Understand The Importance Of Data Analytics And Its Future

By Author: Mindbowser
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The simple definition of the word “analytics” is a study of analysis. An analysis is a 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.

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.

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 are 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, they gained widespread popularity. Since 1999, cloud computing has grown significantly and can now store enormously large amounts of data, can handle multiple projects, and can be used by multiple users simultaneously.

What Is Exploratory Data Analysis, And How Does It Work?
Now that we have understood the importance of data analytics and what it 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.

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.

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 a 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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