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Understanding the Difference Between Data Science and Business Analytics

In today’s zeitgeist, we’ll often hear some buzzwords that might not actually mean very much to the average person. Terms like big dataare thrown around in the media and in popular culture and are becoming increasingly important to professionals in many different industries and disciplines. The ability to understand large sets of data and to interpret and gain valuable information from the data we’re accumulating at a rapid pace means that the need to properly understand this data is crucial. For this reason, data scientists and analytics are on the rise.

There’s a distinct difference, however, between the disciplines that manipulate and gain insights from this data. Data science and business analytics might seem very similar and interchangeable at first, but in fact, they require very different skills and produce very different information. But what exactly is big data? How do data scientists use it, and what actually is a data scientist? And how does this all relate to business analytics?

What is Big Data?

Gartner, a prominent business research and advisory company defines big data as follows:

“Big data is data that contains greater variety arriving in increasing volumes and with ever-higher velocity.”

This definition has given rise to the popularity of the 3 Vs of Data Science – variety, volumes, and velocity. These three words do very well at defining just what big data is. Big data is an incredibly large amount (volume) of data that can be arriving very quickly (velocity) and might encompass many different aspects or areas of interest to the business (variety).

Big data is complex, and often we can’t rely on traditional ways of storing data in relational databases because of its size and complexity. Here is where we find the data scientist, on hand to help manipulate and present the data to us in a way we can understand.

With the idea of what big data is, let’s now unpack what the differences are between data science and business analytics.

What Is a Data Scientist?

Data science at its core is all about that big data. A data scientist is a technology expert, masters of the tools of the trade and the ability to house and manipulate bog data. If you took a mathematician, a statistician and a computer scientist and blended them together, you’re getting close to a data scientist. On any given day, a data scientist might be collecting data, they might be storing or ‘warehousing that data’ or they might be transforming it using programming languages like SAS, R, and Python into something that humans might be able to read or interpret.

They use data to solve business-related problems by using statistical analysis methods. It all sounds quite complicated, but the data scientist is the expert in manipulating data to present it in a way that assists businesses with their goals.

Examples of Data Science

Let’s consider some real-life applications of where you might find a data scientist.

  • Healthcare– As with many other things, the healthcare industry was on the cutting edge of data science, and it has used data extensively for many things. Perhaps the most prevalent use of data in health science is in the identification and prediction of diseases – how they’re likely to spread by tracking cases in real-time. If you’ve ever seen the CDC’s FluView or Google’s Flu Trends, you’ve seen the work of a data scientist using big data in healthcare.
  • Traffic– It’s almost impossible to talk about big data without talking about Google – and while we are, let’s consider Google Maps. Users of the service will know that it provides real-time traffic information, but it’s also able to predict traffic and travel time in advance. That’s because it’s using historic data collected from millions of users over time and calculating likely traffic and travel times using an algorithm that was almost certainly developed by a data scientist.
  • Logistics– There’s room for this kind of data intelligence in many aspects of travel and transport. Courier companies like UPS use data to ensure all shipments are routed through the most efficient and effective delivery route, considering a great many things like weather or problems with air travel in certain regions.
  • Professional Sports – Big data finds a home in professional sports too. Many sports use statistics and data to try and improve their chances of winning games or buying the best players for their team. We’ve seen data used extensively in baseball, basketball, and football to great effect.

There are many other places where big data and data scientists can be found, and these few examples are only scratching the surface of the impact that data science has on our daily lives. This data can sometimes form the basis of the data used by business analysts.

What Are Business Analytics?

Business analytics makes use of statistical models and methods to collect, sort and process business data and create business insights. The primary goal of business analytics is to decide what data can be of use to a business and how that data can be used in the pursuit of improving the business, solve issues and problems, and ultimately increase the efficiency of business processes, the productivity of staff or workflows and increase revenue and profits. This is where it differs from data science in a fundamental way – here we aren’t trying to create an algorithm or manipulate big data, but rather we’re trying to take these prepared data sets and find value to a business within them.

What is a Business Analyst?

A business analyst is a very varied career and tends to evolve based on the needs of the company they are working in. At a core level, the business analyst will work with the stakeholders of a particular area or process to identify ways that the operations or processes can be improved. They’ll often be the ones communicating between departments or heading up meetings that involve more than one department or team within the business and tying together or interacting with the various stakeholders.

To understand where a business analyst fits in to business analytics, we need to understand where it fits in to the business. Business analytics is a subset of business intelligence and works in the realm of business data. While business intelligence itself encompasses many parts of business processes and improvement, including the actual collection and categorization of data, business analytics hones in and focuses on the actual methods used in making data useable.

Some Components of Business Analytics

Let’s have a look at some of the core components of business analytics. Each is an important part of building datasets for use in analytics.

  • Aggregation of Data– The first step in preparing data for analysis is to collect, centralize and clean the data. Depending on the source of the data, this might take no time at all, or it might take a lot of work to clean it up for use.
  • Data Mining– Data mining is the process of identifying trends and patterns in data that might not have been used before. It’s the process of classifying data and identifying what it can be used for.
  • Forecasting– Using data to forecast events or requirements of the business is a big part of data analytics. A good example of this is stockholding of a retail company. What trends would mean the need to purchase more of a specific item for sale, and at what times of the year should you stock up on things – like space heaters in the early winter?
  • Data Visualization– Once data is collected, it needs to be displayed in a readable fashion, and that’s where data visualization comes in. This is where interactive graphics, charts, and graphs might be used.

A Real-World Example of Business Analytics

Let’s have a look at a real world example of where business analytics helped a company increase its effectiveness. For this example, we’re using the Coca Cola Bottling Plant, an independent company responsible for bottling Coca Cola products.

For a long time, the company had a manual reporting process, which meant they didn’t have any meaningful access to real-time sales or operational data. The business intelligence team from Coca Cola harnessed the power of their Business Intelligence platform to automate a reporting process, which not only meant that they were able to get much more up to date information on sales and operations, but they saved an incredible amount of staff time.

According to a report, aside from the invaluable information they were getting from the real-time reports, the Coca Cola Bottling Company saved 260 hours in a year. This is the equivalent of an employee working for 6 full weeks a year.

If you want to learn more about the difference between data science and business analytics, you might want to check the linked article from Suffolk University.

Both data science and business analytics have their roots in the same place, and that’s the use and manipulation of data to get to a goal. Data science relies on technology and computer science to make huge amounts of data useable, while business analytics uses data to improve the business processes and the focus of the business to improve the effectiveness of the business.