What is data analytics?
Data analytics is about using data to help businesses and individuals make better decisions and be more successful. It involves looking at facts and figures and using them to take specific actions. This can be done by studying raw data and understanding how it relates to solving problems and making decisions in an organization.
Using data analytics is a great way for businesses and people to find practical solutions for their ideas. It can help you create a clear plan for the direction your company should take. Here are a few examples of how data analytics can be helpful:
- To identify trends and patterns.
- To seek out new opportunities.
- To determine possible risks and benefits.
- To make a strategy of action.
What is data analysis?
The process of cleaning, manipulating, modeling, and questioning data to discover relevant information is known as data analysis. Data analysis is a vital part of data analytics. It helps us identify solutions by providing information.
There are several approaches we can take when it comes to data analysis. These are some of the approaches you can use depending on what you want to accomplish.
- A/B testing: A comparison of one test group to another.
- Data fusion, integration: It develops accuracy by analyzing and combining data from various sources.
- Data mining: This identifies patterns in massive data sets and extracts them for analysis.
- Machine learning: Here, computer algorithms are used to automate the process of developing analytical research models.
- Natural language processing (NLP) uses computer algorithms to study human languages.
Key differences between data analytics vs data analysis
Data analytics and data analysis are frequently used synonymously. Data analytics is a broad term that covers everything related to managing data, including the methods and tools used. On the other hand, data analysis is specifically about breaking down a dataset into smaller parts and studying each part individually, as well as understanding how they are connected to each other.
In simpler terms, data analytics is the big picture of working with data, while data analysis is the detailed examination of the different pieces of data and how they fit together.
| Data Analytics | Data Analysis |
|---|---|
| Data analytics is a common and general type of analysis that businesses use to make decisions based on data. | Data analysis is a specific kind of analytics that businesses use to examine data and obtain valuable insights. |
| Data analytics involves collecting and examining data and is typically used by one or more people. | Data analytics involves understanding the data, examining it, cleaning it up, and making changes to obtain a useful outcome. |
| Data analytics involves several steps, including collecting data and examining business information. | Data analysis involves the process of making sense of data. Before we can extract useful information from raw data, it needs to be organized and transformed through data cleaning and conversion. |
| Data analytics involves using different technologies like Tableau, Python, Excel, Google Analytics, and more to process and work with data. | Data analysis involves examining data using various tools like SPARK, Google Fusion tables, Node XL, Excel, and more. |
| This does not offer itself to descriptive analysis. | This can be subjected to descriptive analysis. |
| With the use of this, one might discover anonymous relationships. | This cannot be used to find unknown relations. |
| Inferential analysis is not included here. | Inferential analysis is supported here. |
Which Is the better option?
The methods and techniques used by analytics specialists to make predictions and draw conclusions can be difficult for someone without expertise to understand. Non-experts may find it challenging to comprehend advanced processes like creating new data from existing datasets to reach better conclusions.
However, there are improved ways to represent data analysis visually, using graphs and charts. These visual representations make it easier for even people who are not well-versed in reading and interpreting data to understand the information contained in the dataset.