Data has become the primary component for growth in the industrial landscape!
The world is now an ocean of data. Everywhere you look, there is data. The simplest examples can be found on your smartphone. For instance, the order history on your e-commerce account will show you how much and what type of items you have ordered in the last six months and their order value. Your OTT streaming account will have information about the shows you are watching along with their genres and typically, at what time of the day you watch your favorite shows. The list goes on and on. Every company on the planet today has these data oceans (technically, can be called data lakes), which they can use to understand and potentially serve their customers better.
Corporations also leverage data to increase profitability and provide the best customer experience. Monopolies are hard to find in any competitive industry landscape today. Companies are thriving by offering superior products with the best purchasing and sales experience at an optimum cost, resulting in maximized profits. These are some of the areas where data is used at every step of the way to find solutions and increase efficiency, productivity, and profitability.
What is the best product or service for a customer? How does the need change from one customer to another? If I like a watch from Brand A, would you choose the same brand as well? If I like to order biryani, would you order paneer masala from your food delivery app? What is best for customer A, may not be the ideal choice for customer B. The entire pre-sales experience is about how companies customize solutions as per customer needs. Now you need to ask yourself, “how do we customize solutions for an individual customer?”, and “what do you need to achieve that?”
In a similar case, when you try to book a cab at 1 PM for MG Road versus 7 PM, do you see a price difference? Mostly, the answer would be yes. Do you ask yourself why? The answer seems obvious that higher cab demand leads to higher fares. But how are the cab companies determining the exact price? All the demand data gets matched with supply in the backend and then shows you the price. Variably, there could be many other parameters like location, customer loyalty, new/old customer, demographics, etc. based on which the fare is shown. We should keep in mind that all of this happens in real-time.
Here, the Art is to determine how to solve customer problems in a cost-efficient way while ensuring they receive a best-in-class experience. Interestingly, Art goes hand-in-hand with the Science of executing it.
The Data Engineering & Machine Learning infrastructure behind this is the science of it all that takes place in the background. On one hand, the Data Engineers will ensure that the data is captured, stored, and recycled effectively and efficiently. While on the other hand, the AI-ML teams would be on top of the algorithms that will talk to the systems and crunch the customer’s real-time data to decide the most accurate and relevant options.
The world is increasingly becoming digital with each passing day. In the past decade, most of our activities have gone digital – from ordering vegetables to official meetings. This is not even the tip of the iceberg. Globally, across many regions, digital adoption still remains underpenetrated by a huge margin, largely due to inaccessibility to the internet. Even then, with the considerable amount of digital activity that we are currently accumulating, our data ocean is increasing at a rapid pace. Hence, the eagerness to harness Data Infrastructure &Data Science is increasing as well. This is nothing but a means to collect and interpret data to achieve the best possible results.
In my opinion, Data Engineering & Data Science will be the most sought-after stream of study in the coming years. There are many studies across the world that will infer the same. Tomorrow’s business owners and product managers will need to understand these areas like never before. Every single business decision needs to be driven by data and not my gut feeling. There are insights that are hidden in the data. We need Data Scientists to uncover those secrets and present them to the business leaders to make informed, data-supported decisions that are unlikely to go wrong. In the next century, the companies that are able to leverage and accurately interpret Big Data and bring it to action will have a higher chance of success.
