Top Most Big Data Inhibiting Factors

Big data is the art and science of collecting large data sets (unstructured video, emails, sensor reports, logs) through conventional and digital sources to determine market trends and partners. This information is processed and analyzed by companies to improve their decision making so that they can connect with the right path that brings maximum opportunities and limited risks to their organization. However, an unfortunate reality is, big data has lots of enemies. Data is a majority of the word datum.

The interpretation of big data done by experts is somewhat similar – a quantity of data that is difficult to curate, process or analyze through relational database due to its burgeoning size (created by Internet of Things (IoT) including machine generated and transaction processes)) . However, the question that strikes the mind is why this big data is so difficult to manage, and what factors act as a roadblock to this business-related data?

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

IT infrastructure: Technology plays the leading role in expanding the world economy. Sometimes it also puts a button on certain good things. Technology itself is one of the big data problems – how? In short, the incompetence of IT architecture to integrate elements and data models makes it a problem. Today, the biggest problem is the increasing varieties of data types and repository systems that make IT architecture to keep data seamless and updated around the clock. The architecture should be planned and designed accordingly to meet data security and data science challenges. Additionally, determining layoffs and gaps for data is indispensable to bring the right data management and management strategies into operations.

Unaware Data Scientists: That does not deny the fact that big data you have helped many organizations and individuals move to the top level; and now these people have started calling themselves the data scientists. Unfortunately, this has created a mess where they derive their own conclusions and explain their assumptions to others. This is a major problem as they employ statistical techniques without understanding its functionality. Remember, the potential of this developing data is around 13; and those who make real implementations can take advantage of it.

Lack of ressources: The other problem associated with big data is the lack of analysts who can analyze the data; draw real conclusions and help companies of all sizes make pragmatic decisions based on data. Studies say big data and analytics will change companies’ faces in the coming years. There is a dearth of data analysts who can handle, analyze and draw insights from this data. That is why many universities have gone a step ahead in running specialized analysis courses. It is expected that this approach will gradually bridge the gap. It is important for organizations to hunt for real talents (analytics experts) who can help them draw an analytical framework and handle different business challenges in a crisp manner.

Dependence on conventional approach: Every company endeavors to find ways to help them innovate. Usually, they consider their past positions and strategies to begin their future operations. It is true that by utilizing analytics, companies can grow big through strategic decision making. However, the main problem here is to integrate analytics into a reluctant mindset that is cautious about changing and complacent with conventional legacy systems. Until the timing of this approach does not change, the adoption of analyzes cannot be fully adopted. In this regard, forward-thinking business leaders should make their efforts to encourage their business to make analytics-driven decisions.

Data Segmentation: Another challenge that comes with big data is – its management. Every day, huge amounts of data are generated, which IT professionals find difficult to manage. To put it simply, companies command their IT professionals to locate where their data lies and determine how to best utilize it. The problem with IT experts is that they get lost in the black hole (the amount of data is so large that they don’t know which direction to go.) Sometimes, data is not properly classified at the point of creation, which means companies have no idea to know which way they are heading (looking for sales, customer information and profiles).

This is why it is important to classify the data according to their types so that the right things can be done at the right time. It is also important to determine which data is most needed in the near future.