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 and partner trends. This information is processed and analyzed by companies to improve their decision making so that they can get hooked on the right path that brings maximum opportunities and limited risks to their organization. However, an unfortunate reality is that big data has many enemies. Data is plural of the word data.
The interpretation of big data by experts is something like: an amount of data that is difficult to select, process or analyze through a relational database due to its increasing size (created by the Internet of Things (IoT), including transactional and machine-generated processes). ). However, the question that arises is why this big data is so difficult to manage and what factors act as a barrier to this business-critical data.
This article will highlight some of the opponents of big data:
That infrastructure: Technology plays the main role in the rise of the world economy. However, at times, he also put a knob on certain good things. The technology itself is one of the big data problems, how? In a nutshell, the IT architecture’s incompetence to integrate data elements and models makes it a problem. Today, the biggest problem is the ever-increasing variants of data types and repository systems that cause IT architecture to keep data seamless and up-to-date 24 hours a day. The architecture must be planned and designed accordingly to meet the challenges of data truth and data silos. In addition, it is essential to determine data redundancies and gaps to carry out correct 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 move to the next level; and now these people have started calling themselves data scientists. Unfortunately, this has created a mess, where they are drawing their own conclusions and explaining their assumptions to others. This is a big problem, since they apply statistical techniques without understanding their functionality. Remember, the potential of this evolving data is countless; and those who make correct implementations can reap its benefits.
Lack of sources: The other problem related to big data is the lack of analysts who can analyze the data; draw correct conclusions and help companies of all sizes make pragmatic decisions based on data. Research indicates that big data and analytics will change the face of business in the coming years. There is a lack of data analytics professionals who can handle, analyze and extract insights from this data. That is why many universities have stepped forward to offer specialized analytics courses. This approach is expected to gradually close the gap. It is important for organizations to seek out the right talents (analytics experts) who can help them design an analytics framework and tackle various business challenges in astute ways.
Addiction to the conventional approach: All companies strive to find ways that can help them innovate. They usually consider their past records and strategies to start their future trades. It is true that by leveraging analytics, companies can grow with the help of strategic decision making. However, the biggest problem here is integrating analytics into a reluctant mindset that is wary of change and complacent with conventional legacy systems. Until such time as this approach changes, analytics adoption cannot be fully embraced. In this regard, forward-thinking business leaders should strive to encourage their company to make decisions based on analysis.
Data segmentation: Another challenge that comes with Big Data is its management. A large volume of data is generated every day, which IT professionals find difficult to manage. Simply put, businesses are directing their IT professionals to locate where their data resides and determine how it can best be used. The problem with IT experts is that they get lost in the black hole (the amount of data is so high that they don’t know which way to go). Data is sometimes not properly classified at the point of creation, meaning companies will have no idea where they are going (looking for sales, customer details, and profiles).
This is why it is important to classify data according to its types so that the right things can be done at the right time. Likewise, it is essential to determine which data will be most needed in the near future.