Most businesses have undergone a digital transformation over the past decade. That has led to unimaginable amounts of new data types. More and more firms recognize the value of data and its ability to improve all areas of their business, both with customers and internally. Therefore, the growth in the number of data engineers has grown exponentially in recent years. It’s no secret that companies are looking for ways to gain an advantage over the competition. Sometimes getting that edge is a matter of hiring the right person – in this case, a data engineer.
From the very beginning of the digital transformation, it is evident that companies needed data scientists to understand the data that companies were sourcing. However, much less obvious was that you needed a separate person to organize your data, keep it secure and accessible so that a data scientist could do their job efficiently. We expected that the person in this position would build the appropriate infrastructure and data flows independently. However, this often goes beyond the competencies of a data scientist. As a result, companies have struggled to derive optimal value from their data designs.
Today there is no doubt that companies around the world need data engineers who build architectures such as databases and computing systems to extract value from data. That enables organizations to implement successful data science initiatives successfully. Learn and master Data Science from industry experts by taking up an Intellipaat Data Science Certification course.
Data engineer vs. data scientist
Before we move on to identifying ten reasons why a business needs data engineers, it is worth highlighting the differences between a data engineer and a data analyst.
Unfortunately, many people think these concepts are the same, even though there are apparent differences.
Data engineers have experience in managing and organizing many large data sets and cloud solutions. They can extract essential data from extensive and unprocessed collections. They can also write code and scripts. In addition, they know system monitoring, alerting, and creating dashboards. A data engineer makes the data understandable and then passes it on to a data scientist.
Data analysts are mainly business-oriented people. In their work, they focus on economics, mathematics, and statistics. Database administration and work closely with the company’s partners and customers to achieve business goals using data.
If you’re looking to start your journey in the field of data science, you can start learning from courses on Dataquest.
Why should you hire a data engineer for your company?
In 2023, the data engineer was ranked eighth among the fastest-growing positions in the emerging LinkedIn job report. So let’s find out why a data engineer has become so crucial to businesses.
1. Data analysts can focus on their work
Large amounts of data require proper preparation to be analyzed. The lack of a data engineer means that data analysts spend up to 50% of their time organizing data sets. Data engineers have the option of reducing this percentage to zero by providing scientists with data ready for analysis, which is related to the next point.
2. Increase in the productivity of the Data Science team
Hiring a data engineer spreads the workload and improves the performance of your data science team. Rather than incur the costs of hiring more data analysts, you can free up their time by hiring a data engineer. That will develop not only the performance of your Data Science team but also its results. Remember that your data is an asset to the company, and you should treat them as such.
3. Data engineers help grow your business
Nowadays, data is the foundation of enterprises. Product, employee, customer data, network data, and more are used many times a day to make significant and small decisions. The data engineers are accountable for managing the data infrastructure, which will scale with the growth of the enterprise and thus contribute to the development of the organization.
4. Make decisions faster
Properly organized and structured data sets by data engineers allow companies to make faster and more accurate decisions that increase competitiveness.
5. Data engineers enable innovation in business
Building data pipelines is one of the essential tasks of a data engineer. Thanks to pipelines, it is possible to transform unstructured data into data ready for applications, artificial intelligence systems, or machine learning. Thus, proper data pipelines are the key to maintaining the data flow for business innovation.
6. Real-time data access
In business, you often have to make quick decisions in real-time, and outdated data is useless. So data engineers keep all data sent to algorithms and dashboards up-to-date. That enables companies to make quick and effective decisions whenever necessary.
7. Accurate business predictions
To predict accurately, good models, good artificial intelligence, and sound machine learning are necessary, which is not possible without the already mentioned data pipeline, and data engineers are responsible, among other things, for their creation.
8. Data engineers solve problems
Data engineers are persistent, patient, and focused. They can know why and how data pipelines work, and if something goes wrong, they work to find a solution. The best data engineers learn and develop constantly. Their interest in data allows you to immediately identify potential problems and quickly find the right solution for them. They are familiar with numerous data sets and various programming languages and can connect formerly extracted business units using data-driven systems.
9. Data integration
Many industries rely more and more on SaaS platforms, but vendors may not offer the services or expertise that result in seamless integration with the enterprise data warehouse. Data engineers can make this process work and help the business obtain an integral, complete view of its data.
10. Automation of internal processes
Data engineers implement services and devices that automate typically manual duties. For example, they can automate data acquisition, metric calculation, metadata management, A / B testing, and more to facilitate the work of other enterprise functions. Thus, companies that rely on data engineers to develop, build, and integrate data and tools will create data-driven practices that are increasingly automated, and this is the primary goal of digital transformation.
Unlock Analytics with Data Engineering
You already know why today’s business needs data engineers. Now is the time to act.
You have two ways to choose from; you can hire an experienced data engineer or use one of the many third-party companies specializing in Data Engineering Services (https://addepto.com/data-engineering-services/).
Whichever option you choose, keep in mind that companies today need to consciously invest in developing their data engineering capabilities to have a genuinely successful analytical program. Companies that will increase in the years to come are those that not only recognize the challenges of model development and manufacturing but also actively invest in engineering talent to maximize value from data.