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Data Challenges Are Halting AI Projects, IBM Executive Says
May 28, 2019 | WSJ | By Jared Council.

About 80% of the work with an AI project is collecting and preparing data. Some companies aren’t prepared for the cost and work associated with that going in,......“And so you run out of patience along the way, because you spend your first year just collecting and cleansing the data,”.....“And you say: ‘Hey, wait a moment, where’s the AI? I’m not getting the benefit.’ And you kind of bail on it.”....A report this month by Forrester Research Inc. found that data quality is among the biggest AI project challenges. Forrester analyst Michele Goetz said companies pursuing such projects generally lack an expert understanding of what data is needed for machine-learning models and struggle with preparing data in a way that’s beneficial to those systems.

She said producing high-quality data involves more than just reformatting or correcting errors: Data needs to be labeled to be able to provide an explanation when questions are raised about the decisions machines make.

While AI failures aren’t much talked about, Ms. Goetz said companies should be prepared for them and use them as teachable moments. “Rather than looking at it as a failure, be mindful about, ‘What did you learn from this?’”
artificial_intelligence  data_collection  data_quality  data_wrangling  IBM  IBM_Watson  teachable_moments 
may 2019 by jerryking
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