Big data and analytics are the two terms running in the heads of the board of directors of many corporations. So, their target is to get into this era of Big Data and Analytics as soon as possible. Since Big Data is grabbing the big eyes of the industry, it unburied the extensive benefits for them. There are a few companies that need to have in-depth functional information from their Big Data algorithms, but they are still fighting to reckon it. What most companies make mistakes are, they skip out to perform the big data testing rigorously.
Here is a list of a few reasons why an enterprise needs to perform Big Data & Analytics Testing:
1. Increased Down-time:
Most of the organizations face a lot of issues while working on predictive analytics and one of the issues is deploying the Big Data applications. And, in this kind of cases, it is so obvious that those issues have remained unnoticed while collecting the data. If the testing is performed while collecting and deploying the data, then such cases can be resolved faster. Also, this will reduce the overall downtime.
2. Reliability & Efficiency at Risk:
While performing data analysis, Big Data applications fetch the data in real-time from data sets of various channels. So, doing this frequently, there are huge chances of getting complex, yet inaccurate data resulting in creating a risk in the applications. This will highly affect the reliability of the data obtained. Here, it is highly recommended to test the data to check on its quality from the root to its end. This will not only ensure the reliability of the data but will also improve its efficiency.
3. Threat to Quality of Data:
The size of the enterprise does not matter while working on Big Data. They need to ensure the data to be valid, consistent, precise and unique. If the data lack any of these, then there are high chances of having a threat to the quality of the data. The enterprise has to go in-depth while testing the data to make sure the functionality is working properly.
4. Scalability with Data Sets:
Generally, in any of the application development, we consider small data sets to begin with and slowly and gradually we shift to the larger ones. We start performing initial tests of the applications as per its design and it works great sometimes. But, we need to check for the results from the various data sets. Here, the problems with scalability of the data set arises. We all can avoid this kind of situations by preparing a smart sample of data sets that can be used to test the application at various intervals.
5. Lack of Data Security Maintenance:
For any enterprise, it is highly important to secure the confidential data of all the clients to maintain the level of their trust. If the security of these confidential data is not maintained, the enterprises can face a big forfeiture. So, it becomes mandatory to perform different types of testing at multiple intervals to secure the data from hacking.
6. Inappropriate Process Optimization:
Big Data and predictive analytics are of great help to most of the manufacturers because it helps them to plan their business processes. In future, if they are unable to maintain the data properly, there are chances that the manufacturers have loopholes in their business processes that will affect to deliver the desired output to the clients.
7. Performance Testing on Live Data:
Now, all the Big Data applications are required to gather live data for the analysis. Filtration needs to be done to ensure the data captured in real-time is valid and productive. To test the performance of the live data coming through various channels become extremely difficult. So, this testing needs to be performed along with the integration testing to kick the competition out.
8. Inconsistent Results:
Why is Big Data important? The reason is that the Big Data can access the data from various data sets. So, if the data is accurate and valid, then the enterprise can gain much benefit from them. But, say, for example, the results obtained from Big Data & Predictive analytics are not consistent. Then the enterprise will face a big fat disgrace. So, it is a must for an organization to choose the right kind of testing at the right time to ensure the accuracy and certainty.
9. Data Digitization:
Though we are in a period of digitization, each and every organization will have some data or documents that are in the paper format. And, if the organization plans to convert them into the digital format, they need to ensure that the confidentiality remains the same and the data does not get corrupted or hacked. This can be only done through testing the data at regular intervals.
10. Allows to Compete with Competitors:
Today, in the competitive market, all the organizations need to compete with their competitors. And this can only be achieved by enforcing the testing team to implement the right kind of testing tools. This can not only get the best outputs but can also improve the ROI of the organizations.
These points show that the Big Data testing is equally important for an organization just as the other types of testing. An organization needs to perform the right kind of testing strategies and they will get the best results to meet the client’s requirements.