Today, it would be right to say that Big Data is reigning the field of Information Technology. Big Data analytics has become indispensable for organizations of all kinds to progress and preserve their competitive edge. Data, if utilized effectively, can transform the fortune of an organization.
However, it comes as a surprise that only less than half of the organizations leveraging Big Data analytics are achieving the expected outcomes from it. Their attempts at uncovering valuable insights from data processing have not been able to yield a substantial result.
Big Data analytics offers a plethora of advantages, enhanced operational efficiency, and improved decision making being the foremost. In such a scenario, it becomes crucial to find out exactly what big data challenges the organizations are encountering and how to tackle them effectively.
Without a comprehensive understanding of Big Data, it is not possible to attain success from it. On the contrary, it may even result in more harm than good as organizations will continue to invest copious amounts of money on assets and technologies that they don’t know how to utilize correctly.
Moreover, when the mindsets of the employees are rooted in the current processes and operations, they might fail to see the potential of big data. Hence, the adoption of such technology eludes the organization and therefore, its progress
As with everything new, businesses should give themselves enough time to be acquainted with such technology. Members present at the top management levels need to understand and accept Big Data as vital for their growth. It will enable them to conduct training and workshops centered on its application and the benefits it can provide. Hence, an adequate understanding and acknowledgment of Big data technologies will be able to permeate all the departments. The implementation of new technology should be carefully monitored to yield the right results.
This one of the more complex big data challenges plaguing organizations. Data streams from numerous sources, in structured and unstructured formats. Hence its integration poses a problem. Big Data isn’t all authentic. There is a risk of gathering wrong or duplicate data that might have a deleterious impact on your operations.
To prevent the data from being contaminated, it must be refined, reviewed, and verified. In order to extract the maximum benefit from your data, it must match the standards of data quality like consistency, accuracy, completeness, ability to audit, and orderliness.
Being careful of the data sources from where the data is coming from will ensure that the information you’re working with is reliable. Your data needs to be stored properly, and you must ensure that a robust cleaning mechanism is in place.
Data needs to be aligned and corresponded with other records to make it consistent. Besides, regular automatic and manual audits will ensure you don’t run into any data quality issues.
One of the big data challenges that may pull a company away from implementing it is the expenses that come with it. Both the on-premise and cloud-based solutions demand new administrators and developers. At the same time, the configuration, development, and set-up of the Big Data project are also very expensive. Moreover, the expansion of data comes with its own set of financial complications.
To resolve this challenge, a careful examination of your business’s technological requirements is crucial. A cost-effective route can be going ahead with a hybrid solution. In it, the data storage and processing are partitioned between the cloud and on-premises. Other options like Data Lakes and Optimized Algorithms offer inexpensive data storage options and minimized utilization of computing power, respectively.
It is one of the critical big data challenges that arise from not prioritizing the security aspect of Big Data. Most of the businesses implementing Big Data projects can be found guilty of not paying adequate attention to security. In simple words, Big Data technologies lack the requisite amount of security.
Security should be the prime concern when designing the architecture of Big Data solutions. It might be a good option to consult a Big data Company to create a tailored solution where the security aspect is given due prominence.
One of the most critical big data challenges lies in its tendency to grow at an exponential rate. Enhancing storage capacity and computing power are the possible ways by which businesses seek to upscale their Big Data projects. Upscaling endangers the optimal performance of your system. It is also not very feasible if you’re operating on a tight budget.
This challenge can only be solved by giving due attention to the architecture of your solution. A robust architecture will ensure that problems pertaining to this aspect are minimized. Besides, Big Data algorithms should be developed, keeping in mind the scaling concerns.
Complications that arise out of the expansion of data can be dealt with proper planning of your system’s maintenance and support. Regular monitoring and tracking of the system’s performance through audits will also eliminate any upscaling issues.
You know that data analytics is used to uncover relationships and patterns out of the past information on consumer behavior. One of the most bewildering big data challenges that companies face is related to finding meaningful insights through their data analytics.
Data is being continuously generated from a variety of sources at a rapid pace. In such a scenario overlooking information from any one source can render your observations and reflections about your customers inaccurate.
This challenge can be countered by maintaining a system of reliable data sources whose analysis will help you arrive at the right conclusion. Considerable thought should be given to these data sources to ensure that you’re not missing out on any one of these sources through which you may gain valuable information.
To successfully reap the benefits of Big Data, it needs to be analyzed properly. The challenges described above are typical for any organization utilizing Big Data technologies to encounter. These challenges may seem hard to overcome but can be effectively resolved through a good system architecture and an organized approach. There is a need to evaluate your company’s requirements and needs before going ahead with any significant data analytics project.