The importance of data in today’s modern world cannot be stressed enough. A large number of companies all around the world, for instance, Facebook, Amazon, Google, etc. rely heavily on data for their day-to-day businesses. We live in a world where quintillions of bytes of data are being generated every day from different sources.
Every time we search for our favourite product on Amazon and magically an ad for that very product starts to pop up each time we use Google (or some other platform), this is to make our experience more rich and useful and this all is done through data and its analysis. This is just one scenario where the smart analysis and usage of data can help drive decisions for companies. It is predicted that trillions of Gigabytes of data will be generated in the world in the time span of just one year and seeing these insights, it is self-evident as to why so many companies have opened up multiple positions for Data Analysts in various domains.
The job of Data Analysts is to provide companies with insights after having analyzed the data available to them and help the business grow.
- Top 10 Data Analytics Tools of Today’s Times
- 1. Python and R Languages
- 2. Tableau
- 3. Microsoft Excel
- 4. Microsoft Power BI
- 5. RapidMiner
- 6. Apache Spark
- 7. KNIME (Konstanz Information Miner)
- 8. QlikView
- 9. Splunk
- 10. Talend
- Frequently Asked Questions (FAQs)
In order to deal with data on such a huge scale, it becomes extremely important to wisely select tools that help in the analysis of data in an efficient and faster way to ensure better results.
The term “Data Analytics Tools” is used to describe software and applications which are being used by data analysts in order to develop and perform analytical processes (for instance, filtering out meaningful data from raw data) that help companies to make better, informed business decisions while decreasing costs and increasing profits. Some of the common questions which Data Analysts and Business owners need to answer before selecting a Data Analytic tool are:
- How well is it placed in the market?
- What is the cost of using the tool?
- How easy or hard is it to learn it?
- How many users use that tool currently? etc.
This article aims to answer these common queries and gain more knowledge about the most recent Data Analytics Tools available in the market.
Top 10 Data Analytics Tools of Today’s Times
Now that we have a fairly good understanding of what Data Analysis is and what Data Analytics Tools do, let us dive a little deeper in order to understand what are the best Data Analytics Tools available in the market today and what is the variety of features they have to offer us:
1. Python and R Languages
Two of the most widely used programming languages in the field of data analytics are R and Python. Both Python and R are open sources and rich in libraries which makes them act as extremely useful tools for data analytics. Python can be used for almost everything from data scraping to analysis and reporting while R is mostly used for data mining and statistical analysis. Now let us take a look at some of the features of Python and R individually.
Some of the features of Python are as follows:
- One of the fastest programming languages in the world today, Python is being used in a lot of industries like Software Development, Machine Learning, Data Science, etc.
- Python is an Object Oriented Programming language.
- It is easy to learn and has a very rich set of libraries because of which it is being heavily used as a Data Analytics Tool. Two of the most well-known libraries of Python – Pandas and NumPy – are being used a lot as they provide lots of features for Data Manipulation, Data Visualization, Numeric Analysis, Data Merging and many more.
Some of the features of R are as follows:
- One of the most widely used programming languages for statistical modelling, visualization, and data analysis, R is used a lot by statisticians for statistical analysis, Big Data and machine learning.
- R is extremely good for Exploratory Data Analysis or EDA. EDA is an approach used by statisticians to analyze data sets to summarize their main characteristics. It is done often with visual methods.
- Using packages like plyr, dplyr, and tidy, it becomes very easy to do Data Manipulation in R. Data analysis and visualization can be done in R with the help of packages such as ggplot, lattice, ggvis, etc.
Some of the famous companies using Python are NASA, Netflix, Spotify, etc. while some of the famous companies using R as a Data Analytics Tool are Uber, Twitter, Google, Facebook, etc.
One of the most in-demand, market-leading Business Intelligence tools, Tableau is used to analyze and visualize data in a very easy format. It is a commercially available tool that can be used to create extremely interactive data visualization and dashboards without having a lot of expertise in coding or technical knowledge. Let us take a look at some of the features of Tableau:
- Tableau is an easy-to-use tool which can be used for understanding, visualising and analyzing data.
- It provides fast analytics, that is, it can be used to explore any type of data, for instance, spreadsheets, databases, data on Hadoop and cloud services, etc.
- It can be used to create smart dashboards for visualizing data using drag-and-drop features. Moreover, these dashboards can be easily shared live on the web and mobile devices.
Some of the famous companies using Tableau for Data Analytics are LinkedIn, Amazon, Barclays, etc.
3. Microsoft Excel
Microsoft Excel is one of the most popular Data Analytics Tools worldwide. It is spreadsheet software that has a variety of features for calculations and graphing functions. Features like sharing a workbook so that multiple people can collaborate together in real-time, adding data into a spreadsheet directly from a picture, etc. make Microsoft Excel really an ideal tool for data analysis. Now, let us take a look at some of the features of Microsoft Excel:
- Microsoft Excel is a spreadsheet which can be used very efficiently for data analysis. It is part of Microsoft’s Office suite of programs and is not free.
- Data is stored in Microsoft Excel in the form of cells. The statistical analysis of data can be done really very easily using the charts and graphs which are offered by Excel.
- Excel provides a lot of functions for data manipulation like the CONCATENATE function which allows users to combine numbers, texts, etc. into a single cell of the spreadsheet. A variety of built-in features like Pivot tables (for the sorting and totalling of data), form creation tools, etc. make Excel an amazing choice as a Data Analytics Tool.
Some of the companies which use Microsoft Excel are IKEA, Marriott, McDonald’s, and many more.
4. Microsoft Power BI
Released in 2011, Power BI is Microsoft’s yet another solution for Data Analytics. Being part of the Microsoft Power Platform, Power BI aims to provide interactive visualizations and business intelligence capabilities with an interface simple enough for end-users to independently create their own reports and dashboards. It can be used for data visualization, predictive analysis, and many more areas. Let us now take a look at some of the key features of Microsoft Power BI:
- Power BI comes in three different versions: Desktop, Pro and Premium. The Desktop version is free of cost while the other two are paid.
- It allows importing data to live dashboards and reports and sharing them.
- It can be integrated very well with Microsoft Excel and cloud services like Google Analytics and Facebook Analytics so that Data Analysis can be seamlessly done.
Some of the companies which use Microsoft Power BI are Tenneco, Ecolab, Nestle, and many more.
RapidMiner is a data science software platform that was developed for providing an integrated environment for data preparation, machine learning, deep learning, text mining, predictive analytics, etc.
RapidMiner can be used for the development of business and commercial applications. It can be also used for research, education, training, rapid prototyping, and application development. It also supports all steps of the machine learning process including data preparation, results in visualization, model validation, and optimization. RapidMiner is developed on an open-core model (An open-core model is a business model which is used for the monetization of commercially produced open-source software). Let us take a look at some of its key features:
- RapidMiner makes use of a client and server model. The server of RapidMiner can be offered both on-premises or in public or private cloud infrastructures.
- It has a very powerful visual programming environment which can be efficiently used for building and delivering models in a fast manner.
- RapidMiner’s functionality can be extended with the help of additional extensions like the Deep Learning extension or the Text Mining extension which are made available through the RapidMiner Marketplace. The RapidMiner Marketplace provides a platform for developers to create data analysis algorithms and publish them to the community.
- RapidMiner has lately extended the platform to full-time coders and BI Users. It is a fully transparent, end-to-end Data Science platform which enables data preparation, Machine Learning, and model operations.
Some of the companies which use RapidMiner are HP Enterprise, BMW, Sanofi, and many more.
6. Apache Spark
Apache Spark is a data processing framework that is used a lot for Big data processing and Machine Learning. It allows data analysts to process huge datasets really fast. It is extremely easy to analyze big data and perform computationally heavy analytics on them using Apache Spark. Apache Spark is one of the most active Apache projects at the moment and it comes with an amazing open-source community and an interface for programming, which, ensures fault tolerance and implicit data parallelism. Let us take a look at some of Apache Spark’s key features:
- Spark is a free and open-source data analytics engine which can be used for Big Data Processing. It performs really very well for batch and streaming data.
- Apache Spark is extremely easy to learn. Along with that, we can also use it interactively from the Scala, Python, R, and SQL shells. It can run on a lot of platforms, for instance, Hadoop, Apache Mesos, standalone, or in the cloud. Apache Spark can access diverse data sources.
- Spark has extremely rich libraries for SQL (SparkSQL), Machine Learning (Mlib), Live dataStream processing (SparkStreaming), Graph analytics (GraphX) and many more fields.
Some of the companies which use Apache Spark are Uber, Slack, Shopify, and many more.
7. KNIME (Konstanz Information Miner)
The Konstanz Information Miner, also known as KNIME, is a free and open-source data analytics, reporting, and integration tool or platform. It integrates the different components for machine learning and data mining by allowing users to create workflows for data analytics and making reusable components accessible to everyone. Let us now take a look at some of the key features of KNIME:
KNIME is currently providing the following two software:
KNIME Analytics Platform – The KNIME Analytics Platform is an open-source software used to clean and gather data. It is also used to make reusable components accessible to everyone and create Data Science workflows.
KNIME Server – It is basically a platform that can be used by enterprises for the deployment of Data Science workflows, team collaboration, management, and much more.
- KNIME provides a simple, easy-to-use drag and drops graphical user interface (GUI) which makes it ideal for visual programming (Visual programming is a kind of programming language which helps in letting humans describe processes using illustration.).
- KNIME offers in-depth statistical analysis and no technical expertise is required to create workflows for data analytics in KNIME.
Some of the companies which use KNIME are Siemens, Novartis, Deutsche Telekom, and many more.
QlikView, a business analytics tool is a SaaS (Software as a Service) software company that provides the QlikView Software, a classic guided analytics solution. which lets us rapidly develop and deliver interactive guided analytics applications and dashboards. Two of the most important products of QlikView are Qlik Sense and Qlik Replicate. Both products are cloud-based software for business intelligence and data integration. Some of the key features of QlikView are as follows:
- It is a Self Service Business Intelligence, Data Visualization, and Data Analytics tool which can be used for accelerating business value via data by providing features such as Data Integration, Data Literacy, Data Analytics, etc.
- One of the most important features launched by QlikView is an intelligent alerting platform Qlik Alerting for Qlik Sense. This helps the organizations handle the exceptions, and notify users of potential issues. It also helps users analyze further and prompts actions based on the derived insights.
Some of the companies which use QlikView heavily are CISCO, NHS, KitchenAid, SAMSUNG, etc.
Splunk is yet another software or tool for searching, monitoring, and analyzing machine-generated data. It does so using a Web-style interface. Using Splunk, we can capture, index, and correlate real-time data in a searchable repository. From it, we can generate graphs, reports, alerts, dashboards, and visualizations. It can be also used for identifying data patterns, providing metrics, and diagnosing problems from machine data. In this way, Splunk provides intelligence for business operations.
There are three main products of Splunk available in the market right now:
- Splunk Free or Splunk Light: Splunk Free allows search, report and alert on all the log data in real-time from one place. It has limited functionalities and features as compared to the other two versions.
- Splunk Enterprise: It is mostly used by companies which have large IT infrastructures and IT-driven businesses since it helps in gathering and analysing the data from websites, applications, devices and sensors, etc.
- Splunk Cloud: As evident from the name itself, Splunk Cloud is a cloud-hosted platform which has the same features as Splunk Enterprise. We can avail of it using Splunk itself or through the Amazon Web Services (AWS) cloud platform.
All three products vary by the bandwidth of the features they offer. They are all available for free download and trial versions.
- Splunk is really useful in the Data Ingestion of a variety of data formats, for instance, JSON, XML and unstructured machine data like web and application logs.
- Splunk also supports features like Data Indexing and Searching, Alerts and Dashboards for better analytics of data.
Some of the companies which use Splunk heavily are Dominos, Otto Group, Intel, Lenovo, etc.
One of the most powerful data integration ETL (Extract Transform and Load) tools available in the market, Talend was developed in the Eclipse graphical development environment. Talend is one of the most useful data analytics tools which allows us to easily manage all the steps involved in the ETL process and aims to deliver compliant, accessible, and clean data for everyone. Talend provides software solutions for data preparation, data quality, data integration, application integration, data management, and big data. Some of its key features are as follows:
- Some of the products of Talend are Talend Open Source, Stitch Data Loader, Talend Pipeline Designer, Talend Cloud Data Integration and Talend Data Fabric.
- Talend can help us to deliver complete and clean data at the moment we need it. It does so by maintaining data quality, providing Big Data integration, cloud API services, etc. It also Prepares Data and provides Data Catalog and Stitch Data Loader.
- Talend started to adhere to the lakehouse paradigm for data and the path to reveal intelligence in data. Talend Cloud is also available in Microsoft Azure Marketplace.
Some of the startups which use Talend are ALDO, ABInBev, EuroNext, AstraZeneca, etc.
So, in conclusion, we hope that we were able to make our readers understand what Data Analytics Tools are and what are the various types of Data Analytics Tools are being used in the world today. With each passing year, the amount of data being generated is expected to grow exponentially, and therefore, the role of Data Analysts will become extremely crucial for companies in the near future. Hence, any budding Data Analyst should be familiar with various types of Data Analytics tools available in the market so that he or she can help his or her company bring in a lot of business and excel in his or her career.
Frequently Asked Questions (FAQs)
Q.1: How to select the best Data Analytics Tool?
Ans: Different types of Data Analytics Tools offer different types of features and the answer to this question will vary from person to person. In order to select the best Data Analytics Tool, first of all, it would be great if one could understand his or her business use case. After that, one should explore all the options and tradeoffs of Data Analytics tools that suit their use case and finally account for the performance and experience of those tools in order to finally decide which is the best Data Analytics tool for them.
Q.2: Which Data Analytics tool is easiest?
Ans: Some of the easy to use Data Analytics Tools are as follows:
- Microsoft Excel
Q.3: Which Data Analytics tool is in demand?
Ans: Some of the most in-demand Data Analytics Tools are as follows:
- The language R
- Apache Spark
- Jupyter Notebook
- Microsoft Power BI