With the advancement of technology, cloud computing has been able to make a mark in numerous companies worldwide. It has got an array of benefits but it also has its downsides.
There are specific challenges for data quality that arise when data, as well as the data applications, move around in the cloud and in between cloud and on-premise.
At first, let us take a look at the benefits of cloud-based data management.
We understand that this article is about how the cloud complicates data but that does not mean that the cloud computing system comes without benefits.
The most obvious benefit of the cloud computing system is scalability which is the capacity to increase or decrease the amount of infrastructure for the processing and hosting of data. With the cloud computing service, you can easily meet the customer requirements because of its scalability.
The benefits also include includes easy deploy Data Analytics tools, acts as a cost-cutter, keeps your data secure, provides customer support, enhances mobility and Insight, and a lot more. It also helps with increased collaboration quality control as well as disaster recovery.
When you are moving specific data within the cloud or even between the cloud and infrastructure, you definitely have a chance of formatting problems or data loss. Not only that you can also have an accurate timestamp and other trivial issues that can actually hamper your data quality.
For example, never move data from the virtual server into a cloud-based file storage service, then some of the data might get formatted and can even be damaged while the transfer is taking place over the network.
Cloud data can become magnanimous in a fast-paced manner. Since it is so scalable, it is easy to store enormous amounts of information in the cloud. And as we all know excessive quantity can actually cause hindrance to the quality, therefore, it will be harder for you to maintain the quality of your Cloud Computing service.
Cloud services are an ever-changing process, and they are forever getting updated. But the fact of the concern is that unlike the software the cloud-based tools might not notify you whenever they are getting modified. This unnoticed changes to your tools can cause issues in data quality.
Let us suppose that your cloud-based tool has been modified and your other tools are not. So whenever it will structure data in the new format, the other tools will not be configured to handle it.
If you want to maximize the data quality in the cloud then the answer is definitely not avoiding the cloud computing service, because that would mean that you are denying the latest advancements of science.
A forward-thinking data management team would ensure that while enjoying the cloud services, one should also put data quality measures and incorporate that into place.
The most obvious and basic way of quality management document control is to ensure that automated data quality checks are being run on every part of your data, indifferent of whether it is being placed on the cloud or not.
Timely data quality checks can make sure that you can find the problem areas where the data has been subjected to damage and rectify it in time.
Also, steps must be taken in order to minimize data migration between different networks and services. If data mobility can be controlled between the cloud and on-premise platforms, then there will be lesser issues with data quality. Make sure to maintain a policy of archiving or deleting the data when you no longer have the need for it, nearby avoiding the data sets to grow unhindered and magnanimous.
Also, remember that you do not need to use every one of your cloud vendor’s data management and analytic tools. Just because they are given there, does not mean that you have to use them essentially. It is always a better option to take advantage of cloud computing in order to manage your data while performing other tasks. For example, if you are setting up your own Hadoop environment, then instead of using the Hadoop-as-a- service, make sure to use a distribution of your own choice in the Cloud.
It might sound like the very first method but in reality, it works well. If the detail issues can be rectified right at the source then the hosting will occur in case of the automatically cleansed data. It is advisable that you set up your source system like your website, run a quality check test, and then normalize the issues with your data.
It might sound similar to the former method but it is different. If you can rectify the source system only, then it will definitely cleanse the data before entering the database.
Before analyzing the customer data, it is put through a phrase of extract, transform and load process. If you have the ability to fix data in this stage, you can definitely solve numerous data quality issues.
It is comparatively a difficult method of fixing data quality problems but it is the most powerful as well. The most important issue with many of the customer databases is that they have numerous records for the same customer and there is no way of seeing whether these pieces of information are relevant or not. By giving solid entity resolution, the data can be identified and used for more targeted and efficient marketing.
It is absolutely possible that you can enjoy the benefits of the cloud computing service without compromising on data quality. If you are able to abide by the aforementioned steps in the correct manner then there will be no stopping you!
This is a guest post by Danish Wadhwa for zipBoard Blog.
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