Technology Report: Data access hurdles impact AI adoption for 71%...

Report: Data access hurdles impact AI adoption for 71% of enterprises


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Even as decision-makers and CXOs remain optimistic about the potential of AI, enterprises are struggling to make the most of it at ground level. Example: A new report from data integration giant fivetran that says 71% of companies find it difficult to access 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 discovered gaps in data traffic and access within their organizations. The finding is important because data is vital for the training and implementation of models. You can’t run a successful AI program without building a solid foundation for data storage and movement, starting with a data warehouse or more to automate data ingestion and preprocessing.

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

Obstacles to data access

In the questionnaire, almost all respondents confirmed that they collect and use data from operational systems at some level. But 69% said they struggled to access the right information at the right time, while at least 73% said they had trouble extracting, loading and transforming the data and translating it into practical advice and insights for decision-makers.


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As a result, while a large number of organizations (87%) view AI as essential to the survival of businesses, they are not getting the most out of it. Their broken, manual data processes lead to inaccurate models, ultimately resulting in a lack of trust and circling back to humans. Survey respondents claimed that inefficient data processes force them to rely on human-led decision making 71% of the time. In fact, only 14% of them claimed to have reached advanced AI maturity, using general purpose AI to automatically make predictions and business decisions.

In addition, there is a significant financial impact, with respondents estimating that they are missing out on an average of 5% of global annual revenue due to models built using inaccurate or low-quality data.

Talent is wasted

The challenges associated with data movement, processing and availability also mean that the talent hired to build AI models ends up wasting time on tasks outside their main job. In the Fivetran survey, respondents claimed that their data scientists spend an average of 70% of their time preparing data. A whopping 87% of respondents agreed that the data science talent within their organization is underutilized.

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

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Shreya Christina
Shreya has been with for 3 years, writing copy for client websites, blog posts, EDMs and other mediums to engage readers and encourage action. By collaborating with clients, our SEO manager and the wider team, Shreya seeks to understand an audience before creating memorable, persuasive copy.


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