Data, data, data: In today’s business world, we create and transfer massive amounts of data constantly. From a business standpoint, having access to this much data presents a multitude of opportunities, but how can we turn data into action? The answer: using business intelligence. Business intelligence refers to a type of solution that collects and merges data, creates visualizations of datasets, discovers trends and insights hidden within data and helps users make data-informed decisions. There are many subcategories of BI tools that target more specific needs; one of these kinds of tools is statistical software.
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Statistical software, or statistical analysis software, refers to tools that assist in the statistics-based collection and analysis of data to provide science-based insights into patterns and trends. They often use statistical analysis theorems and methodologies, such as regression analysis and time series analysis to perform data science.
SelectHub’s analyst team took a look at what’s currently on the market for statistical software, and we determined that these are the top five in their class.
There are many, many solutions on the market that can perform statistical analysis, so it can be difficult to find one that addresses your needs and best assists you in the decision-making process. To help you choose the best statistics software for your business, let’s take a closer look at the ins and outs of the industry.
Here’s what we’ll discuss:
What is Statistical Analysis?
Statistical analysis is a form of quantitative data science. BI software vendor SAS defines statistical analysis as “the science of collecting, exploring and presenting large amounts of data to discover underlying patterns and trends.” As the name suggests, it employs statistics, which is “the science that deals with the collection, classification, analysis and interpretation of numerical facts or data…by use of mathematical theories of probability.”
Researchers, data scientists and analysts may use statistical analysis to:
- Investigate and present information revealed by datasets
- Explore the relationships between data points
- Identify underlying trends and patterns in data
- Generate and prove or disprove the validity of probability models
- Use analytical algorithms to make predictions for the future
- Uncover actionable insights
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Types of Statistical Analysis
There are two important statistical methods used in data analysis — descriptive and inferential statistics. Both methods are important and give different insights.
Descriptive statistics is the kind of statistics that generally comes to most people’s minds when they hear “statistics.” Descriptive statistics refer to the analysis of data that helps describe or summarize data in a meaningful way. They simplify large quantities of data for easy interpretation, without making conclusions beyond the analysis or answering any hypotheses. Instead of proceeding data in its raw form, descriptive statistics allows us to present and interpret data more easily.
In contrast, inferential statistics allows analysts to test a hypothesis based on a sample of data from which they can make inferences and generalizations about the greater whole. Inferential statistics tries to make conclusions about future outcomes beyond the data available.
For descriptive statistics, we choose a group to study, measure all the subjects in that group and describe the group in exact numbers. Descriptive statistics can be helpful in looking into such things as the spread and center of the data, but because descriptive statistics are stated in exact numbers, they cannot be used to make broader generalizations or conclusions.
For inferential statistics, we instead start by defining the target population and then plan how to obtain a representative sample. After analyzing the sample and testing hypotheses based on the sample data, the result will be expressed in confidence intervals and margins of errors, based on the uncertainty of using a sample that cannot perfectly represent the population.
Both kinds of statistics are at the heart of the statistical analysis that powers statistical software, used hand in hand to solve business problems with intelligence.
Why Use Statistical Software?
Statistical software can help with business intelligence in many different ways. As business intelligence is the practice of collecting and analyzing data and transforming it into actionable insights, statistics can add even more value to your business’ proprietary data. Statistical analysis can give insight into how effectively your business is operating, and help you think ahead with predictive analytics models based on historical data.
Statistics can be difficult to perform, but with the right BI tools, it can be a breeze.
So what are the benefits of using a statistical analysis tool for business intelligence?
- Increases efficiency from streamlined and automated business data analysis workflows
- Returns more accurate predictions based on machine learning, statistical algorithms and hypothesis testing
- Easy customization allows you to ensure the software correctly processes the data and results you want
- Grants access to larger databases which reduces sampling error and enables more precise conclusions
- Empowers you to make data-driven decisions with confidence
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How to Choose the Right Statistical Analysis Tool
There are many factors to consider when choosing statistics software. The “best” tool for you and your business depends on your requirements and what you want to do with your data.
Here are some questions you can answer to help determine the perfect solution for you.
What kind of data do you need to analyze?
Using a complicated advanced tool like statistical software for simple data sets is impractical; statistical analysis tools work best with complicated sets of quantitative data. If your analysis needs are less demanding, a business analytics tool may be more suitable for you.
Products tend to offer different ranges of statistical theorems and algorithms, but some users may only need to use a small percentage of these functions. If you have a massive amount of data to analyze, you may want to invest in a tool built to handle large data sets with speed. You should look for a tool that performs exactly the kind of data analyses you need it to. Who will use the tool?
Will your analysts be experts, amateurs, or somewhere in between? Will they analyze data continuously in real-time, or will they do more statistical analysis on an ad-hoc self-service basis? Are they primarily data analysts or scientists?
Your statistical analysis software should meet the needs of the person using it, so make sure to choose a package that does exactly what your user needs it to.
How easy is it to use?
Statistical analysis is by no means easy, and many statistical software platforms can be confusing and downright unintelligible to the average user. Some tools also have a higher learning curve than others, making them more difficult to master. After considering who will be using the tool, determine what their level of experience with statistics is.
Expert data scientists will feel at home crunching numbers with equations and programming languages, but novice users may feel overwhelmed with a software presented in that format and prefer using a more familiar menu-based interface.
Do your engineers need a robust statistical analysis platform with powerful coding capabilities, or do your analysts need a simpler statistical tool that can display basic models, or do you need something in between?
How will your tool integrate with your business’ existing solutions?
Considering the interoperability and integration capabilities of prospective statistics software is an important step in the vetting process. While statistical software helps businesses derive deeper insights from their data, they are often just a cog in the machine of their technology ecosystems. More frequently than not, your business may need more than just one solution to address its analytical needs.
Will the new solution play well with others? If your business currently uses any other programs, it can be helpful to get a statistical analysis tool that supports the databases, file formats and frameworks of your existing solutions.
What quality of graphics do you need?
Some statistical packages are feature-packed with data visualization options, while others generate graphics that are much more bare-bones, with less customization available.
Do you prefer interactive or static visualizations? Will you need your statistical analysis software to produce visually appealing graphics outright? Or if you’ll output the graphics to another program, can the software export in the form you prefer?
If visualization is an important prerequisite for you, it’s certainly worthwhile to look into the graphical output capabilities of your would-be statistics software.
What is your budget?
Statistical software packages range in price from free for open-source tools like Python and R, to thousands of dollars per license for more robust offerings. Will you need just one license, or several? There are also many statistical analysis platforms that have academic versions available to students and teachers at a discounted rate.
The cost of your solution will affect which statistical analysis software is best for your business.
Does the solution have documentation or support?
There’s nothing more frustrating than a solution creating more problems than it solves. It’s much easier to use programs with comprehensive documentation than ones where you have to figure it out yourself. Before choosing a solution, make sure that your tool of choice comes with documentation that your users can understand, or at the very least, access to technical support should they have questions.
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The Best Statistical Software Tools
Now that we know what to look for, let’s look at the top statistical analysis solutions currently on the market and see if one of these is your perfect match.