The process of acquiring, cleaning, analyzing, and mining data, as well as interpreting and reporting the results, is known as data analysis. Through data analysis, we uncover patterns in data and relationships between distinct data points. Insights and conclusions are produced and drawn based on these patterns and relationships. Data analysis aids organisations in comprehending their historical performance and informing future decisions. Businesses may confirm a plan of action through data analysis before committing to it. saving time and resources, as well as ensuring higher results. We’ll look at four different kinds of data analysis, each with its own aim and position in the data analysis process.
By summarizing historical data and presenting the findings to stakeholders, descriptive analytics may assist in answering questions about what happened over a period of time. It assists in providing crucial historical context. For example, cash flow analysis or historical performance based on the organisation’s key performance metrics.
Diagnostic analysis contributes to the answer. What went wrong? It uses descriptive analytics insights to go further into the reason for the outcome. For instance, a sudden rise in traffic to a website with no apparent explanation or a surge in sales in a region where marketing has not changed.
Predictive analytics can assist you in figuring out what will happen next. Future results are predicted using historical data and trends. Risk assessment and sales forecasting are two areas where firms use predictive analysis. It’s vital to emphasise that predictive analytics’ goal is to anticipate what could happen in the future, not to predict what will happen in the future. All projections are based on probabilities. What should be done about it?
Prescriptive analytics can assist in answering that question. The chances of distinct scenarios may be calculated by evaluating past decisions and occurrences. is a figure based on which a plan of action is chosen. Prescriptive analytics is exemplified by self-driving automobiles. They assess the surroundings before making judgments about speed, lane changes, and route selection, among other things. Alternatively, airlines may alter ticket rates automatically in response to client demand. Gas prices, weather, and traffic on linked roads are all factors to consider.
Let’s have a look at some of the most important phases in any data analysis procedure. Understanding the issue and the intended outcome. Understanding the problem to be solved and the intended outcome to be obtained is the first step in data analysis. Before you can begin the analytical process, you must first establish where you are and where you want to go. Establishing a clear metric Choosing what will be measured is part of this stage of the process. For instance, the amount of product X sold in a given location and how it will be measured. Once you know what you’re going to measure and how you’re going to measure it, you determine the data you’ll need, the data sources you’ll need to collect this data from, and the appropriate tools for the task in a quarter or throughout the festival season.
Data cleansing after gathering the data, the following step is to address any data quality concerns that might compromise the analysis’ correctness. This is a crucial step since only clean data can assure the correctness of the study. You’ll look for missing or incomplete numbers, as well as outliers, in the data. A customer demographics data set with a value of 150 in the age column, for example, is an outlier. You’ll also standardize the data that comes in from various sources.
After the data has been cleaned, you will extract and evaluate it from several angles. To comprehend trends, detect connections, and find patterns and variations, you may need to edit your data in various ways. Results interpretation: It’s time to interpret your findings after reviewing your data and maybe undertaking further study, which might be an iterative loop. As you interpret your findings, consider whether your analysis can be defended against challenges and whether there are any restrictions or conditions in which it could not hold true. presenting your results Any analysis’ ultimate purpose is to influence decision-making. The ability to convey and present your results in a clear and effective manner is as crucial as the study itself in the data analysis process. You may show your data in a variety of ways, including reports, dashboards, charts, graphs, maps, and case studies.
