Artificial or not, intelligence requires cleaned and mastered data

Machine learning, for instance, is used in facial recognition, speech recognition, object recognition, and translation projects, with ramifications across many global industries, and deep learning can approach the level of neural networks to advance those abilities. With object recognition software, for instance, the ultimate question to ask about its efficiency will be: does it have enough data sets to distinguish among types? In this context, the well-worn field of master data management (MDM) assumes a crucial role in the age of AI. In this framing, markets for second- and third-party data will thrive, and the beneficiaries will be the businesses that have intelligent MDM platforms to accumulate and store the data, allowing them to create a data field from which AI can silo data, rather AI having to break down siloed data.