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Top 10 Tools Through Which Data Analytics Can Be Automated

The process of analyzing, collecting, cleaning, transforming, and interpreting data systematically, using mathematical and statistical techniques for making data-driven decisions, is called ‘Data Analytics’. The insight gained through data analysis can not only be used for decision-making but also for guided future research. In today’s world, where data is becoming extremely important and massive, data analytics can be automated in order to simplify. Thus, in simple words, data analysis is the process by which raw and scattered data is made useable, by using statistical and mathematical methods.


Tools through which data analytics can be automated


Automation of Data Analytics

Data analytics is becoming very crucial day by day in every field, be it business or science. And, in modern times, where automation of literally everything is becoming possible, even data analytics can be automated. Automation refers to the process of analyzing data through computer programs and simulations.

It is the process wherein data is collected, either manually or digitally, and it is processed by specific software. Automated data analysis saves time and funds, which makes the decision-making process smoother and faster. Even big data analysis, which is a tedious and mistakable process, becomes easier and more precise.

As there is no human intervention in the process, it is faster and more accurate. Moreover, automation in the process of data analytics, enables us to see various patterns and trends, which would not have been caught by humans.

Due to all of these, productivity can be increased. Thus, data analytics automation is very helpful in organizing and computing data, and then making decisions based on it.

In this article, we shall try to understand what data analytics is and its automation process, how data analytics can be automated, some tools used for automation of the process of data analytics, and its advantages in depth.

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Tools Through Which Data Analytics Can Be Automated

Data can be of any and all types, making data analytics a very vast and varied field. Because of this, not every human can analyze all types of data. This is where the automation of data analytics comes in handy, as it brings uniformity, accuracy, and precision to the analysis of the vast variety of data. Data analytics automation is done by various tools on computer devices. Here we have made a list of the top 10 such tools, through which data analytics can be automated.


1. Tabeleu

Tableau is a widely used visualization tool. It has an easy-to-use drag-and-drop interface which helps in creating interactive projects and dashboards. The visuals created in Tableau can be used by organizations to give context and meaning to raw data and to make it understandable quickly and easily.

On Tableau, there is mobile support for both Android and iOS. Moreover, hidden data can be found and worked upon, in Tabelu because of the ‘Data Discovery’ feature. But, the outstanding thing about Tableau is the quality of visuals that it creates. That being said, it is only a tool through which one can make visuals.


2. Power BI

Power BI is a business analytics tool by Microsoft. Its user-friendly interface offers dynamic visualizations and business intelligence functionalities. This allows even users with less proficiency to analyze intricate data easily. It is also integrated with various Microsoft products which helps in data analysis.

It allows the users to access Excel, cloud-based data sources and on-premises data sources. Some of its innovative features include Natural Language queries, Power Query Editor Support, and an intuitive User Interface.

The limitation of Power BI is that it cannot handle data bigger than 250 MB. So, only small data analysis can be automated using PowerBI. Moreover, a user would have to pay extra to utilize all features.


3. Apache Spark

As the name suggests, Apache Spark is known for its in-memory processing, which makes it incredibly fast in data processing and data analytics. Due to its API, it is very easy and straightforward to learn. It can also handle and manipulate data in real time, making it a very handy data processing tool.

Moreover, it is open source, which makes it accessible to everyone. It can run platforms like Hadoop, Cloud, and Kubernetes. It also has great connectivity, thanks to the support of Python, Scala R, and SQL shells. The drawbacks of Apache Spark include the absence of an integrated filing system and very few algorithms.

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4. R

R is an open-source Programming language. It is also used widely in Statistical Computations, Graphic Development, and Data Analytics. It can be used to handle large data sets and is very versatile as it can be used for data processing, data visualization, etc. R is also useful for processes like data cleaning, machine learning, and Natural Language Processing.

R also provides extensive packages for data manipulation, visualization, and modeling. These features make R a very helpful tool through which data analytics can be automated. Although, it has certain setbacks. Like, it is quite slower than its competitors C++ and Java. Besides, learning R is not an easy task, making it difficult for laypersons to use it.


5. Python

Python is another programming language that is used for Data Processing and Machine Learning, through which data analytics can be automated. Python, unlike R, has an easy syntax, making it easier to use even for a layperson.

It has a large scalability, which enables it to handle large data sets. Moreover, its extensive packages and libraries like Pandas and NumPy, increase its data analysis and manipulation functionalities.

Even though Python has all these features, it is not flawless. Just like R, it is slower compared to Java and C++, because unlike the two it is not a compiled language, but is an interpreted language. Besides, Python also consumes a lot of memory.

6. SAS

SAS stands for Statistical Analysis System. It was created by the SAS Institute. It is used a lot in Business for sophisticated analytics, multivariate analysis, business intelligence, data management, and predictive analytics.

It is quite user-friendly as it has both a Graphical User Interface and a Terminal Interface. The user can use the one she is acquainted with. SAS also makes large data analytics automation simple and easy once mastered.

SAS also has various Analytical Tools through which data analytics can be automated. All of these factors and functions make SAS very powerful, but it is very costly and is also quite difficult to learn.

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7. Hadoop

Hadoop is an open-source software by Apache. Apache Hadoop is a data processing as well as storage software, where big data can be analyzed using the MapReduce model. Hadoop enables scaling from a single server to thousands of machines.

It can operate even after some nodes fail, because of its fault-tolerant nature. As it is open-source, it is free to use. Moreover, because it supports various data formats, it can be customized to suit the specific needs of users.

On Hadoop, data analytics can be automated effectively, only when there is powerful hardware. Moreover, its MapReduce model makes its learning curve difficult to grasp.


8. Google Analytics

Google Analytics is developed by Google, and so is naturally very trusted and widely used. It is a web analytics software used to monitor web traffic. It provides website analysis and also provides insights on web performance. It also has an e-commerce and conversion tracking mechanism.

Although Google Analytics provides one of the best web analytics services, it has a few drawbacks. Like, it does not have a dedicated customer support mechanism. The free version only allows analysis of up to 10 million hits per month.


9. TensorFlow

TensorFlow is an open-source software, which is also developed by Google. It is a Machine Learning Library, used widely by businesses across the world. Due to its worldwide recognition, it is preferred by a lot of businesses for data analytics automation, as its tutorials are available in abundance.

TensorFlow supports various programming languages like Java, C++, Python, JavaScript, and Java and also runs on CPUs, GPUs, or TPUs. The built-in visualization helps in the automation of data analytics, through graphs. The major setback of TensorFlow is that it is very difficult for beginners to learn.

One must have a thorough knowledge of coding to utilize TensorFlow optimally. Moreover, the installation and configuration of TensorFlow can quite tricky depending on the user’s system.


10. QlikSense

QlikSense is a business data analytics automation tool provided by Qlik, a business analytics solutions company. QlikSense provides data visualization and data analysis by supporting various sources like spreadsheets, databases, and cloud services.

Especially the data visualization features are very extraordinary and can be used to make amazing visuals. It uses AI and Machine Learning features for the automation of data analysis.

Moreover, it has user-friendly features like Instant Search and Natural Language Processing, which can make data analytics easy. In the operation of large data sets, QlikSense has proven to be quite slow and inactive. Moreover, the pricing model of QlikSense is very complicated and the data extraction is quite difficult to comprehend.

These are some tools through which data analytics can be automated. There are many other such tools as well, like, Microsoft Excel, KNIME, RapidMiner, Jupyter Notebook, MangoDB, Microsoft Azure, etc. These tools enable the automation of data analytics, which is very advantageous in this modern, data-driven world for any organization. Let us look at some of its key advantages in detail.

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Advantages of Automation of Data Analytics

Automating data analysis is done by organizations as it helps them in various ways. Like, it saves time and money, increases accuracy, helps to ensure whether the work done is going according to the plan, etc.

Data analytics brings uniformity and provides direction to companies and organizations pertaining to their plan of action. Thus, it is an integral part of any organization’s planning and decision-making units.

Here, we have listed down a few of the advantages of data analytics and its automation, which help organizations and business units attain their set goals.


1. Saving Money

Although many tools required for the automation of data analytics require paid licensing, it is still cheaper as compared to the manual process. This is because, unlike the manual process, data analytics can be automated, requiring fewer staff members. The hours of work of the minimal staff are also very low compared to the unautomated method. Thus, the licensing fees prove to be a lot more affordable as compared to the wages of the employees.


2. Saving Time

Data analytics involves the collection of data, its processing, and its analysis, which is a time-consuming and tedious process for human beings. But automated data does this work a lot faster than humans. This gives the data analyst a lot more time to reflect on the automated data and interpret it. This enables the organization to make more insightful decisions, which will in turn lead to the attainment of the goals.


3. Handling Big Data

Automated data analytics can handle a lot of data. Unlike humans, data analytics tools can automate big data at once. Tools like QlikSense, R, Python, etc. can handle and automate large data sets easily. The time taken to analyze the data is also much less as compared to the employees, which is an added benefit. So, in this modern data-driven world, where there is a lot of data in huge quantity, automation of data analytics proves to be very helpful.


4. Increased Accuracy

It is obvious that humans cannot provide the level of accuracy that machines and software do. It is the same when it comes to data analytics as well. The data analyzed by software is more precise, accurate, and complete.

This is because, the data analytics automation tools are able to pay great attention to detail, which helps in finding patterns and trends which are not easily identifiable by humans. The fatigue factor in humans also leads them to make mistakes, but automating data analytics avoids this possibility as well.

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5. More Frequent Insights

With the automation in the field of data analytics, insights are now available on a more frequent basis. The insights that were only available monthly or weekly earlier, are now available on a real-time basis. This allows faster adjustment to trends and emerging patterns in the market.

Moreover, it enables firms and organizations to detect diversions from the plan and take necessary actions. It also makes it easier for organizations to detect a problem before it has risen or when it has not become too severe.


6. Increased Productivity

Automating data analysis increases productivity as, it is faster than the manual process, with the error rate being very low. This will lead to the elimination of bottlenecks and will result in a proper flow of work.

The real-time data insights allow the staff to incorporate the new findings frequently to get better results. Moreover, automated data provides a level of accuracy that can never be reached by manual processes. Due to all of these, productivity increases and the achievement of goals.

This shows that automating the process of data analytics helps an organization or a business unit a lot. It saves time and money, handles all types of data – big or small, provides real-time insights, and thus, increases productivity in the unit.

So, it would be fair to say that, in this modern era, when data has become a very powerful asset for any individual, organization, or business unit, automating the process of data analytics is not a voluntary activity, but is a basic necessity.



1.    What is data analytics automation?

The modern world is driven by data. Data analytics has thus become very essential. Due to this, automating data analytics is becoming very important. Automating data analytics means eliminating the human process of analyzing data and instead using computer tools and software. There are many tools or software available using which processes can be automated, like PowerBI, Tableau, ApacheSpark, Google Analytics, etc. Using these tools, large data files can be automated, and the automated data will be more reliable and the process of data analytics will become faster.

2.    Can data analytics be automated?

Yes, data analytics can be automated. In fact, automation of data analytics has become a must in the modern data-driven world. Automating data analytics has become quite a simple process these days. One just needs access to certain tools for data analysis automation. A lot of these tools are free of cost or are comparatively cheap, and easily accessible. The tools can then automate data analytics. Using automated data, even large data can be handled. Moreover, automation ensures the accuracy of the analyzed data. So, data analysis automation is not only possible but is the need of the hour.

3.    What is an example of automating data analytics?

There are many places where the process can be automated. One such example of data analytics automation is ETL. ETL stands for Extract, Transform, and Load. It enables engineers to extract data, transform the extracted data into a usable form, and load the data into systems where end users can utilize them. Data analytics automation is not only possible here, but at every place where data is analyzed, automating the process is possible.

4.    What is data analytics?

In the modern world, data is gaining a lot of prowess. In these times, the study of data is very essential as it can help us understand the market trends and patterns, and can help us prepare for future dangers and opportunities. So, the systematic study of data, including the collection, cleaning, transforming, and interpreting of raw data using statistics and logic, and in modern times, even computer software, to convert it into actionable insights is called data analytics. The process of data analytics is used in fields like science, business, agriculture, banking, etc. for better and faster insights, saving time and money, and for increasing productivity.

5.    What problems can data analytics solve in business?

Data analytics is the process of converting raw data into actionable insights using statistical and logical methods. Data analytics can help organizations and businesses in solving problems like improving customer satisfaction, increasing revenue, reducing costs, optimizing operations, etc. This helps the business earn more profit and solve many other problems. It can also prepare the firm for upcoming dangers and help in the prevention of problems as well. Thus, data analytics can solve many business-related problems, and data analytics automation can make the process of data analytics very simple, easy, and reliable.

6.    What are the disadvantages of automating data analytics?

Data analytics is very beneficial in modern times where data has become very important. Automation in the field of data analytics has made data analytics simpler, easier, and more reliable, but it has a few drawbacks. Because of the automation of the data analytics process, it has lost the human touch and thus it lacks creativity and judgment. Moreover, it can sometimes prove to be very expensive. The other problem is that automated analysis can lead to the formation of a pattern. Data analytics can be automated, only when precautions are taken to avoid its disadvantages.


Data analytics is the process of converting raw data into actionable insights, which helps an organization or a business unit stay up-to-date with the ongoing market trend patterns, predict future dangers and avoid them, and detect diversions from the plan. In recent years, the importance of data has increased by many folds, and so has the amount of data. To handle the huge data, data analytics can be automated.

There are many tools like Tableu, PowerBI, R, Python, QuikSense, etc. which automate data analytics. Using these tools the whole process of data analytics can be automated. Automating data analytics is better than manual data analytics as it saves time and money, can handle big data, provides more frequent insights, and thus, is a lot more productive. Thus, in order to achieve success in business, data analytics can be automated, instead of the manual analytics process.

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