Most people get mix up between the terms, data science and data analytics, even though they’re completely different disciplines. Think of it like this: data scientists design the crystal ball, while analysts read it. To say it another way, one creates the tools that make analytics and predictions possible, while the other uses those tools to solve real business problems. In this article, I’ll explain the explosive origins and massive technological forces propelling these scientific disciplines. Further, I’ll provide clear definitions and concise comparisons that clarify the differences between data analytics vs data science.

1. The Origins and Driving Forces Behind The Explosive Growth of Data Analytics and Data Science.

In fact, for centuries accountants, mathematicians, and statisticians have been crunching numbers. Now, thanks to computers these numbers and equations have turned into data and algorithms. Consequently, data analytics and data science have come into their own as a field of study and profession. To further explain, below I’ll detail the origins and current challenges these two data disciplines are tackling.

a. Computers And Its Data Output Are The Driving Forces For The Origins Of Both Disciplines.

It was in 1962 that John Tukey, a mathematical statistician, published a paper, “The Future of Data Analysis”. In this paper he pointed to the existence of an as-yet unrecognized science called data analysis. Further, he pointed to three driving forces in data analytics which still apply today (see 50 Years of Data Science) for both data analytics and data science. These driving forces are:

• Computing Power. Indeed, more than 50 years later computer capabilities continue to expand rapidly to store, crunch, and display data.
• Computer Data Output – “Big Data”. Back in the day, data was a new phenomenon. Now, it is ever increasing because of more powerful computing power, cheap data storage, and the rapid expansion of Internet of Things (IoT) devices.
• Fast, Global Internet. Computers are now transferring data at lightning speed to other computers and the “cloud”.

As a result of these computing phenomena, every industry and profession is coming to terms with “data wrangling” and squeezing insights from “Big Data“.

b. Businesses Start Talking Data.

Remarkably, it was just in 2008 that we started hearing the term “data scientist”. Also at that same time, businesses and IT departments started talking about and working with “data”. Moreover, they started to regularly use terms like “Big Data“, business intelligence (BI) dashboards, and data analytics. Now, business leaders are desperately seeking to harness all this data and find ways to make it effective. In fact, they see leveraging data as key to their business’ success.

“Data are becoming the new raw material of business.”

Craig Mundle

c. We Are Now Awashed In Data Where We Need Data Professionals To Make Data Useful.

Now, we are awash in data. According to TechJury: “if you were to take all of the data generated by humanity in 2020 and divide it among the world’s population, you’d find that each person created 1.7 megabytes of data every second. In fact, it’s estimated that more than 90 percent of the total data created by humans has been generated in just the last two years.” Indeed, it is now obvious that businesses need data professionals. For instance, Data analytics and data science are both popular fields where the demand for data professionals are now in the millions.

“It’s amazing how much data is out there. The question is how do we put it in a form that’s usable?”

Bill Ford

2. Data Analytics Vs Data Science – Definitions.

In fact, it is easy to get confused about the difference between data analytics vs data science. For instance, employers routinely place thousands of job openings for both Data Analysts and Data Scientists. However, in many cases, companies are using the same job descriptions and skill sets list for both job titles. Now, in reality these terms should not be interchangeable. This is because businesses do need both data scientists and data analysts. To better understand and distinguish the differences between these two job titles, below are some definitions.

Scientific Method Example: Data Scientists Use An Unique Way To Achieve The Best Results

a. Data Scientist Definition.

“Uses scientific methods (data science) to liberate and create meaning from raw data.”

Data Science Association

So a data scientist conducts basic and applied research using data. Further, they use the scientific method to create meaning, data models, and software applications. For more how data scientist use the scientific method, see my article, Scientific Method Example: Data Scientists Use An Unique Way To Achieve The Best Results.

b. Data Analyst Definition.

“Examine large data sets to identify trends, develop charts, and create visual presentations (i.e. data analytics) to help businesses make more strategic decisions.”

Kristin Burnham, Northeastern University

In this case, the data analyst uses a wide range of software tools (software applications, algorithms, methods, etc.). Then they apply these tools against large data sets. Hence with this data, they create actionable information for decision makers. Moreover, many, if not most, of these software tools that data analysts use originated from data science.

Lastly, for a more detailed discussion on data analytics vs data science definitions, see my article, Data Science Definition – The Truth About This Discipline And Its Massive Growth.

3. Data Analytics Vs Data Science – A Breakout Of Each Discipline By Sub-Type.

a. Six Types Of Data Analytics

Moreover, you can apply data analytics in a variety of ways. In particular, this includes budgeting and forecasting, risk management, marketing and sales, and in product development. More importantly, there are six types of data analytics. See below:

Types of Analytics

A Data Analytics Perspective To Better Empower Supply Chain Managers

• Descriptive Data Analytics. Here, the analyst asks what happened?
• Diagnostic Data Analytics. Next, the analyst why did this happen?
• Predictive Data Analytics. The analyst asks what is most likely to happen?
• Prescriptive Analytics. Here, the analyst asks what action should we take?
• Real-Time, On-Demand Analytics. In this case, the analyst asks what do I do now?
• AI-Powered Analytics. Lastly, the analyst asks what questions did I not know to ask?

Also, for more details and examples on types of data analytics, see Tim Stobierski’s article, WHAT’S THE DIFFERENCE BETWEEN DATA ANALYTICS & DATA SCIENCE?, and my article, A Data Analytics Perspective To Better Empower Supply Chain Managers.

“Data is the new science. Big Data holds the answers. Are you asking the right questions?”

Patrick P. Gelsinger

b. The Six Divisions Of Data Science.

Indeed, data science is now influencing all disciplines, organizations and industries. Further, data science’s domain knowledge, and its software tool sets are rapidly growing. Moreover, the amount of data continues to explode. To better illustrate the vastness of data science, David Donoho, a professor of statistics at Stanford, describes and classifies the various activities of data science in his paper, 50 years of Data Science. Specifically, he describes GDS (Greater Data Science), the science of learning from data, as divided into six divisions. To list, the 6 types are:

What Is Data Science? Its Focus, Applications, 6 Components Explained

The Types of Data Science

1. Exploring and Preparing Data.
2. Representing and Transforming Data.
3. Computing with Data.
4. Modeling Data.
5. Visualizing and Presenting Data.
6. Science about Data Science.

For a more detail description, see my article, 6 Divisions of Data Science.

“The best thing about being a statistician is that you get to play in everyone’s backyard.”

John Tukey

Data Science Definition – The Truth About This Discipline And Its Massive Growth

Need help with an innovative solution to make your supply chain analytics actionable? I’m Randy McClure, and I’ve spent many years solving data analytics and visibility problems. As a supply chain tech advisor, I’ve implemented hundreds of successful projects across all transportation modes, working with the data of thousands of shippers, carriers, and 3rd party logistics (3PL) providers. I specialize in launching new analytics-based strategies, proof-of-concepts and operational pilot projects using emerging technologies and methodologies. If you’re ready to supercharge your analytics or if you are a solution provider, let’s talk. To reach me, click here to access my contact form or you can find me on LinkedIn.

For more from SC Tech Insights, see the latest articles on Data and Decision Science.