6 steps to Data Analysis.

One can have different goals in performing data analysis. Maybe one wants to describe, explore or diagnose. There are several methods to implement these goals with data analysis. If you want to know more about patterns, you can, for example, use cluster analysis, with which you organize data into groups (or clusters) that have common characteristics. Cluster analysis is the main task in exploratory data analysis and a common technique that, i.a., is used for pattern recognition and image analysis. Other methods can be data mining and text analysis.

The insights that come out of one's data analysis are precious for decision making in all kinds of companies. Without data analytics, companies would face mountains of data without being able to create meaning from them. Data analysis helps companies solve problems and improve and optimize from the past, leading to better profits, error reduction, more customers, etc. Today's flow of information requires refining and understanding data effectively. Without such efficiency, the cost of sorting through data will outweigh any benefit.

Today, the ability to analyze data is not limited to data scientists. You can quickly learn to analyze and interpret your data with the proper practice. We have made it easy for you and divided the analysis into six simple steps that can help you tackle some of your biggest business problems.

1. Ask the right questions!

Define your question or goal behind the analysis: what are you trying to discover, and what problem are you trying to solve? Your question could be a specific business problem, and from there, you’ll create a set of measurable, clear, and concise questions that will help answer that.

2. Collect the correct data to help answer your initial questions. 

You’ll want to determine if the data is already available within your organization as a starting point. Will you also need to source some data externally, or do you have everything? Your end goal of this step is to have a complete, 360-degree view of the problem you want to solve.

3. Perform a data cleaning/data wrangling. 

Cleaning up your data is a fundamental step because, ultimately, the accuracy of your analysis will depend on the quality of your data. The data cleaning could be getting data into the proper format, getting rid of unnecessary data, correcting spelling mistakes, etc. Here’s where you’ll spend some time polishing the data to ensure it’s in tip-top shape, and preparing it for analysis and interpretation.

4. Manipulate data using Excel or Google Sheets. 

This may include plotting the data out, creating pivot tables, etc.

5. Analyze and interpret the data using statistical tools.

(i.e. finding correlations, trends, outliers, etc.). Using the techniques and methods of data analysis, you’ll look for hidden patterns and relationships in the clean data and find insights and predictions. 

6. Present your insights in meaningful ways.

This could be by designing graphs, visualizations, charts, tables, etc. You are now ready to report your findings to project managers, department heads, and senior-level business executives to help them make decisions and spot patterns and trends.

Once you've learned to analyze your data, you might want to implement a tracking plan. To learn more about this, read our article Why you need a tracking plan.

Explaining the importance of data analytics is simple: Data is the future, and if you want greater insight into how your business is performing online, to create a better customer experience, or to solve other business problems, data analysis is a great place to start.