In today’s digital world, businesses produce voluminous data from sales, customer behavior, social media handles, and in-house operations. This data needs to be stored, managed, and analyzed to enhance its utility. Companies are embracing big data analytics to improve data productivity and operational performance. Analytics allows them to make accurate predictions, and most of all, to acquire a competitive edge.
However, due to the often unstructured and massive nature of Big Data, companies face numerous unavoidable challenges. These issues become prominent right from storing the data and managing the in-house workforce. So, companies cannot use data analytics to the fullest extent at all. But, what are the challenges in Big Data Analysis? Let’s discuss them below.
Analyzing data is quite Expensive.
Even if you are satisfied with your investment in data analyzing systems, it is never too late to check whether you are overpaying or not. Every day new technologies emerge which can process more volumes of data in faster and cheaper means. Consequently, sooner or later, your data analytics technology will be rendered outdated and become more expensive.
So, adapting to new technologies is the best way out in the long run. It will help you in reducing the costs and improving the reliability of the corporate datasets.
Inefficient data workers
Companies need skilled data analysts, scientists, and engineers to work with big data tools and technologies. These workers should have enough experience to handle cutting-edge tools and understand enormous datasets.
With data handling tools are evolving rapidly, there is a scarcity of data professionals who meet with above criteria. Companies cannot find employees who deeply understand data and keep up with the customers simultaneously.
One of the most daunting issues faced by analytics-driven companies is safeguarding the massive volumes of daily data. Companies are often so preoccupied with understanding and managing data they undermine the importance of data security. Such unprotected data attract malevolent hackers quickly.
Cybersecurity professionals take steps like data encryption and real-time security monitoring to secure the database. Other measures may include data segregation, execution of end-point security, and tools like IBM Guardian for Security.
Systems often rely on poor-quality data, full of defects and errors or sometimes incomplete. Such inconsistent and unvalidated data provides miserably poor results.
To ensure the incoming data quality, compulsory data validation and data quality management are necessary. It allows you to spot inaccuracies and helps in filtering your data, making it pure and error-free. High-quality testing and data validation will ensure that data sets are of proper configuration and reliable for better decision-making.
We can see that in the way to data transformation, organizations face multiple challenges. From cybersecurity risks to data quality issues, companies need to streamline and optimize many operability facets. Businesses need to develop a culture where everyone can get familiar with the basic concepts of data analytics. Once a data understanding gets established in the industry, the already existing problems won’t stop anyone from reaching the heights of success.