Why SPSS is a much better choice than Excel for data analytics

The volume and nature of the data that most organisations hold these days mean that the analytical possibilities are great. Organisations are constantly seeking ways to extract valuable insights from their data to gain a competitive edge, using advanced analytics to help them make data driven decisions. However, as with everything, it’s really important to use the right tool for the job and we still talk to organisations who are trying to use Excel for their advanced analytics.

Whilst of course Excel does have some analytics functionality it is really not designed with advanced analytics in mind. IBM SPSS Statistics offers a much powerful and specialized platform for advanced analytics. In this blog post, we will explore the reasons why organizations should consider using SPSS for their data analytics needs instead of relying solely on Excel, and counter some of the concerns (for example around ease of use and cost) that might make organisations nervous of switching from Excel to SPSS.

  1. The quality of your analytics will be more robust if you use a specialised tool such as SPSS

Excel is a versatile spreadsheet tool, but its primary function is not statistical analysis or advanced data analytics. SPSS, on the other hand, was specifically designed for advanced statistical analysis and now includes machine learning techniques. It offers a much wider range of statistical tests and procedures, including regression analysis, ANOVA, factor analysis, neural networks and more. SPSS also integrates with open source analytics languages such as R. These specialized tools enable organisations to perform in-depth statistical analysis with ease, making SPSS a better choice for researchers and data scientists. In contrast, the analytical options in Excel are much more limited and also much harder to use. Because SPSS is designed with advanced analytics in mind, it’s easier to get to where you want to go with your analysis.

  1. Much more sophisticated data visualisation in SPSS

Effective data visualization is crucial for conveying insights to stakeholders. It’s not enough just to run the analytics, you also need to be able to communicate your results clearly and in a way that supports effective decision making. Excel includes some basic charting options but SPSS’s data visualisation capabilities are far superior. With SPSS, you can create a huge range of visually appealing and informative charts, graphs, and plots that help you present your findings more effectively. This is particularly valuable when dealing with complex datasets that require sophisticated visualisations, or when needing to present your findings to non-specialists who need to be able to grasp the implications of your results as simply as possible.

  1. Advanced data preparation and data transformation functionality in SPSS

Effective data cleaning and preparation has a huge role to play in ensuring the success of your analytics project. Data cleaning and preparation are often time-consuming and challenging tasks in data analytics. SPSS simplifies this process by offering features such as data transformation, missing data handling, and data validation. Indeed, we have written before about the many ways in which SPSS makes the data cleaning phase of a project easier. These features streamline data preparation and ensure that your analysis is based on clean and reliable data. By contrast Excel does not really offer such specialised data cleaning tools. Data cleaning in Excel can be a time consuming and fiddly process and is prone to error which can mean that the quality of your analytics project and the robustness of your findings are compromised right from the start.

  1. Automation and reproducibility using SPSS syntax

SPSS allows you to automate repetitive tasks and create reusable scripts using syntax, making your analysis more efficient and reproducible. This is especially valuable for organisations that need to analyse data regularly or share their analysis processes with others. Excel lacks the automation and scripting capabilities of SPSS, which can lead to errors and inconsistencies in analysis. We have a whole series of video guides showing you how to use SPSS syntax to automate common processes which can save a huge amount of time during any analytics project, particularly when compared to trying to achieve the same results in Excel.

  1. Predictive analytics and advanced modelling is only really possible in SPSS

If your organisation aims to use its data to make data-driven predictions and forecasts, SPSS is by far the better option. Its predictive analytics capabilities, including machine learning algorithms, can help you build predictive models for numerous business applications, from sales forecasting to customer churn prediction whilst Excel lacks this capability.

  • Better handling of large datasets

Excel has limitations when it comes to handling large datasets. SPSS, on the other hand, is specifically designed to handle large volumes of data efficiently. This is particularly important for organisations dealing with big data or complex research projects that require extensive data processing.

While Microsoft Excel is a valuable tool for basic data analysis, row and column mathematics and reporting, it falls short when it comes to advanced statistical analysis, data visualization, automation, and predictive analytics. IBM SPSS Statistics, with its specialised capabilities and features, is the preferred choice for organisations looking to extract meaningful insights from their data efficiently and effectively. By adopting SPSS, organisations can elevate their data analytics efforts, make more informed decisions, and stay ahead in a competitive business landscape.

But isn’t SPSS expensive and hard to use compared to Excel?

This is a concern we here from time to time when talking to potential clients about switching their analytics practice from Excel to SPSS. There may have been a time when these concerns were at least somewhat justified but that’s certainly not the case anymore. SPSS offers a whole range of flexible pricing options designed to ensure that analysts only pay for what they need. Not everyone needs a multi user perpetual license giving unlimited access to the software forever. If you have a particular project in mind and just need one person to be able to use SPSS for a short period, even just a month, you can just pay for that using SPSS’s monthly subscription options.

SPSS also comes with many more support and training options than you would get with Excel. At Smart Vision we offer a whole range of support and training options designed to fit with the specific requirements of the particular organisation. For a light touch if you just need to find out how to do a particular thing or want to refresh your skills in a particular area we have a large library of free video guides breaking down key aspects of SPSS analytics into bite sized chunks. We also offer all our customers with support contracts access to our technical support helpline which includes not only technical advice but also statistical and analytical support. If you want to skill up your team we have a whole range of online training courses covering the areas of data analytics that we’re most often asked about, and we frequently work with clients on a consultancy or one-to-one guided training basis. Whatever you need to ensure you get the most out of your analytics project, we can provide it.    

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