Data Is No Longer The New Oil- It Is The World`s Most Valuable Resource

A few decades ago, the most valuable resource was oil. Data was being compared to oil. Hence the famous quote ‘Data is the new oil’.

That statement has entirely changed today. Data can no longer be metaphorically compared to oil.

Here is why

Data is essentially infinitely durable and reusable, whereas oil is a finite resource.

Data being compared to oil, implies that data has no value after use and decreases in utility like in the case of oil.

To be transported to where it is required, oil needs huge quantities of resources. On the other hand, data can be repeated endlessly and transported across the world via fibreoptic networks at the speed of light, at the lowest possible costs.

The more it is used, rather than its energy being lost as heat or light in case of oil, data often becomes more usable. Data also shows more applications once processed. It would definitely be a mistake to treat data like oil: using it once then believing its value has been exhausted and disposing it.

In this digital era, Data is no longer the new oil. It is the world`s most valuable resource!

Data is not recent, but it is growing at an incredible rate. The increasing interactions between data, algorithms, and analytics of big data, connected data and individuals are opening enormous new prospects.

Enterprises have now started developing products and services based on data-driven analogies. For many communities and economies, the formulation of national data strategies may help in realising the potential of data. Several countries are in the process of implementing such strategies.

The ability to provide an agile environment to serve the data workload is critical with data powering so many innovative approaches, whether it be artificial intelligence, machine learning or deep learning. Data undoubtedly offers them the chance to enhance or redesign almost every part of their business model.

Let us consider the BFSI domain

Tonnes of unstructured, semi-structured and structured data that has been stored had no use two decades ago. Thanks to data analytics solutions and big data technologies, we can use all the data now!

Data that has been stored can be used in numerous ways. Let`s take the banking and financial service institutions domain as an example.

1. Improved levels of customer insights and engagement : Customers are now gradually engaging on digital channels with BFSI organisations. The BFSI institutions are enhancing their product/ service quality by utilising big data technologies to dissect digital channel data as it would help gain a better understanding of customer pain points, needs, and wants. It is important not only to boost the customer experience, but to also keep ahead in the competitive market to obtain customer insights.
2. Improved fraud detection and prevention : Over time, hacking, breaching techniques & strategies have developed and strengthened to become more complex and sophisticated. This is where it comes to data. To detect trends and predict the frauds, big data analytics and instruments can process and analyse large datasets. This helps to a great degree to mitigate financial losses.
3. Improved market trending analysis: The rapidly growing demand for speedy execution of financial sector market trading has led to the adoption of big data for the same reason. Big data`s primary benefactor is trading methods that rapidly exploit advanced algorithms to trade financial markets. The BFSI is a huge ocean of data. The data collected from different markets (based on geographical location, asset classes, type of market, etc.) can be compiled with other structured and unstructured data to generate rich, hybrid datasets using big data tools. This can be used to analyse real-time 360-degree view of business scenarios.
4. Enhanced risk management: Data sees use in fields such as business risks, automated risk control, fraud management, credit management and retail & industrial loans. Data tools can aid in exponentially boosting the predictive power of the risk models, they can also enhance the system`s response time and performance thereby reducing costs and offering extensive risk coverage.
5. Enhance Employee Engagement: Data has another advantage that is to provide employee engagement. Companies can track, control, and evaluate their employee`s performances by implementing data analytics techniques. Motivating and recognising employees through the conduction of employee engement programs and award giving events, a firm can retain many employees thereby increasing employee performance. This has now turned into the new culture of work. and techniques. 

Using data for data`s sake

Some companies are struggling to understand the role that data strategy can play in transforming and delivering productivity in their business operations. Without having a good reason, there is a propensity to try to use data. The first step in incorporating knowledge into strategy should concentrate on finding business issues.

The approach or technique that can illustrate solutions is data analysis, but it is rarely the ‘silver bullet’ for success without a specific target. As several aspects of digital transformation, data is not something that can be supplied as a separate business entity in a silo. It must be a cross-functional service incorporated within an enterprise.