Statistical data analysis does more work for your business intelligence (BI) than most other types of data analysis.
Also known as descriptive analysis, statistical data analysis is a wide range of quantitative research practices in which you collect and analyze categorical data to find meaningful patterns and trends.
Statistical data analysis is often applied to survey responses and observational data, but it can be applied to many other business metrics as well.
See below to learn more about statistical data analysis and the tools that help you to get the most out of your data:
Statistical Data Analysis
- Types of statistical data analysis
- Benefits of statistical data analysis
- Use cases for statistical data analysis
- Statistical data analysis market
See more: What is Data Analysis?
Types of statistical data analysis
Before you get started with statistical data analysis, you need two pieces in place: 1) a collection of raw data that you want to statistically analyze and 2) a predetermined method of analysis.
Depending on the data you’re working with, the results you want, and how it is being presented, you may want to choose either of these two types of analysis:
- Descriptive statistics: This type of statistical analysis is all about visuals. Raw datadoesn’t mean much on its own, and the sheer quantity can be overwhelming to digest. Descriptive statistical analysis focuses on creating a basic visual description of the data, or turning information into graphs, charts, and other visuals that help people understand the meaning of the values in the data set. Descriptive analysis isn’t about explaining or drawing conclusions, though. It is only the practice of digesting and summarizing raw data, so it can be better understood.
- Statistical inference: Inferential statistics practices involve more upfront hypothesis and follow-up explanation than descriptive statistics. In this type of statistical analysis, you are less focused on the entire collection of raw data and instead take a sample and test your hypothesis or first estimation. From this sample and the results of your experiment, you can use inferential statistics to infer conclusions about the rest of the data set.
Benefits of statistical data analysis
Every company has several key performance indicators (KPIs) to judge overall performance, and statistical data analysis is the primary strategy for finding those accurate metrics. For internal, or team metrics, you’ll want to measure data like associated deals and revenue, hours worked, trainings completed, and other meaningful numerical values. It’s easy to collect this data, but to make meaning of it, you’ll want to statistically analyze the data to assess the performance of individuals, teams, and the company. Statistically analyzing your team is important, not only because it helps you to hold them accountable, but also because it ensures their performance is measured by unbiased numerical standards rather than opinions.
If your organization sells products or services, you should use statistical analysis often to check in on sales performance as well as to predict future outcomes and areas of weakness. Here are a few areas of statistical data analysis that keep your business practices sharp:
- Competitive analysis: Statistical analysis illuminates your objective value as a company. More importantly, knowing common metrics like sales revenue and net profit margin allows you to compare your performance to competitors.
- True sales visibility: Your salespeople say they are having a good week and their numbers look good, but how can you accurately measure their impact on sales numbers? With statistical data analysis, you can easily measure sales data and associate it with specific timeframes, products, and individual salespeople, which gives you better visibility on your marketing and sales successes.
- Predictive analytics: One of the most crucial applications of statistical data analysis, predictive analytics allow you to use past numerical data to predict future outcomes and areas where your team should make adjustments to improve performance.
See more: What is Raw Data?
Use cases for statistical data analysis
In virtually any situation where you see raw quantitative and qualitative data in combination, you can apply statistical analysis to learn more about the data set’s value and predictive outcomes. Statistical analysis can be performed manually or through basic formulas in your database, but most companies work with statistical data analysis software to get the most out of their information.
A couple of customers of top statistical data analysis software have also highlighted other uses they found in the software’s modules:
- “[TIBCO Spotfire is a] very versatile and user friendly software that allows you to deploy results quickly, on the fly even. Data transparency and business efficiency is improved tremendously, without the need for an extensive training program or course. On the job is the best way to learn using it, figuring problems out with the aid of the community page and stackoverflow, and if all else fails there are committed consultancies that can sit with you and work out complex business needs, from which you will gain another level of understanding of the software onto which you can build further. We use this software not only for data analytics, but also for data browsing and data management, creating whole data portals for all disciplines in the business.” -data scientist in the energy industry, review from Gartner Peer Insights
- “Although not a new tool, [IBM] SPSS is the best (or sometimes the only) tool to effectively analyze market research surveys — response level data. our team has explored many other solutions but nothing comes close…We conduct many consumer surveys. we need to analyze individual respondents, along with their individual responses or answers to each question — which creates an unlimited number of scenarios. SPSS is flexible enough for us to get answers to questions we may not have predicted at the beginning of a project.” -senior manager of consumer insights and analytics in the retail industry, review from Gartner Peer Insights
See more: Qualitative vs. Quantitative Data
Statistical data analysis market
The market for statistical analysis software hit $51.52 billion in 2020 and is expected to grow to $60.41 billion by 2027, growing at a steady annual rate of 2.3% between 2021 and 2027, according to Precision Reports. Statistical analysis software is used across industries like education, health care, retail, pharmaceuticals, finance, and others that work with a large amount of quantitative data. Companies of all sizes implement this kind of software, but most of the latest implementations come from individuals and small-to-medium enterprises (SMEs), Precision Reports says.
10 Statistical analysis software makers
Are you curious about the different statistical data analysis tools on the market? Looking for a new solution to replace your current approach? Check out these top statistical data analysis tools or use this Data Analysis Platform Selection Tool from TechnologyAdvice to guide your search.
- IBM SPSS
- IHS Markit EViews
- MathWorks MATLAB
- SAS Institute
- StataCorp Stata
- TIBCO Spotfire