When every report tells a different story
Marketing reports one lead count, the CRM shows another, and the board deck a third. Nobody is lying: each report pulls from a different system with different filters. So meetings open with an argument about which number to trust, and your analyst spends the week reconciling tables instead of answering questions.
A data strategy fixes this at the foundation. It is not a slide deck of principles: it means deciding which sources matter, where they come together, how they are transformed, who owns each metric and who gets to see what, then building it so it runs on its own. With that in place, every dashboard, AI model or conversational agent on top gives the same answer.
We are from Analytics, a data strategy consultancy based in Madrid, Spain, working with mid-market sales and marketing teams across Europe. We bring your sources into a single source of truth on whichever cloud platform fits, ready to activate. No more dashboards that contradict each other, no more manual exports.
Data sources we bring into your data architecture
The end-to-end data pipeline we build
- 1IngestionAutomated, scheduled loads from each source into the data warehouse, so nobody has to export anything by hand.
- 2Transformation with dbtCleaning, joining and renaming in version-controlled SQL, with tests that flag when a source changes or bad data arrives.
- 3ModelingBusiness-ready tables such as customers, deals, campaigns and orders, with the relationships already resolved.
- 4Metric definitionsWhat counts as a qualified lead, how margin is calculated, when a deal is won: defined once and used in every report.
- 5Data governanceClear ownership for each source, controlled access to sensitive data, and documentation so your team knows what every table means.
What a sound BI architecture looks like
Good BI architecture keeps three layers apart. At the bottom sits storage: the data warehouse or lakehouse that holds clean data. In the middle, the modeling and metrics layer, where each calculation is defined once. On top, what your team actually touches: dashboards in Power BI, Looker or Tableau, predictive models such as churn risk, and conversational agents such as Sales Copilot.
The payoff is practical. Switch visualization tools next year and the foundation stays intact. Ask the AI agent a question and the answer comes from the same tables that feed your sales director's dashboard.
Choosing a platform: Google Cloud, Azure, AWS, Databricks or Snowflake
We don't start from a favorite platform. We start from what you already run and where you are heading:
- If most of your marketing data comes from GA4 and Google Ads, BigQuery on Google Cloud is usually the most direct route.
- If your company works in Microsoft 365 and Power BI, Azure and Microsoft Fabric fit what your team already knows.
- If your infrastructure already lives on AWS, we build there instead of adding another vendor.
- If you need data science alongside BI, or you handle large volumes, Databricks and Snowflake are worth weighing up.
We also look at who will maintain it, what your security policy requires and what each option costs to run at your real volume. You get the reasoning, not a sales pitch, and if the platform you already have does the job, we won't ask you to move.
Data strategy consulting: common questions
What does a data strategy actually include?
Deciding which data the business needs, where it comes from, where it lives, how metrics are defined and who can see what, and then building the pipeline so it runs without manual work.
Do I need a data warehouse if I already use Power BI or Looker Studio?
With one or two sources, maybe not. Once you combine CRM, ERP, web analytics and ad platforms, wiring everything straight into the visualization tool leads to duplicated logic and dashboards that disagree.
Will we have to switch cloud providers?
Not necessarily. If what you have works, we build on it, and we only suggest an alternative when there is a concrete reason we can explain.
Why do you use dbt?
Because it keeps transformations in SQL that is version-controlled, documented and tested, instead of scattered queries that only their author understands.
Let's talk about your data sources
In 30 minutes you tell us which sources you have and which numbers don't add up. We'll tell you whether we can help, and if we can't, we'll tell you that too.
Book a 30-minute call