Data Virtualization 101
What is Data Virtualization?
Data virtualization is an approach that you can take to integrate data from multiple sources or systems. It creates a virtual, logical data layer to integrate and transform an organisations data and makes it available to end users or applications.
Maybe you’re trying to combine your sales data with your customer information, both in different places; and don’t forget your manually updated data file – data virtualisation would enable you to combine these, as though you had a fully integrated single system.
The data isn’t moved and stored in a separate location. Instead, it’s all held in a virtual layer so you can pull data from multiple sources instead of replicating it. It doesn’t reside anywhere, but you can use this layer to create different views, different data sets for your end users to then be able to use for whatever business purpose they need.
You can have a unified, organised, and comprehensive version of data from unstructured and structured sources while keeping your data in original source systems. This solves the data movement challenge and ensures end users have seamless access to real-time data.
Setting the record straight
It’s not uncommon to confuse data virtualization to some other, similar process or name in the data world, so, what ISN’T data virtualization?:
Data Virtualization is not Data Visualisation
Even though they sound a bit alike, data virtualization and data visualisation are different entities. Data visualisation is all about turning data into easy-to-understand charts, infographics or even animations and graphs. Data virtualization, on the other hand, acts like a go-between, linking data sources with the apps that want to visualise them.
Data Virtualization isn’t just a copy of your data
It’s important to know that a data virtualization platform doesn’t create copies of your data. Instead, it uses metadata, and some smart logic to show the data in a unified way.
Another one to add to the list is mixing up data virtualization with things like virtual database software or storage solutions. Virtual storage solutions don’t have the ability to merge data from different sources in real-time, which is a key feature of data virtualization. Instead, virtual data storage is the pooling of physical storage from multiple storage devices into what appears to be a single storage device – or pool of available storage capacity.
What does a successful data virtualization include?
To be successful, you need to understand what your end users are trying to achieve.
Without that understanding, you won’t know what the virtualized layer needs to look like or what data it needs to include. So, one of the key things is understanding what the business challenge is or understanding what your end users intend to use it for. However, there are other crucial elements that lead to successful data virtualization.
Successful Data Virtualization Must Be Accessible
- Unified Interface: It should provide a unified, single point of access for different types of data sources like databases, flat files, web services, and more.
- Ease of Use: The system should be user-friendly so that even non-technical users can query data and build reports easily.
- Metadata Management: A good virtualization platform maintains a comprehensive catalogue of metadata that users can refer to while working with data.
It Must Perform Well For The End User
- Low Latency: Data should be retrievable in real-time or near-real-time.
- Caching: Intelligent caching mechanisms can store frequently accessed data temporarily to improve performance.
- Query Optimisation: The system should be able to optimise queries so that the minimum amount of data is transferred over the network.
The Data Virtualization Must Be Secure
- Data Masking: The ability to hide certain data within a field (such as masking the first 12 digits of a credit card number).
- Role-Based Access: Different users and systems have access to only the data that they are authorised to see.
- Encryption: Data should be encrypted both at rest and during transmission.
How to know if you need data virtualization
Scattered Data Landscape
If your organisation has a complex ecosystem of data sources – spanning multiple databases, cloud storage solutions, legacy data – it may benefit from the unified access layer that data virtualization provides.
Data Integration Challenges
If you think about integrating data and cry in despair, you may need data virtualization. When integrating data proves cumbersome and resource-intensive, especially in handling disparate data formats or schemas, data virtualization can offer seamless data integration without the need for physical data movement.
Need for Real-Time Insights
If your organisation’s business operations demand real-time or near-real-time analytics, and the existing architecture struggles to deliver timely insights, then data virtualization can be a viable solution, as it enables quick access to data.
Security and Governance
A requirement for robust security and governance measures across diverse data sources can be efficiently managed through a data virtualization layer. This centralises access control, encryption, and audit capabilities.
Key Considerations to Successfully Implement Data Virtualization
Neglecting the reason for data virtualization
It’s really important to first get a handle on what your business actually wants to achieve and what the end users need. If you skip this step, you’re risking time and resources on a system that might not be usable by the people it was designed for. Don’t forget, this isn’t a one-person show. You’ll want your IT department, your data governance team, and your business units all pulling in the same direction.
Poor communication = useless data virtualization
Communication is key here. A lack of chat between the people setting up the system and the ones using it can lead to some head-scratching moments when it doesn’t do what it’s supposed to. And speaking of expectations, data virtualization is cool, but it’s not a magic wand. It can’t fix every data issue you’ve got, so be realistic about what it can and can’t do.
Increased network traffic
On the techy side, keep an eye on network traffic. Pulling data in real-time from all over the place can clog up your network, so be mindful of that. And make sure someone’s actually in charge of keeping the system up and running. Nothing brings the house down faster than a system crash with no one to fix it. Being aware of these pitfalls can help you navigate the tricky waters of data virtualization a bit more smoothly.
So where do Amplifi come in?
Amplifi data expertise is not just limited to data integration or virtualization.
We have the capability to support you from start to finish all along your journey to a modern data ecosystem. Ultimately there’s a lot more that comes along with that, things like your governance, your data quality and your data strategy.
We’re data people who think in a way that you need us to. We want to make sure your data journey is a seamless one and continues to be supported post implementation.