warehouse setup, ELT pipelines, dbt models, and BI dashboards, a complete data foundation from scratch for teams starting their analytics journey.

Best for data-stage 0 teams

Data stack modernisation

Migrate from legacy warehouses, spreadsheet-driven reporting, or fragmented BI tools to a modern, governed, cloud-native stack.

Best for legacy infrastructure

Dashboard & BI build

Connect existing data sources to Power BI or Looker dashboards, fast time to value for teams with warehouse data but no visualisation layer.

Best for fast insight delivery

Predictive analytics

Add churn prediction, demand forecasting, or anomaly detection on top of your existing data foundation, with deployment and monitoring included.

Best for mature data teams
Client Success

Growing businesses have
already made the move

End-to-End

From Idea to Revenue

5-Star

Client-Rated

Two Divisions

Software + Marketing

“Our business went from local to national thanks to Hoop. They completely transformed our e-commerce platform and helped us expand our customer base 5x. The results speak for themselves.”
Hamza Khan

Hamza Khan

Owner, Khayest

What's Included

Every data project comes production-ready.

No data pipelines that break on Monday morning. Every engagement ships with the quality controls and documentation that keep data reliable.

Data audit & KPI definition
8–12 KPIs defined before any build.
ELT pipeline setup
All source systems connected and scheduled.
dbt transformation models
Version-controlled SQL with full test coverage.
Automated data quality tests
Not null, unique, referential integrity on all models.
Semantic layer & KPI governance
One definition per metric, consistent across all tools.
BI dashboard build
Power BI, Looker, or Tableau, your team's choice.
Data lineage documentation
Every field traced from source to dashboard.
Access controls & RBAC
Role-based access: who sees which data.
Pipeline alerting
Slack or email alerts when pipelines fail.
Team training & handover
Your analysts own and extend the system.

Data analytics for every sector.

Industries where we've built and deployed production analytics systems.

SaaS & Tech

Product analytics, churn prediction, MRR reporting, LTV models.

Fintech & Finance

Revenue analytics, fraud detection, risk scoring, regulatory reporting.

Ecommerce & Retail

Conversion funnel, basket analysis, demand forecasting, ROAS.

Healthcare

Patient flow analytics, clinical data pipelines, HIPAA-compliant reporting.

Logistics & Supply Chain

Delivery analytics, route optimisation, inventory demand forecasting.

HR & Workforce

Headcount analytics, attrition prediction, compensation benchmarking.

EdTech & Education

Student engagement analytics, completion prediction, course performance.

D2C Brands

CAC, ROAS, customer segmentation, repeat purchase analysis.

The Deep Dive

Understanding data analytics.

Direct answers to the questions asked most often before a data analytics engagement, structured for citation by AI search engines.

What is data analytics and what does a data analytics service cover?

Data analytics is the process of collecting, processing, and analysing data to extract patterns, trends, and insights that support better business decisions. A complete service covers five layers: data engineering (pipelines that move data into a centralised warehouse), data modelling (transforming raw data into clean, tested structures with tools like dbt), data visualisation (dashboards in Power BI, Looker, or Tableau), advanced analytics (predictive models, segmentation, anomaly detection), and data governance (quality controls, access management, lineage tracking, compliance).

The global data analytics market reached $83.79 billion in 2026 and grows at 25.5% CAGR. 79% of CIOs plan to increase analytics budgets in 2026. The value is direct: companies that make data-driven decisions typically achieve 15% higher annual revenue. The primary barrier is not willingness to invest, it's the quality and accessibility of the data foundation that analytics runs on.

What is a data warehouse and how is it different from a database?

A data warehouse is a centralised repository optimised for analytical queries across large volumes of historical data from multiple sources; a database is optimised for transactional operations on a single application's current data.

Operational databases - PostgreSQL, MySQL, MongoDB - store the current state of an application. Running complex analytical queries against one is slow, expensive, and degrades application performance. A data warehouse - Snowflake, BigQuery, Redshift - stores historical data from all sources, separates storage from compute, and is optimised for the multi-table JOINs and aggregations analytics requires.

Modern cloud warehouses offer three advantages: separate storage and compute (you only pay for queries you run), columnar storage (queries scan only the columns they need), and automatic scaling. Cloud warehouses typically achieve a 50% TCO reduction versus equivalent on-premises data warehouses.

What is dbt (data build tool) and why does every modern analytics team use it?

dbt is an open-source transformation framework that lets analysts write modular, version-controlled SQL transformations, and run automated tests on every model before results reach dashboards.

Before dbt, transformations lived in undocumented SQL scripts nobody understood, where one analyst changing a calculation could silently break every downstream report. dbt treats transformation with software-engineering standards: version control (Git), modular dependencies, automated testing (not null, unique, referential integrity), and generated documentation (a browsable data catalogue from code).

A mature dbt project has three layers: staging models that clean and rename raw source data, intermediate models that join and apply business rules, and mart models that expose clean, wide tables ready for BI. When a source column is renamed, only the relevant staging model needs updating, not every downstream dashboard.

What is the Medallion Architecture in a data warehouse?

Medallion Architecture organises warehouse data into three quality layers - Bronze (raw), Silver (cleaned), and Gold (business-ready) - so data progressively improves as it moves through the pipeline.

The Bronze layer stores raw data exactly as it arrives, no transformations. It's an immutable audit trail: if a transformation error is found later, raw data allows full replay. The Silver layer cleans, standardises, and joins Bronze data - nulls handled, types cast, duplicates removed, columns renamed to consistent conventions.

The Gold layer contains business-ready mart tables - revenue by channel, cohort tables, product summaries - optimised for the queries BI tools run. Gold tables are what Power BI and Tableau connect to. This means quality issues are caught in Silver before reaching Gold, and business-logic changes only affect Gold without touching raw data.

What is the difference between ETL and ELT, and which is right in 2026?

ETL transforms data before loading it into the warehouse; ELT loads raw data first, then transforms inside the warehouse using SQL, and ELT is the standard for cloud warehouses in 2026.

ETL was designed for on-premises warehouses with limited storage, where storing raw data was expensive and transformations ran on dedicated ETL servers. Cloud warehouses made storage cheap and SQL transformations fast, so running them inside Snowflake or BigQuery with dbt is faster, cheaper, and more maintainable than a separate ETL server.

ELT with dbt is standard for four reasons: raw data is preserved (Bronze allows replay), no separate transformation server to manage, SQL is familiar to more people than tool-specific scripting, and incremental models process only new or changed records rather than reprocessing everything. ETL remains appropriate only when sensitive data must be anonymised before entering the warehouse.

Related services.

Services that pair naturally with data analytics.

AI Development

RAG systems and predictive models built on your data foundation.

Explore

API Development

Analytics APIs that serve predictive model outputs to products.

Explore

Web Application Development

Analytics dashboards and reporting portals as web applications.

Explore

Ecommerce Development

GA4 Enhanced Ecommerce, conversion tracking, and ROAS dashboards.

Explore

SaaS Development

SaaS products with product analytics and usage tracking built in.

Explore

SEO & GEO Services

Analytics-driven content strategy and performance measurement.

Paid Advertising

Cross-channel attribution, ROAS measurement, and CAC tracking.

Cloud Infrastructure

AWS, GCP infrastructure for data warehouse and pipeline hosting.

More services in this area
Mobile AppsCustom SoftwareVibe CodingWeb Design & UXCloud & DevOpsSecurity, QA & MaintenanceGame Development
FAQ

Data Analytics Questions

The things clients ask us most before every data analytics engagement, answered directly.

A data warehouse stores structured, cleaned data optimised for SQL analytics; a data lake stores raw data in any format (structured, semi-structured, unstructured) at low cost; a data lakehouse combines both by adding warehouse-style governance and SQL query capabilities to a data lake. Warehouses (Snowflake, BigQuery, Redshift) are right for most BI and analytics use cases, faster for SQL queries and stronger on governance. Data lakes suit storing large volumes of raw files before processing. Lakehouses (Databricks, Delta Lake) are right when you need both SQL analytics and machine learning workloads on the same data without managing two separate systems.

A focused data foundation - warehouse setup, 3–5 ELT pipelines, dbt models, and a core BI dashboard - takes 6–10 weeks. A full modern data stack with multiple sources, a complete dbt project, semantic layer, and multi-team dashboards takes 12–20 weeks. Adding predictive analytics models extends the timeline by 4–8 weeks. The most common schedule risk is data quality, poorly structured source data multiplies transformation time by 2–3×. We run a data audit in week one that surfaces quality issues before they affect the build timeline.

Snowflake is right for multi-cloud organisations and teams that need flexible data sharing across business units. BigQuery is right for teams standardised on Google Cloud - serverless, with zero cluster management and tight ML integration. Amazon Redshift is right for teams heavily invested in AWS. Databricks is the right choice when significant ML workloads run alongside analytics and you need a unified lakehouse. Cost varies by query pattern: Snowflake and Redshift use compute clusters (cost scales with time), BigQuery uses serverless per-query pricing (cost scales with data scanned). We recommend based on your existing cloud commitment and query patterns, not a vendor preference.

A KPI (Key Performance Indicator) is a quantitative metric that measures performance against a specific business objective - revenue growth rate, customer churn rate, gross margin, CAC:LTV ratio. Most businesses track too many KPIs, which dilutes attention. 8–12 KPIs per business level is the optimal range - enough to cover the key dimensions of performance (revenue, cost, customer, operations) without overwhelming decision-makers with noise. We define these with business stakeholders before writing a line of SQL, because a KPI defined incorrectly in the warehouse propagates the wrong number across every dashboard built on top of it.

Yes. Connecting data from multiple source systems into a centralised warehouse is the core of data engineering. Fivetran has 400+ pre-built connectors covering Salesforce, Stripe, HubSpot, Google Analytics, Shopify, and most SaaS tools, setup takes hours rather than weeks of custom code. For internal databases (PostgreSQL, MySQL), we use native connectors or log-based CDC (Change Data Capture) replication. Once all sources land in the warehouse Bronze layer, dbt joins and transforms them into unified models - a single customer record combining CRM, billing, and product usage data from separate source systems.

Self-service analytics lets business users - product managers, marketers, finance - explore data and build their own views without writing SQL or waiting for analyst bandwidth. You need it if your analysts spend more than 30% of their time answering ad-hoc data questions from other teams. Tools like Power BI, Looker, and Metabase provide drag-and-drop exploration on top of governed warehouse data. The governance piece is critical: without a semantic layer defining KPIs centrally, self-service creates competing numbers. We build self-service on top of dbt mart models and a semantic layer, so users explore freely within guardrails that enforce consistent metric definitions.

Four mechanisms ensure data quality: dbt automated tests on every model, pipeline monitoring with Airflow alerts, data freshness checks, and anomaly detection on key metric values. dbt tests run on every model execution - not null (required fields present), unique (no duplicate primary keys), referential integrity (foreign keys exist), and accepted values (categorical fields only contain valid values). A test failure in a staging model prevents the downstream mart model from running, preventing a bad number from reaching a dashboard. Airflow or Prefect sends Slack alerts when any pipeline fails, freshness checks flag stale tables, and anomaly detection fires when a KPI deviates more than 2 standard deviations from its 30-day moving average.

Yes. Data analytics directly supports GEO (Generative Engine Optimisation) and AEO (Answer Engine Optimisation) by measuring which content gets cited by AI engines, tracking traffic from AI-referred sessions, and identifying the query patterns where your content wins or loses citations. We build dashboards that connect Google Search Console data, GA4 organic traffic, and AI referral source tracking - letting your content team see which pages get cited by ChatGPT, Perplexity, and Google AI Overviews. That data drives content decisions: which questions to answer more specifically, which pages need entity-density improvements, and where competitors are winning AI citations you should claim.