Data Science Vs Data Analytics: What Are the Differences?

Data is everywhere. If you want to pursue a career in understanding the data that makes or breaks businesses, there's never been a better time to begin a course in data science and analytics.

With a field as broad and as general as data, though, how can we understand the difference between key industry roles such as the data scientist and the business analyst? Let's take a moment to dive into data - where it comes from, and how it can be useful. Then, we'll explore how a masters of data science online can help you discover which data career you prefer to jump into in the workplace, and how each of these careers can be massively beneficial in understanding data in the world around us.

Data: Why is it so important?

Whether or not you notice it, data is formed all around us. From the digital footprint that we leave online, browsing the web, to the purchases we make at retailers at home and abroad, data is generated. In fact, it's estimated that by 2025, we'll use more than 180 zettabytes of it.

While data can be captured and created in a variety of ways, the way it's captured and structured can be critically beneficial to organizations. Using tools such as a data lake or data warehouse, businesses capture commercially critical data such as transaction logs, and usage data. Businesses can then opt to dive into the data, which they can then use to help run their business. 

Depending on the sort of business you run, there are many questions that can be asked - consider some of the big ideas that businesses must consider these days:

• In the logistics space, fuel and time can be expensive, particularly for deliveries that require complex routing. To minimize cost and emissions, what are some strategies that businesses can make to improve operational efficiency?

• In supermarkets, space is precious - understanding what customers want is not only beneficial in a sales sense but can also be useful in a cost-minimization sense. When developing new stores, how can businesses leverage the data they have to build stores that are not only high sales drivers but also energy efficient?

Why is understanding data critical to business success?

Understanding data is a great way to enhance business operations. By understanding the patterns and behaviors that may exist in business processes, managers can utilize the domain expertise of business analysts and data scientists to make data-driven changes within their organization.

A recent study by Accenture found that data is used widely in modern workplaces - with some 63% of survey respondents saying they used it to make data-driven decisions within the workplace. Being able to utilize the talents of data professionals is not only critical, but it's also therefore imperative - particularly for businesses that wish to remain relevant in the years ahead.

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Business Analyst - Influencing Business Operations

One way to use data to make informed decisions is as a business analyst. As a strategic role, business analysts take advantage of data sources within a business to make operations run more smoothly, and to the benefit of the business.

For example, a business analyst may work on projects, providing advice to cross-disciplinary teams to ensure that strategic and corporate standards are met as part of a project. This can provide operational efficiencies by using past business practices to inform future decision-making.

In another example, a business analyst may review internal sales data to provide critical insights on product categories that may need to be evaluated. This can be extremely helpful in fields such as consumer staples, where understanding what trends and ideas can be relevant can be crucial in taking advantage of customer interest.

Data Scientists - Solving the Problems of Tomorrow, Today

Another way to utilize data in the workplace is by taking advantage of the domain expertise of a data scientist. Using statistical methods, a data scientist brings value to business by preparing, testing, and creating powerful data models using a range of internal and external data sources.

For example, a data scientist may work on an algorithm that can improve operational efficiency in a multinational pizza chain, such as Domino's. By developing models to improve customer outcomes in areas such as pizza quality, delivery speed, and customer retention, data scientists can take advantage of data and test it in a way that can be massively advantageous when compared to the family-owned pizzeria of old.

In another case, data scientists may leverage advanced tools such as machine learning and artificial intelligence (ML & AI) to develop tools that may previously have only been considered in the realm of possibility. Take, for example, a predictive model that incorporates automated weather data to optimize shipping routes for high-risk items. While it may seem like the stuff of fantasy, in fact, this is something that major businesses such as FedEx are undertaking with their data sources.

Where Will Data Take You?

Depending on your interests, there's a data opportunity available in almost every industry. With demand for data roles such as analysts expected to grow dramatically over the next decade, there's simply never been a better time to consider a career as a data professional.