Data Analytics: The 4 Methods of Analyzing Data and Their Effective Use
In today’s digital world, companies rely on vast amounts of data. However, just collecting the data is not enough. It is important to derive meaningful insights and conclusions based on the data to make better decisions. This has made the role of a data analyst professional relevant and critical. They are the professionals who perform data analytics by examining data, extracting information, and helping companies make informed decisions.
The data analytics market is expecting an incredible surge. The expected growth is more than 650 billion dollars by 2029, with the increasing significance of data analytics in the economy. It is important to know what data analytics is, its importance, and the best approaches to use the same effectively.
What is data analytics?
Data analytics is collecting, refining, and processing huge amounts of raw data into meaningful insights. By analyzing the data, you can improve company performance, lead your company towards growth, and give a competitive edge to your firm.
Why is data analysis important?
Data analysis is highly crucial for all businesses.
- Data analysis allows company managers to determine the ROI of their marketing efforts.
- It allows marketing managers to understand consumer behavior, buying patterns, and market trends. Analyzing your data helps you understand customer needs and how to better customer support, etc.
- Using the data, the company can make data-driven decisions and increase the chances of success. Data analysis can boost your confidence by enlightening you with various business aspects like what is working and what is not, business risks, opportunities, scope for improvement, etc.
- Using the power of data, you can do competitor analysis. Data analysis helps you with competitor information like their strengths, weaknesses, marketing tactics and strategies.
- Data analysis is futuristic. From helping you know your customer base to identifying the areas of improvement, data analytics helps businesses in every way.
Data Analysis Approaches
To understand the working process of data analysis, learn about its types or approaches.
👉Predictive Analytics
Predictive analytics is a form of data analytics in which the analysts work on historical data and predict what will happen in the future. It is helpful as it allows you to plan your future. Predictive analytics is a method that uses statistical and machine learning techniques. You can use this approach to augment business operations, mitigate risks, lower costs, and increase growth. One of the examples includes predicting the sales number with the onset of a fresh season. The prediction is based on the previous sales figures. In the predictive analysis approach, you know whether the sales will decline in a particular season. Accordingly, the marketing managers can create an effective campaign.
👉 Prescriptive Analytics
The second most popular approach to data analysis is prescriptive analytics. In this type of data analysis, you get recommendations or suggestions for the course of action you must take. It uses optimization techniques to find and suggest the best action. The approach also studies the given scenario, constraints, and business objectives. And then it answers what you should do or how to improve. To use this approach, you must study the data, simulate the same in various business scenarios, and then arrive at the best decision. Prescriptive analytics helps you solve business issues like staff planning, marketing and product mix, capital budgeting, resource management, etc.
👉 Diagnostic Analysis
You must explore and evaluate data to determine the link in diagnostic analytics. This helps to explain the problem through regression analysis, hypothesis testing, and causal analysis. For example, it is common for companies to use diagnostic analysis to know the reason for quality issues, customer complaints, security breaches, and more. Diagnostic analytics is very helpful in finding the underlying reasons for problems and identifying the solutions to those problems.
👉 Descriptive Analysis
Descriptive analysis helps to turn the raw data into easily understandable language. It works to classify, distribute, and manipulate data to generate meaningful insights into a study. Using this approach, you can detect anomalies and errors in a report. Descriptive analysis generates facts that allow the businesses to understand the hits and misses.
Wrapping up
Data analytics is a powerful tool for business growth. Using different data analytics approaches can make understanding data and making informed decisions easier. Every data analysis approach has its strengths and weaknesses. Choosing a suitable approach is one of the important data analyst skills. You can keep certain factors in mind while selecting the approach, like the nature of the problem, quality of available data, availability of resources, etc.
