The 3 Most Underappreciated Aspects of Big Data

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In the today’s technology driven world, Big Data analytics has turned to be critical to IT services. This technology has allowed many firms in the tech world to gain invaluable insights into their business processes, what client’s want and the competitors’ behaviors, all thanks to the capability to tap both unstructured and semi-structured data. But certainly, not all businesses have benefited from this evolution. As much as benefits of Big Data have been widely known, what is less clear is precisely how businesses should undertake Big Data analytics efforts to achieve the desired results. There is no doubt that the technology has tremendous potential value, but to achieve it is not guaranteed. Firms have to make a connection between the use of this resources and the business strategy if they want to achieve results. Some aspects of Big Data analytics are widely known and well used, such as collecting a large volume of raw data from online platforms and the need for advanced analytics software. However, there are facts that are frequently overlooked. The following are some of the underappreciated facts about Big Data.

Brand Implication Identification

In a world grappled with competition and customers who demand high expectations from the businesses, building a strong brand is critical to surviving in the today’s ever dynamic business landscape. It’s not only the competition and customers that have seen change but also the way people connect with each other that has made social media a major resource in the brand development and recognition. Social media adoption has and is increasing at exponential rates. Data available shows that, one in nine people around the world is on Facebook, Twitter recently showed that it handles 1.6 billion queries per day while YouTube’s page views are more 92 billion per month.

Businesses have been forced to ensure consumers get the best experience for their products and establish a positive vendor relationship. Negative consumer experience or vendor relationship can affect the activities of a firm across the globe in a short span of time negatively. The role played by social media to promote brands is crucially important but negative campaign of poor brand experience could hurt a brand globally and lead to devastating effects on the financial viability of a firm. The link between Big Data and social media is largely on large scale data mining that is crucial for a holistic approach to brand protection. Usually, this is the function of media relations, human resources or communications within organizations.

The reality of brand protection is that when negative media campaign is launched from the social media, the company deals with it in a reactive manner. Most organizations opt to deal with these issues when they emerge, and when they seem like they cannot be ignored. However, this turns out to be an underestimated fact when most of these organizations have Big Data analytics in place to help them collect social media data. For instance, data collected from social media about a specific brand would help personnel within a company to act proactively to prevent social media negative campaign before it’s too late. Negative information can be captured soon enough at infancy and managed before it becomes a trending topic on social media.

Business Interoperability

Businesses can shape their course through open source harvesting of data. It is this strategy that helps businesses achieve a great deal of agility that is important dynamic monitoring of large data sets and set up the mitigating efforts. With effective data harvesting platforms, a firm can develop new market-oriented strategies, make changes or react more quickly to market changes. When firms harvest data and add what they have, they can establish trends, and reports can be produced with relative ease. As organizations strive to establish a connection between departments, those using analytics from big data are regarded as pioneers. However, those using the harvested data in a comprehensive manner, ignore the need to deep-dive into the technological foundation. Pioneers hence become the pacesetters and hence the value of intelligence gained through data harvesting is not well utilized by all employees in the organization.

Implementation of big data to help forecast investments

Irrespective of the industry, firms makes use of Big Data in a time-consuming process to realize the return on investment (ROI). The reason it is time-consuming is because the manual process of harvesting information is tedious and involves mining data for specified content, analyzing the data to find important information, make the data usable by decision makers, analyzing the information and making the actual decision using the information gathered. Open source harvesting is thought to fill this void to automate the whole process, and the process avoids the human error and results in quality information. With this advanced software, people are now left with the role of analysis and action. However, since the software is much more complicated especially to untrained personnel, analyzing the data to forecast investment still proves difficult. For instance, untrained real estate personnel may not be able to analyze data from real estate investor software hence missing an opportunity to recognize industry trends or changing consumer preferences.