Data analytics is a process that uses data to make better decisions, take more intelligent actions, and uncover new opportunities. Data analysts use tools and techniques to extract insights and trends from data.
Data analytics is often confused with data analysis, which is a subset of data analytics. Data analysis is “an analytical study aimed at extracting knowledge from data to make effective decisions”.
In this post, we’ll explore the differences between these terms to help you make the most of your data analytics initiatives.
Analytics and Analysis: A Look at the ABCs
Before moving on, let’s delve into the fundamental meaning of the two terms.
Analytics is the systematic study of information with the help of several tools. On the other hand, analysis is the detailed examination of analytical data.
So, analytics and analysis are interrelated; in fact, these terms are like night and day. Without proper analytics, one cannot perform analysis. Thus, in simple terms, we are able to analyze due to analytics.
Data pertains to all the facts and stats related to a business or entity. This data is like the content in digital marketing. Without its adequacy, one cannot perform a study to make crucial decisions.
Hence, connecting these three terms defines the gradual growth of a business.
What Is Data Analytics?
Let us move beyond technical definitions. You might have heard about trending job profiles like data analysts and data scientists. These professionals work in the field of data analytics.
These experts simplify existing data to make it easy to analyze for a business. This simplification often leads to:
- Trend analysis
- Highlighting areas of opportunities
- Predicting triggers
- Better management decisions
In layman’s terms, data analysts help management make better business decisions. Let us understand data analytics with a simple example.
Data Analytics: Example
CocaCola is a prominent brand that uses concrete data analytics. This process led to a phenomenal strategy of a digital-led loyalty program.
Coca-Cola used customer data to understand elements that redefine advertisements and promotions. This process of business analytics led to strong customer retention of the brand.
What Is Data Analysis?
If data analytics is a cake, analysis is a vital piece of this cake. As said earlier, data analysis extracts what’s needed from the initial data.
Typically, data analysis consists of techniques like:
- Regression analysis
- Cluster analysis
- Sentiment analysis
- Statistical analysis
- Prescriptive analysis
- Factor analysis
- Text analysis
- Diagnostic analysis
Furthermore, efficient data analysis simplifies the existing data. Hence, you can make the best use of available resources for making major business decisions.
Data Analysis: Example
The most common example of test-based data analysis is the survey questionnaire. A research and marketing consultancy, Anstice used effective data analysis to gain customer insights.
The MonkeyLearn text analysis analyzed nearly 14,000 responses. Thus, this analysis proved beneficial for the company’s planning and design team.
Data Analytics vs. Data Analysis
The following table depicts a head-to-head comparison of the two generally confusing terms. Importantly, we have parameters that highlight the crucial comparison points.
| Parameter | Analytics | Analysis |
| Basic Action | Exploring | Extracting |
| Number of Stages | High | Comparatively Low |
| Quantity of Tools | High | Limited |
| Cost of Execution | More | Less |
| General Order of Execution | First | Second |
| In a Nutshell | Broad Concept | Process |
Generally, data analysis output is more straightforward for users to understand and interpret. The text, graphs, spreadsheets generate a simplified presentation of information.
On the contrary, data analytics involves structuring unstructured data. Moreover, a data engineer can interpret these findings to assist the management.
In terms of real-time application, data analytics is beneficial to predict future demands and numbers. On the contrary, data analysis studies past information to gain valuable insights.
Data analytics uses standard tools like:
- Python
- SAS
- MS Excel
- Apache Spark
- RapidMiner
- KNIME
- Tableau
- Weka
- R Programming
- Google Analytics
Data analysis uses tools like:
- SQL
- Google Fusion Tables
- DATAPINE
- MySQL Workbench
- Talend
- Qualtrics
Data Analytics vs. Data Analysis Comparison: An Example
With all the technical information, it is time to distinguish analytics from analysis in the simplest way possible. We believe that real-life examples offer the simplest explanation of a concept.
So, consider you own an electronic store. Now, you have the customer data that pertains to sales in the past year.
Thus, with data analytics, you will find what customers can purchase in the near future.
In contrast, with data analysis, you will find what happened in the past year related to sales.
So, in a nutshell, analytics talks about the future, whereas analysis evaluates the past.
Data Science and Data Analytics
With a growing emphasis on data science, you may wonder about its similarities and differences with analytics.
This section covers everything about data science. In addition, you will understand what data analysts and scientists do.
What Is Data Science?
Data science is a field that involves obtaining value from data through the following:
- Statistics
- Scientific methods
- Data analysis
- Artificial Intelligence
According to IBM insights, 90% of data resulted from user interaction in the last two years. So, with an abundance of data at our fingertips, businesses must utilize it wisely.
Data scientists develop new processes for data modeling. They write algorithms and indulge in the development of statistical models.
Data Analyst vs. Data Scientist
The simplest way to differentiate an analyst from a scientist is by comparing them to an engineer and architect.
In this case, the data analyst is an engineer who works to extract information out of data. The data scientist is like an architect who designs ways to capture data for further analysis.
Data analysts:
- Query data using SQL
- Use Excel for forecasting
- Use software to create dashboards
Data scientists:
- Conduct analysis using machine learning algorithms
- Develop big data infrastructure
- Create libraries using programming skills
Both these profiles, however, possess some similar skills like:
- Data mining
- Data warehousing
- Mathematical and statistics knowledge
- Data visualization
What Is Data Mining?
Data mining falls under data analysis. It involves analyzing a massive volume of data to answer common business questions.
Data mining is typically prevalent in professional areas like:
- Research
- Business
- Education
- Sales
- Product development
- Marketing
Data mining involves steps to achieve high-quality data that profits a business. These steps are paramount for data miners to create a structured process.
- Understanding a business: Data miners study the current business condition, goals, and ambitions to evaluate data needs.
- Preparing the data: Data preparation includes all activities that segregate, re-arrange and fix missing information.
- Data modeling: In this step, data miners study and find patterns in the collected data. Generally, they use algorithms and other software tools for efficient modeling.
- Assessment: This step involves evaluating whether the current data proves advantageous for business goals. Moreover, this step can consume time for effective implementation.
- Data disposal: Finally, data miners present the final draft to top-level management. Consequently, it is up to the management to decide the final execution of steps to justify the overall data.
These steps and explanations justify that mining falls under data science. Analytics and analysis are integral parts of data mining.
Can Analytics and Analysis Work Together?
Data analytics and analysis are indeed two different terms. Still, they are inseparable in most cases.
Let us understand how analytics fuels the process of data analysis. This example will highlight the interdependency of the two concepts.
Data Management: Example
Consider a small automobile sales store. A data analyst uses tools to interpret and study the existing data.
Further, this analyst will use tools like AI, machine learning to predict future sales. Accordingly, the business owner will know when to launch or sell a particular car model.
Building on this example, the team needs to analyze this data to understand sales better. Thus, the business owner gets a clear idea of profit, loss, break-even points, etc.
Hence, although data analysis can be an independent activity, it needs raw material. This raw material in the form of data has simplified representation due to systematic analytic techniques.
Big Data vs. Data Analytics
As the name suggests, big data is unorganized information about a particular subject. This data comes from various sources like social media, smartphones, online surveys, the internet, etc.
Data analytics uses techniques to structure the existing data in proper channels. Thus, big data needs efficient analytics to find use in practical applications.
Managing big data is often a daunting task to data scientists or big data experts. This concept relates predominantly to companies like Google, Amazon, and Facebook.
Wrapping Up
Many people often assume that both “analytics” and “analysis” have the same meaning. You now know that analytics is a broader term, and analysis is a part of this process.
But it is vital to understand the difference between both these terms.
Data will redefine and elevate business in the upcoming decades. Hence, using these techniques to gel out favorable results for your business or organization is essential.
