Extracting Business Value through Data Engineering

Data isn't just a mere collection of numbers and texts — it's the lifeblood of an organization. For businesses with a customer-centric focus, the vast oceans of unstructured data hold immense potential, but only if they can navigate its depths effectively. This is where the magic of data engineering comes into play. By efficiently managing, maintaining, and interpreting data, businesses can transform these raw bytes into actionable strategies that propel them ahead of the competition. In this blog, we'll dive deep into the world of data engineering and its undeniable correlation with business value. From understanding the intricacies of data lakes and pipelines to realizing the tangible benefits it brings, we'll guide you through how enterprises can harness their data for unparalleled success.

The Power of Data Engineering for Customer-Centric Organizations

Data can be incredibly powerful for a customer-centric organization if they know the importance of extracting real insights from it. This phenomenon is the art of data engineering, a process to manage and maintain the unstructured business data flowing in the pipelines and extracting meaningful insights from it in the business’s favor.

According to a recent study from EY, 93% of organizations said that they will continue investing in data engineering and analytics to leverage its benefits.

But how can a business achieve so much value from just some raw data? This blog entails the process for enterprises to leverage the data and extract insightful, actionable strategies from it to improve the overall business.

Correlation of Data Engineering with Business Value

Data engineering is a technical field that involves the management and handling of bulk data flowing through data lakes and pipelines, its utilization, and then the implementation in an organization. In simple terms, the concept of data engineering revolves around working with the organization and its people, data, and techniques to leverage this data for extracting insights and metrics, and then developing strategies for improving the organization. The definition may look simple, but the process is not, as it involves the optimization, integration, governance, and flow of enterprise data for the analytical needs within the organization’s data-handling ecosystem.

This process is executed by data engineers, data analysts, and data architects who leverage the data in the best way to extract necessary features and trends through advanced data analytics models.

The importance of enterprise data is understood from its impact on the organization’s business value. Business value describes the factors and metrics that affect an organization's well-being, productivity, and success in the long run. But how does data engineering contribute so much towards its business value?

Data enables organizations to develop better and smarter business models through decision-making capabilities. This decision-making capability is acquired through the power of big data and by deriving analytical insights from it. Organizations leverage these techniques to analyze the trends and insights in the data, they then use the extracted metrics to develop effective enterprise work models that boost business value and growth.

Extracting Business Value and Insights through Data Engineering Process

Enterprises deal with tons of data flowing through their pipelines. However, without the application of proper engineering techniques and data management processes, all this data goes to waste. Let's explore how to harness raw data through data engineering and extract valuable insights for business purposes.

Develop a Business Case

The first step when considering data engineering is to set up a business case around the available raw information. A business case is when you define your business focus around the available resources, set the goals, and pre-define the expected outcomes.

Any business that proceeds to analyze the data without defining the objectives and goals of the engineering process is likely to fail those data engineering projects. Goals set your path and allow you to unveil the associated challenges with ways to tackle them. This way, you get to discover some possible strategies to work on a data engineering model for your business. Hence, it is mandatory to align the business goals, missions, and objectives with the data to seek efficient results.

Insights Extraction from Data

A data engineering process demands a complete comprehension of the data. Comprehending what’s available allows us to unveil the real value in it. This is why the initial data analysis step is one of the most fundamental steps in process engineering. It allows you to extract crucial insights, features, and metrics from the data that are later used to feed into the machine learning algorithms.

A data engineering design process involves the use of advanced analytical and data visualization tools to identify the possible features that can be beneficial for business value. Therefore, you must have enough resources and an expert team with brilliant computation capabilities to make the most out of it.

Spotting the Challenges and Opportunities

The insights extraction step in the data engineering design process involves looking for insights, key features, and challenges and hurdles. The target becomes visible once the data engineers look for correlations and trends that can generate a massive impact (both positive and negative) on business value and growth. The extracted metrics are the ways to accelerate business values, as well as the source to unveil the possible issues and situations that can erode the entire business model. This is why it is crucial to keep an eye out for the potential pitfalls associated with the data, so the right steps to strengthen the business model can be taken as needed.

Handling Data Sets

The data pipelines carry massive amounts of data. However, that doesn’t mean that all of that will be useful for the business in the long run. This is why it is important to divide the data into sets based on their properties and the potential they carry for business value.

Managing the data this way makes the process of engineering much easier and saves time for the data scientists and analysts. However, it’s important to note that this is not the case in all situations. Some enterprises might need to make sets of bigdata and use them all together, while others may only have to work with a few sets of data depending on the developed business case.

Data can be grouped into sets based on demographic, behavioral, situational, psychological, and geographical parameters. This data segmentation makes it easier to understand the right focus of the data engineering process, saving both time and processing costs.

Big Data, Machine Learning, and AI Deployment

Data engineering involves the use of big data, data analytics, and machine learning. After the data is segmented in the form of sets, the next step involves using machine learning and big data to extract real value from it in real terms. All of this becomes possible only with the support of data engineers, architects, and scientists.

Data scientists make use of the labeled datasets and the extracted features to tune and refine a deep learning algorithm that works to analyze the data based on available resources and inputs and extracts the true value of data.

Extracting Actionables through Process Engineering

Once the deep learning algorithm provides you with the raw metrics, data scientists have to work on extracting the actionables stemming from the results. The action extraction process involves advanced analytical techniques and data-driven tools that help extract the most realistic actionable when combined with big data. Handling these data-driven tools with agile methods opens doors to potential efficiencies.

Data scientists make use of the processed data and analyze it, then they work on understanding the intelligent business changes and perks associated with it.

Building a Healthy and Scalable Process

Once you have everything you need from the data, define a process around those actionables. It’s also important to keep an eye on the business changes that you’ll have to implement and manage. The process sometimes becomes harder than one may have anticipated given that there could be resistance to change management. However, when executed in the right way, data engineering activates the most fruitful results.

Process Implementation and System Adjustments

The last step is to optimize the process concerning the business model and work infrastructure in order to make the required system adjustments. This step entails careful attention, real-time communication, and consistent updates between the data scientists, architects, and engineers. Data engineers initiate the process implementation with a step-by-step method and make changes to the business operations as needed. Once the process is successfully implemented and the volume of data grows, the real-time insights demonstrate whether they took the right steps or not. Essentially, the process auto-authenticates itself.

Get More Value of Your Data with Data Engineering

Data engineering is far more than just a technical exercise, it is an essential strategy for extracting maximum business value from an ever-growing sea of raw information. Its impact spans across better decision-making, improved business models, and long-term organizational growth.

For more helpful tips, advice, and best practices on how to unlock business growth through data engineering processes, you can read more of our articles on data analytics and machine learning.