At every single second of the day, 92,000 searches are being conducted on Google, 71,666 videos are watched on Youtube, at least 101 connections are made on LinkedIn, more than 3,600 photos and videos go up on Instagram, TikTok users watch 2,780 videos, and about 4 million emails are being sent and received across the globe. Data is everywhere, effectively changing our world and shaping the future.

Successful businesses leverage data to improve their services, offering quality products and more personalized experiences to their customers. These companies understand how critical data is in helping them figure out their next steps. They know that, without data, decision-making becomes almost impossible.

To make accurate decisions and take advantage of new opportunities, companies need quality data. Data saves them a lot of time and resources by helping them identify what the exact problems are. Data = knowledge = actionable insight.

But in its raw, unrefined form, data is an enigma — a vast collection of disparate pieces that lack cohesion, meaning, and purpose. In this state, data’s true value is concealed beneath layers of unstructured mess, leaving a company confused about what to do with it or how to extract value from it. The result is that 68% of data collected by enterprises lie stale and unused.

This is where data engineers emerge as the crucial architects of data transformation. Part of their job is to build the infrastructure (called a data pipeline) that collects large volumes of complex data, processes it, and stores it in a way that is discoverable and accessible to data consumers, namely business analysts, data scientists, and ML engineers. The pipelines they build must be robust, scalable, and able to move data quickly and efficiently from various sources to a destination, usually a central repository called a data warehouse.

They tame this vast amount of data and bring it to life through harmonization (bringing diverse, often inconsistent data from various sources and systems), standardization (imposing a consistent and uniform format for the data), and validation (assessing the quality and accuracy of data to ensure its reliability).

According to Gartner, businesses lose an average of $15 million every year because of poor data quality. The problem isn’t about the data itself, or the technology. Big data is complex, messy, and unruly. controlling and managing it requires the skill and expertise of a data engineer.

Data engineers understand the ins and outs of data-producing systems — how they work and generate data, the velocity at which data is generated, the types of data they generate, and of course the limits of these systems. Collecting data from various source systems (IoT devices, e-commerce apps, etc) and storing them in a format suitable for querying and analytics is one of the basic duties of data engineers. Their job is to ensure that big data doesn’t become a big disappointment for data-driven organizations.

In today's business world, just having a good product or service isn't sufficient for success. To thrive, a company must understand its customers' behaviors and preferences. Data engineering, through operations research and big data, enables businesses to track customer insights and trends. Companies use this data to predict future needs and adjust their strategies accordingly, staying ahead of the game instead of simply reacting to what's happening now.

in addition to ensuring the quality and integrity of data, data engineers play a significant role in safeguarding sensitive information by implementing robust security measures throughout the data lifecycle. In a world where data breaches can lead to catastrophic consequences, the efforts of data engineers are pivotal in maintaining trust with customers and preserving the reputation of organizations.

Data engineers, as gatekeepers of companies’ data, have become business growth enablers, helping organizations thrive and transform through data-driven insights. They bridge the gap between raw data and meaningful information, elevating businesses to new heights and empowering them to adapt to the ever-evolving challenges of the modern business world.

The demand for data engineers will only intensify as data continues to grow exponentially. With their coding prowess, knowledge of cloud technologies and distributed systems, and business acumen, data engineers harness the raw power of data and convert it into actionable insights that drive growth, efficiency, and competitive advantage across industries, including finance, healthcare, climate science, artificial intelligence, transportation, and education. From designing robust data pipelines and optimizing data storage to ensuring data security and compliance, their work forms the foundation upon which data scientists, analysts, and business leaders build their strategies.

Embracing the importance of data engineers is not just a choice; it is a necessity for any organization seeking to lead the way in the ever-evolving, data-centric future.