Statistical data analysis, also known as descriptive analysis, involves collecting and analyzing categorical data based on a predetermined method for quantitative research. It is often used to find meaningful patterns and trends in survey responses, qualitative data, observational data as well as other business metrics.
Compared to other types of data analysis, statistical analysis offers greater benefits for business intelligence strategy.
Portions of this definition originally appeared on Datamation.com and are excerpted here with permission.
What are the types of statistical data analysis
The precise type of analysis used will depend on the collected data, your desired results, and how the data is presented.
- Descriptive statistical analysis involves organizing large amounts of data into visual representations, such as graphs and charts, to help you understand the meaning of values in the dataset.
- Inferential statistics involves using a sample of raw data to experiment and test hypotheses to infer conclusions about the rest of the larger dataset.
What are the benefits?
In practice, statistical data analysis allows companies to measure key performance indicators (KPIs) related to employee and sales performance. It offers an unbiased numerical value to encourage accountability, predict future outcomes, and identify areas of weakness.
A few areas where this kind of analysis can inform decision making and improve business practices include:
- Competitive Analysis: Identify the objective value as a company, and compare performance to competitors.
- True Sales Visibility: Gain greater visibility on marketing and sales successes by measuring sales data and associating it with specific timeframes, products, and individual salespeople.
- Predictive Analytics: Use past numerical data to predict future outcomes and areas where teams should make adjustments to improve performance.
Take a deeper look at some of the benefits of statistical data analysis at Datamation.com.