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Barriers in Adopting AI Revealed in Alation State of Data Culture Report

Data quality issues are a barrier to successful implementation of AI according to 87 percent of survey respondents.

Note: TDWI’s editors carefully choose vendor-issued press releases about new or upgraded products and services. We have edited and/or condensed this release to highlight key features but make no claims as to the accuracy of the vendor's statements.

Data quality issues are a barrier to successful implementation of AI in organizations according to 87 percent of respondents in the new quarterly Alation State of Data Culture Report. Almost half (46 percent) say they are very or extremely concerned about data quality. The survey found that just 8 percent of the data professionals say AI is being used across their organizations; 68 percent say AI is being used in some parts of the business.

Produced by Wakefield Research for Alation, Inc., a vendor of enterprise data intelligence solutions, the report provides a quarterly assessment of the progress enterprises have made in creating a data culture, the challenges they face in embracing data-driven decision-making, and the progress they have made in leveraging data to drive business value.

Among other key findings in the report:

  • Inherent bias creates risk. 87 percent say that inherent biases in data being used in AI produce discriminatory results, creating risk for organizations. Solutions to this risk include:
    • Better modeling skills among analysts (42 percent)
    • Better curation and governance (38 percent)
    • Better literacy and understanding of data (38 percent)
    • Collecting data from more and more varied sources (36 percent)
    • Cataloging data for visibility (35 percent)
    • Core diversity in employees (35 percent)
    • Ability to crowdsource information (35 percent)
    • Stricter scrutiny of outcomes (33 percent)
  • Innovation and efficiency are primary drivers. When it comes to deploying AI, improving and innovating products and services is the top driver (43 percent), followed by improving operational efficiency (33 percent), and improving the customer experience (24 percent).
  • Skills are not the issue -- executive buy-in, is. 55 percent say getting buy-in from executives who control funding for AI is a bigger obstacle to using AI effectively than employees without skills to create AI models (45 percent).
  • Data quality issues are paramount. The top data quality issue to solve is inconsistent standards across data collection (50 percent), followed by compliance/privacy issues (48 percent), and lack of democratization or access to data (44 percent).
  • Success factors are many. Of organizations that have deployed AI, respondents cited better modeling skills among analysts (44 percent), cataloging data for visibility and access to available data (38 percent), and ability to crowdsource info (38 percent), as ways to combat bias in AI. Almost one-third (31 percent) say that incomplete data is a top data issue that leads to AI failing.

Additional key findings:

  • Fewer than 20 percent of respondents report that their companies have fully enabled the three disciplines of data culture across all departments in the new study.
  • More than a third (37 percent) of top-tier data culture companies were more likely to exceed their revenue goals versus 28 percent overall, showcasing a link between data culture and revenue.
  • The vast majority (92 percent) of top-tier data culture companies are also more likely to have a corporate initiative to become more data driven, compared to 69 percent overall.
  • Breaking down silos to foster a data culture -- and in particular, increasing collaboration between the data and analytics team and business units -- was far more common at top-tier data culture companies (58 percent) than it was overall (46 percent).

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