I've noticed recently that some of the major brands are investing more in opportunities to harness the full power of Artificial Intelligence and Machine Learning technologies. If ever there was a time to examine and refine your current automated operations, that connect your data with A.I., it's now.
However, it's important to first note that in order for companies to maximise leverage, solid and structured automation processes must be put in place. Equally, before brands dive in to use one of the many much-hyped A.I. and ML tools available today, the importance of automation as a prerequisite must be understood.
It can be very tempting to jump straight in and use these latest new and exciting technologies. The ability to process and optimise online marketing activity using Machine Learning and A.I. is a dream for most marketers, and this presents an exciting future with boundless opportunities.
Yet without refining your automated operations, and ignoring the importance of data governance, the quality of learnings out of A.I. and ML will only be as good as the data you integrate it with.
iProspect has helped many brands move over from legacy data systems to a more compatible and robust clean data architecture. A global brand can find themselves generating vast amounts of data. Our experts cleanse and restructure this data so that it's ready to connect for machine learning.
With such a large volume of data, strict data governance processes are then put in place to ensure the quality of data is maintained, which will form the roots of the advanced discipline of A.I. and ML.
Our automated processes increase productivity of a complicated daily work list by allowing our integrated platforms to perform the repetitive tasks and collect data accurately. This then means that time is freed up to fine tune and use an A.I. system to make better sense of brand data.
Intelligent automation is not only smart for productivity; it also ensures that the data you connect to your A.I. and ML technology is correct and leverage is truly maximised.
