Most people mix up data science and data analytics, but they’re completely different disciplines. Think of it this way: data scientists design the crystal ball, while analysts read it. Indeed, 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 recent, explosive origins and driving 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.

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

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 explain, below is a summary of the origins and current circumstances faced by both the fields of data analytics and data science.

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

In 1962 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 the 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. This is the phenomena of computers that continue to expand its capabilities to store, crunch, and display data.
• Computer Data Output. The ever increasing amount of data that computers are producing and cheaply storing known as “Big Data”.
• Fast, Global Internet. Computers are now transferring data at lightning speed to other computers and the “cloud” as well as receiving data from Internet of Things (IoT) devices.

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

2. Businesses Start Talking Data.

Remarkably, it was just in 2008 that we started hearing the term “data scientist”. Also at the 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 seeking to harness all this data and find ways to make it effective. In fact, they see leveraging data as key to the success of their business.

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

Craig Mundle

3. 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 was over 2.7 million by 2020.

Data Analytics Vs Data Science – Definitions.

It is important to beware of these fuzzy “Big Data” terms like data analytics, data science, and data mining. For example, Dan Ariely, a well-known behavioral economics expert, says this about big data:

“Everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it.”

Employers literally have thousands of job openings for both Data Analysts and Data Scientists. In many cases, companies are using the same job descriptions and skill sets list for both job titles. Yet in a technical sense these terms are very different. On the other hand, both of these professions are growing rapidly due to “Big Data” and the fact that businesses do need both data scientists and data analysts. To better understand and distinguish the differences between these two job titles, I like these definitions:

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

1. 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.

2. 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

So, 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 the definition and span of data science, see my article, Data Science Definition – The Truth About This Discipline And Its Massive Growth.

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

1. Six Types Of Data Analytics

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. Below are six types of data analytics:

“The greatest value of a picture is when it forces us to notice what we never expected to see.”

John Tukey

Types of Analytics

1. Descriptive Data Analytics. Here, the analyst asks what happened? They examine, understand, and describe. For example, a dashboard displaying year-over-year pricing changes.
2. Diagnostic Data Analytics. Next, the analyst why did this happen? For example, examine the market to determine the reasons behind product demand.
3. Predictive Data Analytics. The analyst asks what is most likely to happen? Specifically, they rely on historical data, past trends, and assumptions to answer questions about the future. For example, real estate brokers provide projected home values to buyers.
4. Prescriptive Analytics. Here, the analyst asks what action should we take? To detail, they identify specific actions that should be taken to reach future targets or goals. For example, a transportation company provides cost-effective delivery through better route planning and auto-correction of shipping addresses.
5. Real-Time Analytics. In this case, the analyst asks what do I do now? To detail, analytics enters into the realm of operations offering real-time insights. For example, analysts can evaluate real-time data to identify choke points and disruptions anywhere in the world.
6. Cognitive AI Analytics. Lastly, the analyst asks what questions did I not know to ask? In this case, they leverage both Artificial Intelligence (AI) and data analytics to do knowledge-based tasks. For instance, analysts can prompt an AI Chatbot to evaluate data, and then automatically create a trend chart.

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.

2. The Six Divisions Of Data Science.

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

John Tukey

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:

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.

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

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