fbpx
01
/
01
/
Sana Shafaqat Official

What’s the Difference Between Data Analysis and Data Analytics?

Facebook
Twitter
LinkedIn
Pinterest
Reddit
Email
difference-between-data-analytics-and-data-analysis

YOU READ:

The world of data analytics has become the new frontier for businesses. Data analysis is examining data, extracting insights, and making decisions based on those findings. The critical point, however, is that it is essential to realize that we use several other terms to describe this process as well – data mining, data analytics, artificial intelligence (AI), and so forth. 

One significant difference between data analysis and analytics is how they approach data analysis; analysis tends to focus on the past, whereas analytics tends to focus on the future.

Here, we’re going to look at what data analytics is and how it differs from standard data analysis methods so that you can choose the most suitable for your needs. 

It is common to hear people use the terms “Data Analysis” and “Data Analytics” when referring to the act of analyzing data to derive meaningful insights from it. However, there is much more to these terms than meets the eye.

In data analysis, an analyst examines raw data and identifies patterns and trends. Then conclusions are drawn based on these observations using these trends and patterns as a guide. 

You will need a spreadsheet, a database (like Excel), and one or more tools to perform this analysis properly. However, you do not need advanced programming skills to perform this work.

Data analytics uses technology such as artificial intelligence (AI) software or machine learning algorithms to extract actionable insights from raw data sets and sometimes even to predict future outcomes based on these insights.

In contrast to traditional methods, such as statistical analysis, you can access data only with your eyesight, experience, knowledge, and understanding. We can see things differently using algorithms because they are not biased toward any particular perspective. So they are far more accurate than humans could ever be on their own without some assistance by the side of their backs to help them along on their journey towards perfectionism.

What is Data Analysis?

Data analysis refers to examining raw/primary data and computing results describing the behavior of the data. It can be done manually or using software and can be used to test hypotheses or answer questions.

Data analysis refers to identifying patterns in data sets and using those patterns to generate predictions or other insights about the future. It requires extensive knowledge about different types of databases and how one can use them together with additional tools to make sense of large data sets (e.g., social media).

Data analytics is a combination of both the two methods above. Still, it differs from them in that it is more concerned about how you get at your data rather than what you do with your findings once you have them (e.g., whether your hypothesis is valid based on your results).

Data analysis involves examining and interpreting data using statistical methods and models to discover patterns. It is sometimes also called exploratory data analysis, which consists in finding patterns in the data by making graphs and visualizing them, thereby arriving at conclusions.

What is Data Analytics?

In data analytics, the objective is to take analyzed data and apply it in a meaningful and helpful manner to develop well-educated business decisions. There are many types of data analytics, but the most common is studying data to understand it. It’s a subset of data science, which uses statistics and machine learning to turn large amounts of unstructured information into meaningful conclusions.

Data analysts use statistical techniques to analyze and understand the relationship between different pieces of information to make decisions about their business processes or products.

It refers to using technology that transforms raw data into actionable insights. It involves various advanced statistical techniques that help convert heaps of complex and unstructured data into relevant information. Contextualization of data through the use of analytics helps businesses make informed decisions, thereby achieving their goals.

By analyzing your existing information sets, data analytics allows you to extract valuable insights through different methods, such as machine learning or natural language processing (NLP).

As a result, you can conduct this analysis using a variety of tools available to you today, such as R, Python, Tableau Public, SAS, Apache Spark, Excel, etc. Still, they all share one thing in common: they extract meaning from raw numeric values stored within data stores such as SQL databases or flat files stored on hard drives.

Conclusion

The phenomenon of data analysis is the study, refinement, transformation, and training of historical data to gain valuable information, suggesting conclusions, and make decisions based on that information. To improve insight and design better strategies, data analytics uses data, machine learning tools, statistical analysis, and computer-based patterns to gain better insight.

Data analytics and data analysis extract actionable information from raw data. The two terms are often confused but refer to two different things. Data analysis refers to examining data using statistical methods and models to discover patterns in the data. It is sometimes also called exploratory data analysis, which involves finding patterns in the data by making graphs and visualizing them, thereby arriving at conclusions. By applying analysis and insights to past data, it is possible to flip them into actions to help the organization make decisions and solve problems more effectively.

share this post

Facebook
Twitter
LinkedIn
Pinterest
Reddit
Email
Sana Shafaqat

Sana Shafaqat

I am a professional statistician and data analyst. I have worked in the field for over five years and have experience with various statistical software packages. I am passionate about data analysis and interpretation and love finding new ways to visualize data. I enjoy reading, spending time with my family, and playing tennis in my spare time.

Keep Reading