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Statistical analysis software helps businesses collect, organise, interpret, and present data in meaningful ways. It allows companies to analyse existing data using mathematical models and simulation techniques, and generate business-relevant insights to facilitate new opportunities, study past sales trends, and forecast future demand.
The benefits of statistical analysis software
- Manage large volumes of data: Statistical analysis software can handle large amounts of data, and a variety of data types. Statistical analysis programs can process structured, unstructured, sensory, mobile, web, and social media data faster and more accurately than traditional data management tools and/or manual efforts.
- Obtain valuable business insights: Statistical analysis tools allow businesses to collect and analyse data to deliver actionable insights, which can help businesses explore new revenue streams, identify gaps in current workflows, and study past patterns to predict future needs. Thoroughly analysed data can result in insights that accelerate decision-making and optimise current workflows.
Typical features of statistical analysis software
- Data visualisation: Analyse raw data and present insights via interactive charts and graphs.
- Reporting/Analytics: Track and analyse data, and organise it into informational summaries that can be printed and exported in various formats.
- Regression analysis: Establish a relationship between two or more variables of interest and understand how independent variables are related to the dependent variable.
- Multivariate analysis: Observe and analyse multiple dependent variables simultaneously in relation to other variables.
- Time series: Capture sequences occurring at various points within a specified period and make predictions based on past trends.
- Statistical simulation: Create statistical models to imitate operations of a real-world process or system.
Considerations when purchasing statistical analysis software
- Business needs: Modern statistical analysis software can do a lot, from simple calculations and charting to statistical modelling and spectrum analysis. However, licensing costs increase alongside advanced analytics features. Buyers should carefully identify their unique business requirements, data forms, and types of models needed to analyse their data before beginning their statistical analysis software search. Identifying specific requirements can help purchasers select software that meets their needs within their budget, and avoid paying for features they may never use.
- Learning curve: It can take time to learn more about statistical analysis software and become familiar with its features, coding, and interface. While these depend on the software's ease-of-use and user learning speed, consider testing out several statistical analysis tools to obtain a rough idea of the learning curve for each before making a purchase.
Relevant statistical analysis software trends
- AI and machine learning augmented analytics: Artificial intelligence and machine learning augmented analytics is the next big thing in the data analytics field. Instead of using regular statistical models and adopting traditional approaches, statistical analysis programs will understand the data and share insights and discoveries. Gartner predicts that in 2020, most data science tasks will be automated and will allow users to produce a higher volume of advanced analysis (research available to Gartner clients).
- Self-service analytics will scale up: Self-service analytics is a form of data analytics in which business professionals are encouraged to run statistical analysis queries on their own without assistance from IT or data analytical teams. This trend is on the rise, especially in the small and medium-sized business segment, which suffers from a resource crunch and is always seeking employees with multiple skill sets. This is a key factor driving statistical analysis software vendors to shift their products from coding-based to graphical user interface (GUI) oriented tools, which are designed to help non-technical users perform data-backed analysis without depending heavily on codes or programming languages.