As a business, your golden resource is your data. Either you agree with me or not, at the end of the day, your data is the only thing that tells you where you currently stand in your business — what needs to be improved, what works, what doesn’t, and what should be optimized moving forward.

The problem is, with the growing number of data sources — CRMs, ad platforms, websites, apps, social media, spreadsheets, and more — it becomes more and more complicated to effectively track everything and extract actionable insights. You’ve probably figured out by now that using Excel or some off-the-shelf “all-in-one” tool isn’t the best long-term solution.

Then, how do you manage all that data?

By building an efficient and scalable data infrastructure.

What is a data infrastructure?

A data infrastructure is a system composed of multiple tools and layers that handle your data end-to-end — from ingestion and storage to transformation, analysis, and visualization. It’s the backbone that ensures data flows smoothly and becomes accessible and usable for decision-making.

Components of a Data Infrastructure

How to Extract Data From Your Sources

The logical first step is to extract all your data from your various sources.

There are two main schools of thought here:

→ Team Code-First: These are the engineers who prefer building everything from scratch, even if it means handling dozens or hundreds of data sources manually.

→ Team Efficiency-First: This is the team I resonate with. Why reinvent the wheel when tools already exist to automate most of the data ingestion process? For standard sources like Google Ads, Facebook Ads, HubSpot, Salesforce, or Stripe, tools like Airbyte, Fivetran, or Stitch are perfect. For custom APIs or internal tools, I turn to Python scripts for more control.

If you’re still leaning toward the build-from-scratch approach, consider tools like DLT (Data Load Tool) to manage pipelines with code while keeping it modular and reusable.

Where to Store That Data

Once your data is extracted, it needs a centralized place to live. This is where storage comes in:

→ Data Warehouse: Great for structured data used in business reporting and analytics. Tools like BigQuery, Snowflake, and Redshift dominate here.

→ Data Lake: Best for unstructured or semi-structured data and when your end goal involves machine learning, AI, or advanced analytics.

→ Lakehouse: A hybrid model that combines the reliability and performance of data warehouses with the flexibility and scalability of data lakes. Tools like Databricks have popularized this concept.

I personally prefer the data warehouse model for its simplicity and ease of integration, especially when building for business users. But for large enterprises and AI-heavy use cases, the lakehouse model can offer more power and flexibility.

How to Make That Data Usable

Having data stored isn’t enough — it needs to be clean, reliable, and structured properly. That’s where data transformation comes in. They help you apply naming conventions, create joins, filter noise, and build logical data models.

→ For data lakes, tools like Apache Spark and Delta Lake help in large-scale transformation and data engineering.

→ For data warehouses, tools like DBT (Data Build Tool) or SQLMesh are excellent.

In my workflow, I use DBT extensively, often alongside orchestration tools like Dagster or Apache Airflow to schedule and manage data pipelines.

How to Make Your Data Accessible

Once your data is modeled and transformed, it needs to be made available to decision-makers and stakeholders who may not have SQL or technical skills.

→ Dashboards: Use tools like Looker Studio, Metabase, Tableau, or Power BI to build interactive dashboards.

→ Automated Reports: Set up automated weekly reports using tools like Zapier, Make (formerly Integromat), or custom scripts that send Excel or PDF reports via email or Slack.

→ Embedded Analytics: For more advanced use cases, embed analytics directly into internal tools or customer-facing apps.

Final Thoughts

In a data-driven world, building a strong, modular, and scalable data infrastructure is not a luxury — it’s a necessity. Without it, your business decisions will always be reactive instead of proactive.

The tools are there. The challenge is in making the right choices that align with your team’s capabilities, your company’s goals, and your stakeholders’ needs. Whether you go full-code or lean on no-code/low-code tools, the key is consistency, automation, and scalability.

Remember: your data is not just a reflection of the past. It’s the fuel that drives the future of your business. So, invest in your data infrastructure wisely — it’s one of the best strategic decisions you’ll make.

— Dorian Teffo