Table of Content
- Introduction to Data Analytics
- Different Types of Data Analytics Tools
- List of Top 10 Data Analytics Tools
Introduction to Data Analytics
The progress of any discipline can be assessed by analyzing the advancement of the tools and the ease with which their use spread in the concerned community. The invention of the telescope marked the change in the discipline of astronomy whereas the microscope revolutionized biology. As the discipline progresses, so does the tools deployed in the field. The latest and major discipline is of Data Analytics or Business Analytics which has revolutionized the way modern-day businesses function. The importance of business analytics can be appreciated when we understand as to how it allows the companies to gain an immense amount of information from their otherwise often unused data. Data Analytics allows for the companies to have-
- A better understanding of what has happened in the past
- What is happening exactly in the present
- What could happen in the future given certain circumstances
All of this allows for the leadership to come up with a much-informed decision which earlier was not possible as earlier many decisions were based more on leadership’s own experience, intuition, and very little data. Now, major tactical and strategic decisions are taken sometimes solely on the basis of the analytical results.
With the acceptance of data analytics as a legitimate and essential part of a company’s operations, the spread of it has reached companies where analytics was unheard of and this has revolutionized the data analytics tools. The process of data analytics is reflected directly on the analytics tools with they now being much more advanced, sophisticated, and easy to use.
Different Types of Data Analytics Tools
There are numerous Business Analytics tools that have grown immensely in popularity and capabilities especially in the last few years. All these tools, however, cover a particular aspect of analytics and specialize in it. These different type of tools can be understood from the following table:
|1||Collection and Storage||These tools are often responsible for storing a large amount of data and help in extracting relevant data in an easy and quick manner||Hadoop, Apache Spark, Apache Hive, Apache Cassandra, Amazon Redshift|
|2||Analytics||A number of tools allow the user to gain quick insights from the data. There are a several APIs that allow for specific analytics with relative ease and higher efficiency||KNIME, Rapid Miner, Splunk, TIBCO Spotfire, Qlik, SQL, MS Excel|
|3||Reporting & Visualization||One of the most important aspects of analytics is to present often complex information in easy to understand format and this is where the reporting aspect of analytics comes into play. Various tools help in creating reports and help especially in the visualization aspect by easing up the process of creating complicated graphs||Tableau, MS Excel, Power BI, Chartio, Redash, Google Data Studio|
|4||Modeling||The backbone of advanced analytics is predictive modeling and a certain statistical and programming tools allow the user to create such models using libraries that help in developing complex statistical, machine learning and deep learning-based models||Python, R, SAS|
All these tools cover are considered highly relevant in the industry and are used in one or the other process of analytics. Data Analytics tools however can be further divided into 2 categories based on the type of their availability as some are commercially available while others are open source.
|Cost||Such tools are expensive and require a license for commercial use||Open Source tools are available for free and it is not obligatory to pay for their commercial use|
|Extendability||New functionalities cannot be added as the source code is not available||The source code is available which can be modified to add and increase the capabilities of the tool|
|Support||Professional Assistance is provided by the creators of such tools for troubleshooting, tutorials, etc.||The online and in-person community provides help for solving problems faced during the use of such tools|
|Adoption||Such tools are often used by large multi-national companies especially those working in the field of BFSI (Banking, Financial Services, and Insurance)||Open Source tools are often used by startups, mid-level and large companies as well. They are much less expensive and often are at par in terms of the capabilities when compared with a commercial tool|
|Examples||1. Tableau (used by Citibank, Dell, Barclays) |
2. SAS (used by HDFC, HSBC, Citibank, Netflix, Accenture, Google)
3. Microsoft Excel (used by everyone)
|1. Python (used by Cognizant, Google, Genpact, Facebook)|
2. R (used by Google, Facebook, Fractal Analytics)
3. Apache Spark (used by Wipro, Ola, Infosys)
List of Data Analytics Tools with Descriptions
While there are hundreds of data analysis tools out there that can help in solving the above-mentioned aspects, the top 10 tools that can be easily be considered as the most important are the following:
|Sr. No.||Tool||Primary Usage|
|4||MYSQL||Descriptive & Diagnostic|
|5||Rapid Miner||Descriptive & Diagnostic|
|6||MS Excel||Analytics & Visualization|
|7||Tableau||Analytics & Visualization|
|8||Power BI||Analytics & Visualization|
|9||Apache Hadoop||Big Data Analytics|
|10||Apache Spark||Big Data Analytics|
The first three tools that are discussed below are the ones that are used for high-level analytics involving the development of diagnostic analytics, predictive and machine learning models, etc, and are also the rivals of each other. These are Python vs R vs SAS.
Python is an open-source, high level, a scripting language developed by Guido Van Rossem in 1991. It is recently being considered as a data analysis tool because of the wide range of libraries that the python user community has developed over the last few years which has allowed it to compete with the traditional data and business analytics tools. Following are the important features of this tool:
- Easy learning curve and can be easily learned by those who are new to the world of programming.
- It is an open-source and object-oriented language which allows the user to add new functionalities making the tool extremely versatile
- Python can work with a number of IDE (Integrated Development Environment) and notebooks especially Jupyter notebook which makes for the storing, debugging, and reusing of code extremely easy.
- It is considered one of the fastest languages, however, it requires a large amount of RAM for it to work efficiently.
- Python can work well with Big Data platforms and has data mining, manipulation as well as model building capabilities. Packages such as pandas, scipy, and numpy allow this tool to perform any kind of data manipulation whereas sci-kit learn, keras and tensorflow provide the much-needed capability to develop machine learning and deep learning algorithm based predictive models. Other packages include stats and statsmodels that help this tool to perform statistical functions and create statistical models.
- A primarily advanced tool for modeling, python can be used for reporting and visualization with the help of its packages such as matplotlib, seaborn, altair, etc. helping in automatic generation of reports.
R is known as the statistical language made by the statisticians for the statisticians. Like Python, R also requires a bit of coding and programming capabilities from the user for it to function. Developed by Ross Ihaka and Robert Gentleman in 1995 (released in 1997), R is an open-source, statistical language that primarily found its place in the world of academia and research and was later adopted by the BFSI companies. Following are the primary features of R-
- R has a bit of steep learning curve (if compared to Python), however, once mastered R provides a lot of capabilities that justified the effort to learn it.
- As R is an open-source language, it has a very strong community with CRAN acting as the quality control organ for this community providing good quality, high capability libraries that cover data analytics needs of almost all domains, from medical to insurance.
- R is commonly used with R studio as the preferred IDE. R Studio has an easy to customize the layout and has good code debugging capabilities and even allows for the objects of this object-oriented language to be saved in the physical memory for further use. This makes R studio a much more user-friendly IDE making the task for code reusability much easy.
- The biggest accomplishment of R is the sound statistical capabilities that it possesses which has allowed for its widely accepted use in universities and government organizations alike. Unlike the statistical packages of Python, R has statistical packages that have been developed by CRAN (or in other words: by the developers of R) and not by some third party which makes the results of the statistical tests computed from R much more reliable. The libraries such as caret and h20.ai provide machine learning and deep learning capabilities whereas dplyr and reshape allow for data manipulation to be done easily in R.
- Reporting and Visualization is one of the best features of R as it has extremely advanced and sophisticated libraries such as ggplot and plotly that can create beautiful and complicated graphs. Also, R allows for the user to easily create dashboards using R shiny which makes it a one of its kind tool and with a simple learning curve.
Statistical Analysis Software or commonly known by its abbreviation SAS, is one of the earliest tools that was adopted by companies once they decided to perform fulltime inhouse analytics. Unlike the above two mentioned tools (Python and R), SAS is a proprietary tool which means that its source code is not available and its capabilities can only be expanded by its creator- SAS Inc. Like Python and R, SAS also has the capability to perform data manipulation, reporting, visualization as well as advanced analysis using predictive models using statistical and some machine learning algorithms. As mentioned earlier, SAS has been mainly adopted and continues to be used by multinational companies involved in the BFSI domains as they prioritize customer security over anything else, and also, the cost is not much of a big concern for them. Following are the main features of SAS because of which it has stayed relevant-
- SAS has one of the easiest learning curve and people with no programming background can easily learn to use it. Moving from SPSS or SQL to SAS is particularly easy as SAS also has procedural commands which make the switch very convenient.
- Unlike R and Python, SAS has proper support infrastructure where SAS Inc. provides help in solving problems and help in training individuals to use various SAS functions.
- Server support is something that makes SAS stand apart from the other tools as data can be saved on the secure SAS servers which makes it a particularly attractive option for those businesses where securing data is of high concern.
- There are a number of licenses that SAS provides which provide a varying degree of capabilities which range from simple data manipulation and simple statistical models to advanced predictive models and visualization, however, these functionalities come at a very high cost.
- SAS is relatively slower than Python and R, however, it can easily connect with servers and thus can help with big data operations.
All of the above can be summarized in the following table:
|Commercial Software||Open Source||Open Source|
|Expensive||Free of Cost||Free of Cost|
|Statistical Software||Statistical Programming Language||Scripting Language|
|Can create sophisticated Statistical Models||Can create sophisticated statistical models||Can create decent statistical models|
|No or very less Machine and Deep Learning capabilities||Advanced Machine and Deep Learning capabilities||Advanced Machine Learning and highly advanced Deep Learning capabilities|
|Advanced Server capabilities provided by SAS Inc.||Optional Server Capabilities provided through R Studio||No Servers are provided (though can connect to other servers and DBMS)|
|Advanced reporting and visualization capabilities||Advanced reporting and visualization capabilities||Average reporting and visualization capabilities|
MYSQL is a query-based language which is one of the most commonly used languages. Before the advent of SAS, R, and Python, the most common tools used for any type of analytics were SPSS and SQL. Today, SPSS sees very limited use, however, SQL has successfully transitioned and adjusted itself to the modern-day requirements of Big Data. It is for the following reason that MYSQL has continued to be in popular use-
- MYSQL can easily be connected with various software that makes it a very attractive DBMS (Database management system). The recent version of MYSQL even provides a high level of data security and support making it to be adopted by a range of companies.
- The biggest advantage of SQL is the extremely easy learning curve as a large population of analysts is familiar with it as the SQL queries are mostly in almost plain English.
- The reason that SQL has survived the wrath of time is because of its speed and it is a high-performance tool that allows for processing of an extremely large amount of queries and is the reason that a number of E-commerce companies prefer to use MYSQL.
- MYSQL falls in a unique place as technically it is a free, open-source tool making it inexpensive with the security of a proprietary software allowing for a wide range of companies, from startup to big multinational to opt for it.
5. Rapid Miner
One of the most versatile tool for performing analytics is Rapid Minder. The reason for the sudden success of Rapid Miner is the variety of tasks that it can perform which range from basic ETL functions to data mining and machine learning. It is one of the rare tool that allows the user to perform advanced forms of data analytics such as Predictive Analytics and Text Mining using drag and drop functionalities. Among the range of advantages that Rapid Miner has, following are the most crucial ones-
- It can perform almost all aspects of data and business analytics. It can be used for performing segmentation, data preparations, visualization, development of predictive models, and their evaluation along with various kinds of statistics such as descriptive statistics.
- The user interface of Rapid Miner is what makes is stand apart from the rest of the tools. People with no programming background can easily work on this tool and can efficiently process data and come up with insights without putting in much effort.
- Rapid Miner’s capabilities can dramatically increase as it can work with a number of other tools. Its machine and deep learning capabilities can be increased significantly by integrating it with R and Python.
- Being an open-source platform, RapidMiner has a large number of libraries that allow for it to continuously expand in terms of its capabilities making it possible to have widespread acceptance, from startups to large corporate entities.
- Compared to other open-source tools, RapidMiner has a superior data security system with having a robust 4-layer security system making the users confident to use it when dealing with sensitive data.
6. Microsoft Excel
Often ignored and not taken as a serious tool for performing data analytics is MS Excel. The main reason that some sections of data analysts do not consider Excel as an important enough tool is mainly because of its limitation in dealing with large amounts of data. However, one must know that not always there is a need to deal with a large amount of data, and often after passing the data through other tools, for microanalysis, MS Excel is the greatest tool and also is the preferred tool for a preliminary inspection of a sample or a subset of a large dataset. The ease with which Excel is able to perform typical day to day business analytics tasks is the reason that almost all companies on the face of earth deploy it and it is expected from all the analysts that they know at least the basics of it. It is for the following reasons that MS Excel is so famous and a widely accepted tool:
- Excel has an extremely easy learning curve. Because of its graphical user interface, it becomes easy to have a connect with the datasets which is highly important especially for those who are new at dealing with structured data.
- Excel being a commercial tool provides great assistance and detailed material regarding the use of the various excel formulas that allow the user to perform often complex analytical procedures.
- Excel can connect to other DBMS especially SQL servers and with certain plugins, the user can perform data manipulation on even large amounts of data too. The GUI environment when combined with these capabilities makes it a unique and preferred tool for data cleaning and basic aggregation.
- One of the less talked about advantages of Excel is the wide range of plugins available for it which can dramatically increase its capabilities. These tools are often domain-specific and as they are screened through Microsoft, they are of superior quality.
- The wide reach of Microsoft Excel has led to the creation of a highly vibrant and supportive community as methods for solving specific problems in Excel can be easily found through Excel’s online community.
- Lastly, a decent enough aspect of Excel is its visualization capabilities. Most of the typical graphs along with few advanced ones can be created in Excel and is the reason that a lot of companies often after cleaning and aggregating the data use Excel’s simple graphs to visualize their analysis.
While MS Excel can be used for creating graphs and other advanced tools such as SAS, Python and R can also create sophisticated graphs but still, the place of a dedicated visualization tool cannot be taken by any other kind of tool. There are a number of tools for visualizing data such as D3, Vega, Google Charts, High Charts, etc but one of the widely accepted tools for visualization is Tableau. Tableau has the disadvantage of having a lack of support for higher-level SQL queries and cannot deal with extremely large amounts of data but still is highly popular. We can refer to the following features of Tableau for its widespread use-
- Tableau has the advantage to connect with various data sources such as a number of DBMS, OLAP, and spreadsheets such as MS Excel with which it is especially compatible (especially with the pivot table feature). It can also connect with languages such as R and Python after a few adjustments which take the load of aggregation and other calculations away from Tableau making it more efficient. This makes it easier for the user to connect no matter how their data is stored.
- The biggest advantage of Tableau is the extreme ease with which one can use it as there is no programming pre-requisite and people with less background of computer sciences can easily learn it. As several times there are dedicated teams for reporting and visualization, knowing Tableau can provide a much-needed credential in a candidate’s profile who is trying to enter the field of analytics.
- Continuing the ease of use, the very interface of Tableau allows for quick reporting and creation of advanced graphs. As tableau has a graphical user interface, most of the graphics can be created using simple drag and drop functionalities which helps in effortless discoveries of patterns and insights.
- Tableau can be used with Tableau Public which is free for the users, however, it has limited capabilities. The users can also look for the commercial paid version which has higher capabilities and the price is also not very high.
- Lastly, Tabluea can be used (line R Shiny) to create dashboards which in this case can be created with much ease, can be made to get updated in realtime, and can be shared with clients through social media.
8. Power BI
The success story of Power BI is incredible as it started as just a plugin for MS Excel, however, because of its superior business intelligence capabilities it has developed into a separate tool that now sees widespread support and appreciation. Like Tableau, it provides multiple licensing options that range from free for personal use to premium which has the complete functionality. Disadvantages of PowerBI include its lack of big data handling capability, difficult learning curve as it is tough to master because of its use of DAX formula which is a complicated language to work with and high complexity because of the sheer number of options that are tough to comprehend. Still, PowerBI continues to see success for the following reasons-
- PowerBI is a highly compatible tool as it can get data from multiple sources ranging from the typical Excel, XML, JSON to Databases such as SQL Server, Oracle Database to even Azure, and other cloud-based sources. It can also connect to numerous online services such as Facebook and Google Analytics making it a highly versatile tool.
- As Power BI is considered to enter a bit late in the world of analytics, it has compensated this shortcoming by releasing constant updates to its capabilities making it one of the most up to date tools.
- Like Tableau, Power BI too has easy methods for performing visualization as it too has drag and drop functionalities for understanding and analyzing data in quick and easy to understand manner. Power BI can also create interactive dashboards and reports and has filters and options for customizing the graphs to accommodate maps, key performance indexes, etc.
- With the recent updates, PowerBI has introduced a few basic concepts of Augmented AI where simple text-based commands can be written in plain English and Power BI provides quick visual friendly analysis and all of this can be accessed through mobile and other platforms and can be shared easily.
All the above-mentioned tools allow for quick visualization and help in reporting, however, they have some differences which can be understood and summarized with the following table
|Commercial Software. Not Free||Free Version available through Tableau Public||Free Version available are reasonable cost|
|Provides Basic Visualization options||Provides Highly advanced visualization options||Provides Highly advanced visualization options|
|Has limited Dashboard capabilities. Tough to update graphs in realtime||Can provide Dashboards and can update graphs in realtime||Can provide Dashboards and can update graphs in realtime|
|Can be learned easily||Has an intermediate learning curve||It is tough to master and has a relatively steep learning curve|
Tools for Storing and Accessing Data
9. Apache Hadoop
With the advent of the internet and higher computer processing capability, the amount of data being generated has skyrocketed. In order to handle this large amount of data or commonly known as Big Data, a number of tools have been developed that allow for dealing with the ever-increasing Variety, Volume, and Velocity of data and among them is Hadoop. Hadoop works on the MapReduce technology and allows the user to access and process large amounts of structured as well as unstructured data. Being an open-source tool, it has wide acceptance and is a highly efficient and cost-effective tool to deal with a large amount of data as it can work with a cluster of machines without adding any financial cost to the operations. The following features have led to the widespread acceptance of Hadoop-
- As mentioned above, Hadoop is an open-source platform that makes it a highly attractive option for dealing with big data. The commercial version of it such as Horton and Cloudera are also available at a reasonable cost that provides troubleshooting support and other assistance.
- The sheer community of Hadoop is one of its impressive feat. Because Hadoop has been in the world of analytics for some time now and has been adopted by a number of companies, it has led to a vibrant community of users.
- Another reason why Hadoop is a highly cost-efficient tool is its capability to use community storage which helps companies in reducing their storage expense and as it also allows for pooling of hardware which further brings down the cost of maintaining high computational machines for companies.
- Like the other tools mentioned in this article, Hadoop too can pride itself for the ease with which it can integrate with other tools. Even though it is developed in Java, Hadoop can easily integrate with languages such as Ruby, Groovy, Perl, and Python. It can also change its processing unit from Map Reduce to other newer processing frameworks such as Apache Spark.
10. Apache Spark
One of the main competitors of Hadoop as well as complementary tool, Apache Spark is considered as the next generation tool for dealing with analytics when a large amount of data is involved. It is also an open-source data analytics tool that has a big data framework and can integrate with Hadoop making it a highly attractive option for those analytical firms that deal with a large amount of data. Following reasons have led to the widespread popularity of this tool-
- Data using Apache Spark can be processed in real-time. This is particularly advantageous in the field of social media analytics, fraud detection among others where the velocity of data is extremely high.
- When compared to MapReduce, Apache spark has a relatively easy learning curve and doesn’t require much coding for it to function properly. This is the reason that a large number of companies adopt it as they can even train their existing workforce to make them work on Apache Spark.
- Apache can connect with other languages in order to write the code such as Java, Python, and Scala making it a versatile tool and accessible for people belonging to different programming backgrounds.
- Apache also allows for numerous algorithms to work with it especially Machine Learning algorithms, SQL queries among others making it to not get stuck as just another big data framework.
- As it is an open-source tool, it too has wide support and a highly informative community allowing for new Apache users to feel confident.
- Lastly, the biggest advantage of Spark is its speed which is significantly faster than Hadoop which is because of its RAM intensive framework, however, this comes at the cost of it becoming a memory expensive tool.
Both Hadoop and Spark provide a range of option to perform analytics on large amounts of data but are different from each other in the following ways:
|Purely a Big Data processing engine that helps in performing analytics where a large amount of data is involved||It can be considered as a data analytics engine as it has the capability of dealing with big data along with supporting analytics-based algorithms|
|It is used to store a large amount of data and helps in sharing the resources of the machine||Apache can deal with the processing of real-time data that makes it an attractive option for social media and surveillance entities.|
|It has a steep learning curve and is tough to master||Compared to Hadoop, it is relatively easy to learn and is compatible with Python, Java, SQL, etc.|
|Works on the local drive and is the reason it is slower than Apache Spark||Works on RAM making it much faster than Hadoop|
There are several tools that allow us to perform data analytics, however, each one of them handles some specific aspect of the analytical process. Where Modeling can be performed by using tools such as Python, R, and SAS, Reporting can be easily done through tools like MS Excel, Tableau, and Power BI.
For performing quick Analytics, tools such as Rapid Miner and MYSQL are particularly of great advantage while for storing and accessing data, Hadoop and Apache Spark can come in handy. With each one of these tools helping in solving the various problems of business, one must try to know as many of these tools as possible.
A practical approach can be to learn any one tool from Python, R or SAS for modeling, choose from Tableau and Power BI for visualization, Apache or Hadoop for dealing with big data, Rapid Miner being optional and Excel and SQL being the must-know tools as they are used almost in all the business organizations.