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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. 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 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 virtualisation 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 catalogue 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.

Is Data Virtualisation the key to Machine Learning?

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It used to be said that technology was the lifeblood of the organization, which evolved to connectivity and now it is data – regardless of industry or size. Thanks to the evolution of technology, everything is captured, and we now have a multitude of data. If this data is used correctly, it has the possibility of improving any business greatly.

This all makes data increasingly valuable and as a result, data lakes (repositories that allow organisations to store all their structured and unstructured data) have become popular when working on a businesses data management architecture. By storing all data in data lakes, organisations can easily access their data and save business time and money. These lakes also allow businesses to have access to a range of business insights that allow them to make well-informed business decisions. Using machine learning on this data allows a business to forecast outcomes and achieve the best results.

Despite all these benefits, businesses are still struggling when it comes to integration as well as data discovery. Storing data in its original format does not take away the need to adapt it for machine learning. Having all your data in one physical place is like trying to find a needle in a haystack. On top this, many organisations use various data storage solutions such as on-premise servers, the cloud and data centres so it is more like trying to find a needle in several haystacks.

Fortunately, some tools have come into the market to assist in integrating all this data, however, more complex tasks need a complex skillset and that’s where data virtualisation comes into play. Data virtualisation provides one access point to access any data – regardless of format. If implemented correctly it can stitch together various bits of data from multiple sources in real-time. It removes the need for data to be replicated into one location for a business to read and gather insight.

As machine learning and big data continue to grow and support modern business decisions, data virtualization is enabling businesses to seamlessly represent their data.