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From the security of large volumes of information to proper ingestion of data for end user purposes, the many challenges presented by Big Data are undeniable. Here, we take a look at the primary Big Data challenges facing organizations in 2016.
Today, a vast majority of organizations are heading in the right direction with regards to managing Big Data effectively. Nevertheless, the challenges of leveraging the many benefits of Big Data, without depleting the available resources, are agonizing developers, administrators and managers in 2016. So, even though business unit leaders are being able to access Big Data solutions and use them in their decisions and initiatives, they are grappling with the latest challenges of Hadoop and Big Data in terms of research, data accumulation and analysis.
For instance, as per the World Quality Report 2015-16, Big Data security issues are being portrayed as the highest ranked priority for IT strategists. Here, we discuss some of the most important Big Data challenges that are plaguing the adoption of sensitive data applications –with respect to web-centric, mobile specific or cloud-based instances alike.
Big Data Security Challenges
With massive information and data being stored, it has become essential to maintain their security effectively. In most cases, data security issues are a result of the absence of firewalls and antivirus software. Given under are the main Big Data challenges pertaining to the scope of information stored on isolated systems, hard disks, and elsewhere (also consider checking out this perfect parcel of information for data science degree).
It is commonplace to find distributed system computations having just a singular level of protection. This in turn leads to grave securities problems.
The absence of advanced security measures is adding to the woes of transferring automated data securely.
The act of information mining unethically is helping fraudulent IT agencies and specialists gather the personal data of unsuspecting users without gaining their permission.
Security solutions are finding it difficult to meet up with the ever-evolving demands of non-relational databases (NoSQL).
There are many organizations that fail to institute proper access controls for dividing the level of authorized usage and confidentiality in operational processes or across different hierarchical levels.
There exists a large gap in the amount of information received by the system and the ways in which it is validated for remaining accurate and trustworthy.
As there is a large amount of sensitive information involved, it often becomes difficult to perform the routine detailed audits as recommended.
Big Data is so voluminous in size that it is not easy to monitor and track its origin consistently.
Organizational connections security and access control encryption systems are liable to become dated and inaccessible for IT specialists relying on the same.
Organizational Improvements and Big Data Challenges
Big Data challenges are making organizations face the music while bringing about much needed improvements; or while analyzing large data quantities and acting on them in real time (Here's the perfect parcel of information to learn data science). Additionally, most Big Data operators keep complaining that the operational processes being handled by them are becoming long-drawn and cumbersome. Mining or using real-time data is also becoming a tough task for organizations implementing manual processes for collecting and analyzing data. In the current Big Data and Hadoop backed scenario, technology executives are initiating their Hadoop adoption and other Big Data processes cautiously. There is an increasing focus on software users (as well as tool enablements or application plans) for driving consumption in planning.
The Impact of Big Data Challenges
Overall, most risk expensive systems, especially those that involve the acquiring and processing of billions of records on an everyday basis, are lacking automated modes for collecting and ingesting data. They are devoid of proper alerting and recovery means too. This effectively means that a small issue has the potential of turning into a very large problem quickly. Once a specific problem is fixed, the backlog needs to be processed. If the process of improvement or repairs is attempted manually, it can be very frustrating and difficult to handle things without going astray (also consider checking out this career guide for data science jobs).
Dealing with Big Data Challenges in 2016
Even in 2016, dealing with the smart solutions presented by Big Data is not at all an easy affair. It is important to understand that the mere adoption of Netezza, Teradata, Hadoop, or any other technology, will not solve the problem for organizations. The data has to be accumulated and parsed appropriately, and transformed into user-friendly data models for the purpose of ingestion by end users. Moreover, meaningful reports have to be created for transforming and cleansing all the loaded data into reliable inputs that Big Data tools can use. Yes, Big Data challenges can be BIG and require a lot of work on the part of organizations to prevent any hold-backs or hold-ups.
Let us know your views on Big Data challenges in 2016 and why you think that organizations should put in more efforts in the IT security measures undertaken by them. We shall be happy to hear from you.
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