BASIC 101 – Everything You Should Know About Data Analysis

Data analysis is the process of cleaning, segregating, and interpreting data into useful business insights for accurate decision-making. Its purpose is to go through pools of data and extract useful information that will benefit the business to understand their present and future business functions. Unknowingly, we apply the basic principle of data analysis in our daily lives too. For example, when we make a life decision by thinking about what happened last time or what will happen in the future after the decision is made, that is data analysis.

The Need For Data Analysis

Calculated and analyzed decisions take us far in life and at work. If you feel your business is stagnated, you have to look back at your mistakes, learn (analyze) from them, and plan better to avoid those mistakes. Even if your business is booming, you will have to make sure that it grows further. For this, all you need to do is analyze your business data and business functions.

Techniques & Methods Of Data Analysis

Data analysis techniques differ from business to business. However, here are the basic analysis techniques.

Text Analysis – Also known as data mining, text analysis is one of the methods used to discover patterns in large datasets using data mining tools. This process transforms raw data into business information which is then communicated to the key decision-makers of the organization like the board of directors and stakeholders. Via business intelligence tools that are present in the market, this method offers a way to extract data and examine it to interpret the data.

Statistical Analysis – Statistical analysis answers the question "what happened?" using past data from the dashboards. This technique of data analysis includes collecting, analyzing, interpreting, presenting, and data modeling a data set or a data sample. Within this methods, there are two categories:

• Descriptive analysis that analyzes the complete data or a sample of the numerical data to show the mean and deviation of continuous data and percentage and frequency of categorical data.
• Inferential analysis that analyzes a sample from complete data to draw different conclusions from the same data set by going through different samples.

Diagnostic Analysis – This answers the question "Why did it happen?" by finding the cause from the result of statistical analysis. When there's a new problem in a business function, this method is useful to figure out behavior patterns of data.

Predictive Analysis – This method of analysis answers the question "what is likely to happen?" by making predictions about the future results based on current and past data. With detailed information, the prediction will be more accurate.

Prescriptive analysis – This technique combines insights from all the previous analyses to examine which action is the right fit in a particular scenario. Most data-driven companies use this technique because the predictive and descriptive analysis doesn't improve data performance.

Process Break Down

Data analysis consists of these steps

Required data gathering – To gather the necessary data, it is important to establish the reason or purpose for it. Once you know the purpose, the next step will involve choosing the right method. You will have to decide what to analyze, how much of it should be measured, and where to use the insights.

Data collection – After gathering the required data, you will know how much of it to measure. So accordingly, you have to segregate and collect the data for analysis. As data will be collected through different sources, it's best to keep a tab of it.

Data cleaning – Not all data that has been collected will be useful to the purpose. Hence, it should be cleaned to remove any duplicate records, spaces, and errors. The outcomes of the analysis will depend on how clean the data is.

Data analysis – Once the data is collected, cleaned, and processed, you can manipulate it to extract the information you need. In this phase, you can use data analysis tools that will help you understand the data set in a better way.

Data interpretation – After the analysis is done, it's time to interpret the result to find out the best outcome. This interpretation can be communicated in the form of a table or chart.

Data visualization – The said result needs to be graphically communicated to the employees of the organization. Only when everyone works towards the common goal, a company will reach its business goals.