Data analysis is the process by which data is examined, cleansed, transformed, and modeled in order to reveal valuable information, help make conclusions, and inform decision-making. It’s a part of the intelligence process, which transforms raw data into valuable information that helps companies gain a competitive edge.
The first step of the process of data-analysis is to identify the business issue or problem you want to address. This requires thorough research, and could include forming an initial hypothesis that can be tested with data.
Once you have identified your question you can begin collecting data from both internal and external sources. They can be as organized or unstructured as you’d prefer, but usually include both qualitative and quantitative data. It’s important that you understand that data collection and analysis are both iterative procedures, meaning that you’ll probably revisit and collect additional data to revisit your initial question, as well as any assumptions you’ve made along the way.
In this stage you will employ various tools and techniques for data analysis to manipulate the data and discover patterns such as trends, outliers, or variations that tell an interesting story. Data visualization software, such as, to convert the data into a visual format that is easy to understand. It is also possible to perform predictive or diagnostic analyses to predict data analysis future outcomes.
The next step is to present and interpret your results. This section should contain an explanation of the statistical methods used and any sensitivity or robustness testing performed, and the results themselves. If you’ve used tables or figures, make sure that you label them clearly and provide clear captions.