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

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Even while decision-makers and CXOs remain bullish on AIs potential, enterprises are struggling to help make the the majority of it at the bottom level. Just to illustrate: a fresh report from data integration giant Fivetran that says 71% of companies find it hard to access all of the data had a need to run AI programs, workloads and models.

Dealing with Vanson Bourne, the business surveyed 550 IT and data science professionals in multiple countries and found gaps in data movement and access across their organizations. The finding is significant as data is essential for model training and implementation. One cannot run an effective AI program without laying a good foundation for data storage and movement, you start with a data warehouse or lake to automate data ingestion and pre-processing.

Analytic teams that start using a modern data stack can more readily extend the worthiness of these data and maximize their investments in AI and data science, George Fraser, CEO of Fivetran, said in the analysis.

Data access obstacles

In the survey, the vast majority of the respondents confirmed they collect and use data from operational systems on some level. However, 69% said they battle to access the proper information at the proper time, while at the very least 73% claimed to handle difficulty extracting, loading and transforming the info and translating it into practical advice and insights for decision-makers.


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Because of this, even though a lot of organizations (87%) consider AI vital for business survival, they neglect to take full advantage of it. Their broken, manual data processes result in inaccurate models, eventually producing a insufficient trust and circling back again to humans. The survey respondents claimed that inefficient data processes force them to depend on human-led decision-making 71% of that time period. Actually, only 14% of these claimed to possess achieved advanced AI maturity using general-purpose AI to automatically make predictions and business decisions.

In addition, theres significant financial impact, with respondents estimating they’re losing from typically 5% of global annual revenues because of models built using inaccurate or low-quality data.

Talent gets wasted

The challenges connected with data movement, processing and availability also imply that the talent hired to create AI models eventually ends up wasting time on tasks beyond their main job. In the Fivetran survey, the respondents claimed that their data scientists devote 70% of their own time normally to just preparing data. As much as 87% of respondents agreed that the info science talent of their organization isn’t being useful to its full potential.

In accordance with Fortune Business Insights, the global AI market is projected to cultivate from $387.45 billion in 2022 to $1,394.30 billion by 2029, with a CAGR of 20.1%

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