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Unlocking the Potential of Machine Learning in a Data Lake
With data becoming the brain food to the intelligence of every organization, regardless of size or sector, it has become crucial to harness this data to achieve the best results, make the most informed decisions and improve productivity. However, with every action, reaction and interaction a fresh load of data is produced, resulting in an avalanche of information. It becomes key to store and manage all the data of interest – both unstructured and structured – in one central repository. This repository, more commonly referred to as a data lake, has become the principal data management architecture for data scientists. The benefits of a data lake are threefold: They make data discovery easier. They reduce the time spent by data scientists on selection and integration.They provide massive computing power, allowing data to be efficiently transformed and combined to meet needs of any process that requires it. A recent analyst report confirmed the success of the data lake discovering that those employing this architecture were outperforming their peers by 9% in organic revenue growth. Perhaps one of the main advantages of the data lake, especially for organizations interested in getting ahead of the competition, are the machine learning capabilities. By using machine learning to analyze the historical data stored, businesses can glean sufficient intelligence and insight to forecast likely outcomes and work out how to achieve the best results for employee productivity, processes and so on.
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