October 9, 2018

Data Industry: A Replica of Oil Industry

Data Industry: A Replica of Oil Industry


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Data in the 21st century has become one of the most important resources on the planet, owing its usage to a variety of aspects. Many experts have proclaimed that, in this century, data will be used to build all-around economy, new businesses and products.

These experts have started referring to data, especially big data, as the oil of the digital era. Data, just like oil, is found in its raw form, and converted by experts into a useful resource that powers our day-to-day life, making tasks simpler and making us more efficient.

Petroleum, which refers to raw oil, is found in its natural form and needs to go through various processes in a refinery before we are able to use it to power cars and planes. Similarly, data needs to be converted and processed in several ways before we can understand it and use it. Both processes are very complex.

In its raw form, data is usually too big and messy. Think about it: your phone, for example, can track your movements, it has information about your activity, your browsing, and your day-to-day plans, and all of this tracking creates raw coded data. Tons of new data is constantly streaming in, but it doesn’t come in a simple, easy-to-read format.

This creates a clear problem: we have information at our finger tips, but we are not able to understand it, and therefore we cannot use it. Users are basically telling us what they want, as well as where and when they would like to have it, but the information is lost in a sea of constant data.

To solve this problem, we needed a "data refinery". Similar to an oil refinery it takes in the raw material and extracts the useful bits so it can be used. In the case of data, it’s a software platform that pulls in huge datasets finds patterns in the data and makes predictions. The data refinery is the missing link between gathering data and extracting value from it.

Data refineries make data more useful. They store huge datasets, apply function, trace patterns and extrapolate predictions, which ultimately help us make informed decisions.

Some major digital tech firms have taken command of the data and its usage. However, many experts believe that there is huge untapped potential in the field of data, and a need to continue exploring ways to make data usage more efficient. They believe that current digital refineries are not capable of gathering specific and targeted results.

In the past few years, the data industry was confined to finding trends. Recently, some experts were able to crack the information within trends and turn it into predictions. This was a major breakthrough. Some experts experimented with the new data technology, so they could even predict the time of customer demand and the product demanded. However, that technology is in the testing phase; it’s going to take some time before we can apply it for commercial purposes.

Another challenge in the data industry is the quantum of data. In order to find real and useful trends from raw data, there needs to be a huge volume of data. Extracting it and storing this so much data can be a challenging task.

Google is one of the best examples of a data refinery in the current age. Google’s cash cow is its search engine, which was a breakthrough idea that used data and innovation.

Attracting people to and maintaining their viewership is one of the many difficult challenges the company has had. To achieve the objective, Google created and internal platform for experts and scientists to analyze a huge dataset of users. The company took out raw data and used algorithms to clean it up and make it usable.

With the inception of data refineries, day-to-day services have improved in many ways. The best example is Amazon’s product recommendation tool—a new feature, which has been optimized over the past few years. Experts believe that improvement in services is due to the availability of more consumer data.

Moving forward, experts believe that data refineries will allow companies to generate and analyze data regardless of the industry. Huge data can be converted into predictions and outcomes according to specific needs. If the data industry is properly looked after, it has a potential to be as vital as the oil industry.

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