{"id":12174,"date":"2023-05-11T07:00:00","date_gmt":"2023-05-11T15:00:00","guid":{"rendered":"https:\/\/formtek.com\/blog\/?p=12174"},"modified":"2022-08-06T12:14:43","modified_gmt":"2022-08-06T20:14:43","slug":"artificial-intelligence-data-quality-can-make-or-break-an-ai-project","status":"publish","type":"post","link":"https:\/\/formtek.com\/blog\/artificial-intelligence-data-quality-can-make-or-break-an-ai-project\/","title":{"rendered":"Artificial Intelligence: Data Quality Can Make or Break an AI Project"},"content":{"rendered":"\n

Getting the data right for AI and data analysis projects may be the biggest factor in making or breaking the project. Without accurate, current, and diverse data sets, it isn’t possible to build models that make reliable predictions.<\/p>\n\n\n\n

Data prep is tedious and one of the most overlooked steps of an AI project. Even frequently used publicly available training sets have been found to have a significant number of problems with data quality, and if commonly used can’t get it right, you have to be suspicious about projects that don’t have as high of a profile. <\/p>\n\n\n\n

A study in 2021 by MIT found that the public ImageNet database<\/a> has “systemic annotation issues” with as many as 20 percent of the collection containing duplicates. Another investigation into a dataset created by Google<\/a> found as many as 30 percent of the entries as mislabeled. A project between IBM and MD Anderson<\/a> was canceled because poor results were obtained due to using outdated data.<\/p>\n\n\n\n

Andrew Ng<\/a>, a professor at Stanford University, commented that<\/a> “AI has a proof-of-concept-to-production gap. The full cycle of a machine learning project is not just modeling. It is finding the right data, deploying it, monitoring it, feeding data back [into the model], showing safety\u2014doing all the things that need to be done [for a model] to be deployed. [That goes] beyond doing well on the test set, which fortunately or unfortunately is what we in machine learning are great at.”<\/p>\n\n\n\n

A Forbes article<\/a> by Kathleen Walch<\/a> suggests that analytics and AI practitioners need to get back to basics and pay more attention to data prep. In the mid-1990’s a set of best practices for data mining projects was developed called the CRoss Industry Standard Process for Data Mining (CRISP-DM)<\/a>. Steps two and three of this process are “Data Understanding” and “Data Preparation” and both of these are crucial in building a project that has high data quality.<\/p>\n\n\n

\n
\"\"<\/a>
Wikipedia: The six steps of CRISP-DM<\/a><\/figcaption><\/figure>\n<\/div>\n\n\n

Arvind Krishna<\/a>, the CEO of IBM, noted that data prep is hard<\/a> and the main reason why AI projects are canceled is the difficulty in preparing quality data. Krishna said that many companies “run out of patience along the way, because they spend their first year just collecting and cleansing the data. And they say: \u2018Hey, wait a moment, where\u2019s the AI? I\u2019m not getting the benefit.\u2019 And they kind of bail on it.”<\/p>\n

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Getting the data right for AI and data analysis projects may be the biggest factor in making or breaking the project. Without accurate, current, and diverse data sets, it isn’t possible to build models that make reliable predictions. Data prep…<\/span><\/p>\n

Read more ›<\/a><\/div>\n

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