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Apr 2019

Using Data Virtualsation to Simplify Machine Learning

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Having all your data in one place doesn’t necessarily make finding things easy, in fact, most of the time it’s like finding a needle in a haystack.

People often call data on the oil of the technology age. It’s a very valuable commodity that drives organisations everywhere. The volume and variety of data that flows through organizations today are so vast that data lakes are now one of the principal data management architecture. According to Forbes “A data lake holds data in an unstructured way and there is no hierarchy or organization among the individual pieces of data. It holds data in its rawest form—it’s not processed or analyzed.” This, interestingly, is supposed to make data easier to find and reduce time spent by data scientists on selection and integration. An added benefit is that data lakes provide massive computing power, thus allowing data to be transformed to meet the needs of processes that require it.
A recent study proved that organisations that applied data lakes outperformed their peers by up to 8%. However, most businesses struggle when it comes to applying machine learning to these data lakes to gain insight from the data. In fact, the majority of data scientists spend 80% of their time on this task, it’s time for a change.

Despite what one would think, having your data all in one physical place does not make finding it easier. Storing data in its raw form requires it to be adapted for machine learning, and that burden falls on data scientists. The past few years have brought out tools that help these scientists with integration but there still remain tasks that require a more advanced skill set.
To address these issues, data virtualization is needed.

Primarily, data virtualization allows data scientists to access more data in the format that they prefer. It provides one single access point to any data, regardless of its location or format. This applies different logical views of the same physical data without the need for replication. In doing so, data virtualization offers fast and inexpensive ways of using the data to meet the needs of different users across an organization.

Data virtualization doesn’t require data to be replicated (with just data lakes in a business’s architecture, you do require data replication) so new data can be added more quickly. The best data virtualization tools will also allow a searchable catalog of all available data sets including extensive metadata.
By employing DV, IT data architects can create ‘reusable logical data sets’ that expose information in ways useful for different specific purposes. Data scientists can then adapt these reusable data sets to meet the individual needs of different Machine Learning processes and, by allowing them to take care of complex issues such as transformation and performance optimisation, data scientists can then perform any final, and more straightforward, customisations that might be required.