A Bridge Over Troubled Data: Giving Enterprises Access to Advanced Machine Learning

They want more intelligent applications for significant use cases such as real-time fraud prediction, a better customer experience, or faster, more accurate analysis of medical images. The problem facing most organisations is they store data in different forms and locations, each of which may belong to a business unit or department. Making this data usable by advanced applications is demanding. Before the advent of the new paradigm – the smart data fabric – the approach would have been to create a data lake or warehouse, using the relatively low cost of storage and compute. The organisation also likely then using time-consuming ETL processes to normalise the data. This approach, which is still in widespread use, has had its victories but creates a centralised repository that leaves data difficult to analyse and often fails to provide consistent or fast answers to business questions.