Data analysis is the process of looking at, cleansing, transforming, and modeling data in order to discover valuable information, support conclusions, and support decision-making. It’s a step in the intelligence cycle that transforms raw data into actionable insights to give businesses a competitive advantage.
The first step of a data analysis is to pinpoint the question you’re trying answer or business problem you’re trying to solve. This requires extensive research, and may involve forming an initial hypothesis that can be tested with data.
Once you’ve identified your issue Once you’ve identified your question, you can begin collecting the required data sets from internal as well as external sources. They can be as organized or unstructured as you’d prefer, but usually include both qualitative and quantitative data. It’s important to remember that data collection and analysis are both iterative processes, which means you’ll need to return to collect additional data to review your initial question and any assumptions that you’ve made in the process.
In this phase, you will use different methods and tools to manipulate the data and identify patterns in the form of trends, outliers, trends or variations that tell the story. For instance, you may employ a data visualization program to transform the data into easily understood graphical representations. Additionally, you can conduct predictive or diagnostic analyses to anticipate future outcomes or events.
The next step is to present and analyze the results of your analysis. This section should describe the statistical techniques employed, including any sensitivity or robustness tests performed, as well as the results themselves. If you’ve used any figures or tables, make sure you clearly label them and provide detailed captions.