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IS IT BAD DATA OR BAD DESIGN CAUSING AI PROJECTS TO FALL SHORT OF EXPECTATIONS ?
Derek Cowan , Director of System Engineering APAC at Cohesity , discusses how the right data can have a transformational impact for Artificial Intelligence and Machine Learning projects .
The growing adoption of Artificial Intelligence ( AI ) and Machine Learning ( ML ) technologies are becoming key drivers for organizations looking to become leaders in their fields and earn the label of being ‘ best in class ’. However , sadly , too many AI and ML projects fail to reach their full potential .
There can be numerous or varying reasons for this , including poor goal setting , budgetary constraints , and function creep during the planning , proof of concept and realization phases .
Bad data is also a key reason for project failure . It comes down to the oft-repeated truism , ‘ Garbage In , Garbage Out ’ which is as relevant today as it has ever been .
Two of the four key roles and responsibilities for AI projects outlined by analyst firm , Gartner , relate to data . Alongside AI Architect and ML Engineer , the Gartner lists Data Scientist ( responsible for identifying use cases , determining which data sets and algorithms are required and building AI models ), and Data
Only 32 % of data available to enterprises is put to work , while the remaining 68 % goes unleveraged .
Engineer ( responsible for making the appropriate data available , with a focus on data integration , modelling , optimization , quality and self-service ).
Organizations ignore these data-related roles and the wider importance of data at their peril and risk falling on the wrong side of the 50 % of IT leaders Gartner has identified that will struggle to move their AI projects beyond proof of concept before the end of 2023 .
Work backwards from your intended outcome
No AI or ML project has a chance of being successful if there is not an accompanying data strategy . A key aspect of getting this in place is to work backwards
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