Data Engineering Services: How It Is Impacting The Enterprises

By Author: Neeraj
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Data is no longer a byproduct of any operation; instead, it plays a critical role in companies’ growth in today’s market. It’s not data that helps enterprises boost their business operations and profits; it is the insights that they extract from the collected data. Here comes the need for robust data engineering services. It helps them to streamline data pipelines, integrate various data sources, and store data effectively. Let’s understand how effective data engineering services can help you drive strategic advantage.

Data Engineering

It is a process that focuses on building an infrastructure for data collection, storage, and transforming into usable formats to be used for transforming into insights. Unlike the traditional IT infrastructure, which focuses on operational data, data engineering services focus on building pipelines that support complex data processing, handling, and analysis processes.

Key Trends in Data Engineering Services

Cloud Data Engineering

Enterprises are moving their datasets to cloud platforms such as GC, AWS, and Azure. Cloud platforms provide flexibility, ...
... scalability, and cost efficiency to enterprises.

Over 85% of companies are expected to adopt a cloud-first approach by 2025. - Gartner

Real-Time Data Processing

Real-time insights require real-time data processing. Enterprises demand real-time data processing capability to make informed decisions instantly. Technologies such as Apache Kafka and Spark Streaming help data engineers ingest real-time data to process it in real time to get instant insights.

Automation and Low-Code Solutions

Low code platforms and automation technologies are helping enterprises build data pipelines easily and faster. Tools like DataRobot and Alteryx help data engineering teams create data pipelines without having more coding knowledge and with limited resources.

Focus on Data Governance and Compliance

Since data privacy has become one of the major concerns for enterprises, they should focus on data governance and compliance practices.  There are some laws that enterprises should comply with, such as GDPT and CCPA, to ensure data privacy. Data engineering services help enterprises protect confidential information by automating the compliance and data governance authoring process.

Microsoft Fabric

Microsoft Fabric is a one-stop solution for all your data analytics needs. It gives you combined experiences, data engineering powered by Syanpse, data integration by data factory, and more. You get all the tools to extract business insights from raw data here. Hence, it is called a unified data analytics platform. Microsoft Fabric helps you manage the data engineering process; it eliminates redundant data storage, which saves costs on creating copies of duplicate data. It also lowers the implementation and overall data engineering costs, leading to higher financial efficiency and a highly streamlined data engineering and analytics process.

Here are some Facts and Figures about data engineering impacts

Data Usage and Growth

According to IDC, global data volume is going to grow to 175 zettabytes by 2025, and most of the data will be generated and processed in real time. Here comes the data engineering services to manage this influx of data effectively by creating data pipelines, storage, and data ingestion approaches.

Time Savings in Data Management

Enterprises having robust data engineering services in place experience a 30% reduction in the time they spend managing their data processes- McKinsey. It helps them to get more time to spend on their crucial tasks and extract insights to make decisions rather than cleaning or managing data.

Advanced Analytics ROI

Deloitte found in its research that 70% of leaders are investing in data management efforts. Data engineering services help enterprises ensure that data is of high standard and easily accessible for analytics. Ultimately, enterprises experience more impact at reduced costs involved in the analysis process.

Here are some common use cases of Data Engineering Services

Retail: Retailers are using data engineering services to provide personalized shopping experiences to their customers by merging data from various customer interaction points, past purchase data, and behavioral data. Retailers build a robust recommendation engine based on these data.  Customers having the right recommendation based on their behavioral analytics are more inclined to purchase more, i.e., higher conversation rate and customer retention. Every data insights goes through various methods and processes, data engineering takes care of most of the steps. It helps them in ensuring that data pipelines are built properly.

Financial Services

Most of the financial institutions are fighting against the fraudulent transactions. They need faster data processing and real-time data insights. Data engineering services help them set data pipelines that monitor various kinds of data patterns to flag any fraudulent transactions. It helps them to prevent such fraud risks and secures the banking process for the customers. There are various other activities that fintech companies can help to boost their customer experience with data engineering services.

Healthcare

Data engineering is an integral part of healthcare organizations, helping them improve patient care with predictive analytics.  Millions of data are collected, processed, and stored effectively with the help of data engineering services. These predictive analytics models help them to store, collect, and analyze the patients’ data in real-time and monitor them, leading to better patient care. Boosting the patient care is what healthcare industry is more focused. Data engineering activities can help them to extract the better insights out of the patient data.

Manufacturing

In the manufacturing sector, predictive maintenance helps them manage unplanned downtimes that lead to huge revenue losses. Professionals use data engineering services to build IoT pipelines to collect information from equipment in real-time to avoid any downtime. It also helps them to improve the productivity of their team members. It all starts with how you get your data prepared for the analysis process. Manufacturing enterprises ensure that they deploy a robust data engineering strategy. It helps them to prevent any kind of unplanned downtimes.

Conclusion

Data engineering services have come a long way from back-end operation to a must-have asset for enterprises. While building an effective data strategy, a reliable and scalable data engineering service is very critical. It helps enterprises across industries boost customer experiences, team productivity, and decision-making. If you want to start your data engineering journey, let’s connect with the certified experts at the Saxon AI team.