Do we need a data warehouse?

Once you have more than two or three sources, or need history, usually yes. Below that, a well-built model directly on the sources can be enough and we will say so.

Who owns the data platform?

You do. It is built in your cloud account, with your credentials, and fully documented.

How do you keep the numbers right?

Automated data quality tests run on every refresh and alert before bad data reaches a dashboard. Key figures are reconciled against finance during the build.

Power BI is usually the better value for Microsoft-centric organisations; Tableau suits complex visual analysis and existing Tableau estates. We work in both.

Do we need a data warehouse?

Once you have more than two or three data sources or need reliable history, usually yes. Below that, a well-modelled dataset directly on the sources can be enough.

Data and Analytics

Name: Texas Solutions
Address: 1201 Fannin Street, Suite 260, Houston, TX 77002, Houston, TX, US
Telephone: (838) 910-3147
Price range: $$-$$$$

One version of the numbers, trusted by everyone who reads them.

Get a Rough EstimateTalk to a specialist (838) 910-3147

Starting price · United States

$10kproject

BI dashboard set

First dashboards: 3-6 wks
Metric definitions: One each
Platform: Your cloud

Dashboard set 3-6 weeks · data warehouse 2-4 months · predictive model 2-3 months

The problem

Three reports, three different revenue numbers

Every department exports to spreadsheets, defines metrics its own way and argues about whose number is right. Monthly reporting takes a week and is out of date when it lands.

Metrics defined differently by each team
Manual exports and spreadsheet stitching
Dashboards nobody trusts or opens
No history, so trends cannot be seen

What we do about it

Model it once, use it everywhere

We build pipelines from each source into a warehouse, define every metric in one semantic layer, and publish dashboards on top. Refresh is automatic and every number can be traced to its source.

Automated pipelines from every source system
One documented definition per metric
Dashboards designed around decisions, not data
Data quality checks that alert before bad numbers ship

What is inside

6 ways we run data and analytics

Each one has its own page with what is delivered and what it targets in search.

Data engineering

Reliable pipelines from every source system.

Business intelligence

Dashboards built around the decisions they support.

Power BI and Tableau

Implementation, redesign and performance tuning.

Data warehousing

A modern warehouse on Snowflake, BigQuery or Fabric.

Predictive analytics

Forecasts for demand, churn, cash and capacity.

Big data services

Pipelines and storage for data at serious volume.

Always included

What comes with every engagement

Not an upsell list. These are part of the price, on every package.

Source system inventory and access plan

One documented definition per metric

Automated refresh, no manual exports

Data quality tests with alerts

Row-level security where needed

Dashboards designed with the people who use them

Training for report owners

Full documentation of the model

Warehouse cost monitoring

Everything in your own cloud account

Packages

Three ways to start

Prices shown for United States in USD. Change your region in the header to see another market.

Starter

A trusted dashboard set.

$25k

one-time project

Up to three data sources
Five to eight dashboards
Metric definitions
Automated refresh
Owner training

Estimate this package

Most chosen

Growth

A warehouse and BI layer.

from $50k

one-time project

Cloud data warehouse
Up to ten sources
Semantic layer
Data quality tests
Departmental dashboards
Three months of support

Estimate this package

Pro

Warehouse plus predictive models.

$150k

one-time project

Full data platform
Predictive models
Streaming where needed
Governance and lineage
Managed analytics team

Estimate this package

Full price table for United States

Projects are fixed-price per phase. Managed analytics runs monthly.

🇺🇸 United States · USD

Data and analytics pricing for United States
Deliverable	USD range	Billed
BI dashboard setUp to three sources, five to eight dashboards	$10,000 – $40,000	project
Data engineering pipelinesAutomated ingestion with quality tests	$25,000 – $120,000	project
Cloud data warehouseDesign, build and semantic layer	$50,000 – $250,000	project
Predictive analytics modelDemand, churn or cash forecasting	$40,000 – $150,000	project
Managed analyticsPipelines, dashboards and requests	$3,000 – $12,000	per month

US figures are benchmarked; UK, Canada, Australia, Europe, the Gulf and Asia are scaled from them and are indicative until confirmed on a call. BI and warehouse licences are billed separately by the vendor.

Try it yourself

Data project ballpark

Move the inputs and the numbers move with them. Nothing is submitted until you choose to.

BI dashboard set · United States

$10k – $40k

project

Typical timelines: Dashboard set 3-6 weeks · data warehouse 2-4 months · predictive model 2-3 months

Ballpark only. Scope, integrations, data readiness and compliance move the figure, and the full calculator asks about each of them.

Get a detailed estimate

Process

How the work actually runs

1Weeks 1-2
Audit
Sources, existing reports and metric definitions reviewed. Decisions the data must support are listed.
2Weeks 3-8
Model
Pipelines and warehouse built, metrics defined once in a semantic layer.
3Weeks 6-10
Publish
Dashboards built with their users, tested against known figures, rolled out with training.
4Ongoing
Extend
New sources, forecasts and analysis added as the questions evolve.

Tools and platforms we work in

Power BITableauLookerSnowflakeBigQueryMicrosoft FabricdbtAirflowFivetranDatabricksPythonSQLPower BITableauLookerSnowflakeBigQueryMicrosoft FabricdbtAirflowFivetranDatabricksPythonSQL

Case study

Sample profile: multi-site retailer, 40 stores

Finance, merchandising and store operations each reported revenue differently, and month-end reporting took six working days.

What we did

Built pipelines from POS, ERP and e-commerce into a warehouse
Agreed one definition for each of 24 core metrics
Published role-based Power BI dashboards
Added a weekly demand forecast per store

Month-end reporting

6 days4 hours

-97%

Competing revenue figures

Unified

Forecast error

24%9%

-15pts

Weekly dashboard users

12140

+1,067%

Sample engagement profile showing the kind of result this service targets. It is replaced with a client-approved case study, with names and logos, before launch.

Business intelligence and data engineering explained

What is the difference between BI, data engineering and data science?

Data engineering moves and cleans data from source systems into a warehouse. Business intelligence turns that data into dashboards and reports that people use to make decisions. Data science and predictive analytics use statistical and machine learning models to forecast what happens next. Most companies need them in that order.

How much does a Power BI or data warehouse project cost?

A set of five to eight Power BI or Tableau dashboards on up to three data sources typically costs US$10,000 to US$40,000. A cloud data warehouse with a semantic layer runs US$50,000 to US$250,000. Licences are billed by the vendor.