Big Data in the Cloud: Reaching the Tipping Point

Big Data in the Cloud: Reaching the Tipping Point

In the contemporary world of Information Revolution, big data has come to the fore, and business intelligence is the torchbearer in the exploration of uncharted territories of new business opportunities. Companies such as Uber and Airbnb, which run purely on data generated from apps, are the biggest disruptors on today’s business scene. They have accurately shown the enormous actionable potential of big data generated through the internet.

In this backdrop, we are going to discuss Big Data in the Cloud as today there are plenty of cloud-service providers that offer data storage and analytics services to enterprise customers.

We are considering Big Data keeping in perspective Cloud since early big data projects were in the realm of in-house exercises. But, today there are increasingly great success stories of big data are coming out in public; it shows the newly found world of the digital-oriented business environment. Further, it is staunchly advocating in favour of cloud-led data and analytics to get the utmost success in business endeavors. Some strong points make a case for cloud-led data and business analytics to make the businesses ever-prospering.

Big Data: A Big Project:-

A decade or five years ago when most of the enterprises exercised big data projects in-house, even knowing that it is not going to easy, with excellent planning and organization, they failed. The glaring reason was – lack of requisite skill sets that were scarce at that point of time, “brain drain” was also a plausible reason as data scientists ventured out to more rewarding careers.

The role of the Data Scientist: At the inception, even though there were vast lacunae on the infrastructural front, the major fault was with the working module: IT leaders in in-house projects shielded those who analyzed data from those who were managing the operations. But, for smoother conduction of the projects, it is highly recommended that data scientists should work in tandem with the operations team this is because it allows data scientists to fine-tune their models better, streamline processes and at the end of the day bring effectively lower the time needed to squeeze actionable information from humongous data.

Tipping Point: It is an agreed fact that a decade, we were unable to comprehend the vastness and immense potential of big data holistically. Our judgment was proven wrong as we not only inept in accurately understood the complexities of building and executing a big data platform but also in understanding the critical big data roles. But, the current reality is enterprises are leveraging the cloud support as they are now in an enabling position to focus on what’s important: highly tuned analytics. Therefore, companies or enterprises are increasingly migrating their big data endeavors to the public cloud because in coming years that is the actual place where most of the data will reside or is still residing; thanks to the low cost of cloud storage.

Big Data: Great Redemption Coming Into Its Fold: Big data came onto the floor of the business world with a promise to enable business leaders and enterprises to let them have a conscious awareness of their business endeavors and operations. So, herein, data is analyzed to a point where efficacy and efficiency of decisions made will be not compromised, and the process will be smooth even if it relates to real-time analytics. But, early experiences with big data analytics were quite sour in nature so bleak in prospects. Now, these are bygones, outsourcing back-end duties to a third party (big data and analytics to the cloud provider) have unleashed a great opportunity to focus more on making accurate decisions regarding company’s operations in the current and prospective environment.

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