Businesses deal with a lot of data every day, and this volume of data is growing at an exponential rate, necessitating analysis and inspection. This is exactly where the concept of big data comes in.
Big data identifies relationships between data points from a set of raw data to gain fresh perspectives and forecast the future. The raw data set is used to create models, run simulations, and change data points to see how each change affects the conclusions and forecasts that are produced. This tactic has been employed by organizations to assist staff in making more informed decisions.
A few significant misconceptions have clouded the functionality of big data due to its rising popularity. There are a few myths about big data that everybody needs to know. Keep reading this article to find out Common Big Data Myths
The most prevalent myth, which is held by all, is that big data is present everywhere. It is true that at present Big data technologies and services are centers of attention in industries with a record high usage. However, a report claims that only 73% of all organizations intend to invest in big data. However, they are still in the early stages of adapting to big data.
Additionally, as it is a complex technology, many organizations encounter challenges during the pilot stage because they fail to connect the technology to real-world use cases and operational procedures.
The second Big Data Myth which everyone believes is that big data is too complex to handle.
Given the volume of big data generated on a real-time basis from multiple sources like video, audio, and images, it can get a little messy. Big data has been developed to automate manual processes and lessen the challenges of managing all of this data.
Big data analytics make use of different technologies and simulation tools that may seem too big and daunting to handle at first, but so did cell phones and computers at first, and now everybody uses them. But with patience and practice, even the most difficult technology is simple to learn.
The same case is for big data tools. A few specialized tools are used to store, process, analyze, and visualize data points, and they take some getting used to.
One of the Common Big Data Myths is that big data in the cloud faces a potential risk. Hacking has put a number of data protection services to the test, which has enraged cloud technology detractors. However, it's crucial to debunk the misunderstanding that the cloud poses a threat.
With every major data hack, there is a constant upgrade of security measures and protocols to ensure the protection of sensitive data.
To ensure data security and restriction to third-party authorization against all threats, cloud vendors routinely update their software and hardware. To keep cloud services safe from various types of security breaches, audits have become routine.
Another Common Big Data Myths is that big data is here to replace humans. You might have heard the sentence, ‘Machine algorithms will replace humans forever!’. You may fear the fact that fictional movies such as Terminator and Prometheus may become reality in the future and humans will lose all their jobs. The reality is that no machine can be a substitute for human insights and intelligence. Even for big data to work, you need data analysts to operate the analytical tools and machines. Not only this, but machine algorithms also cannot provide unique reasoning as humans do.
Moreover, big data cannot replace human creativity. Based on the various insights provided by big data, decisions are made by human brains and the creativity therein. Machine algorithms cannot produce creativity because even robots lack a human-like creative moment. As a result, this particular myth lacks a solid foundation.
One of the other common Big Data Myths is that big data guarantees a higher return on investment. People think technology can automatically produce the best results. This isn't exactly accurate because, despite technological advances, it is impossible to control all of the outside influences on businesses and their decisions. Making data-driven predictions wouldn't be necessary if external factors could be managed. It is as simple as that.
Big data certainly cannot completely guarantee a higher return on investment (ROI), but it can be manipulated to do so with the help of mature decisions.
Big data should be approached as an asset that can be used to solve many of the problems faced by a business on a day-to-day basis. Big data has the ability to elevate even the most alienated of business operations. Big data is evolving into a corporate phenomenon, so it must be treated and utilized as such.
