Do I Need Data Analytics, Data Science, Or Data Engineering Services? Here’s How To Find Out

By Author: Jency Jane
Total Articles: 5
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Last year, Cognizant published a report titled Transitions to Digital Experience Making. The report aimed to understand how business leaders around the world are "preparing for a world reshaped by the pandemic and digital imperatives."

Cognizant surveyed 4,000 business leaders for its study. The expansive list could be divided into all kinds of different industries. Over 280 surveyed, for example, were executives at the world's leading media and entertainment enterprises.

Let us stick with media and entertainment for now. Cognizant found that over 60% of the executives were confident that the pandemic would accelerate the adoption of data, data analytics, and other digital initiatives in the industry. In fact, over 75% admitted having used data analytics in "some degree" already.

And that is just media and entertainment. The use of big data and big data analytics is taking over the world, touching businesses across every industry, media and entertainment, or manufacturing and education.

And that is how it should be. Without data, businesses are blind and deaf, making key, costly decisions, uninformed. ...
... It is rightly called the new oil for propelling modern businesses to new heights. In 2022, attaining success, especially spectacular, sustainable success, is going to be virtually impossible without using big data and big data analytics.

It is no surprise then that data analyst is one of today's hottest jobs. Or was it data scientist? Actually, it is providing big data engineering services. What is even the difference?

Data analyst vs. data scientist vs. data engineer

As a business owner in 2022, you want to solve customer problems and make business decisions with data. The point is, you need all three: data analytics, data science, and data engineering services. But when do you require what? Here is how to find out.

1. Data analytics

The aim of data analytics is to look at data, ask the right questions, and find their answers. The questions may ask, what do our customers want? And why? The answers to these questions are hidden in data—marketing, sales, customer, etc.—and it is the job of a data analyst to unearth them.

The answers are found in trends or patterns in data. But more difficult is determining whether the trends are correlated or does one trend cause the other or vice versa. In any case, businesses learn trends to make better decisions. More specifically, businesses employ data analytics services to make better decisions—or solve problems—in the present.

2. Data scientist

In contrast, businesses employ data science services to solve problems in the future. Data scientists are proficient in mathematics, specifically, statistical analysis. They combine proficiency with sophisticated tools like machine learning to forecast future occurrences based on past data.

Given the rise in data and its types (text, audio, video, etc.), data science has grown more and more challenging. Modeling now involves increasingly more powerful computers and increasingly more complex algorithms. However, the problems we are trying to solve are also increasingly ambitious: understanding and translating natural language, developing an ethical AI, and predicting the climate, to name a few.

In any case, insights from data science inform forecasts. The forecasts could shape a business strategy. Or they could shape an algorithm or machine learning/AI model. But who can vouch for the accuracy of a forecast?

3. Data engineer

A forecast is as accurate as the data on which it is based. And that is what data engineering services guarantee: extracting, transforming, storing, managing, and moving data in a way that ensures it is trustworthy, reliable, and consistent. The result is insights—and therefore forecasts—that are trustworthy, reliable, and consistent.

Data engineering represents the foundation of a data-driven framework. A data engineer designs the pipelines that ensure efficient movement of data and that the right data is always available to the right people at the right time. He or she works behind the scenes, and it is on their work a data analyst or scientist builds.

In conclusion, it should be noted that while we have divided the three into neat columns, businesses do not work this way. The practices overlap and feed off each other's ideas. Businesses thrive when the three work—it is not hard to guess—together. In synergy.