Challenges faced in Data Analysis and Big Data
Big Data and Data Analytics is the buzzword of the modern technology and the business community. Here we are going to some of the probable and frequent challenges and issues to be faced before we could navigate them effectively.
But identifying these probable challenges beforehand would be of utmost importance to understand how Big Data Analytics works and how we can be benefited from its resourceful work abilities. The amount and the variety of data available be an overwhelming thing to be noticed by any data engineer. So it is vital to make the data easy to access and resourceful for brand owners and managers.
Data storage
Large organizations and companies are at the breakneck speed and this growth inevitably leads to the rapid increase in the amount of data produced every day. So data storage is becoming a formidable challenge. Data lakes and warehouses options are widely used to gather and store a large amount of unstructured data in its original format. The problem with this process is, it encounters errors when these data warehouse lakes or data warehouses try to blend unstable data from different kind of sources. So the major challenge a data analyst may face is issues like duplicate data, logic conflicts, inconsistent data, and, worse yet, some data might be missed at all.
Understanding the entire analytical process
Although we produce a huge amount of data, it is worthless if data analysis is not done very properly. So the role of Big Data analysts and Data Scientists is indispensable. So storing the data to the utmost quality makes the job of a data scientist very demanding. A data scientist should possess enough diverse skills as the job is multi-disciplinary. When it is compared to the amount of data being produced, the available number of data scientists are far lesser.
So it makes all the more important to analyze the data which is being produced every minute in a voluminous amount. So this exponential rise of data has created a huge demand for big data scientists and Big Data analysts in the market.
Qualitative analysis
Big data is the major key player in helping the enterprises and organizations to draw best decisions possible in a given scenario. In addition, the data they produce should have the quality of accuracy. If the data, which is the fundamental source, used to draw decisions and conclude is not accurate it will lead to an ill-advised decision-making process that would ultimately be a hindrance to the future success of the business operations. And this high trust in data quality makes testing all the more a high priority. So we are in need of a lot of resources to make sure the accuracy of the information provided. So this process of analyzing the quality, thus creating accurate data is somewhat expensive and unaffordable.
Security of the data and the database
If the companies and enterprises learn to figure out how to manage and handle big data, it provides them with a wide variety of opportunities. But the security and the privacy of the data is highly vulnerable and indefensible from the external factors. The varied tools to manage, analyze, store, and utilize the data runs the risk of being exposed. So as more and more data is produced, it also increases the privacy and security concerns. So it is critical for data scientists and analysts to take into account these issues and handle the data in such a way so as this will not disrupt the privacy of the data involved.
Managing the sources of the data
The volume of data being produced is at a high velocity and it causes the multitude of problems when we manage the source of the data. The majority of data stems from the company’s internal sources like marketing and finance. As this is not enough, social media is an external source that produces a huge volume of data, thus making the data extremely diversified and so large to handle. So the optimal management of the data is highly unlikely.
As data sets are becoming larger, data analysts face a challenge to constitute them into a solid analytical platform. But if this is ignored, it will generate gaps and pave the paths to wrong insights.
Conclusion
So all these concerns apart, all of these can be resolved in a stroke of a moment if we could get the underlying problem. It all stems from the inadequate knowledge of how the fundamental aspects of big data analysis and its inner workings. So with good knowledge and practical understanding of this most important technological innovation, we can shine through in big data and data analysis.
