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As my colleague Wim Stoop previously shared, “A well-planned enterprise data strategy helps companies get the most of their data, making it known, discoverable, available, trusted, and compliant. In an industry that is subject to stringent regulatory requirements, it is critical to use data to accurately scale up riskmanagement.
The complexity of regulatory requirements in and of themselves is aggravated by the complexity of the business and data landscapes within most enterprises. Creating and automating a curated enterprise data catalog , complete with physical assets, data models, data movement, data quality and on-demand lineage.
A well-designed strategy can help organizations balance business growth with environmental, social and governance (ESG) responsibility while improving operational efficiency. Encourage cross-functional collaboration : Partner with IT, operations and finance teams to align data-driven sustainability efforts with broader businessobjectives.
A modern, cloud-native dataarchitecture with separation of compute and storage, containerized data services (for agility and elasticity), and object storage (for scale and cost-efficiency). Customer use cases can be grouped into three categories. .
Without a doubt, Artificial Intelligence (AI) is revolutionizing businesses, with Australia’s AI spending expected to hit $6.4 However, according to The State of Enterprise AI and Modern DataArchitecture report, while 88% of enterprises adopt AI, many still lack the data infrastructure and team skilling to fully reap its benefits.
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