We have a certain level of expectation when it comes to driving; we expect the ride to be smooth and comfortable, with the car gliding over road surface irregularities with ease. This is owing to the active suspensions, which controls the vertical movement of the wheels relative to the base of the car, as opposed to passive suspension where the movement is determined entirely by the road surface.
Data virtualization as active suspension for your data
Now imagine the road you travel is made up of data, as different from each other as different surfaces a car can find on its journey, and where instability is represented here by their heterogeneity, format and meaning, with the volume replacing the concept of length, with straight lines and sudden curves that translate into planned activities and extemporaneous needs, to which they must react and respond very quickly.
If the road is the data, then the driver of the car is anyone who needs it, for example a data scientist, who needs to be able to reach their destination and concentrate on the road ahead without losing sight of what is in front of the vehicle that could compromise the vehicle.
If the road is the data and the driver is the data scientist, then a good data virtualization system can only be the active suspension, capable of adapting to varying surfaces so that anyone can enjoy it without jolts and without skidding, being able to concentrate on where the data leads, rather than on how it is made.
Just as active suspension acts as a leveling layer, a sort of resin that fills the holes, making the road smooth, data virtualization creates an abstraction layer over the data, on which anyone can drive smoothly regardless of the surface of the road or analyze the data from the abstraction layer regardless of the origin of the data sources. The road, or data, in this analogy will present itself in a homogeneous way, thanks to the insulating layer between it and the one on which the car will slide, comfortably, without jolts and in complete safety.
This is what makes a good data virtualization system, isolating from the harshness of a terrain made up of heterogeneous data, in terms of format, volume, quality and meaning, making possible for those who use them to do so by focusing on what the data represents and not about how they are made.
Data virtualization promises a smooth ride
The more efficient and effective a data virtualization system is, the smoother the surface on which the data analyst works will appear, which will guarantee the best possible experience, allowing him/her to reach the destination quickly, and often ahead of the competition.
Data virtualization, just as active suspensions, shouldn’t be an optional accessory included in the basic equipment of your car, but an essential to ensure a smooth ride. After all, the end goal is to become a data-driven company, right?