A Comprehensive Understanding Of Data Analytics And Its Importance In The Future

The simple definition of the word “analytics” is a study of analysis. An analysis is the process of conducting a detailed examination of the elements of something. The modern term data analytics is formed by putting together the terms data and analytics. Therefore, data analytics refers to a tool or a method that is used to gain valuable insights into the inner mechanisms of a specific activity or phenomenon. Usually, the term data analytics is used in the context of businesses and their activities. By performing data analytics using various tools, companies can gain meaningful insights from the large datasets they have collected. This will enable them to provide responses and services that are specifically catered to their customers’ needs. The importance of data analytics is more evident in recent years. The term data analytics is often abbreviated to analytics, and it is essential not only for large organizations but for businesses of all sizes. However, due to the multitude of data analytics platforms that are available, it can be quite challenging to choose one for your organization. In such a case, you should consider using a data analytics consulting company like Mindbowser.

US_data_analytics_outsourcing_market Source

What Are The Various Branches Of Data Analytics And How Did They Originate?

Though data analytics might seem like a product of modern technology incepted due to the exponential rate at which we generate data every day, the first use of data analytics by a business can be traced all the way back to the early 19th century. The first documented use of data analytics is when Henry Ford measured the speed of the assembly line to gauge the efficiency of his manufacturing plant. Before Ford, Fredrick Winslow Taylor used data analytics to initiate time management exercises. Soon, with the arrival of the era of computers and other digital technologies, computers became decision-making support systems. As a result, there was significant growth in the amount of data that we were generating. Therefore, data analytics started to receive more widespread global attention. Ever since then, the amount of data we generate on a daily basis has also increased at an exponential rate. The most significant growth in the adoption of data analytics was seen with the advent of Big Data, Data Warehouses, and the Cloud. These new technologies not only played a role in the increased adoption of data analytics, but they also contributed significantly to the evolution of data analytics and its journey to what it has become today.

Let us have a look at various components of data analytics, when they were first used, and how they evolved over time.

A timeline of inventions in data analytics Fig: A timeline of inventions in data analytics

  • Statistics and Computers

Traditional descriptive statistics is one of the most vital foundations of modern data analytics. Statistics have been used for various analytics purposes as far back as Ancient Egypt. For decades, governments around the world have been using statistics for planning a wide variety of activities, including censuses and taxation. The development of the computer and the evolution of computing technologies have also dramatically improved the process of data analytics. In the year 1880, before the use of computers, the US Census Bureau took seven years to process all the information they had collected and complete their final report. Today, it can be done in a matter of days.

  • Relational Databases and Non-Relational Databases

Invented by Edgar Codd in the 1970s, relational databases gained widespread popularity just a decade later. As relational databases (RDBMs) improved over time, it allowed users to write in Sequel (SQL) and retrieve data from their database. SQL provided organizations with the ability to analyze their data on demand, and even today, it is being used widely. When Larry Page and Sergey Brin designed Google in the 1990s, they too based their search engine on the same principle. Google was designed to search for a specific website while processing and analyzing big data stored in various physical locations.

  • Data Warehouses

In the late 1980s, as the costs of hard drives reduced, the amount of data that was being collected by users began to grow significantly. During this time, an architecture called data warehouses was developed with the goal to help in transforming data coming from operational systems into decision-making systems. Unlike relational databases, data warehouses are optimized for quick response time to queries, and they are usually part of an organization’s mainframe network.

  • Business Intelligence

Though the term business intelligence (BI) was first used in 1865, only in 1989 was it adapted by Howard Dresner at Gartner to describe making better business decisions through searching, gathering, and analyzing the accumulated data that was saved by an organization. Today, business intelligence serves as a description of decision-making based on new and innovative data technologies. Large organizations adopted BI as a way of analyzing customer data systematically, but over time, it became one of the most vital steps that is taken before making any business decision.

  • Data Mining

Data mining is the process of identifying hidden patterns within large datasets that first began in the 1990s. Data mining became more popular as a direct consequence of the evolution of database and data warehouse technologies. Advanced features in these technologies allowed organizations to store more data while analyzing it quickly and efficiently. Data mining and its non-traditional methods provided results that were both surprising and beneficial to organizations. It allowed them to predict the potential needs of customers based on trends identified in the analysis of their historical purchasing patterns.

  • Big Data

The advent of big data is a relatively recent phenomenon, but it has played the most significant role in the evolution of data analytics. The name big data was coined by Roger Magoulas in 2005 when he described a massive amount of data that was almost impossible to cope with using the business intelligence tools available at the time. In the same year, open-source software that could process big data, called Hadoop, was developed. Hadoop was an incredibly powerful software that could process structured, and unstructured data streamed in from almost any digital source.

  • Cloud Analytics

Although the cloud and being able to store data on the cloud feels like a very recent phenomenon, the concept was actually first introduced in 1997 by Emory University professor Ramnath Chellappa. He very accurately described the cloud as “a new computing paradigm where the boundaries of computing will be determined by economic rationale rather than technical limits alone”. Two years later, in 1999, Salesforce provided us with an early example of successful analytics implementation of a cloud computing architecture. As businesses gained a better understanding of the potential of cloud services, it gained widespread popularity. Since 1999, cloud computing has grown significantly and can now store enormously large amounts of data, can handle multiple projects, and it can be used by multiple users simultaneously.

What Is Exploratory Data Analysis, And How Does It Work?

Now that we have understood what data analytics means and the components that fall under its broad umbrella, it is time to delve deeper into a data analytics technique that will help your organization to maximize its pattern recognizing capabilities. Exploratory data analytics is a statistical approach that is used to analyze and produce descriptive graphical summaries. The most significant advantage of using EDA instead of a statistical model is that, with EDA, analysts will be able to foresee what the data can reveal beyond formal modeling. When using EDA to analyze your data, you can use your data as it is without having to make any assumptions. It further validates and expands the practice of using graphical methods to explore data. Since it gains all its insights from well-known statistical theories, they are quite easy to decipher. If a large dataset is unsuitable for formal statistical analysis, you can use EDA to derive hidden trends within the data.

The primary objective of analyzing data using EDA is to study a dataset without making any assumptions about it. It is critical to be able to do this because it allows data analysts to authenticate any assumptions that they have made while devising the problem or operating a particular algorithm. This will enable analysts to recommend new and innovative schemes that would not have been possible previously. While implementing EDA, you are essentially using inductive reasoning to obtain results. It can also help you better understand the relationship between variables, detect various issues such as data entry errors, identify the basic data structure, test your assumptions, and gain new data insights. However, the most vital aspect of implementing EDA is that it has the potential to uncover hidden information that might further open up new areas for research. Here are some of the most effective methods that are used to carry out exploratory data analytics.

Different Types of Exploratory AnalysisFig: Different types of exploratory analysis

  • Univariate Visualization

In this type of analysis, the dataset to be analyzed only consists of one variable. It is mainly used to trace and report patterns in the data.

  • Bivariate Visualization

This is used to not only determine the relationship between two variables, but it can also help you understand the significance of those relationships.

  • Multivariate Visualization

When the complexity and the size of datasets increase, a multivariate analysis is used to trace the relationships between different fields. It also significantly reduces a specific type of errors. However, this approach is unsuitable for small datasets.

  • Dimensional Reduction

This type of analysis helps analysts deduce which parameters contribute to the maximum variation in results and enables fast processing by reducing the volume of data.

Data analysts can use these methods mentioned above to understand the problem at hand adequately. They can then proceed to select the appropriate models to corroborate the generated data. Once they have studied the distribution of data, they can finally check if there is any missing data and find ways to solve it.

How Data Analytics Can Drive Innovation And Give Birth To New Trends

If the interdependencies between people, institutions, entities, and technological processes are made more apparent through the study of data relationships, it could potentially drive organizations to come up with innovative new methods and business strategies. Organizations can use tools such as exploratory data analysis, business intelligence, and data management to gain a better perspective on how making changes in one function will affect another process. In every industry, market leaders want faster decision cycles and to reduce delays in the process of researching new approaches and implementing them. However, the biggest problem that most organizations face is that as they move towards being more data-driven, their instincts or gut-feelings are taking the backseat. Without natural human instincts guiding businesses, the potentially innovative ideas they have can be buried under a mass of data and other research resources. But with further advancements in the field of cognitive analytics, organizations have started to use their data insights to align their research and financial resources behind innovative ideas.

data analytics maturity modelSource

Another field of data analytics that has immense potential to give birth to innovative ideas is cloud computing. Cloud services can provide organizations with a high degree of flexibility to adjust their systems according to the new ideas that they are testing. It also allows them to create “what if” simulations and perform data discovery. Numerous organizations are using cloud platforms to develop data sandboxes for users to test out their new ideas. This enables them to experiment without having to wait for IT to acquire and configure on-premise resources that support experimentation. The cloud environment has also turned into space where many organizations are experimenting with open-source tools such as Apache Hadoop and Spark, to analytical tools and languages such as R and Python. The use of cloud platforms ensures that an organization’s innovative ideas are not killed off due to a lack of in-house infrastructure before they get a chance to flourish.

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The Top Tools In Data Analytics That You Must Know About

As the demand for data analytics services in the market is increasing at a rapid rate, many new data analytics tools have emerged. Each of these tools fulfils various data analytics functionalities. Most of the tools mentioned below are either user-friendly or open-source. Here are the top tools in the data analytics market.

data analytics marketFig: Data analytics market

  • R Programming

The R programming language is one of the top data analytics tools that are used for statistics and data modeling. An added benefit of using R is that it can be compiled and run on various platforms such as UNIX, Windows, and macOS. It also provides tools that automatically install all packages as per user requirements.

  • Python

When it comes to the top data analytics tools that are used today, Python is right up there with R. It is a programming language that is exceptionally easy to read, write, and maintain. It also offers various Machine Learning and data visualization libraries such as TensorFlow, Matpotlib, Pandas, Keras, etc. In addition to its vast library, Python can also be assembled on any platform like SQL server, a MongoDB database, or JSON.

  • Tableau Public

This free software connects to any data source such as Excel, corporate data warehouse, etc. Then, it creates visualizations, maps, dashboards, etc. with real-time updates on the internet.

  • Qlikview

Qlikview delivers results to users significantly faster than other data analytics, and it also offers in-memory data processing. Its data association and visualization features allow you to compress data to almost 10% of its original size.

  • SAS

This is a very easily accessible tool and is capable of analyzing data from a variety of sources. It is a programming language that offers an environment that is ideal for data manipulations and analytics.

  • Microsoft Excel

Even today, Excel is still one of the most widely used data analytics tools. Though it is usually used for the client’s internal data, it is capable of analyzing tasks that summarize the data with a preview of the pivot tables.

  • RapidMiner

RapidMiner is a highly robust, integrated platform that is capable of integrating various data source types such as Access, Excel, Microsoft SQL, Tera data, Oracle, Sybase, etc.


Konstanz Information Miner is an open-source data analytics platform that allows users to analyze and model their datasets. Since it has the added benefit of visual programming, KNIME provides a platform for reporting and integration with the help of its modular data pipeline concept.

  • OpenRefine

This software by Google is also known as GoogleRefine, and it will help users to clean up their data before they perform data analysis. Its primary purpose is to clean messy data, transform and parse data from websites.

  • Apache Spark

Apache Spark is one of the largest data processing engines that are currently available. Compared to other tools, Apache Spark can execute applications in Hadoop clusters 100 times faster in memory and ten times faster on disk. It is also widely used for data pipelines and Machine Learning model development.

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What Does The Future Of Data Analytics Hold?

Since it first started, data analytics has been continuously evolving. When it first started, data analytics only dealt with descriptive analytics, which merely described datasets. Data analytics has come a long way since then. Today, data analytics is capable of predicting future outcomes in the form of predictive analytics. Recent advancements in technologies such as Artificial Intelligence (AI), cloud computing, the Internet of Things (IoT), and Machine Learning have significantly contributed to the enormous growth and immense popularity of data analytics. Let us discuss some of the platforms and technologies that will play a critical role in the future of data analytics.

  • Augmented Analytics

The integration of Machine Learning and natural language processing into data analytics and business intelligence is referred to as augmented analytics. Starting from this year, this form of analytics is going to play an enormous role in analyzing data in the future. Since augmented analytics can search raw data for valuable parts of the analysis, it can also automate various parts of the process. The use of augmented analytics especially makes the process of data preparation significantly more straightforward.

  • Relationship Analytics

The data analytics tools that we have access to today can be used to find the relationship between a pair of variables. However, one of the biggest problems that analysts face today is that the current data analytics solutions analyze data in isolation. In the future, with the help of relationship analytics, organizations will be able to break down multiple data sources and connect them to analyze their data as a whole rather than in isolation. This will enable companies to draw more comprehensive insights from their data analytics processes.

  • Decision Intelligence

The future of data analytics is not just limited to new platforms that perform the same functions better, but it is also about new intelligent platforms that can help with decision making. Decision Intelligence is a robust new platform that integrates the fields of social science, managerial science, and data science into one. Its ability to draw information from a wide range of disciplines makes it an invaluable business decision-making asset to organizations. This use of this intelligent platform will help your organization to optimize your decision-making and add an extra level of quantitative analysis to it.

  • Continuous Analytics

Just a few years ago, analysts did not expect data analytics platforms to deliver insights in a few days or weeks. However, we can already see a drastic difference in the speed at which analytics tools can deliver insights today. In the future, thanks to continuous analytics, we can expect these platforms to take full advantage of IoT devices and generate insights even faster. By continually analyzing data, organizations will be able to shorten the window for data capture and analysis significantly.



The proliferation of new technologies that augment and expand its functions has pushed data analytics to evolve dramatically. It is highly likely that we will see AI enhancing the capabilities of data analytics in the near future. Automation of processes and the implementation of natural language processing in data analytics will be the most significant contributors to the growth of data analytics in the years to come. Since data analytics is an ever-changing field, it is crucial that organizations use the best data analytics platforms that are currently available. Mindbowser’s data analytics solutions can help your organization identify and obtain the most valuable and meaningful insights from your data. It will help you to make the best of all the tools and platforms that you have at your disposal and turn your data insights into competitive advantages.

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This blog is from Mindbowser‘s content team – a group of individuals coming together to create pieces that you may like. If you have feedback, please drop us a message on contact@mindbowser.com
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