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Mythbusters: Let’s talk about Big Data

Atualizado: 4 de jan. de 2022

Businesses deal with large amounts of data every day, and the amount of data created and stored continues to increase, requiring careful review and analysis. This is the origin of the concept of big data analysis. In the literal sense means, Big Data is a large volume of structured and unstructured data.

Big data uses data points from a set of raw data and identifies relationships to gain new insights and make predictions. The raw set of data is used to build models, run simulations and change data points to see how each alteration impacts the resulting information/insight and prediction. This has been adopted by organisations to help make smarter and more informed decisions.

With the increase popularity of big data, some major misunderstandings have blurred the real function of big data. So, let’s myth-busting five of the top Big Data myths.

1. Big Data Is Too Complex to Handle

Given the volume of big data generated in real-time from multiple sources such as audio, video and images, it can be a mess. Big data was introduced to automate manual processes, thereby reducing the complexity of processing all this data.

Big data analytics uses different technologies and simulation tools that may seem at first too big to handle, but so are smartphones and computers! All inventions need to go through a learning process to become easy to operate and use. It’s the same for big data tools. Some specialised tools are used to store, process, analyse and visualise data points, which require some training to get used to.

The technical framework of big data is not as complicated and sophisticated as claimed or thought!

2. Big Data Is Expensive

The word “big” creates an unfair perception that is nothing less than a building full of supercomputers.

Analysis packages become very accessible, many of which can be run on typical multi-core desktops, laptops, and even phones. Many companies have been working hard to deploy cost-effective data processing software that can be used in various data projects. This means that big data systems can be deployed in small settings, and IoT devices, machine learning, artificial intelligence, and dashboards can be accessed almost anywhere at a reasonable price.

3. Big Data Is for Big Companies Only

This is the biggest misunderstanding, and it is wrong on many levels. Many people believe that because big data is considered an expensive investment, only big companies can afford it. But this is not the case. For example, Hadoop can be an affordable option for large and small organisations.

It is also wrong to assume that small businesses do not have enough capacity to take advantage of big data. Small businesses have more personalised ways to run and manage daily operations, and they are likely to be more efficient to manage big data and using open-source software.

The agility provided to many small companies allows them to gather insights and react within a few weeks, with the cost-effectiveness of modern big data systems, small businesses can quickly take advantage of this advantage. Big Companies bigger problems

3.1 Also, many think that Big Data is all about the IT industry but the goal is to create insights that can be applied in a variety of sectors. It impacts the business and all its departments including sales, marketing, finance, operations and human resources. For example, companies in retail use big data to collect and process information such as inventory numbers, trends, buying processes, or global supply chains. If Big Data can revolutionise the way people buy shoes, it can do a lot in many other sectors as well.

4. Big Data Is Here to Replace Humans

A machine cannot substitute human insight and intelligence. Computers have the advantage of raw speed, being able to sort through more numbers in a few minutes than a person can complete in a lifetime, and can quickly detect patterns and execute formulas in massive amounts of data. However, people are often better at adapting to situations with limited information than machines. BBig data must be combined with human decision-making to finally play its role and ultimately be effective.

Also, big data cannot replace human creativity, which cannot be achieved by machine algorithms. Based on the various insights provided by big data, decisions are made by the human brain and its creativity.

5. Big Data is the Solution to Everything

As we said before, big data can get messy and chaotic, so it is essential to turn these raw data into meaningful insights. This may only be possible by having the right amount of resources at the right time. To do this, we need people with the skills, experience, and ability to handle large amounts of data. Only in this way can insight and data-driven decisions be made.

The success of big data depends on data scientists, analysts, experts, support staff and business managers. Big data analytics are tools that help discover relationships and patterns from a given set of data points, but the ultimate decision-making ability lies in those who use and interpret these patterns and relationships.

Even if trends or correlations have been accurately identified and discovered, external factors will eventually decide determine the quality of the results. Predictions can be made, but they aren’t necessarily always right.

It may seem like an IT term, but it does more than that. Big data will be regarded as an asset that can be used to solve many problems that companies face daily. Big data can play a role in the most remote business operations. It’s a company-wide phenomenon and must be approached and used as one too.

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