In this particular article, we are going to compare 3 of the most renowned and commonly used data analytics software: SAS, SPSS and Excel. To keep things simple and easy to understand, we are only going to compare them on the basis of some key characteristics: User Interface, Data Modeling, Charts and Graphics, and Information Management Let’s begin with a quick comparison between the trio of them.
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Comparing Data Analytics Tools
A vast majority of data analytics software readily available in the market serve 2 distinct functions; programming and software operation. As a result, they have 2 different user interfaces which are present to distinguish one function from the other, helping users perform each task in the best possible manner.
Microsoft Excel has the most basic and user-friendly interface among them as it only allows business users to perform software operation. The lack of programming capability and interface makes it easy to use for new users and does not require one to know programming languages and codes to operate it effectively and efficiently.
However, it does come with built-in statistical analysis tools and functions in the form of pre-installed formulas to help with calculations, spreadsheets, and charts based on that data. These functions are fairly easy to understand, with users only required to choose the function name and enter the numbers while the program does the work for them.
Like Excel, SPSS is also primarily based on the software operation function, although it offers certain programming capabilities to the user. It is known as an advanced version of Excel as it allows users to make their own formulas as well, on top of using those which are already present. For this, users need to be well-versed in the 4GL command syntax language.
Out of these data analytics tools, SAS is by far the most programming-oriented software. Most of the features require the user to know the SAS programming language in order to perform analytics functions and tasks. It requires users to be well-drilled in using programming language, knowing codes by heart to prevent any errors from occurring.
Since Excel only has a software operation aspect to its functionality, all the features can be easily used by simply interacting with the menus on screen, all of which make data entry and manipulation fairly easy. However, the increased simplicity makes it hard for the user to deal with complex data sets to develop predictive models, with support for unstructured and unsorted data almost non-existent. This forces users to spend more time sifting the data manually before they can use the data to generate accurate models.
This is not the case in SPSS. By making use of the 4GL programming language, users can make their own, customized formula to develop models. However, the interface of the software limits its ability to provide a clear-cut way of applying different analysis on the data, inclusive but not limited to regression analysis.
Where Excel and SPSS fail, SAS provides users with the complete solution. This software is extremely well-structured as well as flexible, accurately generating a number of models. Its excellent data management capability allows users to manipulate and use date in different ways and formats as well. Lastly, the step-by-step procedure of applying any type of analysis on the data is recorded in the log window to help users understand the entire process.
Charts and Graphics
Excel is by far the best software when it comes to producing visually stunning and detailed charts and graphs as compared to SPSS and SAS. It grants users the flexibility to choose from a number of layouts and designs as per their liking at the simple click of the button.
On the other hand, even though SPSS also has a multitude of options with regards to the types of charts and graphs which can be produced, the process of generating them is not as simple as that in Excel. Users need to enter the data manually (i.e. assign variables to the x and y-axis) instead of choosing the data on which the graph needs to be modeled.
SAS is the most complex of the three in this department, with users required to make multiple adjustments in certain scenarios to achieve the desired visual representation. This is because graphs are generated on the basis of the programming done: The more detailed the programming, the better the results. However, the major plus point of this is that once perfected, the same coding can be applied later on to stylize other data sets, saving valuable time and effort of the user.
Data management is further divided into 3 distinct levels: Descriptive, Predictive, and Prescriptive Analytics. The first one focuses on providing insights on past and present happenings; the second level is focused on providing insights regarding the future outlook given that all environmental variables remain constant; the third level is concerned with providing an insight into future trends when one or more of the defining variables change.
Currently, Excel is only capable of descriptive analytics So, it’s usage where predicting future trends is concerned is fairly limited.
However, SPSS can be used to provide predictive insights by making use of data mining algorithms. However, the presence of a pre-defined interface makes it hard to provide predictive analysis using complex data sets.
Conversely, SAS is the most efficient in this aspect. This is because the level of programmability allows the software to source information from various resources and databases of information to present valuable insights which fit into all three levels.
The Final Verdict
Now that you have somewhat of a clearer idea regarding the level of functionality offered by these three data analytics software, you might be thinking which would suit you the best. Well, let us clear the air over this troubling situation.
Excel and SPSS should be the preferred option for those individuals and businesses which only deal with small data sets, only need to perform basic descriptive and predictive analytics, and only use it occasionally to perform such tasks. These programs are also best for those individuals who cannot invest a lot of time and are looking for an easy-to-learn software.
On the other hand, businesses which deal with large data sets on a daily basis and are required to generate prescriptive models which take multiple fluctuating variables into account need to recognize and utilize the power of SAS. Although it requires a person to take part in extensive training to master the software and its tools, it is a worthwhile investment which repays businesses in a fitting way.