Introduction to Big Data Analytics Challanges
Today, every minute, sees production of huge amounts of data. Every large company is struggling to find ways to make this data useful. However, this is not an easy task. The amount of data produced makes it very difficult to store, manage, analyse and utilize it. The development of various big data analysis tools has helped with data handling to a great extent.
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However, there is still a long way to go to make Big Data Analysis optimal. We are listing out the main challenges faced in Big Data Analysis:
Data storage and quality
Companies and Organizations are growing at a very fast pace. Moreover, the growth of the companies rapidly increases the amount of data produced. The storage of this data is becoming a challenge for everyone. Options like data lakes/ warehouses are used to collect and store massive quantities of unstructured data in its native format. The problem, however,is when a data lakes/ warehouse try to combine inconsistent data from disparate sources, it encounters errors. Inconsistent data, duplicates, logic conflicts, and missing data all result in data quality challenges.
People who understand Big Data Analysis
Data Analysis is very important to make the huge amount of data being produced, useful. Therefore, there is a huge need for Big Data analysts and Data Scientists. The storage of quality data scientists has made it a job in great demand. It is important for a data scientist to have skills that are varied as the job is multidisciplinary. This is another challenge faced by companies. The number of data scientists available is very less in comparison to the amount of data being produced.
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Good quality analysis
The companies and organisations use big data produced to make the best decisions possible. Consequently, the data they are using should be accurate. If the data used to make decisions is not accurate it will result in ill-advised decisions that would ultimately be detrimental to the future success of their business. This high reliance on data quality makes testing a high priority issue. This requires a lot of resources to ensure the accuracy of the information provided. The process of creating accurate data is very time consuming and requires the use of tools that can be expensive.
Security and privacy of the data
Once, companies and organizations figure out how to use big data, it gives them a varied range of opportunities. However, it also involves big risks when it comes to the security and the privacy of the data. The tools used for analysis, stores, manages, analyses, and utilizes the data from a different variety of sources. This ultimately leads to a risk of exposure of the data, making it highly vulnerable. Therefore, the production of more and more data increases security and privacy concerns. Thus making it essential for analysts and data scientists to consider these issues and deal with the data in a manner that will not lead to the disruption of privacy.
Various sources of data
Dealing with the volume of data being produced and the velocity at which it is being produced is a challenge. Additionally, it is a challenge to manage the enormous number of sources that are producing this data. The data comes from the company’s internal sources like finance, marketing etc. Moreover, external sources like social media produce a huge amount of data. Therefore, making the data extremely diverse and massive. Any number of tools and Big Data experts will not be enough to manage and utilize this amount of data optimally.
Big Data is a great boon to various companies and organizations, as it is helping them take better decisions, thus profiting the company. The use of this data to the best of its abilities, however, remains a dream. The blog mentions the main reasons why Big Data is not used optimally.