With AI and Machine Learning growing at a rapid pace, companies evolve their data infrastructure to benefit from the latest technological developments and stay ahead of the curve.
Shifting a company’s data infrastructure and operations to one that is “AI ready” entails several critical steps and considerations for data and analytics leaders looking to leverage Artificial Intelligence at scale, from ensuring that the required data processes to feed these technologies are in place, to securing the right set of skills for the job.
Therefore, companies usually begin their journey to “AI proficiency” by implementing technologies to streamline the operation (and orchestration) of data teams across their organisation and rethinking business strategy — what data do they actually need?  This is a natural first step for most organisations, given that Machine Learning and other AI initiatives rely heavily on the availability and quality of input data to produce meaningful and correct outputs. Guaranteeing that the pipelines producing these outputs operate under desirable performance and fault tolerance requirements becomes a necessary, but secondary step.
As a recent O’Reilly Media study showed, more than 60% of organizations plan to spend at least 5% of their IT budget over the next 12 months on Artificial Intelligence.

Considering that interest in AI continues to grow and companies plan to invest heavily in AI initiatives for the remainder of the year, we can expect a growing number of early-adopter organisations to spend more IT budgets on foundational data technologies for collecting, cleaning, transforming, storing and making data widely available in the organization. Such technologies may include platforms for data integration and ETL, data governance and metadata management, amongst others.
Still, the great majority of organisations that set out on this journey already employ teams of data scientists or likewise skilled employees, and leverage the flexibility of infrastructure in the cloud to

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