Why Do Businesses Need Data Engineering?

Not a hot trend, but the starting point for data success

Data is data, right? Just ones and zeroes piled up on each other? Wrong. Cloud storage, data warehousing, the data lake or data mart – these are not random masses of gigabytes shoveled onto a server rack in the cloud. They’re masterpieces of design, organized and structured to perform well for a specific use case. Even if the data within them is unstructured. (In fact, this is where they work best.)

That’s the reason for the rise of data engineering. A practical consulting approach that brings order to the data within your business … shaping and sizing it in a way that optimizes how it flows between applications, data storage, and the people who use it. Doing so keeps your technology infrastructure responsive to people’s needs – and your costs under control.

But data engineering isn’t a magical on/off switch. Getting it right for your business means combining in-depth business smarts with full-stack technical expertise, often as a long-term project that adds incremental benefits over time. In this blog, you’ll see the problems it solves – and the opportunities it creates.

Data engineering: what it is …

There’s a Japanese art form called “Hikaru Dorodango”. It involves making shiny and colorful spherical sculptures out of … mud and water. The raw materials don’t sound easy to work with. But with enough focused effort, out of something messy and random comes something structured and beautiful.

Data engineering is surprisingly similar. That finished architecture that looks perfectly suited to its purpose doesn’t appear by chance; it’s the end result of a process that uses talent, analysis, and iteration to reach the beautiful final result. Using application files, daily reports, live data, even video and audio as the raw materials that make a coherent and useful whole.

Doing so involves critical thinking about your data and who uses it, so the right data infrastructure can be decided based on factors like latency, peering, volume and capacity. It needs a strategy for data architecture, so that infrastructure can differentiate between types – real-time feeds? Cached storage? Daily backups? – and prioritize performance. And it needs close attention to data quality, so your data’s growth doesn’t outstrip the value it delivers.

Just as you’d slice a tree into smooth geometric planks before building a wooden house, data engineering formats data, applications, and connections into the form that maximizes utility across your business. And if – like most larger organizations – your needs differ across locations, data engineering becomes even more important. Let’s look at why.

… and why modern businesses need it

Like it or not (and we like it), all businesses are now technology businesses. Competitive advantage has always started with optimizing the resources available. Once, those resources were limited to labor and raw materials; today, applications, cloud infrastructure, and data feature large in the mix. Driving incentives include:

  • Cost control: making sure investment in technology gives sufficient returns to business growth
  • Market knowledge: understanding your market and how it’s changing, based on hard data
  • Customer insight: knowing what your audience wants, so you can improve your products and services
  • Planning your actions: using data-driven decisions to inform your strategy and tactics
  • Communicating effectively: sharing key findings and insights with your worldwide team, so everyone knows your corporate goals
  • Raising barriers: creating and maintaining intellectual property as a source of value
  • Enabling people: giving your team the tools they need to be successful
  • Legal compliance: following procedures for keeping sensitive data under lock and key

There are many more. But perhaps the biggest factor is economic: because everybody else is doing it, you can’t avoid doing it too. Use of technology is now non-optional … and data engineering makes it work harder for you.

The many faces of data engineering – and why all are important

So what does “working harder” mean? Let’s share a few examples.

1. Data engineering can improve data quality and accuracy

From the simplest formatting – like a form that allows too many variants of “Mister” – to the most complex neural network structures for AI, the right approach to how data enters your systems pays huge returns later on. If there’s no thought given to what’s going into your databases, all that happens is the data pile grows ever-larger while the proportion of “dumb” data in it slows down your infrastructure and adds to your costs.

It’s where data engineering solutions like data ingestion and data processing come in, often as part of a data warehousing strategy where the flow of data is structured into standard objects for easy storage and retrieval. This approach can make data access exceptionally fast at scale – making it great for real-time applications in finance and logistics.

2. Enhanced accessibility and availability with data engineering

When data’s in the right place, integrated and open to everyone who needs it, people are more likely to use it. This avoids issues like Proximity Bias (the tendency to use what’s close at hand rather than what’s right) and Familiarity Bias (using only datasets you already know). If data starts off clean and coherent, it increases the chances of confident and reliable decision-making downstream – because the picture it presents of reality is more complete and trustworthy.

This is where you’ll see approaches like data lakes, which bring together data from different sources in one place – but keep that data in its original format, without the application changing it in any way. It’s a way data engineering can maintain the integrity of all data across your business, while making it usable by those who need it. Many video streaming services use lakes in their data engineering strategies, for example.

3. Data engineering can drive greater operational efficiencies

Even if your data’s the right shape and structure, it needs to be optimized for its internal and external audiences to make the best use of it. It’s not much use locating your North American retail data in Papua New Guinea! Data engineering uses both physical infrastructure (distance between data and distribution centers, capacity of cables and airwaves) and technical choices (availability of bandwidth, type of data, volume of users) to find the ideal balance between where the data is and how its users access it.

It’s called data pipelining, and it’s a priority for businesses needing to shift large volumes of data that cluster within markets (like services customized by country) and heavy cloud service users (who want to minimize latency and costs of bandwidth). Data engineering is how they happen.

4. Driving understanding across the enterprise, with data engineering

Any business succeeds or fails on internal communication: how consistently the company’s strategy and goals are understood by its employees and managers, even if international borders and cultural differences are in play.

That’s why data analytics is part of any data engineering project. A critical eye on what data is needed to keep the company’s workforce informed, and how to present that data in a form that makes sense, whether through spreadsheets, narrative reports, or data visualization on dashboards. It may sound high-level, but it’s all part of good data engineering practice.

5. Data engineering for ongoing improvement and iteration

Last, let’s note data engineering isn’t a one-off event. It’s a way of thinking that looks at the landscape the business operates in, understands its market forces and economic drivers, and forms a plan for both current and future needs. Because the world doesn’t stand still.

This is why good data engineering thinks of your data infrastructure as a connected data platform, a broad solution across the business – even if the parts are individually customized per-market. Because “platform thinking” ensures all relevant data is available to the right people, in the right way, whatever its source … exactly what business decision makers need.

What is Big Data engineering?

That’s a lot of data. So let’s make a point about Big Data, and how data engineering makes even terabyte-scale applications manageable.

Data engineering consulting starts by finding “the edges of the jigsaw”: the Big Picture of your Big Data, and how broad and deep it really is. It means the services you add to your IT infrastructure – application seats, cloud services, monthly maximums, burst capacity – are as big as you need them to be … but no bigger, so you’re not wasting money and resources. Of course, this includes a strategy for ensuring they can grow with you as your needs do.

(In brief: the job of a data engineer)

As you’d guess, data engineers tend to be multiskilled and multidisciplinary people. They need to understand how a business works in detail, and what its people need to do their jobs effectively. They have to see those requirements in terms of a viable technology stack, and then specify that stack for developers. And they have to join it all together: visualize how the different pieces of a global solution work as a unified whole.

Fortunately, we’ve got a lot of those people at Strypes. Between our business teams across Europe and our development hub in Bulgaria, you’ll find hundreds of people with the right mix of business know-how and technology smarts. They’re ready to go to work on your data engineering strategy from beginning to end … and beyond, wherever your business takes you.

CONCLUSION: Why Strypes is your partner for data engineering

The greatest reason for partnering with Strypes is simply the fact we’ve donne it before. For hundreds of companies, including global enterprises and market leaders. They trust us to execute their data engineering requirements – and keep on doing so. (Some of our relationships span decades.)

And we’d like to do the same for you. Book a chat with a Strypes expert today:

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