Data virtuality is becoming the new normal, especially with more vendors offering cloud-based software as a service. That being said, just because you’ve started to dabble in data virtualization as a way to get your database more easily accessible online doesn’t mean that you’re fully leveraging what this approach to business intelligence can really offer you. In fact, there are many layers to data virtuality beyond the virtual layer that you work with within your existing data architecture. As such, it behooves you to learn more about how to truly use data virtuality to its fullest, whether you plan on using your new cloud-based data source for analytics or other kinds of queries. Here are five major features and ways to improve your data virtualization in the coming months.
1. Leverage orchestrated services for your data.
One feature you should definitely be utilizing with your source system comes down to orchestrated services for your data. In a nutshell, this means that you’re doing the work to ensure that in using a data virtualization tool for data management you’re not creating duplicate datasets and aren’t creating additional data lakes or siloes in the process. Since these sorts of disparate data sources can ultimately limit the effectiveness of your data virtualization software with its business users, it’s in your best interest to use orchestrated data services for this kind of management.
2. Make sure your platform offers visuals.
Data can be complex, which is why visuals can make it easier to operate data virtualization software. If you have business users who don’t know about SQL or other coding languages, having a graphical user interface that offers a powerful way to build queries without knowing coding languages can be a major boon.
3. Consider governance.
Security protocols are becoming more and more important with big data. Hackers are continually causing major security breaches, which means it’s more important than ever to protect your business data as well as your customer data. Especially when it comes to data virtualization, which pools your data at a single source, it’s vital to consider both security and governance if you want your business to avoid negative press due to sloppy data management in the virtualization process. Many data virtualization tools even allow you to access this information in real-time.
4. Use metadata to your advantage.
Being able to search up and find data with less time has a variety of use cases and advantages for businesses of all sizes. As such, the more you can use metadata to drive more elaborate and complex comparisons and discover new connections. In order to find these sorts of insights, it’s best to have tools that allow for advanced metadata use so that you can build and display models of different data relationships in a way that unstructured data could never offer.
5. Ensure your queries are built on solid ground.
The engine your data integration solution uses for virtualization has a lot of ramifications when it comes to various use cases. For example, reliability and workload both have a lot to do with the engine powering your big data queries. Especially if the amount of available data you have is quite large, you need to make sure that the engine powering your semantic layer as well as your overall data storage are enough to keep your data access trucking day and night.
As you can see, there are several different ways that you can use your source system to improve the way your company does business and utilizes data. Data virtualization is an important step in future-proofing your company and eliminating data lakes; however, it’s not the only thing to focus on if you truly want to succeed. Tapping each and every aspect of your company’s existing data processes in order to ensure that you’re in good shape is crucial if you want to improve your data virtualization approach. With the tips above, you’ll be well on your way to boosting performance in virtualization.
