AI and BI Projects Get Bogged Down With Data Preparation Tasks

AI and BI Projects Get Bogged Down With Data Preparation Tasks

Data quality challenges are often why organizations are reassessing AI and BI projects. Here are four best practices to help you do data prep efficiently.

IBM is reporting that data quality challenges are a top reason why organizations are reassessing (or ending) artificial-intelligence (AI) and business intelligence (BI) projects.

Arvind Krishna, IBM’s senior vice president of cloud and cognitive software, stated in a recent interview with the Wall Street Journal, “about 80% of the work with an AI project is collecting and preparing data. Some companies are not prepared for the cost and work associated with that going in. And you say: ‘Hey, wait a moment, where’s the AI? I’m not getting the benefit.’ And you kind of bail on it.” [1]

Many businesses are not prepared for the cost and effort of data preparation (DP) when starting AI and BI projects. To compound matters, hundreds of data and record types and billions of records are often involved in a project’s DP effort.

However, data analytics projects are increasingly imperative to organizational success in the digital economy, hence the need for DP solutions.

big data data integration data governance data validation data warehouse testing data integrations

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