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.

Data and Analytics

Name: Texas Solutions
Address: 401 W Kentucky Ave, Midland, TX, 79701, US
Telephone: (838) 910-3147
Price range: $$

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

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

Starting price · US dollars

$20per hour

Data analytics / dashboards

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 Monthly reporting takes a week and is out of date when it lands.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 explaining what is delivered and how it is priced.

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

All prices in US dollars. Project ranges are confirmed in a written proposal after scoping; monthly plans are capacity allocations, not unlimited work.

Starter

A trusted dashboard set.

$4,000 – $15,000

USD one-time project · based on: BI dashboard set

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.

$20,000 – $100,000

USD one-time project · based on: Cloud data warehouse

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.

$15,000 – $60,000

USD one-time project · based on: Predictive analytics model

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

Estimate this package

Full price table

Projects are fixed-price per phase. Ongoing work is hourly or a monthly dashboard support plan.

All prices in USD

Data and analytics pricing in US dollars
Item and scope	Price (USD)	Billed
Fixed-scope projects (one-time, confirmed in a written proposal)
BI dashboard setUp to three sources, five to eight dashboards	$4,000 – $15,000project	project
Data engineering pipelinesAutomated ingestion with quality tests	$10,000 – $50,000project	project
Cloud data warehouseDesign, build and semantic layer	$20,000 – $100,000project	project
Predictive analytics modelDemand, churn or cash forecasting	$15,000 – $60,000project	project
Hourly rates
Data analytics / dashboardsHourly, for ad-hoc and short engagements	$20 – $30per hour	per hour
Monthly plans (starting price and starting scope)
Dashboard supportUp to 10 support hours	from $300per month	per month

BI, warehouse and cloud licences are billed separately by the vendor. Hours above an allocation are billed at the hourly rate shown for that role, and only after you approve them in writing. Prices exclude any sales tax, VAT or withholding tax that applies to your invoice.

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 · USD

$4,000 – $15k

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

Illustrative example — not a client result

Example engagement

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 would do

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

Example target: Month-end reporting

6 days4 hours

-97%

Example target: Competing revenue figures

Unified

Example target: Forecast error

24%9%

-15pts

Example target: Weekly dashboard users

12140

+1,067%

This is an illustrative example of how we would approach this kind of work and the outcome we would aim for. It is not a client result, and the figures are targets, not achieved numbers.

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$4,000 to US$15,000. A cloud data warehouse with a semantic layer runs US$20,000 to US$100,000. Licences are billed by the vendor.