3 Reasons Why Data Engineering Matters
In a highly technical world where several trillion bytes of data are generated everyday, it is not enough to make important decisions based on a “hunch” or a “gut feeling” especially when those decisions affect businesses or people’s livelihoods. Across every possible industry, access to big data drives decision-making as we can tie outcomes to historic actions and even forecast key performance indicators (KPIs) with the help of modern analytics tools. With data being so important in the decision-making process, it’s imperative that the data we use is valid and reliable so that it can do its intended job and procure accurate insights. This is where data engineers come into play.
Data engineers work with raw data from various sources and make it usable for business processes and decisions. Data can come from an organization’s ERP systems that they use daily or from other relevant sources like websites, in-house apps and social media platforms, among others. Everytime an employee uses a business application to do their job, data is generated and stored. So, what if we want to determine the effectiveness of a specific business process; or what if we want to forecast our revenue; or what if we want to analyze real-time changes to physical company resources like storage facilities? These insights can be gained from analyzing the data from source systems, but data is not always generated in a clean, ready-to-go format that decision-makers can use, and therein lies the value of data engineering.
Optimizing the Flow of Data
By bringing data from different systems or different parts of one system together, and doing some processing, data engineering enables the creation of pipelines that get raw data from a source or sources to appropriate destinations. Getting the right data in the right place at the right time makes it easier to understand what happened or what is currently happening within an organization. If the flow of data is optimized, analysts can put all the pieces together to see the big picture and answer questions that arise each day.
Growth and Innovation
Data engineering fuels business intelligence by assessing the quality of the data and implementing systems that work together to solve business problems. Business intelligence is the process of transforming data into actionable insights that could lead to creative ways of solving problems, and problems can only be solved if we ensure that the input is good. Dashboards can be built to look at the data in visual formats that are suitable for observing patterns. Therefore, organizations can innovate around the facts, figures and charts that they’ve seen in such reports. With good data coming together in one place, the possibilities for innovation based on insights are endless. Additionally, data engineers can help businesses to keep up with ever-changing requirements by keeping data pipelines and data stores reliable, high-performing, organized and efficient. Thus the possibilities for growth become tangible even as business needs change.
Finding and Implementing Best Practices
Because data engineering ensures the completeness and consistency of data, organizations are able to use it to determine trends and identify best practices that yield maximum results. Data engineering facilitates data science and analytics, so when the data is reliable, analysis can yield accurate and relevant insights about what happened, how it happened, and perhaps why it happened. These details can be intimately assessed for understanding historical revenue and its outlook, performance of business processes or programs that the organization has implemented, the success or failure of new marketing strategies and much more. This leads to prescriptive analytics which can help organizations to determine a course of action based on the findings.
The bottom line is that data engineering enables us to use data in the most effective manner so that we can make better decisions. Before we can gain valuable insights from data and make decisions that will affect the direction of the business, we must ensure that the foundation is solid. You can have data and use it, but without doing some processing, proper storage and optimization, it’s just the same as making a major life decision based on wrong information - chaos. To reap the true benefits of big data, we need to implement strong data engineering practices. An analogy that I’ve come across is that a winning race car driver feels the thrill of driving the car and achieving his goal of winning the race, but the race car builder must first do the work to build the parts of the car that work together to generate that thrill and excitement that come with driving the car and winning. This is the value of data engineering. It’s the work that we do behind the scenes to generate successful outcomes.
