Introduction

With data being the new fuel for businesses that helps gain critical insights and enhance business growth, the need to understand the difference between analysis vs analytics is very important. However, these words are used interchangeably but they are significantly different and possess different meanings and importance. If you are looking for a career in the field of data analyst or data science, then the lack of understanding can affect your ability to make use of customer intelligence to get the best advantage. 

As the use of smartphones, tabs, and laptops is increasing day by day, the data explosion is also increasing many folds. But data is just merely data until it is worked upon to get the business advantage it promises. And, this is the part where lies the importance of understanding the difference between data analysis and data analytics. While both of these help in converting the raw data into actionable insights to deliver business value, both terms seem similar in meaning but are quite different.

The main difference between data analysis and analytics lies in their approach, as analysis looks towards the past while analytics towards the future. That’s the basic difference, let’s dig further to get in-depth knowledge about data analysis vs. analytics and fully understand both approaches and how these are helpful for the businesses.

  1. Data Analysis vs. Data Analytics
  2. Key Difference between Data Analysis and Data Analytics

1. Data Analysis vs. Data Analytics

Let’s start with a general comparison of analysis vs. analytics as per any English dictionary. According to Merriam Webster, analysis is the division of a whole into small components, and analytics is the science of logical analysis. While analysis looks backward over time and works on the facts and figures of what has happened, analytics work towards modeling the future or predicting a result. In other words, the analysis restructures existing available information or data. And, the analytics uses this analyzed information to predict what may happen.

To gain a deep understanding of what is the difference between analysis and analytics, let’s take an example of an apparel brand. The business/brand owner analyzes last year’s sales data to gain insight into profit trends and sales trends as per seasons, months, and weeks. This analysis of what has happened is basically an in-depth review of the past facts. Whereas, analytics combines the results from the analysis of last year’s sales data with logical reasoning to predict future sales pattern and design and plan accordingly.

In practice, this means the brand will employ advanced (machine learning) tools and algorithms to make the best use of the historical review and predict future sales patterns. With analytics, the apparel brand can design its plan of when to launch new products over the coming weeks and months to get the maximum profit. This example clearly indicates the basic English difference between analytics and analysis.

Now, let’s move to data analysis vs. data analytics. Data analysis is the process of studying a given data set (in close detail), dividing them into small components, and studying the subcomponents individually and their relationship with each another. Data analytics, on the other hand, is a more comprehensive term referring to a discipline that comprises the complete management of data, including collection, cleaning, organizing, storing, administering, and analysis of data with the help of specialized tools and techniques. In other words, data analysis is a process or method, whereas data analytics is an overarching discipline (science). 

It is apparent by the definition itself, that data analytics is a broader term and comprises data analysis as a necessary subcomponent. It is the science or the cognitive process that an analyst uses to recognize problems and examine data in the most meaningful ways. Both analysis and analytics are highly significant and help businesses to estimate customers accurately, approach the right audience, and get the best results using their marketing budget. Both of these help businesses explore and analyze the customer data to understand unknown patterns, grab opportunities and gain insights and transform that into productive decision making.

2. Key Difference between Data Analysis and Data Analytics

  • Data analysis is a process involving the collection, manipulation, and examination of data for getting a deep insight. Data analytics is taking the analyzed data and working on it in a meaningful and useful way to make well-versed business decisions. 
  • Data analysis helps design a strong business plan for businesses, using its historical data that tell about what worked, what did not, and what was expected from a product or service. Data analytics helps businesses in utilizing the potential of the past data and in turn identifying new opportunities that would help them plan future strategies. It helps in business growth by reducing risks, costs, and making the right decisions.
  • In data analysis, experts explore past data, break down the macro elements into the micros with the help of statistical analysis, and draft a conclusion with deeper and significant insights. Data analytics utilizes different variables and creates predictive and productive models to challenge in a competitive marketplace.
  • Tools used for data analysis are Open Refine, Rapid Miner, KNIME, Google Fusion Tables, Node XL, Wolfram Alpha, Tableau Public, etc. Tools used in Data analytics are Python, Tableau Public, SAS, Apache Spark, Excel, etc. 
  • Data analytics is more extensive in its scope and encompasses data analysis as a sub-component. The life cycle of data analytics also comprises data analysis as one of the significant steps. 
  • Through data analytics and data analysis, both are essential to understand the data as the first one is useful in estimating future demands and the second one is necessary for gaining insight by analyzing the details of the past data. Data analysis is actually studying past data to understand ‘what happened?’ Whereas data analytics predicts ‘what will happen next or what is going to be next?’ 

The difference between business analysis and analytics is somewhat similar as discussed above in the data analytics vs. analysis section. Business analysis is identifying business needs and outlining solutions to business difficulties while business analytics is analyzing past business performance using tools, techniques, and skills to predict future business performance. In simple words, business analytics works on data and statistical analysis.

Conclusion

Data analysis is a process of studying, refining, transforming, and training of the past data to gain useful information, suggest conclusions and make decisions. Data analytics is using data, machine learning tools, statistical analysis, and computer-based patterns to gain better insight and design better strategies. It is the process of re-modelling past data into actions through analysis and insights to help in organizational decision making and problem-solving. Hope, this guide helped you understand what is the difference between data analysis and data analytics?

It is also important to find the right place to learn and become proficient in all these skills and languages. Jigsaw Academy, recognized as one of the Top 10 Data Science Institutes in India, is the right place for you. Jigsaw Academy offers an Integrated Program In Business Analytics for enthusiasts in this field. The course runs for 10 months and is conducted live online. Learners are offered a joint certificate by the Indian Institute of Management, Indore, and Jigsaw Academy.

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