Big Data Hadoop & Spark

The Top 5 Big Data Myths Busted

Since the term “big data” was coined,  to depict the combination of structured and unstructured data, businesses are effectively using it to draw meaningful insights. However, there’s a lot clandestine and chaos associated to it. Every type of business has come with their distinctive explanation of big data and the definition highly relative towards benefits and usage by these organizations. The abundance of interpretations about big data could easily navigate anyone towards false information. This is certain, it happens with every transformational innovation.  I, in this article, have spoken about five such big data myths and how they could be demystified.

 Myth 1: Big data is literally ‘a lot’ of data

A common Big data myths are that it should go by its name. However, it necessarily doesn’t have to be a lot of data. Even though Volume is a vital aspect of big data, there is no necessity to have any minimum amount of data.

The fact that the origin of big data is real-time. It is through diverse resources and modes like depictions, acoustic, which makes it inevitably big. A simple comparison of the volume of this data with the organizational structured data in spreadsheets and other relative databases, then considerably larger. However more than the size, the data is diverse and unstructured. These characteristics are of big data is what that makes capturing, storing and processing big data exciting. To conclude, “big data” need not always be large in volume, it should majorly come from diverse fields and its complex which is useful.

 Myth 2: Big Data Platforms is a substitute to the data warehouse

One of the obvious big data myths is that it is“the solution” for all queries. This has led to the myth that big data analytics platforms are a substitute for the data warehouse. However, that is not the reality. Big data platforms like Hadoop and others are basically to counterpart out-of-date relational database management systems (RDBMS), not to reduce the data warehouse obsolete. Hadoop is good at storing, managing and analyzing huge volumes and varieties of data. Tasks such as swift processing structured data and handling continual and probable assignments data warehouses are simply better executing and managing. Thus, data warehousing and big data platforms are distinctively important and cannot replace one another.

Myth 3: Big Data implementation is expensive

Another common big data myths amongst companies are that big data comes with the big price. Bursting this myth, there are multiple big data startups offering accessible and pioneering tools to assist businesses to scrutinize the data they collect. There are distinctive tools and services assisting big data at every step from processing, storing, analyzing, and visualizing.

Also due to the large-scale use of Big Data, it is becoming affordable than ever before. Thus, for these tasks, there is no need to build a Hadoop cluster or to hire expensive big data scientists/big data analysts.

Myth 4: As Data Is Huge, Small Flaws Are Acceptable

Big data primarily originates from a variety of sources (structured and unstructured). This does not imply that data quality should be poor in the big data initiatives. Or the distinct data errors will have any impact on analysis results, this is the most trivial big data myths. It’s true individual data errors will not have a flaw may have a minor impact over the complete dataset. This holds goods when there is less data, with volume these minor flaws grow to become prominent. Thus, the overall impact will be poor-quality for the entire dataset.

Most of the enterprise data in a big data environment are usually outdoor or the origin is unknown. This is a high probability that the quality of the data is poor. Thus, the fundamental values of data quality should be taken care.

 Myth 5: Big Data solely for IT

The natural depository for data will seem like IT team if it is inaccessible by rest of the business. They will also miss out on benefits that big data offer. Mostly, big data is presumed to be an IT matter.  This is because big data strategy implementation requires hardware and software.

However, the IT team is just like any other team in the organization that helps in achieving the defined big data goals. The strategy could be to enhance the customer service, or for revamping ideology for revenue generation or could be upgrading the operational efficiency”. For instance, if the strategy is to improve the customer service, the IT manager wouldn’t be a right person to sponsor for the measures to be taken. Thus, big data details must be dealt all vital teams of business.

Conclusion

Myths and misconceptions about big data are profuse, it’s no exception. All that the businesses must consider is the ways to leverage from large data sets for competitive advantage. This happens through the implementing big data platform. The decisions of these businesses should solely depend on overs facts, not fiction. For Businesses, it will surely be a hardship in the beginning. The deeper you go and the more you learn, you will unveil the real treasure. Big data is undoubtedly the trust for the businesses of future.

Pavithra Vasist

Pavithra Vasist is a Content Writer working with Aeon Learning Pvt Ltd. She was previously working with MetricFox, a marketing outsourcing firm as a Copy Writer. She holds a bachelor's degree in Electrical and Electronics Engineering. Besides writing, she's fascinated with electronic gadgets and mostly spends her spare time drawing or traveling. She resides in Bangalore.

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