Top 5 Big Data Challenges and Tips to Resolve them
Big data is used on an extensive scale for accomplishing a plethora of objectives such as prevention of credit card frauds, intervention, and anticipation of hardware failures. It is also useful in the rerouting of traffic to avoid congestion. With the aid of real-time applications and interactions of big data, you will be able to guide your customers.
Firstly i would like you to have a read on What is Big Data? How we contribute in the Implementation?
Here is a list of few of the major challenges in big data along with some tips to resolve them:
Lack of acceptance and understanding of the big data
At times, business organizations fail to understand the basics of big data, the advantages, and the prerequisite infrastructure, needed for big data. Without the proper understanding of these aspects of big data, it adds to the chances of potential risks which may lead to failure of the project. Thus, business organizations may end up in wasting an ample resource and time, if they are not aware of how to use it.
Also, if the employees of the business firm fail to understand the value of big data and they do not want to bring a change in the existing processes, they will not be able to contribute to the progress of the success of the business with the aid of big data.
As big data can introduce a significant change in the business organization, it is a prerequisite that the top management level, as well as the employees in the lower tiers, should accept big data. For ensuring that workers of every tier accept and understand big data, you need to organize a bunch of workshops and training.
In addition to this, the use and the implementation of big data should be controlled and monitored for the acceptance of it. You, however, need to keep in mind that the top management must not indulge in overdoing it as it may lead to adverse effects.
Getting confused about the variety of big data technology
A wide array of big data technologies is available in the market and there are high chances to get confused. You may wonder whether you require Spark or the Hadoop MapReduce speed would be sufficient to achieve the purpose. Finding answers to the specific question can turn out to be tricky. There are chances that you may end up in choosing the wrong technology, in case you are trying to explore from the vast array of technological opportunity, if you do not have a prerequisite understanding of the objectives of your business.
In case you are completely new to the world of big data, it is recommended to seek the assistance of professionals. You can opt for the services of big data consultants. With combined efforts, it is possible to figure out a strategy, and based on the same, you need to adopt the technology stack.
Dangerous security holes
Big data involves a wide array of security challenges. Though there has been a significant evolvement in big data technologies, the security features are neglected.
For preventing the security challenges of big data, you need to put the security at first. It is crucial at the design stage of the architecture of the solution. If you do not focus on data security from the beginning, it will cause major troubles at a later off stage.
Managing the quality of data
The data is collected from a wide array of sources in various formats. Hence, there are high chances that you may encounter data integration issues. For example, the eCommerce companies should be analyzing the data from different call centers, website logs, website scans of the competitors, and social media. The data formats are known to differ and matching them may prove to be an issue.
There is no guarantee that big data is completely accurate. However, it is still possible for you to control the reliability of the data. Unreliable data may consist of wrong and duplicate information. Furthermore, it may also consist of contradictions. Data of poor quality will fail to confer any useful insights, catering to the demands of your business.
A wide array of techniques is available to clean the data. However, it is a prerequisite that your big data should be having the right model. Once you gained success in creating the right model, you should be comparing the data to the point of truth. You should be matching the records after which you require merging them, in case they are related to the same entity.
However, you need to remember that big data is never completely correct. You need to know different aspects of it and deal with the same.
Involves a lot of money
Adopting big data projects involve a lot of expenses. In case you choose an on-premise solution, you need to bear the expenses of new hires, new hardware, electricity, to name a few. In addition to this, you need to make payment for the additional framework, for the setup, for the development, for the maintenance and configuration of the new software.
If you choose a cloud-based big data solutions, you require investing a lot of money for the cloud services and hiring the staff, big data development, maintenance and set up of the required frameworks. In both cases, you should allow future expansion to ensure that the growth of big data does not get out of your hand and you do not end up in spending too much money.
You can opt for hybrid solutions where parts of the big data are known to be processed and stored in parts-on and cloud premises. Restoration to algorithm optimizations and data lakes can help you in saving money.
According to studies, big data is playing a vital role in enhancing the bottom line of the business as it can deliver deep insights into the behavior of the customer by the integration and the analysis of the existing data. The biggest hurdle, encountered by the business firms while using big data is how to get the right value from the data.