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One recent study shows that only 50% follow a product-centric operating model focusing on customer centricity and delivering delightful customer experiences. But there are common pitfalls , such as selecting the wrong KPIs , monitoring too many metrics, or not addressing poor dataquality. Digital Transformation
Part Two of the Digital Transformation Journey … In our last blog on driving digital transformation , we explored how enterprise architecture (EA) and business process (BP) modeling are pivotal factors in a viable digital transformation strategy. With automation, dataquality is systemically assured.
Only Cloudera has the ability to help organizations overcome the three barriers to trust in Enterprise AI: Readiness – Can you trust the safety of your proprietary data in public AI models? Reliability – Can you trust that your dataquality will yield useful AI results?
Companies still often accept the risk of using internal data when exploring large language models (LLMs) because this contextualdata is what enables LLMs to change from general-purpose to domain-specific knowledge. In the generative AI or traditional AI development cycle, data ingestion serves as the entry point.
Current electricity data standards EU legislation, such as REMIT , charges the European Network of Transmission System Operators for Electricity (ENTSO-E) with collecting electricity market data from TSOs. TSOs must provide this data in a format from the IEC’s family of standards known as the Common Information Model (CIM).
An enterprise data catalog does all that a library inventory system does – namely streamlining data discovery and access across data sources – and a lot more. For example, data catalogs have evolved to deliver governance capabilities like managing dataquality and data privacy and compliance.
The recent success of artificial intelligence based large language models has pushed the market to think more ambitiously about how AI could transform many enterprise processes. However, consumers and regulators have also become increasingly concerned with the safety of both their data and the AI models themselves.
Management involves utilizing tools to easily connect publishing and subscribing applications, ensure dataquality, route data, and monitor health and performance as streams scale. This capability converts large volumes of raw data into contextualizeddata that is ready for use in a business process.
The growth of large language models drives a need for trusted information and capturing machine-interpretable knowledge, requiring businesses to recognize the difference between a semantic knowledge graph and one that isn’t—if they want to leverage emerging AI technologies and maintain a competitive edge.
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