Application of Data Engineering in Your Businessby Tech Geekk writer Digital transformation has led to the continuous generation of a huge quantum of data, which has created a need for data engineering services to organize and convert data into usable formats.
Designing and building data pipelines, to transport and transform raw data into usable formats can be termed data engineering. Data from various sources is collected in a data warehouse or a data lake and represented uniformly. Data engineering services assist in designing, managing, and optimizing the data flow.
There has been a visible shift in how businesses have started viewing data as an asset and the use of this data has been a game changer for business operations. Data is tracked, recorded, leveraged & analyzed to make informed decisions, unlock new business opportunities and also increase business growth. Digital data offers huge possibilities for organizations to innovate and also serve customers.
Organizations that adapted to this trend early on have benefited from it and are ahead of the competition. These organizations can redefine their strategies to connect and engage with customers across digital channels and capture market share and customers.
Importance of data engineering -
A decade back, data engineering consisted of data warehousing, business intelligence & ETL (Extract, Transform, Load) (tooling to move the data). In the current scenario, data engineering focuses on
Best practices - Data engineering focuses on best practices in the software development cycle, which includes appropriate use of version control, release management & automated DevOps pipelines.
Security - Data hacking and data breaches pose a serious threat to businesses and customers. Data collected by businesses have to be compliant with System and Organizational Controls (SOC 2) or Personally Identifiable Information (PII) that the business is legally required to protect. Data engineers are expected to grasp the security issues related to this issue and take appropriate action in managing risks involved with data in transit and at rest.
Business domain knowledge - It is now essential for data engineers to have domain expertise to draw insights from the data.
The purpose behind data engineering is to offer an orderly and uniform flow of data that can enable data-driven models like machine learning and data analysis.
The Benefits of data engineering for businesses are -
· Agility - Using data engineering, businesses can be agile in taking decisions based on the insights that the data analytics provides. Businesses that are data-driven and invest in processes that can take advantage of the data streams and their subsequent analysis, can spend more time fine-tuning their companies.
Enhanced efficiency - Integration of data analytics can help enhance an organization’s competencies to become more effective and find better opportunities. Data analytics can provide awareness to take insightful decisions and drive the business forward.
· Revenue generation - Data analysis can be termed as the new revenue stream. Improvements in data collection and subsequent predictions can help in decision-making. A data-driven organization can innovate, improve and generate more revenue streams.
· Digital marketing, product development, and better decisions - Companies leverage data to get better insights into their businesses to make better decisions which can result in better and improved results. To market products and boost sales businesses can run campaigns that are targeted to specific audiences which makes marketing and advertising activities more effective. Companies can also use sales data to measure the effectiveness of such targeted campaigns and take further decisions based on the results. Marketing and ad campaigns are targeted based on different geographical locations, beliefs & preferences.
In 2017, Coca-Cola revealed that its new flavor Cherry Sprite was inspired by the results of data collected from a self-service drink fountain. where customers can mix and make their drinks. Coca-Cola was tagging which flavors were mixed the most in different areas of the world. The analytics were done and the Cherry flavored sprite combination was made into a purchasable item.
· Decision outcomes - Data-driven decisions generate additional data that can be used to evaluate the impact of decisions taken based on previous data streams and their analytics.
· Improving user experience - Data analytics and data engineering assist in the development of new products and make it easier for a product management and development team to spot patterns that can drive the strategic roadmap for new products, services & innovations. Businesses can use machine learning algorithms to forecast future market behavior based on the data. Machine learning can spot patterns and can be used for market predictions based on historical data, ensuring that businesses stay competitive and anticipate market demand to deliver products.
Created on Nov 3rd 2022 03:58. Viewed 39 times.
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