In today’s tech-driven business landscape, organizations greatly rely on data to gain insights about their customers and make informed decisions. By collecting and analyzing data, companies improve their performance, thereby increasing their growth.
However, for those who are not in the technical field, data science jargon is quite confusing. Two such confusing terms which almost seemed similar and are often used interchangeably are data analytics and data analysis.
Most people, especially non-technical users think that both these words relate to the same thing. But there is a major difference between the two. Both these terms have different meanings and have their methods and objectives.
If you are planning to pursue data analyst training, it is extremely essential to understand the key difference between data analytics and data analysis to acquire the required business-specific knowledge to get started with your career.
In this blog, we will look at both the terms and identify what makes the two different from each other.
What Is Data Analytics?
Data analytics is a broader field in data science and refers to extracting, processing and analyzing raw data from various sources to get insights about current problems and present actionable solutions. In other words, we can say that this term refers to the entire action which companies take, i.e. from gaining data-driven insights to making critical decisions.
In a data analyst training program, candidates are taught to work with primary data using various tools and technologies and equip themselves with machine learning and statistical analysis.
What Is Data Analysis?
Data analysis is a subset and part of data analytics. It refers to the process of cleaning, manipulating and visualizing data to make conclusions from it. Raw data often have information which is very difficult to predict.
The role of a data analysis professional is to turn raw data into meaningful conclusions, statistics and explanations. When it comes to information from the raw data, there are several approaches, like A/B testing, data mining, machine learning and data integration.
What Makes Data Analytics Different From Data Analysis?
As an individual who is pursuing data analyst training, you are required to understand the objectives of both terms and understand the circumstances where they are used.
- Use in businesses: Companies need to study consumer behaviour and adapt to the changing demand to thrive in the market. Data analytics aims to examine data and find out the change in consumer patterns to give positive results. Data analysis on the other hand sheds light on the problems faced by the analytics team to interpret raw data.
- Tools used: Data analytics professionals used various tools like Tableau, Excel, Python and Google Analytics to process the data. On the contrary, data analysis involves the use of tools like Spark, Google Fusion tables and Node XL to communicate information.
- The way they work: Data analytics focuses on providing answers to specific questions and challenges that are relevant for organizations to drive potential growth. Data analysis does not work to give specific queries. Instead, they work to find particular questions that might arise while studying the data.
Which One Is A Better Option?
Both data analytics and data analysis are essential in today’s business model. It is hard to compare the two and predict which one is better. After completion of data analyst training, you will find data analytics is preferred by organizations who want to streamline their process by adopting a holistic approach. For example, the finance and retail sector.
On the other hand, data analysis is required in industries that are looking to find solutions using data science. For eg, the healthcare industry.
To Conclude
Data is the new driving fuel for organizations. Hence, you need to understand the two concepts -data analytics and analysis carefully to drive business growth by increasing ROI.
