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5 big data myths busted

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10th Sep 2016
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Data holds the key to success for numerous businesses and organisations. Companies today gather huge volumes of big data through means such as cloud computing. Sifting the useful parts from the rest, organising, and processing them is a challenging task for many. Statistics indicate that by 2020, every individual will generate around 1.7 megabytes of new data per second. Given this scenario, it becomes imperative to get a better understanding of what big data is all about. Here are some myths about data:

5-big-data-myths-busted

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‘Big’ data refers to the quantity

Here, ‘big’ is not to be taken in the literal sense. Rather than the amount of data, it refers to a certain type of data. Structured and unstructured data sets like Excel sheets or metadata from emails taken together with social media analytics is known as big data. This can help provide a picture of trends taking place within a company.

Data is the final product

Nothing could be further from the truth because data is simply unprocessed raw material which is of no use unless it's translated. This involves data management processes such as storage, validation, and, finally, processing. A large team will be necessary for this process.

Data is perfect

There is no such thing as perfect or clean data. Analysing whatever data you have on hand can help you pinpoint quality problems. Waiting for data to be cleaned will mean putting analysis on hold for a long time and, by that time, that data is likely to be stale. As Megan Beauchemin, Director of Business Intelligence and Analytics for InOutsource, says that since organisations’ data is not clean, they put these efforts on the back burner, which is unnecessary. Organising an analytic application will visually illuminate areas of weakness in data. She adds that a clean-up plan can be put into place once these gaps have been identified. The analytic application can then employ a mechanism to call attention to clean-up efforts and monitor the advancement.

Data analysis can be expensive

The large number of free data analysis tools easily available does away with the need for expenses in this segment. Affordable cloud computing has put data analysis within the reach of all.

Human analysts will soon be redundant

The middle path is the perfect solution here. The right combination of machine algorithms and human intelligence is what is required to increase data processing efficiency. The machines can provide answers while data scientists are necessary to provide you with explanations. The presence of human elements in data processing is key to providing real insights.

Last but not least, do not be under the impression that big data analysis is accessible only to large enterprises and multinationals. Small businesses can also develop their own big data strategy to arrive at insights similar to that of larger organisations. The benefits are there, but a small business might take a little longer to fully realise the potential of big data.

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