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.