Data analysis is defined as 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 data and taking the decision based upon the data analysis.
At first glance, data analysis and change management are two completely different categories. Data analysis belongs to hard skills, it provides solutions for messy processes. Change management, on the other hand, is a soft skill that is not only solution-oriented, but also process-oriented.
The benefits of using and analyzing data in day-to-day business are great. People cannot take into account all the parameters when, for example, they predict whether a particular machine could break down. Also, people can repair a machine only when it can no longer work. Data analysis techniques offer more room for manoeuvring. Through algorithmic calculations, various computer programs are able to predict the failure even before a device breaks down and immediately offer the optimal solution. Another example is the program of the consulting company Erst & Young, which has been using the SMART program for several years, with which they analyse data from social networks and determine changes in trends in consumers. It should not be forgotten that more and more companies in the field of human resources, when selecting employees for senior positions, use data analysis to properly predict the outcome of change in a particular senior job.
Examples that point to the benefits of using data analysis in change management are diverse and numerous. What is certain is that data science and its analysis have become an integral part of everyday life. Thus, it can be concluded that those who have not yet started to use this huge potential lag significantly behind business trends.
Data analysis is a somewhat abstract concept to understand without the help of examples. So to better illustrate how and why data analysis is important for businesses, here are the 4 types of data analysis and examples of each.
- Descriptive Analysis: Descriptive data analysis looks at past data and tells what happened. This is often used when tracking Key Performance Indicators (KPIs), revenue, sales leads, and more.
- Diagnostic Analysis: Diagnostic data analysis aims to determine why something happened. Once your descriptive analysis shows that something negative or positive happened, diagnostic analysis can be done to figure out the reason. A business may see that leads increased in the month of October and use diagnostic analysis to determine which marketing efforts contributed the most.
- Predictive Analysis: Predictive data analysis predicts what is likely to happen in the future. In this type of research, trends are derived from past data which are then used to form predictions about the future. For example, to predict next year’s revenue, data from previous years will be analyzed. If revenue has gone up 20% every year for many years, we would predict that revenue next year will be 20% higher than this year. This is a simple example, but predictive analysis can be applied to much more complicated issues such as risk assessment, sales forecasting, or qualifying leads.
- Prescriptive Analysis: Prescriptive data analysis combines the information found from the previous 3 types of data analysis and forms a plan of action for the organization to face the issue or decision. This is where the data-driven choices are made.
“Data will talk to you if you are willing to listen.”