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Informatica Claims Data Fragmentation Is Standing in the Way of APAC Generative AI

March 6, 2024
in Cloud & Programming
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Data leaders in the Asia-Pacific region are actively working on implementing artificial intelligence, according to a survey by Informatica. India is leading the way with 75% of respondents already using generative AI. However, APAC-based data chiefs are facing challenges with managing data for AI, including data fragmentation, data quality, and robust data governance. Richard Scott, from Informatica, emphasized the importance of data literacy and recommended focusing on cloud data architecture and people, processes, and technology.

Data strategy priorities for APAC data leaders in 2024 include delivering reliable data for AI, improving data governance, and processes. This mutual focus on data management and AI is reflected in the increase in data investments predicted by 78% of APAC data chiefs.

Investments in key data capabilities are on the rise in the region, with a focus on data privacy, data quality, data integration, and engineering. Data quality is a major challenge to generative AI, with concerns about bias being particularly significant in APAC. Data literacy outside the data estate is also hindering progress on AI, with a lack of leadership support being a common roadblock.

The number of data management tools needed by APAC leaders is increasing, with a focus on improving governance over data and processes. Enabling more data democratisation is a priority for APAC data leaders, driving vendors to offer governance services and tools. Informatica’s Cloud Data Access Management tool and self-service data marketplace aim to support compliant data management and access.

Scott advised regional data leaders to focus on getting the right cloud architecture in place and to consider people, processes, and technology to meet the data challenge. Starting with the right cloud architecture is essential for scalability and cost-effectiveness. NRMA, an Informatica client, successfully manages over 3,000 datasets by getting the architecture right. Looking at people, processes, and technology holistically is crucial for integrating data effectively across the environment.



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