10 Most Popular Analytics Tools in Business
Update 2017: This article on most popular analytics tools was originally written more than 5 years ago. It has been updated a couple of times since then. However, the industry has changed rapidly in this time and the article continues to generate a lot of interest.
I felt it was time now to do a complete revamp and share the latest and most updated list of analytics tools with the readers. You can read the original article here.
The analytics industry has seen a paradigm shift in how and which analytics tools are used. We have seen the demise of the SAS monopoly, which had for long been the case, giving way to increased adoption of open source tools.
I have worked on SAS for many years and it is a fantastic tool. However, the high pricing of the software was always a deterrent. Most small and medium companies could not afford the tool, and neither did it make sense for them to invest in it as their analysis needs were different. The companies that have and could use it, often ended up paying more for the tool than the analyst who worked on the tool and this was not an optimal scenario.
If we needed analytics to work for us the way we wanted it to, there was a terrible need for industry-wide adoption of cheaper tools, and better if they were free.
In line with the growing importance of open source tools in the field of analytics, I have decided to switch the order of listing the top tools. I will start with the most popular open source tools this time and then move on to the commercial or paid tools.
Open Source Analytics Tools
Here are the 5 most popular open source analytics tools:
R – R is now the most popular analytics tool in the industry. It has surpassed SAS in usage and is now the tool of choice even for companies that can easily afford SAS. Over the years, R has become a lot more robust. It handles large data sets much better than it used to, say even a decade earlier. It has also become a lot more versatile.
1800 new packages were introduced in R between April 2015 and April 2016. The total number of R packages is now over 8000. There are some concerns about the sheer number of packages but this has certainly added a lot to R’s capabilities. R also integrates very well with many Big Data platforms which has contributed to its success.
Python – Python has been a favorite of programmers for long. This is mainly because it’s an easy to learn language that is also quite fast. However, it developed into a powerful analytics tool with the development of analytical and statistical libraries like numpy, scipy etc. Today it offers a comprehensive coverage of statistical and mathematical functions.
Increasingly, we are seeing programmers and other tech folks moving into analytics. Most of these guys are already familiar with Python and therefore it has become a tool of choice for many data scientists.
Apache Spark – Spark is another open source processing engine that is built with a focus on analytics, especially on unstructured data or huge volumes of data. Spark has become tremendously popular in the last couple of years. This is because of various reasons – easy integration with the Hadoop ecosystem being one of them. Spark has its own machine learning library which makes it ideal for analytics as well.
Apache Storm – Storm is the Big Data tool of choice for moving data or when the data comes in as a continuous stream. Spark works on static data. Storm is ideal for real time analytics or stream processing.
PIG and HIVE – Pig and Hive are integral tools in the Hadoop ecosystem that reduce the complexity of writing MapReduce queries. Both these languages are like SQL (Hive more so than Pig). Most companies that work with Big Data and leverage the Hadoop platform use Pig and/or Hive.
Now, let’s take a look at the 5 most popular paid analytics tools:
SAS – SAS continues to be widely used in the industry. Some flexibility on pricing from the SAS Institute has helped its cause. SAS continues to be a robust, versatile and easy to learn tool. SAS has added tons of new modules. Some of the specialized modules that have been added in the recent past are – SAS analytics for IOT, SAS Anti-money Laundering, and SAS Analytics Pro for Midsize Business.
Tableau – Tableau is an easy to learn tool that does an effective job of slicing and dicing your data and creating great visualizations and dashboards. Tableau can create better visualizations than Excel and can most definitely handle much more data than Excel can. If you want interactivity in your plots, then Tableau is surely the way to go.
Excel – Excel is of course the most widely used analytics tool in the world. I have seldom come across a data scientist who does not use Excel. Whether you are an expert in R or Tableau, you will still use Excel for the grunt work. Non-analytics professionals will usually not have access to tools like SAS or R on their machines. But everyone has Excel. Excel becomes vital when the analytics team interfaces with the business steam.
QlikView – Qlikview and Tableau are essentially vying for the top spot amongst the data visualization giants. Qlikview is supposed to be slightly faster than Tableau and gives experienced users a bit more flexibility. Tableau has a more intuitive GUI and is easier to learn.
Splunk – Splunk is more popular than some of the more known names like Cloudera and Hortonworks. It started as a ‘Google for log files’ which means its primary use was to process machine log files data. It has now become much more than that. Splunk has great visualization options and a web interface makes it easy to use.
This is the new and updated list of the most popular tools in the analytics industry. If you are interested in learning these tools, here is what you need to do.
To learn SAS, R, and Python, head to our Data Scientist Specialization. Spark, Storm, and other Big Data technologies are covered in the Big Data Analyst Specialization. We have a course on Analytics with Excel as well that will help you take your step into analytics.
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