Data Transformation Process: How to Get Insights from Raw Data

Companies deal with huge amounts of data every day. Millions of logs, events, sensor signals, or health records are constantly flowing in.

But are companies actually using that data?

Collecting data is easy. Making it reliable, timely, and usable across systems? That’s the hard part. Especially at scale, with legacy tech, siloed teams, and high stakes in industries like healthcare, logistics, and greentech.

Hi, I’m Viktor Lazarevich, CTO at Digiteum. I’ve spent 20+ years helping companies integrate big data analytics services and data management into their operations. In this blog, I’ll share data transformation methods and best practices from the field. Let’s start.

Data often comes from many different sources — like spreadsheets, sensors, or apps — and it might be messy, incomplete, and in different formats. Transformation cleans and organizes that data, so you can actually understand it and start leveraging big data to make smart decisions.

Once it’s done right, you’re not just looking at raw numbers. You’re unlocking the advantages of big data. For example, you can catch a sales drop before it seriously impacts revenue, flag a shipment delay before it becomes a customer complaint, or restock a popular item before it sells out.

From what I’ve seen, companies come to us for data transformation for one of two reasons.

• • Strategic transformation driven by leadership. Sometimes companies need to fix messy processes, cut down on manual work, and get a clearer view of what’s going on. It could be part of a big digital shift, or just fixing what’s broken.

For example, you’ve got a department that basically runs on one person’s gut instinct. They’ve been doing the job for years, they know it inside out. But there’s no playbook, no data to back it up, and no way to scale that knowledge. Think of a supply chain planner who decides what inventory to order based on gut feeling rather than forecasts or numbers. So leadership wants to fix that and turn that kind of tribal knowledge into structured, repeatable processes. And that’s where data transformation comes in.

• • Employees lacking the data they need. Teams can’t access the data they need — or if they can, it’s slow and messy. Data’s buried in spreadsheets, PDFs, legacy systems, or scattered across departments that don’t share. So even though there’s plenty of data, making use of it is too hard.

Fixing this means changing how people work, which is never easy. If someone has been making reports by hand for years, moving to an automated system is a big change. It affects their daily work, who’s responsible, and how much they trust the new system.

At the same time, it forces the business to make real decisions: What’s the source of truth? Who gets access to what? What should be fixed or removed? It’s disruptive, but necessary.

Because doing nothing is worse. Without a clear view of how your business runs, you’re guessing. And that guesswork costs time, money, and growth.

From fixed steps in data transformation to custom solutions

Previously, when needs were simpler, we typically used ETL data transformation processes — extract, transform, load.

But ETL can’t handle the complexity of today’s data anymore.

Modern organizations rely on numerous SaaS tools, connected devices, cloud platforms, and data streams. They produce and consume data in different formats and at different velocities. The environment is much more dynamic and distributed than it used to be.

Today, data transformation usually means building data pipelines. You take data from many sources and send it to different places. Along the way, you process the data to make it useful. For example, you might:

• Add extra context, like data from other sources or AI-generated metadata.
• Validate and check for errors, making sure the data follows rules or matches a known format.
• Hide sensitive info to meet privacy laws like GDPR or HIPAA.
• Sort and group data, so it’s easier to analyze later.

Unlike the fixed three-step nature of ETL, data pipelines are flexible and scalable. They can consist of just a few data transformation steps or hundreds. One pipeline might pull sensor data every second, enrich it with weather info, and send alerts in real time. Another might just clean up a weekly sales file and load it into a dashboard. It all depends on what the business needs.

That’s the whole point. It’s not about following a fixed process. It’s about building exactly what your use case needs, no more, no less.

One of our real-world data transformation examples is the project we did with Diaceutics. Their platform, DXLX, collects lab test data from hundreds of laboratories globally. These labs used different formats and systems, and the data had to comply with strict healthcare and data privacy regulations.

Digiteum built data pipelines that pulled in the data, standardized it into a single format, enriched it where necessary, and anonymized sensitive fields to ensure compliance. This enabled DXLX to consolidate and analyze global data in a consistent and secure way.

As you can see, before your data can work for you, there are a few big questions to answer. What data really matters? How accurate does it need to be? How often does it need to be updated? These are just a few of the big data challenges and solutions we help our clients tackle every day.

That’s where we come in.

• Tailored solutions, not templates. At Digiteum, every project is custom-built to fit your data, goals, and industry. No off-the-shelf shortcuts.
• Proven cross-industry expertise. Dozens of successful projects across healthcare, manufacturing, logistics, and more — we know what works in the real world.
• Business-first approach. We focus on outcomes, not just architecture, and turn complex data challenges into measurable results.

Want to get started?

We offer a free Data Readiness & AI Review. Our team will look at how your data is flowing, who’s using it, where it’s getting stuck, and whether your current setup can support your goals. Then we’ll give you clear, practical recommendations. No strings attached. You can run with them yourself or bring us in to help.