Saket Agarwal, Founder & CEO, Onnivation

Saket Agarwal, the Founder & CEO of Onnivation started off with  building and running 2 startups after finishing education, including failing at building Aao Pao Khao, a Vada Pav chain  based out of Calcutta. He founded Onnivation in 2015 and grew from an individual  consultant to a leader in the Israel-India tech corridor.  Onboarded 15+ partners across Cloud, Data and Security  domains from Israel and have helped 100+ tech unicorns, enterprises and high growth consumer companies transform their  tech stack.  He evolved his role from day-to-day operations to strategy,  government partnerships, USA & Singapore expansion &  strategic relationship building earning himself the moniker of #YourIsraelGuy. 


There is an old adage in statistics that states “correlation does not imply causation”. In business intelligence, the thing that could completely turn things upside down is Assumption. Let me help explain this with an example.

The Toronto Transportation Commission was trying to find the reason between number of complaints and the bus delays on specific routes. One might think, the most obvious reason would be Tardiness. But on using data to analyse and arrive at the exact issue, it was found that Weather was to be blamed for these spikes in complaints. The days on which there would be heavy rains or snow would see an increase in the no of complaints. 

Such use cases will continue to crop up for businesses if Big Data is not analyzed in a more scientific manner so to speak and people run after the wrong issues. 

What is Big Data. It’s not simply “lots of data”, it is rather defined by the 4Vs – 

  • Volume (the quantity of data)
  • Velocity (the speed at which data is generated)
  • Variety (text, audio, video, images, etc.) 
  • Veracity (trustworthiness of data). 

Traditional Business Intelligence tools were designed for low volumes and structured data and the issues of speed always remained. Traditional data operations were not meant to be agile and therefore cannot adapt or incorporate the new sets of data that are now available due to modern logging and monitoring solutions for operations. Furthermore, they run in silos, by the same person, and generally in a  local environment. On top of this, traditional analytics operations are heavily reliant on the human-in-the-loop paradigm, meaning the validation and interpretation of data requires human intervention. 

Businesses are now facing a unique set of challenges as big data continues to become more and more complex. Businesses are in need of more complex insights that may not be very easy to analyze and hence predict how various factors might affect their business. 

That’s when modern data tools emerged. These tools could extract information from highly available but unclassified, unstructured data sets. Researchers in this field and the algorithms they develop help teams manage data-intensive operations and play a crucial role in helping companies monitor, manage, and collect performance measures that can improve decision-making across the organization. For instance, when a business wants to upsell, data science tools can provide insights and predict which users to target. They can also be used to do a lot of A/B tests to determine who to target and how to communicate.

At the heart of it all, while traditional analytics help identify what happened, modern data science tools can go one step further and postulate why it happened and what businesses should do next.

Here are a few other factors that amplify the need for modern data science tools over conventional analytics tools:

Integration capabilities

Despite the fact that traditional analytics tools are based on the same principles as modern data science tools, their inability to integrate with multiple sources of data, such as third-party applications and websites, limits their applicability. For example,in the social media space, for example, traditional analytics solutions cannot identify patterns in consumer behavior or decipher consumer sentiment. Modern data science tools can easily integrate sophisticated technology such as Artificial Intelligence (AI), Robotic Process Automation (RPA), and Machine Learning to collect, structure, and analyze data, irrespective of where it resides; which eventually helps to predict outcomes faster. By connecting the right data at the right time, users can smartly automate analytics processes to save time and energy, which can be redirected to business strategies.

Ease of access and availability

Until a few years ago, analytics tools were considerably expensive and complicated to use.Thanks to constant innovation in the big data space and large strides in computational capabilities of cloud computing, even small and medium businesses can now easily onboard sophisticated data science tools at much lower costs

High-performance Computing Power with Real-Time Automated Reporting

One of the biggest advantages of modern big data science tools is that they can compute large volumes of data at lightning-fast speeds in real-time.These capabilities help businesses keep a close eye on every related development, keeping them at pace with the industry, market, and client sentiments – without the need of an actual person to validate the findings.

By now, most businesses have realized that one cannot improve that one cannot measure. The next challenge now is to extract valuable and actionable insights from these data sets. Fortunately, the ability to do this has also become widely available due to the emergence of data science tools. So in order to reap rich rewards and establish compelling customer loyalty and brand reputation, businesses will have to leapfrog from traditional data analytics to modern data science tools by becoming data-driven, data-informed and data-inspired!

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