The cloud is often advertised as the solution to all your problems, but like any other technology, it has its advantages and disadvantages. These must be weighed carefully before adoption.
Data quality is not the first thing you think about when looking at cloud solutions, but how a cloud solution you choose affects data is one of the most important factors to consider.
Here are some common issues to be aware of:
When you store data in remote data stores, it involves copying data from local data sources to cloud servers.
Unless you have strong data sourcing controls in place, the duplication of files can cause issues if it’s not clear which copy is the latest or best version.
Other issues that can crop up from the duplication of local data to remote cloud servers include file format changes because of differences between local and remote operating systems.
Timestamps will also change when copying files, and it can become confusing when the time zones are significantly different.
Proliferation of Data Volume
The cloud makes it cheap to store enormous amounts of data without the need to observe storage limits. Local data stores need to be physically upgraded to grow larger, but the cloud virtualizes physical resources.
You can rent more storage space automatically as it’s needed with many cloud storage providers.
The problem of data proliferation can impact data quality over time. Multiple copies of the same data or variations in data formatting can degrade the quality of your data. You’ll likely find yourself archiving data that’s no longer useful because you can.
Companies must also consider the bandwidth that will be needed to move data between on premise applications and cloud based data quality options, particularly in remote locations such as South Africa, where international bandwidth remains relatively expensive.
Eventually, it can become difficult to sort out the useful and obsolete data, and the expense of storing and moving useless data will grow over time.
Cloud Software Updates Can Break Data Formats
Another issue that can happen is that the tools that you depend on to format or process your data change on the cloud.
Software as a Service tools are updated continuously, increasing the chances of updates breaking your data format or an integration with your own in-house systems.
Unlike end-to-end in-house solutions, you don’t have complete control over your software dependencies when you move your data to the cloud.
Even changes to the host server’s operating system can cause problems in rare situations.
How to Mitigate These Problems
Most of the issues summarized above can be mitigated with some planning.
The cloud gives your data scientists and developers opportunities to leverage its efficiency and hardware independence.
It may take more discipline to resist allowing data stores to proliferate. Periodic reviews and inventories of your data in storage can keep this problem from impacting your operations.
In the same way, you can plan for dependency changes and find ways to make your data independent of software tools and operating system details you don’t have complete control over.
Careful data structure and formatting design are even more important in the cloud than it is with local solutions.
Finally, duplication of data is largely a process problem.
If you make your data handling process efficient, you can ensure that data reaches its designated cloud data store as soon as it’s collected. There’s no reason for large amounts of data to sit at-rest on local hardware once you’ve migrated to a cloud solution.
The Ways the Cloud Can Improve Data Quality
Data quality can be improved by using the right cloud solution, making it easier to access, read and use data stored in the cloud.
The Cloud Helps You Centralize Data
If you collect data from multiple sources or geographic locations, the cloud can solve your aggregation problems. Every data source with an internet connection can send data collected to a single storage point at a remote data center.
The storage requirements are less of a limitation becausecloud providers can quickly scale your storage capacity up or down dynamically.
The result is a more efficient process once you have your own data collection systems working in sync with the cloud solution.
A good example of this is inventory data collected by a large logistics company at numerous distribution centers across a country or region of the world.
The cloud has become an important part of a modern inventory management strategy.
The lifecycle of inventory items across the entire organization becomes easier to analyze and act upon when using cloud solutions, as the company gets real time insights into inventory levels.
The ability to manage that data in a single cloud application can introduce new efficiencies in inventory management that weren’t possible before.
Better Data Processing Power
Another way the cloud can improve data quality for enterprises with large and diverse data stores is the ability to leverage the processing power of cloud computing.
It’s possible to create data processing systems on the cloud that quickly spin up dozens of servers that process large data repositories and then dispose of the server instances once the processing is complete.
Not only does this make it possible to process data stores much larger than in the past, but it minimizes the cost of the processing hardware.
To continue with our inventory example, companies can now detect patterns they might have previously missed, such as surges or slowdowns in sales of specific items depending on the time of year.
Reporting and forecasting can be processed faster than before, and data filters can be put into place to ensure quality is maintained.
Cloud computing offers new opportunities and challenges to enterprises grappling with the explosion of data sources, growing data storage needs, and the problem of ensuring data quality.
With proper planning, the most common difficulties that the cloud introduces can be mitigated.
The advantages that the cloud brings to the issue of data quality go beyond cost-cutting. The centralization of data collected from multiple locations makes it possible to manage large organizations efficiently.
The processing power that cloud computing brings to the table is also a way that data quality and processing costs are improved.
Joe Peters is a Baltimore-based freelance writer and an ultimate techie. When he is not working his magic as a marketing consultant, this incurable tech junkie devours the news on the latest gadgets and binge-watches his favorite TV shows. Follow him on @bmorepeters