

Microsoft Excel is one of the most popular data analytics tool available in the market. Initially released way back in 1987, its popularity increased manifold in 1993 after the launch of its Version 5. In its most basic version, Excel is a kind of spreadsheet where users can generate and store their data and interact with it to perform all sorts of operations and view them in the form of graphs, charts and other sorts of visualizations. Widely regarded as one of the best tools in data analytics at one time, Excel has lost much of its reputation and prominence to other more advanced software and tools in recent time.

Although a worthy and powerful tool for data analytics, it does not feature in top 5 of most of the industry experts of today. In this article, we are trying to look at some reasons for the breaking up of the acclaimed connection between data analytics and excel in today’s world.

Unfair Comparison with Advanced Tools

With the arrival of more specialized tools for handling different aspects of data analytics, people find them better for their specific needs than a general workhorse like Excel and make the conclusion of Excel to be useless. It is not a fair comparison though. Some experts have pointed out that it is like comparing a Minivan to a large-scale cargo hauling such as Freightliner or comparing a minivan with a Formula 1 car and concluding that minivan is useless since it isn’t a Formula 1 car.

Excel works as a general tool for a wide range of data analytics work and is good for quickly building a wide variety of highly specialized timesaving workflow tools. The newer tools generally try to target and specialize one or two components of data handling and therefore are more advanced and capable in those aspects than Excel.

Relative Ease for Understanding and Operate

One can start working on MS Excel even with a basic knowledge of computer and networking. It does not require a high level of education or knowledge to be a master in Excel. Even kids of elementary schools can be taught to operate Excel with much ease. Excel is also easier to operate than the other advanced specialized tools in the market.

This relative easiness in understating and operating of the tool creates a misconception among many that it is not sophisticated enough to handle complex aspects of data analytics and visualization. It is however a wrong assumption. Even the best tools in data analytics require the knowledge and functioning of Excel in at least some part of their operation. Data analytics and excel have always went hand-in-hand and one is inseparable from the other.

Difficult to Find Errors

Even as one of the best tools in data analytics, Excel functions with the use of simple and complex analytical formulas. And since the formulas are only used for computation and calculation on the data, any errors which may reflect in the outcome becomes very difficult to be searched. Since there is no usage of coding in Excel, it is nearly impossible to automatically detect any error in data without having to go through the complete data manually.

Inability to handle Big Data

One of the major cons of using Excel is its inability to handle big data in data analytics. Since big data is emerging as a major component in today’s world in almost all major sectors, the simplicity of Excel makes it incapable of handling such larger data creating a perception of it being inferior to other such tools which can handle big data efficiently. Even after this disadvantage, it is impossible to overlook the sheer history of data analytics and Excel in any way.