Data Analytics
January 7, 2024
From Antiquity to Algorithms: A Brief History of Data Analytics and its Pervasive Impact on Shaping Businesses Across the Ages
Prasoon Verma

Humans have been analyzing data right from the birth of civilization. The earliest preserved records of writing are examples of data analysis. In ancient Iraq, scribes made lists of workers, creating one of the first databases. They also calculated their wages directly from this raw data, a form of primitive data analysis. Data analysis is rooted in statistics, the beginnings of which can be traced to Egypt, as it took a periodic census to build the pyramids. People have taken on incredible quests and voyages, documenting their experiences along the way, helping us build a reservoir of data. In this blog, we will understand the role of data analytics in the past and how it is becoming crucial with each passing year.

Information is the oil of the 21st century, and analytics is the combustion engine," said Peter Sondergaard.

A few centuries ago, we needed more tools to make the most out of our data and information. Today, the availability of an excess amount of data makes it hard to extract valuable pieces of information. We have collected more data than ever in the last five years.

The 2018 Global State of Enterprise Analytics Report shows that 57% of enterprise organizations use big data and analytics to accelerate process and cost efficiency.


Data remains a valuable commodity when appropriately utilized. The hour needs to analyze these big chunks of data to extract meaningful insights and make the most out of it.

Twentieth-century: First Steps

Calling data analytics a new concept would need to be more accurate. While the terminology may be new, the practice has been around for a long time, way before the age of computers! A century ago, we recorded data in suitable old paper files. Analysis of recorded data took place by identifying trends in statistics observed by the naked eye. Although this was better than basing decisions on intuition, it still needed to be revised as there is a limit to what the eye can observe and analyze.

Pierre Simon Laplace proposed in 1820 that scientific observations had a typical pattern of errors. This idea sparked a revolution in data analytics, statistics, and mathematics. As the ecosystem matured in the 20th century, statistics and mathematics were soon used to solve complex, real-life problems.

From 1962, when John W. Turkey wrote about the "Future of Data Analysis," to 2008, when Dr. DJ Patil and Jeff Hammerbacher first coined the term "data science," the concept of data analytics has undergone a massive shift.

Then came the digital world. Methods of collecting and storing data moved from a physical setup to a cyber setup. The 20th century saw the advent of the first digital computers, which were enormous in size and complexity and were primarily used for military purposes. With computer technology's growth and the microprocessor's advent in the 1970s, computers became smaller and cheaper and were used more for business applications. Data was stored on computers using magnetic tapes or disks, and the main task of business intelligence professionals was to extract information from data stored on these devices. The development of new software technologies, such as structured query language (SQL), allowed business managers to access and manipulate computer data. And so, database and database management systems were born. With more space to keep and process data, high-volume data analysis also rapidly emerged. To accommodate this, we began using relational database models and spreadsheets.

The Digital Revolution

The rise of the Internet and the emergence of social media brought a new era of consumer expectations. They gave birth to the disruptive nature of data and sparked the need for data analytics. 

Data analytics experts found a new way to process, understand, and utilize data to create significant business insights. This began a new digital revolution fueled by big data and analytics. Big data refers to the large volumes of data that are created every day. The data could be from social media posts, IoT devices, images, videos, and other unstructured data sources. The amount of data being created daily is so large that it is difficult for organizations to store it in their computers or servers. The complexity of handling data has increased, along with data analysis. We understood it would become a problem down the line, so we created tools to make this process easier for business users.

Tools such as MS Excel came with various built-in functions to simplify calculations and statistics for business users. Relational databases supported by programming languages such as MySQL and Oracle Database further allowed query data to be sorted, filtered, and perform advanced operations on the database.

While these inventions made things much more accessible, their biggest drawback was that they simplified only one part of the problem – extracting statistics from the data. The analysis part of it, which involves going through the numbers to generate meaningful insights, remained the responsibility of the business user.

Thus, there was a need for automated data analysis in the cloud, and to comply with this need, we saw the rise of Artificial Intelligence and its applications, including data science.


Also Read| Data Analysis: Generate Insights Like A Pro In 7 Steps

The Age of AI

As the name suggests, data science is the science of playing with data to extract some meaning from it. We use a data science methodology to create models to prepare and process the data, analyze it, and generate valuable insights. We first train these data models using machine learning algorithms and then test them on a sample data set to evaluate the accuracy of their predictions. We make additional changes and retest the new model based on the evaluations. The repetition of this process takes place several times to have a foolproof data model. It is then used to analyze new data and give highly accurate insights.

Currently, business analysts are deploying similar data science models to gain accurate insights. They are using it in their business to make analytical forecasts. The power of automation and AI has simplified the work of a data analyst.

Nowadays, self-service, AI-based analytics tools help analyze data from hundreds of sources to find what works and what doesn't. Another advantage of relying on data science is that the algorithms used in these models comprehensively analyze the data, identifying patterns and trends on a much deeper scale. This leaves no room for human errors that otherwise invariably creep in. This allows users to extract useful information that even the most skilled data analytics expert can miss.

The Rise of Business Intelligence

The business intelligence industry has grown exponentially over the last few years. Valued at 20.516 billion USD as of 2020, the business intelligence sector is predicted to grow to 40.50 billion dollars by 2026. 

business intelligence platform is an end-to-end business analytics tool that generates actionable intelligence within seconds of data loading. With business intelligence platforms such as INSIA, you only need to connect your data sources to the platform. Data can be in CSV, Excel, spreadsheets, marketing channels like FB, databases like MYSQL, etc. 

The BI platform performs all tasks associated with data science and data analysis. It gives you ready-to-use insights in various forms. Companies can get a detailed analysis report within seconds through these BI platforms. This allows them to make quick, factual, impactful decisions for the organization's overall functioning. BI tools provide a centralized platform where an organization can access its data and create reports and charts that can be easily shared with colleagues. They also let you visualize the data in graphs or charts to make the information easier to understand.


Data analytics has now become a necessity for businesses. The evolution of AI and machine learning, the Internet, and other technological advances have created new ways to process and analyze data. With big data, the need for real-time analytics has increased. Now, organizations must be able to process the data as it is being created and understand its insights. There is an increased focus on integrating data from different sources into one platform to develop actionable insights. Data visualization and artificial intelligence are used to create interactive data visualizations that are easier to understand. We are seeing a global effort to simplify generating insights from data. A few features that are entering the Business Intelligence sector and are helping in realizing this vision are:

Search bar:

Search engines first come to mind when discussing getting information from the Internet. All it takes is to open Google and type what you want. Companies like INSIA have introduced search engine-like functionality that accepts keywords in natural language. And the platform will automatically show relevant insights.

Personalized dashboards:

Dashboards enable business users to visualize the performance of their business metrics. INSIA provides users with a well-described business analysis report through personalized dashboards. These dashboards often include tables and graphs of metrics and KPIs you must focus on. It also accompanies various insights that help you make critical business decisions. Users can personalize the dashboard per the business needs to make information highly relevant and easily understood.

Push automated insights:

Nowadays, applications give us updates by sending push notifications to our phones and emails. BI platforms are replicating this to provide business users with automated insights tailored for them.

This saves a lot of time as it maintains a live track of data with real-time analysis, giving users performance updates as they occur.

Final Notes

History proves that how we analyze data has reformed the business outlook for organizations. Companies that adopted new methods to increase data analysis volume, speed, and efficiency have gained a competitive advantage and have remained relevant.

Being in business is complex.

Today, it has become necessary to keep one's business up to date with the latest data analytics tools to survive and soar high in the business world. Our Search-based BI and Analytics proprietary platform INSIA fulfills all the abovementioned requirements. Click here to try us for free!

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