Business

Data analysis: the process of systematically collecting, cleaning, transforming, describing, modelling, and interpreting data, generally employing statistical techniques.

Data analysis is a crucial component of both scientific research and business, where demand has increased in recent years for data-driven decision-making. Data analysis techniques are used to gain useful insights from datasets, which can then be used to make operational decisions or guide future research.

With the rise of “big data,” the storage of vast quantities of data in large databases and data warehouses, there is an increasing need to apply data analysis techniques to generate insights about volumes of data too large to be manipulated by instruments of low information-processing capacity.

What are the 5 steps of data analysis?

Data analysts use data to solve problems. As such, the data analysis process typically moves through several iterative phases.

Let’s take a closer look at each:

• Identify the business question you’d like to answer. What problem is the company trying to solve? What do you need to measure, and how will you measure it? 

• Collect the raw data sets you’ll need to help you answer the identified question. Data collection might come from internal sources, like a company’s client relationship management (CRM) software, or from secondary sources, like government records or social media application programming interfaces (APIs). 

• Clean the data to prepare it for analysis. This often involves purging duplicate and anomalous data, reconciling inconsistencies, standardizing data structure and format, and dealing with white spaces and other syntax errors.

• Analyse the data. By manipulating the data using various data analysis techniques and tools, you can begin to find trends, correlations, outliers, and variations that tell a story. During this stage, you might use data mining to discover patterns within databases or data visualization software to help transform data into an easy-to-understand graphical format.

• Interpret the results of your analysis to see how well the data answered your original question. What recommendations can you make based on the data? What are the limitations of your conclusions?