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It was during the Socratic era when humanity first understood the value of knowledge, information, and data. Since then, this persistent thirst for gaining knowledge has increased exponentially several times over. People have taken on incredible quests, voyages, also recorded their experiences along the way, helping us collect a wealth 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.” – Peter Sondergaard.

A few centuries ago, our problem was the lack of data and information. Today, the availability of an excess amount of data makes it hard to extract valuable pieces of information. With computing speed and processing, picking up the pace and so is the inflow of data. We have collected more data than ever in the last five years.

As per the 2018 Global State of Enterprise Analytics Report, 57% of enterprise organizations make use of big data and analytics to accelerate process and cost efficiency, apart from aiding changes in the strategy.

Data remains a valuable commodity. The need of the hour is to analyze these big chunks of data to extract meaningful information.

Twentieth-century: First Steps

It would be inaccurate to call data analytics a new concept. We may have used this terminology only a couple of decades back, but it has been in practice well before the age of computers. A century ago, we recorded data in good old paper files. Analysis of recorded data took place by identifying trends on statistics observed by the naked eye. Although this was better than deciding on intuitions, it still was very flawed as there is a limit to what the eye can observe and analyze.

Pierre Simon Laplace proposed in 1820 that scientific observations had a common 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 soon was used for solving 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 and using it to generate actionable insights 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. Data entry was no longer manual. Data storage moved from piles of pages covered in dust to floppy disks and later hard drives. And so database and database management systems were born. With more space to store and process data, the concept of high-volume data analysis also increased rapidly. To accommodate this, we began using relational database models and spreadsheets.

The Digital Revolution

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 make calculations and statistics easy for business users. Relational databases supported by programming languages such as MySQL and Oracle Database further allowed query data to sort, filter, and perform advanced operations on the database.

While these inventions made things much easier, the biggest drawback was 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 get meaningful pieces of information, remained a 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.

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 data models that can prepare and process the data, analyze it, and generate valuable insights. We first train these data models using machine learning algorithms. Then test them on a sample data set to evaluate the accuracy of their predictions. Based on the evaluations, we make additional changes and then retest the new model. 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 added advantage of relying on data science is that the algorithms used in these models do a comprehensive job of analyzing the data. This leaves no room for human errors that otherwise might exist, identifying patterns and data trends on a much deeper scale.
 
This allows users to extract extremely useful bits of information that even the most skilled data, analytics expert can miss.

The rise of business intelligence

The business intelligence field has grown exponentially over the last few years. Valued at USD 20.516 billion as of 2020, the business intelligence sector can grow to a whopping USD 40.50 billion by 2026.
 
Now you might wonder, is it that simple? All a business user has to do is pump in the data, use one of the data models, and wait for the algorithm to work its magic. Well, turns out you couldn’t be farther from the truth.
 
 
A 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, all you need to do is connect your database to the platform
 
The BI platform performs all tasks associated with data science and data analysis by itself. It gives you ready-to-use insights in various forms. Through these BI platforms, companies can get a detailed analysis report within seconds. That allows them to make quick decisions that are factual and impactful for the overall functioning of the organization.

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, all you need to do is connect your database to the platform. 

After that, the BI platform performs all tasks associated with data science and data analysis by itself, giving you ready-to-use insights in various forms. Through these BI platforms, companies can get a detailed analysis report within seconds, allowing them to make quick decisions that are factual and impactful for the overall functioning of the organization.

Future prospects

We are seeing a global effort to generate insights from data that is as simple as getting factual information from the internet. A few features that are entering the Business Intelligence sector and are helping in realizing this vision are:

Search bar:

When we talk about getting information from the internet, the first thing that comes to our mind is search engines. All it takes is to open Google and type what you are looking for. Companies like Insia have introduced search engine-like functionality that integrates natural language search into BI platforms. The search-based BI platform lets the user simply enter keywords around what they’re looking for, and the platform will automatically show relevant information.

Personalized dashboards:

Dashboards enable business users to visualize their business metrics’ performance. We provide users with a well-described business analysis report through personalized dashboards. These dashboards often include tables and graphs of metrics and KPIs that you need to focus on. It also accompanies various insights that help you make critical business decisions. Users can personalize the dashboard as per the business needs, to make information highly relevant and easy to understand.

Push automated insights:

Nowadays, applications provide us with 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 is proof that the way we analyze data has reformed the business outlook for organizations. Companies that adopted new methods to increase volume, speed, and efficiency of data analysis have gained a competitive advantage and remained relevant.

Being in business is not simple.

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 corporate world.

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