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.

Data democratization, the concept of empowering any employee to make data-driven decisions for their company regardless of skill set, was supposed to rival the Elysian Fields in its paradise-worthy promise of analytics. It's easy to see why this concept made such a big splash in the enterprise. Since the early 2000's, companies have been amassing raw data, which has morphed into the $203 billion big data analytics market. But this data always lacked transparency. Housed in messy data architectures that led to siloed information, companies struggled to gain a singular big picture into what their data was telling them. IT departments were left to sort through data warehouse integrations and complex extract, transform, load processes to try and create structure so any data analytics tool would single view into solving business problems.