Table of Content
- Introduction to Data Analytics
- Different Types of Data Analytics Tools
- List of Top 10 Data Analytics Tools
Introduction to Data Analytics
Any discipline’s progress can be assessed by analyzing the advancement of the tools and the ease with which their use spread in the concerned community. The telescope’s invention marked the change in the discipline of astronomy, whereas the microscope revolutionized biology. As the domain progresses, so do the tools deployed in the field. The latest and major discipline is Data Analytics or Business Analytics which has revolutionized modern-day businesses’ function. The importance of business analytics can be appreciated when we understand how it allows 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 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 sometimes taken solely based on the analytical results.
With the acceptance of data analytics as a legitimate and essential part of a company’s operations, its spread has reached companies where analytics was unheard of, which has revolutionized the data analytics tools. The process of data analytics is reflected directly in the analytics tools, with them now being much more advanced, sophisticated, and easy to use.
Different Types of Data Analytics Tools
Numerous Business Analytics tools 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||Several tools allow the user to gain sharp 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 analytics aspects is to present often complex information in easy to understand format. This is where the reporting aspect of analytics comes into play. Various tools help in creating reports and support, 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 specific 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 analytics processes. However, data analytics tools 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 tool’s capabilities.|
|Support||The creators of such tools provide professional Assistance 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 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 rivals of each other. These are Python vs. R vs. SAS.
Python is an open-source, high-level scripting language developed by Guido Van Rossem in 1991. It is recently considered a data analysis tool because of the wide range of libraries that the python user community has evolved over the last few years, which has allowed it to compete with traditional data and business analytics tools. Following are the important features of this tool:
- Easy learning curve and can be quickly learned by those who are new to the world of programming.
- It is an open-source and object-oriented language that allows the user to add new functionalities making the tool extremely versatile.
- Python can work with many IDE (Integrated Development Environment) and notebooks, especially Jupyter notebook, making 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 to work efficiently.
- Python can work well with Big Data platforms and has data mining, manipulation, and model building capabilities. Packages such as pandas, scipy, and NumPy allow this tool to perform any data manipulation. In contrast, sci-kit learn, Keras, and TensorFlow provides 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 its packages such as matplotlib, seaborn, Altair, etc., helping in the 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 a 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 powerful 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 easily customize the layout and has the right code debugging capabilities. It 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 easily in R.
- Reporting and Visualization is one of the best features of R as it has significantly advanced and sophisticated libraries such as ggplot and plotly that can create beautiful and complicated graphs. Also, R allows the user to easily create dashboards using R shiny, making it a one of its kind tool and with a simple learning curve.
Statistical Analysis Software, commonly known by its abbreviation SAS, is one of the earliest tools companies adopted once they decided to perform full-time in-house analytics. Unlike the above two mentioned tools (Python and R), SAS is a proprietary tool, which means that its source code is not available. Its creator can only expand its capabilities- SAS Inc. Like Python and R, SAS also can perform data manipulation, reporting, visualization, and advanced analysis using predictive models using statistical and some machine learning algorithms. As mentioned earlier, SAS has been mainly adopted. It continues to be used by multinational companies involved in the BFSI domains to prioritize customer security over anything else. 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 most effortless learning curves, and people with no programming background can quickly learn to use it. Moving from SPSS or SQL to SAS is particularly easy as SAS also has procedural commands, making the switch very convenient.
- Unlike R and Python, SAS has proper support infrastructure where SAS Inc. provides help in solving problems and training individuals to use various SAS functions.
- Server support makes SAS stand apart from the other tools as data can be saved on the secure SAS servers, making it a desirable option for those businesses where securing data is of great concern.
- SAS provides types of licenses, which provide a varying degree of capabilities, ranging 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 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 they 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 that 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 minimal 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 widespread use-
- MYSQL can easily be connected with various software that makes it a very attractive DBMS (Database management system). The recent version of MYSQL provides a high level of data security and support, making a range of companies adopt it.
- The most significant 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.
- SQL has survived the wrath of time because of its speed, and it is a high-performance tool that allows for the processing of a tremendous amount of queries and is why many 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 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 tools 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 tools 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, the 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 it stand apart from the rest of the tools. People with no programming background can easily work on this tool and efficiently process data and develop insights without putting in much effort.
- Rapid Miner’s capabilities can dramatically increase as it can work with several 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 many 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 in MS Excel. The main reason that some 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 can perform typical day-to-day business analytics tasks is why almost all companies on the face of earth deploy it. 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 a straightforward learning curve. Because of its graphical user interface, it becomes easy to connect with the datasets, which is highly important, especially for those who are new at dealing with structured data.
- Excel is a commercial tool that provides great assistance and detailed material regarding the use of the various excel formulas that allow the user to perform 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. When combined with these capabilities, the GUI environment makes it a unique and preferred tool for data cleaning and basic aggregation.
- One of the less-discussed 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 creating 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. This is why a lot of companies often, after cleaning and aggregating the data, use Excel’s simple graphs to visualize their analysis.
While MS Excel can create graphs and other advanced tools such as SAS, Python and R can also create sophisticated graphs. However, the place of a dedicated visualization tool cannot be taken by any other kind of tool. There are several tools for visualizing data, such as D3, Vega, Google Charts, High Charts, etc., they are widely accepted tools for visualization in Tableau. Tableau has the disadvantage of having a lack of support for higher-level SQL queries and cannot deal with enormous amounts of data, but it still is highly popular. We can refer to the following features of Tableau for its widespread use-
- Tableau has the advantage of connecting with various data sources such as a number of DBMS, OLAP, and spreadsheets such as MS Excel. It is especially compatible (especially with the pivot table feature). It can also connect with R and Python languages 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 in 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, which is trying to enter the field of analytics.
- Continuing the ease of use, the very interface of Tableau allows for quick reporting and the creation of advanced graphs. As tableau has a graphical user interface, most graphics can be created using simple drag and drop functionalities, making 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 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, it has developed into a separate tool that now sees widespread support and appreciation because of its superior business intelligence capabilities. Tableau provides multiple licensing options that range from free for personal use to premium, which has 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. It can get data from multiple sources ranging from the typical Excel, XML, JSON to Databases such as SQL Server, Oracle Database to 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 for this shortcoming by releasing constant updates to its capabilities, making it one of the most up-to-date tools.
- Like Tableau, Power BI has easy methods for performing visualization. It also has drag and drop functionalities for understanding and analyzing data in a 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. Power BI provides quick visual friendly analysis and 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 is at a 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|
|It 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. To handle this large amount of data or commonly known as Big Data, some 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 and unstructured data. Being an open-source tool, it has wide acceptance. It 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 desirable 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 feats. Because Hadoop has been in the world of analytics for some time now and has been adopted by many companies, it has led to a vibrant community of users.
- Another reason Hadoop is a highly cost-efficient tool is its capability to use community storage, which helps companies reduce their storage expense and 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 on the ease with which it can integrate with other tools. Even though Java is developed, 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 a 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 with a big data framework. It can integrate with Hadoop making it a desirable 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 social media analytics, fraud detection, among others, where the velocity of data is very high.
- Compared to MapReduce, Apache spark has a relatively easy learning curve and doesn’t require much coding to function properly. This is why many companies adopt it as they can even train their existing workforce to make them work on Apache Spark.
- Apache can connect with other languages 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 and SQL queries, making it not stuck as just another big data framework.
- As it is an open-source tool, it has comprehensive support and a highly informative community, allowing new Apache users to feel confident.
- Lastly, the most significant advantage of Spark is its speed, which is significantly faster than Hadoop 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 perform analytics where a large amount of data is involved.||It can be considered a data analytics engine to deal with big data and support analytics-based algorithms.|
|It is used to store a large amount of data and share the machine’s resources.||Apache can deal with real-time data processing 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.|
|It works on the local drive and is the reason it is slower than Apache Spark.||Works on RAM, making it much faster than Hadoop|
Several tools allow us to perform data analytics. However, each one of them handles some specific aspect of the analytical process. Modeling can be performed using tools such as Python, R, and SAS; reporting can be quickly 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 solve the various business problems, 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.
You may also like to read: 16 Best Big Data Analytics Tools And Their Key Features