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Report: Data access barriers affect AI adoption for 71% of businesses


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Even as decision makers and CXOs remain optimistic about the potential of AI, businesses are struggling to make the most of it at a fundamental level. Typical: a new report from data integration giant Fivetran that said 71% of companies have difficulty accessing all the data needed to run AI programs, workloads and models.

Working with Vanson Bourne, the company surveyed 550 IT and data science professionals in multiple countries and found gaps in data movement and access within their organizations. This finding has important implications because the data is important for model training and execution. One cannot run a successful AI program without laying a solid foundation for data storage and movement, starting with a data warehouse or lake for automatic data entry and preprocessing.

George Fraser, CEO of Fivetran, said: “Analytic teams using a modern data stack can more easily scale the value of their data and maximize their investments in AI and data science,” said George Fraser, CEO of Fivetran, in the study.

Data access obstacles

inside survey, almost all respondents confirmed that they collect and use data from operational systems to some extent. However, 69% said they had difficulty accessing the right information at the right time, while at least 73% said they had difficulty extracting, loading and transforming data and turning it into words. Practical advice and insights for decision makers.

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As a result, even though a large number of organizations (87%) consider AI vital to business survival, they are not taking full advantage of it. Their manual, corrupted data processes lead to inaccurate models, which ultimately lead to a lack of trust and a fall back to humans. Survey respondents say that inefficient data processes force them to rely on human-led decision-making 71% of the time. In fact, only 14% of them claim to have achieved advanced AI maturity – using general-purpose AI to automatically make business predictions and decisions.

On top of that, there is a significant financial impact, with respondents estimating they are losing an average of 5% of global annual revenue as a result of models built using inaccurate or quality data. short.

Talent is wasted

The challenges associated with data migration, processing, and availability also mean that the talent hired to build AI models waste time on tasks outside of their primary jobs. . In the Fivetran survey, respondents asserted that their data scientists on average spend 70% of their time just preparing data. Up to 87% of respondents agree that the data science talent in their organization is not being used to its full potential.

Based on Fortune Business Insightsthe global AI market is expected to grow from $387.45 billion in 2022 to $1,394.30 billion in 2029, at a CAGR of 20.1%

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