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Report: Data Access Barriers Impact 71% of Businesses Adopting AI

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While policymakers and CXOs remain bullish on the potential of AI, businesses are still struggling to make the most of it. Case in point: A new report from data integration giant Fivetran says 71% of companies are finding it difficult to access all the data they need to run AI programs, workloads and models.

Job The company, in partnership with Vanson Bourne, surveyed 550 IT and data science professionals in multiple countries on data movement and access between their organizations There is a gap. This finding is significant because data is critical for model training and implementation. Running a successful AI program, automating data ingestion and preprocessing, starting from a data warehouse or data lake, is not possible without a solid foundation for data storage and movement.

“Businesses leveraging a modern data stack can more easily extend the value of their data and maximize their exposure to artificial intelligence and Investing in Data Science.

Data Access Barriers

In the survey, almost all respondents confirmed that they collect and use data from operating systems to some extent. However, 69% say they have difficulty accessing the right information at the right time, while at least 73% report having difficulty extracting, loading and transforming data and turning it into practical advice and insights for decision makers.


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So even though a large number of organizations (87%) believe AI is critical to business survival, they are failing to take full advantage of it. Their broken manual data processes lead to inaccurate models and ultimately Leads to a lack of trust and a return to humans. Respondents claim that inefficient data processes force them to rely on human-led decision-making 71% of the time. In fact, only 14% of businesses claim to have reached advanced AI maturity— —Using general artificial intelligence to automate forecasting and business decisions.

Beyond this, there are significant financial implications, respondents estimated, due to inaccurate or low use of Models built with quality data, they lost an average of 5% of annual global revenue.

Talent is wasted

Challenges related to data movement, processing power and availability also mean the talent hired to build AI models You end up wasting time on tasks outside of your primary job. Respondents to the Fivetran survey claimed that their data scientists spend an average of 70% of their time preparing data. As many as 87% of respondents believe that within their organization of data science talent are not reaching their full potential.

According to Fortune Business Insights, the global artificial intelligence market is expected to grow from USD 387.45 billion in 2022 to USD 1,394.3 billion in 2029 at a CAGR of 20.1%

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