Data Analysis

A huge amount of data are produced and stored each day. These data come from various forms or you produce them daily, every time when you post blogs or even when you post on social networks.

But what are the data? The data is not information, it is just the formless stream of bytes. The Information is the result of processing such data. The process of extracting information from raw data is called Data Analysis. It is a process of cleaning, transforming, and modeling data to discover useful information for business decision-making.

The purpose of Data Analysis is to extract useful information from raw data and based on that information make better decisions for the business.

Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. --Wikipedia

Data analysis is used in business to help organizations to make better business decisions. Which customer segments are most likely to purchase your new product? What forms of advertising reach your customers effectively and make an impact that will make them buy your products?.

With the help of data analysis, you will be able to make decisions on customer trends and behavior prediction, increasing business profitability, and drive effective decision-making.

So, Data analytics is a crucial part of any successful business decision. But how to do this data analysis to get meaningful insights from raw data. There are several methods and techniques that data analysts use depending on the type of data and the kinds of insights they want to uncover.

In this post, we'll discover some of the most useful methods of data analysis. By the end, you'll have a much clearer idea of how you can transform meaningless data into business intelligence.

4 Types Of Data Analysis Methods.

There are four types of data analysis that are used to make better decisions. Even though we separate them they are all linked together and build upon each other. We start with the simplest type of analytics to more complex, as the degree of difficulty and resources required increases, the level of added insight and value also increases.

1. Descriptive Analysis - What happened.

A descriptive analysis is the first step to any analytics process and it helps you to answer the question such as what happened? it gives you an idea of the distribution of your data, helps you detect outliers, and turns it into valuable insights to your business.

Descriptive analysis is the most essential step in your data analysis, as it allows us to present our data in a meaningful way. Although this analysis will not help you to answer questions like why something happened, it will leave your data organized, clean, and ready to conduct further analysis.

2. Diagnostic Analysis - Why it happened.

One of the most powerful types of data analysis. Diagnostic data analytics describe the techniques that will use to answers the question like why did it happen?.

The diagnostic analysis takes insights found from the descriptive analysis and takes it further to find the causes of those outcomes. This analysis is useful to identify behavior patterns of data.

When a new problem arrives in your business process, you can look into this analysis to find similar patterns of that problem. It is possible you have already collected certain data pertaining to the issue. And you may use similar prescriptions for the new problems.

3. Predictive Analysis - What will happen.

Predictive Analysis helps you to answer the question what will happen using the previous result from descriptive and diagnostic analysis in addition to machine learning(ML) and artificial intelligence(AI) to make predictions about the future.

Using predictive analysis, we could find future trends, potential problems, or inefficiencies. If you understand why a trend, pattern, or event happened through data, you will be able to develop a model that will predict what will happen next.

4. Prescriptive Analysis - How will it happen.

Prescriptive data analysis combines all information found from the previous 3 types of data analysis, and helps you answer the question as now that you have an idea of what will likely happen in the future, How will it happen? .

Prescriptive data techniques use the patterns or trends to develop responsive, practical business strategies. Artificial Intelligence (AI) is a perfect example of prescriptive analytics. AI systems consume a large amount of data to learn and use this information to make meaningful decisions.

In Summary: Both descriptive analytics and diagnostic analytics look to the past data to explain what happened and why it happened. Predictive analytics and prescriptive analytics use historical data to forecast what will happen in the future and what action you can take to affect those outcomes. Organizations use a variety of analytics together to make smart decisions that help businesses to grow.

Data analysis is a huge topic to discuss and there is a lot to know. But I'll keep this blog short. This is all about today's blog. Thank you for reading. I hope it helps you. 🙂

see you in my next blog. Take care!!