I was in school back in 2004. We had a computer lab with a dozen of PCs and I remember seeing a bunch of floppy discs which were still in regular use. Fancy squares with a whole in the middle and a capacity to hold a little over two megabytes of data. Let that sink in.

According to a recent report 2.5 quintillion bytes of data are created every day. There being around three billion internet users the average amount of data created by one person in one day would be something around 0.5 GB. That is definitely a leap. I drew your focus towards this just to put the changing scenario of the world of analytics into perspective. It is a harsh world that keeps replacing its assets with new ones.

We will look at a few tools that did not get replaced or removed; tools that have stayed relevant by adapting to the constantly shifting landscape of business analytics.

MS Excel

Good old Excel has been in business for more than three decades. This spread sheet application was released in 1985. It is still one of the major tools for business intelligence. Nearly 15% analytics professionals use Excel for laying down the data and finding insights. In fact if you undergo an advanced Excel training in Malaysia you will be able to achieve much more with Excel than you may think. As long as the amount of data is limited to a few hundred thousand rows, Excel is quite efficient a tool for applying filters and presenting charts and tables. Millions of people have been using Excel for decades; hence it is easily acceptable in any scenario.


You can take the game forward by learning R. It is a widely appreciated tool used for statistical analysis and computation. If you are working with big data to find business insights, Excel may not suffice. You will need a more powerful tool like R to handle the computation. R also comes with cool visualization features and it can process data from a wide range of different sources in different formats. You can easily load data from R onto other visualization tools like Tableau. R first appeared in 1993 and it has gained its ground gradually over the last 27 years.


Data visualization is one of the key business analytics skills. Not only does a good piece of visualization create a lasting and moving impression on the stakeholders it also opens new vistas of thought for the business analyst. Data, when presented in a diagrammatic for usually makes much more sense. With Tableau it all becomes much easier. It is an incredibly efficient tool which takes a lot of load off the analysts shoulder by providing readily available choices in terms of visualization and analysis. Tableau was founded in 2003 and it is now one of the few essential tools for business intelligence.


Standard Query Language or SQL was developed in the 1970s by engineers from IBM. Known as SEQUEL back then it was the primary language used for acquiring data from databases. It is still widely used by data analysts and business analysts.


It is a language for everyone. From web developers to machine learning experts, Python has taken hold of more than 60% of the market. It has great libraries for statistical analysis and data processing which makes it a great choice for data professionals. The first implementation of Python was in 1989.