Top 5 Concerns for Big Data in 2018

by December 8, 2017

In today’s world where the IT sector is booming, data is an aspect of research that is flourishing the most, yet is facing setbacks in terms of the value it offers to the people. The quantity of data that is produced every day, at every minute by people and machines makes it extremely difficult to save, analyze, manage and finally utilize it. Hence, the development and evolution of various kinds of tools for data analysis have helped immensely with the handling of customer data.

According to research, almost 90% of  big data was produced in just the last couple of years. Apps are being developed and used to improve the service levels, utility and customer support, etc. The Data technologies are evolving to a stage in which more organizations are prepared to acquire data as a core part of the analytics infrastructure and information management. Hence, we can say that the big data explosion has occurred and strategies have to be taken up in order to utilize it to its full extent.

There are a number of big challenges that are faced by data every day. Five of the most challenging issues are:


Different Sources of Data

It is anyway a challenge to manage the large number of sources that produce data, let alone dealing with the amount and volume of data and speed at which it is being produced. The data originates from the organization’s internal sources like marketing, finance, etc. and the external sources like social media. This, in turn, makes the data extremely diverse and voluminous. It is anyway difficult to manage and optimize the use of this produced data irrespective of the expensive tools and varied methods and processes.


Quality of Data Storage

With the fast-growing pace of various companies and organizations, the growth of the produced quantity of data is rapidly increasing too. It is hence becoming a huge challenge to store this data. Multiple options called data lakes or warehouses are being used to gather, store and process huge amounts of data that is unstructured, in its original format. The challenge nonetheless occurs when data lakes or warehouses try to merge this unstructured data from dissimilar sources. This is when the error occurs. Missing data, Inconsistent or unstructured data, logic conflicts, duplicates, etc. are all results of poor quality of data storage.


Improved Quality of Data Analysis

The large sum of data produced by companies and organizations are used to come to the best probable solutions, hence obviously the data that they use must be correct and accurate in all probability, otherwise as a result, wrong decisions would be taken, which would ultimately snowball into being harmful to the future working and success of the company. This dependency on the data analysis makes it extremely important to maintain the quality of the analysis. It needs a lot of resources and people with the proper talent and proficiency in order to make sure that the information that is provided by the data produced is accurate. This process is, however, an expensive affair and is immensely time-consuming.


People who Comprehend Big Data Analysis

It is extremely important to analyze the data that is being produced in huge amounts in order to make complete use of it. Hence, the need for data analysts and scientists arises, for the storage and optimum use of quality data. It is also important for a data scientist to have the required skills that are as varied as the job is. But, the number of people pursuing the job of a data scientist is very less as compared to the amount of data that is being produced every day. This is another major challenge that is faced by most organizations.


Privacy and Security of the Big Data

The organizations and companies get a diverse range of prospects once they devise ways of utilizing the big data. But there are big risks involved too when the question of security, protection and data privacy arises. The tools that are used for the various stages of processing the data are from diverse sources. So, this eventually leads to the major risk that is the disclosure of the data which is produced, thereby making it extremely vulnerable. Thus, the increased rate of data production automatically increases the concerns regarding privacy and security. This aspect of data production, therefore, makes it crucial for the data analysts and scientists to keep in mind these important concerns and deal accordingly with the produced data in such a way, that it will not result in any kind of disruption or breach of privacy.

Even though Big Data Analytics Solutions is definitely a great boon to the 21st century for a number of companies and businesses, because it is assisting them in ways more than one. That includes taking improved decisions thereby earning profits for the company and pushing it to further success. However, optimizing the data usage, to help the companies to the fullest, is still quite farfetched, even though the progress towards achieving this goal is being fulfilled to the fullest by the concerned sector. Here’s hoping 2018 will see further movement towards this goal.