May 28, 2025

Viktor Lazarevich

big data

Big Data

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.

What is data transformation, and why do businesses decide to do it?

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.

What makes data integration hard

So, what makes a data transformation project complex? Based on my experience, several key factors contribute to that complexity — both on the technical and organizational sides.

Number & variety of data sources

If you’ve got 10 sources that all look pretty similar, great — you can set up one logic and reuse it.

But when each source speaks its own “language,” things get tricky. One file has dates like “01/02/25,” another says “Feb 1, 2025,” and a third just says “yesterday.” Some fields are missing, others don’t match. Before you can use the data, you have to clean it all up. That’s what makes transformation hard — getting everything to speak the same language.

Volume of data

When your data volumes are low, it’s easier to manage. Daily batch jobs work fine, and a relational database is often enough to store and query the data without major performance concerns.

But once the volume spikes — especially with real-time data from sensors, apps, or customer activity — you run into real constraints. Storage has to scale without slowing down. Processing has to happen fast enough to be useful. And you have to do all this without letting costs spiral out of control.

Compliance

Compliance plays a huge role in many of the transformation projects I’ve worked on — especially in regulated industries like healthcare, finance, or education. You’re not just cleaning and moving data. You’re making sure it’s handled in a way that follows strict legal standards.

So compliance affects how you store data, who can access it, and what you’re allowed to do with it. That’s why it helps to work with partners who know the rules and use tools that are built to follow them — like HIPAA-compliant solutions or GDPR-ready platforms for European data. It saves time, reduces risk, and keeps you ready for audits.

Organizational change & cultural shift

Data transformation isn’t just a tech upgrade. It changes how people work.

Teams get used to their routines, even if they’re clunky. So when you bring in new tools — like switching from manual reports to real-time dashboards — people can push back. It’s not always about the tool. It’s about habits.

That’s why leadership needs to stay involved. Not just signing off on budgets, but helping teams understand why the change matters, what success looks like, and how to get there. A big part of transformation is helping people adjust — and that takes support, not just software.

Data transformation techniques and tools

Out-of-the-box solutions

On the market, you can find a variety of data transformation tools that provide out-of-the-box solutions. But if each data pipeline is unique, what is ther role?

Tools like Fivetran, Airbyte, and others help you build data pipelines without starting from zero. They come with ready-made building blocks for common steps: pulling data from sources, cleaning it up, and loading it somewhere useful. Engineers combine and customize these blocks to build pipelines tailored to each business’s unique needs.

They’re not plug-and-play but a flexible toolkit that lets engineers build pipelines that fit your needs.

Monitoring and tolerance

Most tools come with monitoring built right in. The real challenge is deciding when to jump in and fix something. Some data streams need you watching every second — like patient vital signs in healthcare or fraud detection in finance. Others can wait without causing problems.

At Digiteum, we help you decide which data needs real-time attention and which can wait. Streaming everything live costs more — more servers, storage, and monitoring. So, we focus on what’s important while keeping costs down.

Artificial Intelligence

There’s a whole range of ways AI can make data transformation smarter and more efficient. Here are a couple of common examples:

  • Data enrichment. Say you’ve got thousands of patient notes or support tickets written in plain text. AI can read through them and tag each one with things like the company mentioned, medical condition, product issue, or urgency level. It’s not just picking out keywords — it understands that “high BP” means high blood pressure, or that “can’t log in again” signals a recurring login issue. That way, you turn messy, unstructured text into clean, labeled data you can actually sort, filter, and act on.
  • Anomaly detection. Instead of setting manual rules to flag sensor data or system issues, AI learns what “normal” looks like and spots unusual patterns on its own. When something’s off, it triggers alerts or actions right inside your pipeline, catching problems early.

But this is just scratching the surface. AI can also help with predictive modeling, data quality checks, automation in data transformation, and much more.

Start your data transformation project with Digiteum

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.

With Digiteum, get value before the project even begins

Start with a free Data Readiness & AI Review. We’ll look at your current setup, spot roadblocks, and show you where to begin.

Book your free consultation