Data Analytics Software Technologies

Dec 01, 2019

After completing this video, you will be able to recognize common types of analytics software technologies. Whether you're conducting data analysis yourself or contributing to a project team, you'll be more productive once you understand the various types of analytics and statistical software available.

Whether you're conducting data analysis yourself or contributing to a project team, you'll be more productive once you understand the various types of analytics and statistical software available. These can range from simple spreadsheets to complex advanced analytics tools. Analytic software tools are generally designed to match the analytical process being performed. And they tend to increase in cost and complexity, the more sophisticated the analysis being conducted. Matching the appropriate tool to the job at hand, is a critical first step in analytics technology selection.

There are three common types of analytics software technologies, spreadsheet software, specialized statistical software, and analytics modeling software. Let's now look at each of these in turn. For the most common and basic analytical processes, spreadsheet software tends to be the tool of choice. Microsoft Excel is the most common spreadsheet tool in use today. But many alternatives exist, some at little to no cost. Many are available online through a web browser, such as Google Sheets. Although Microsoft Excel's capabilities have grown since it was first released, it's still limited to analysis of simply structured data in rows and columns for data volumes that generally fit on a desktop or laptop computer.

The benefit of spreadsheets is familiarity and ease of use. Some of the more sophisticated spreadsheet programs include advanced statistical calculations such as simple regression analysis. Beyond spreadsheets are specialized statistical software. These tools can be complex and difficult to master for most non technical or non data science professionals. They come pre-populated with a library of statistical modeling modules. However, they do require significant expertise to know which statistical methods to use. And it requires similar expertise to understand how to test the efficacy of these statistical models.

The most recognizable brand of software in this category is SAS which holds a leading position in the market today among large enterprises. The last of the three common types of analytics software technologies for businesspeople is basic analytics modeling software. It's focused on making analytics modeling easier for businesspeople. Vendors, such as Tableau and Click promote the idea of the citizen data scientist and self-serve analytics. Analytics modeling software tools do require some basic understanding of data structures common in spreadsheets. And they also require ability to visualize how certain data sets can be combined and presented for rich insights.

These tools also strive to make the loading, cleaning, and manipulating of data as easy as possible for users through intuitive interfaces. One of the drivers behind the development of these software applications is to remove the complexity from analytics. Analytics software vendors talk about democratizing data, bringing into a far larger mass of knowledge workers. And many point solution applications have emerged to solve isolated, high value analytics challenges. For example, although a specific piece of human resources software might function as an attendance monitor, offering time tracking, record keeping, reporting, and the ability to see trends over time, it probably wouldn't incorporate more diverse options such as a payroll and benefits management.

This is why enterprises are adopting an ecosystem of analytic software solutions, ranging from traditional spreadsheets, to multi purpose solutions, to point solutions aimed at specific tasks. Analytics tools and statistical packages are becoming ever more sophisticated. There are many single purpose applications emerging to solve narrow and discrete business analytics problems. The analytics software sector is an active and innovative one, and one that's well worth keeping on top of.