Top Most Big Data Inhibiting Factors

Top Most Big Data Inhibiting Factors




Big data is the art and science of collecting large data sets (unstructured video, emails, sensor reports, logs) by traditional and digital supplies to determine market trends and partners. This information is processed and analyzed by companies to enhance their decision-making so that they can hook themselves to the right path that brings maximum opportunities and limited risks to their organization. However, one unfortunate reality is, big data has lots of enemies. Data is plural of the information datum.

The interpretation of big data done by experts is something like – an amount of data which is difficult to be curated, processed, or analyzed by relational database owing to its thriving size (produced by Internet of Things (IoT) including machine generated and transactional processes). However, the question that hits the mind is why this big data is so difficult to be managed and what factors are acting as a roadblock to this business-basic data?

This article will highlight some of the opponents of big data:

IT Infrastructure: Technology plays the principal role in the augmentation of the world economy. However, at times, it also put a knob to certain good things. Technology itself is one of the big data issues – how? To put it simply, the incompetency of IT architecture to integrate elements and data models makes it a problem. Today, the biggest problem is the increasing variants of data types and repository systems that make IT architecture to keep data seamless and updated round the clock. The architecture should be planned and designed consequently to meet data veracity and data silos challenges. Additionally, it is indispensable to determine data redundancies and gaps in order to bring right data management and governance strategies in operations.

Unaware Data Scientists: There is no denying the fact that big data has helped many organizations and individuals to move the upper level; and, now these people have started calling themselves the data scientists. Unfortunately, this has produced a mess, where they are deriving their own conclusions and explaining their suppositions to others. This is a big problem, as they apply statistical techniques without understanding its functionality. Remember, the possible of this evolving data is umpteen; and, those who make right implementations can leverage its benefits.

Dearth of Resources: The other problem allied with big data is the without of analysts that can analyze the data; draw right conclusions, and help businesses of all sizes to take pragmatic decisions on the basis of data. Research states that big data and analytics will change the confront of the companies in the approaching few years. There is a without of data analytics professionals who can manager, analyze, and draw insights from this data. That is why many universities have gone a step ahead to run specialized analytics courses. It is expected that this approach will little by little bridge the gap. It is important that organizations should hunt for right talents (analytics experts) that can help them draw analytics framework and address various business challenges in an perceptive manner.

Addiction to traditional Approach: Every business strives to find ways that can help them original. Usually, they consider their past records and strategies to begin their future operations. It is true that by leveraging analytics, companies can grow big with the help of strategic decision-making. However, the biggest problem here is integrating analytics to a reluctant mindset that is careful to change and is complacent with traditional legacy systems. Till, the time this approach will not change, adoption of analytics cannot be adopted completely. In this regard, forward-looking business leaders should put their efforts to encourage their company to make analytics-pushed decisions.

Data Segmentation: Another challenge that comes with big data is – its management. Every day huge quantity of data is generated, which IT professionals feel is difficult to manage. To put it simply, companies command their IT professionals to locate where their data resides and determine how it can be best utilized. The issue with IT experts is they are lost in the black hole (the amount of data is so high that they do not know which direction to head.). At times, data is not properly classified at the point of creation, which implies, companies will have no idea of knowing which way they are heading towards (seeking sales, customer details, and profiles).

This is the reason why it is important to classify the data according to its types so that right things can be done at the right moment. Equally, it is basic to determine which data will be most needed in the near future.




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