Data Analytics vs. Data Science: How Do They Differ?

What is the difference between data analytics and data science?

The difference between data analytics and data science lies in the scope and focus of their processes. Data analytics deals with limited data sets to provide users with specific answers to their questions while data science concentrates on larger sets of data to discover what questions users are not asking, but should, to move their business forward. Where the parameters are set in data analytics (e.g. a website’s unique visits, CPC ratio, employee turnaround rate), they are more fluid in data science, where the aim is to discover possible insights buried in tons of data.

Data analytics and data science are often used interchangeably. However, if you take a closer look, the two fields have quite a number of differences from the types of data sets they focus on to the kinds of results they produce. Thus, it is highly possible that you are undervaluing data analytics software because you only focus on what you want to know, not what you might know.

This can not only be frustrating to deal with; it can also be counterproductive to your operational efforts. By using the right tools for the wrong objective to analyze your data, you might yield insights that do not necessarily reflect what your collected data implies.

To help you understand the difference in data analytics vs, data science, we have prepared a guide explaining the difference between data analytics and data science. Hopefully, by breaking down their definition, focus, methodologies, and practical applications, you’ll get a better idea as to how you can use both disciplines to your advantage.

data science

Once you have a clear picture of data analytics vs, data science, you should opt for a business intelligence solution that can perform both. One such tool is Sisense, a highly intuitive and scalable platform. You can sign up for Sisense free demo here and get to see how its features can help your business grow.

Digital native or digital immigrant, humans generate valuable data at a surprisingly fast pace. A recent study shows that we create up to 2.5 quintillion bytes of data every day and this number is expected to increase annually.

This is why it comes as no surprise that 97.2% of businesses leverage big data in their operations. Consequently, they have also started using disruptive technologies such as machine learning (96.4%), cloud computing (90.5%), digital technologies (77.4%), FinTech solutions (47.6%), and blockchain (41.7%) to reinforce their use of data, according to the 2019 New Vantage Big Data Executive Survey. What’s more, while big data is often associated with large corporations, there’s also plenty of small business data analytics solutions on the market so this trend isn’t limited to big companies.

However, the same study also revealed that businesses feel they are failing to transform their businesses using big data with 71.7% reporting that they have yet to forge a data-driven culture and 53.1% stating that they are currently not treating data as an asset to their company. These challenges brought about by the implementation of new technologies alongside the use of data have led to companies simply hiring professionals to do it for them. In fact, data science and analytics jobs are expected to increase to 2.7 million by 2020.

While it may be a smart move to get experts who can handle the technicalities of big data, as an entrepreneur, you know it is not enough to simply pass the task and hope to get good results. Not only do you have a wide range of easy-to-use analytics tools today, but you have access to free business intelligence software.

So, to guide in you in maximizing big data for your operations, we have prepared a detailed comparison of data analytics and data science. With this, you will better understand the roles that these two processes play in the utilization of big data.

Comparison of Data Analytics and Data Science

Definition of Data Analytics and Data Science

Data analytics is the process of capturing, analyzing, and organizing data to uncover actionable insights. With it, you can collect raw data from limited sources, remove unnecessary information, and create organized data sets that may be analyzed and visualized to get streamlined insights. This is great for uncovering solutions for business scenarios that require immediacy as well as getting answers to questions that you already have in mind.

Meanwhile, data science is a multidisciplinary field that consists of different processes. Used mostly for large sets of raw and structured data, it can predict trends in your data, find potential problems, as well as discover opportunities for your business. Unlike data analytics, this process delves more into helping you understand aspects of your business that you might not know. This is why it is a better option for those who are looking to drive innovation within the company.

In a nutshell, data analytics can be seen as the branch to data science’s tree, as the latter deals with broader concepts while the former is used to drill down to more specific aspects of your data.

Data analytics as applied to financial KPIs whose metrics are typically well-structured (Sisense).

Focus of Data Analytics and Data Science

Both data analytics and data science are concerned with making sense of your collected company data. However, there are major differences as to what these two fields focus on.

As mentioned previously, data analytics aims to help you find solutions to questions you know. In order to do this, data analytics focuses primarily on mining structured data for actionable insights that you can apply immediately. Otherwise, data science might be the process that you need to do.

Unlike data analytics, data science concentrates more on helping you ask the right questions by going through unstructured data. It locates potential avenues of study, or in simpler terms, it finds out what questions you have yet to ask. This way, it is easier for you to understand the meaning behind the numbers and guide your analysis in the right direction.

Methods and Skills Used in Data Analytics and Data Science

Comparing data analytics vs, data science means understanding their approach to utilizing information. Thus, they also call for different methods and skills. For instance, data analytics requires extensive data querying and data visualization while data science will require more focus on data cleansing and results exploration. It can also be noted that data science also uses a handful of algorithms in order to predict trends and patterns in data–something that is not often seen as part of the data analytics process.

But, of course, these two fields also have their similarities. As both tackle different types of data, these fields will require you to have ample background in computer science, extensive mathematical know-how, and statistical prowess. Moreover, both of these processes involve SAS, R, Python, and similar programming languages so understanding how these disciplines work is a must.

Data science is especially useful for companies with global operations, where you can do a variety of data mashups (Sisense).

Applications of Data Analytics and Data Science

The practical applications of data analytics and data science are quite different considering the focus of the two fields varies.

For one, data analytics is often used by industries that have immediate data needs. Some examples of which are:

  1. Healthcare. This can be used to find out how to improve the quality of care by tracking the efficiency of equipment, understanding how to maximize the time of healthcare professionals, as well as knowing what they should be focusing on in terms of workflow.
  2. Gaming. This can be used to understand how users are responding to your game. It can track feedback on interface and gameplay based on collected data. Moreover, this is great for finding out what gamers expect from their game.
  3. Retail. This can be used to help you understand your customers. You’ll be able to monitor the impact of loyalty programs and other promotions, understand how they interact with your online store, and the like.

Meanwhile, data science is often applied for cases that don’t call for specific results. These include:

  1. Search Engines. This is used to collect and organize the best results for search queries quickly and accurately.
  2. Recommender Systems. This is used to find trends in user demands as well as connect them with relevant information. As such, it can sift through the hundreds of products that a store offers and suggest ones that a customer is more likely to buy. This allows you to promote other products as well as checkout processes.
  3. Corporate Analytics. This can be used in order to find ideas on how to move your company forward. Instead of simply patching up existing problems and working on what you already have, data science will allow you to discover untapped opportunities.

It bears noting that whatever the application, it is essential to visualize data for everyone to grasp the insight no matter the complexity. Many business intelligent software features excellent KPI dashboard and reporting.

What Should You Use for Your Business: Data Analytics or Data Science?

While data analytics and data science clearly have their differences, it is important to note that they will allow you to yield various results depending on what you want to find out for your company. Instead of seeing them as separate fields, data analytics and data science should be interpreted as two parts of a whole.

Data science lays a solid foundation that will help you discover potential insights. However, these data can be close to useless on their own as they don’t necessarily provide you with ways to leverage it. You should use data analytics to help you find actionable insights with practical applications that you can roll out in the immediate future.

The bottom line here is that you should consider using both data analytics and data science for different aspects of your business. They should be, in fact, part of your reasons for getting business intelligence software. When used hand in hand, these tools are a force to be reckoned with. They will allow you to maximize your data, enhance your understanding of your business, improve efficiency, as well as visualize your operations in a new light.

Ready to try data analytics and data science? You can sign up for Sisense free demo here.

You might also want to explore the best data visualization tools around, many of which can be used for both data analytics and data science.

Chris Miller

By Chris Miller

Chris Miller is a senior customer service analyst at FinancesOnline. For more than 5 years now, he has witnessed and written about the tremendous impact of digital technologies that have deeply disrupted the customer service industry. The onset of chatbots and other AI/ML tech, omnichannel platforms, highly personalized service, the emerging blockchain methodologies specially created a deep impact, all of which are reflected in his writing. His reviews of customer service applications serve as invaluable resources for businesses of any size and scale.

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