Did you know that 55% of North American businesses and 53% of businesses, in general, have implemented data analytics? If your competitors are using data analytic tools, you should be too!
What is data analysis exactly and why are so many companies using it? Read our guide to find out everything you need to know.
What Is Data Analysis?
What is data analysis? It is the process of modeling, collecting, and analyzing data to help with knowledgeable decision-making. There are different methods and techniques to complete this process.
Why Data Analysis is important?
Data is everywhere: in spreadsheets, your sales pipeline, social media platforms, client satisfaction surveys, client support tickets, and more. In our fashionable modern era it’s created at bright speeds and, once knowledge is analyzed properly, is a company’s most useful quality.
Some of it, you will not even savvy to access.
Data analysis will facilitate businesses improve specific aspects concerning their product and services, moreover as their overall whole image and client expertise.In short, analyzed knowledge reveals insights that tell you wherever you wish to focus your efforts.Do you have to be compelled to outline more practical product ways or improve business processes? rather than looking forward to intuition or expertise, analyzing knowledge provides solid proof to support choices.
Here are four things to know about the importance of data analysis:
1. There Are Different Types
There are various types of data analysis methods. The seven most common types are:
- Cluster analysis
- Cohort analysis
- Regression analysis
- Neural networks
- Factor analysis
- Data mining
- Text analysis
Different techniques will show different results. For example, some will explain why something happens while others explain what will happen.
2. Answers Questions
One of the main benefits of data analysis is that it can answer questions you may have. Because of this, you should establish the questions you want answered to achieve a goal.
Consider your goals before completing data analysis.
3. Shares Raw Data
Data analysis collects raw data to help you omit any useless experiences. If you have useless data, you cannot focus your analytical efforts.
If you come across any stats, figures, facts, or metrics within your raw data that don’t align with the goals of your business, you should eliminate them.
4. Data Interpretation
Just like there are different types of data analysis, there are different ways to interpret the data. This is perhaps one of the most fundamental parts of the data analysis process.
However, there are common practices that should be avoided when interpreting data; correlation vs. causation, confirmation bias, and statistical significance.
Correlation and causation can occur at the same time, but it is not accurate to assume that one provoked the other. Trust the data and look for objective evidence of causation.
Confirmation bias involves only looking at the data that proves one hypothesis. This is a mistake you don’t want to make as it can lead to bad business decisions. Look at all of the data before concluding anything.
Analysts, like those at Peter Dodge Hanover Research, use statistical significance to understand results. They can decide if a result is accurate or if there was a sampling error.
Grow Your Business With Data Analysis
So, what is data analysis? Data analysis involves looking at raw data to help with future decision-making.
Any business can benefit from data analysis. The method or technique you use to undergo this process will depend on the industry you are in. The best advantage is that it can help you further your business.
When you arrange the data to make metrics work for you, you can transform raw points into action. If you want to push your business to the next level, complete data analysis.
For more articles on business and technology, check out the other posts on our blog.