Big data has caught the attention of business owners as a lucrative technology. But, has it been fully understood? As common features of Big data are resulting in increased expenses and decrementing the quality of the company. 

FREMONT, CA: Big data is said to be a lucrative business strategy, but not all businesses implementing it are set to thrive. Certain key features are often misinterpreted, resulting in errors that invariably creep in. To assist the identification of such standard features, the main factors have been highlighted and briefly discussed to prime up the new business owners or startups.

Excess Data:

The collection of every piece of information, and assembling it for mining the entire volume is a recipe for disaster. The main idea is to be selective and analyze the type of large datasets that the mining should be carried out for and what the customer ultimately wants. According to a survey by Big Data Executive, almost 85 percent of enterprises target to be data-driven, out of which 37 percent is the number of successes. Data saturation is a complex problem, resulting in more unstructured data to deal with.

Poor Data Quality:

Gartner has reported that the total value of the poor data quality costs companies almost $15 million per year. This is the price to be paid for following messy practices and deals with unstructured data collection practices. It is also observed that the situation can become much worse if the sources of data are complex, unstructured, and massive in volumes. This feature not only affects the financials of the enterprise but also causes a detrimental effect on its quality producing useless data.

It is vital to invest in a systematic collection of data and maintain the quality as well. Big data can only be the future of smart businesses only when the good quality of data is used, to begin with.

Overestimating Predictive Analysis:

Predictive analytics is one of the promising features of big data for new businesses. It is a unique method of recognizing patterns and constructing algorithms to generate models that assist in better performance for personalization services. But the expectations need to be maintained at a realistic scale from the Big data projects and needs to be reinforced with human intellect and business knowledge. , and that context should be factored while designing the action plan.

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