Transform Partner的动态

1.???Strategies for Improving #DataGovernance 2.???Co-Existence of #DataWarehouse and #DataLake Architecture for a Banking #DataModel 3.???What is the value that might come out from the combination of #EnterpriseArchitecture and #AI / GenAI practices? a.???Real-world examples of successful integration of Enterprise Architecture and AI/GenAI practices: b.???Key Challenges in the Integration of EA and AI / #GenAI c.????How can organizations start integrating EA and AI? 4.???15 Action Points of the National #AIStrategy of the Singapore 5.???2024 #CDO Agenda Includes Governance, Generative AI 6.???Roadblocks to International Data Governance ? These are the Six Headlines of Our Today’s Premium Content. ? Details available ONLY to our email subscribers ? (To subscribe to our Premium Content, you can DM me or email to [email protected] for the details) ? ? Image Source: #DataManagement and #DataStrategy, Global Data Strategy Ltd ? #TransformPartner – Your #DigitalTransformation Consultancy ?

At IBM, our commitment to responsible technology extends to the data we use to build and train our #AI systems. After all, #trustworthy AI starts with trusted data. ? Throughout this year, IBM collaborated with the Data & Trust Alliance and other industry leaders to develop the #Data Provenance Standards, the very first cross-industry standard metadata taxonomy. These new standards are helping IBM realize exciting gains in operational efficiency as a result, showing that good data governance is good business. ? Learn more about how data provenance transparency is helping us unlock new business value ?? ?Rob Thomas Jon Iwata Saira Jesani Lee Cox Steven Eliuk https://lnkd.in/efuka-kY ?

Thank you,?Christina Montgomery,?IBM Institute for Business Value, and the broader IBM team for adopting and demonstrating the value of Data Provenance Standards. "The most notable impact we’ve observed since more closely aligning our internal data standards with the Data Provenance Standards is a reduction in the time it takes to process data clearance requests. In the eight-month period during which we tested the Data Provenance Standards and implemented other technology and process enhancements, we observed that the average data clearance processing time decreased by 58% for third-party data and 62% for IBM-proprietary data. . . . By August 2024, the number of clearance requests for both third-party and IBM-proprietary data had already surpassed the total number for all of 2023." #AIGovernance #DataGovernance #DigitalPolicy #DataScience #MachineLearning

One of the best here... take a read on how data / models, including the data for Granite, is managed at IBM. #transparentAI #enterpriseAI #aiethics #watsonx IBM watsonx watsonx

Trusted data provides the fuel for an effective AI strategy.

This is another example of why ethical AI is important to the success and quality of AI solution development!

In highly regulated industries like Financial Services, new mandates can often mean employees will be burdened with time-consuming tasks.? When the SEC issued a new regulation regarding the format for financial reporting, many companies experienced this pain.? One company thought this might be a good opportunity to leverage #generativeAI and reached out to IBM Client Engineering (CE) for help.?? Leveraging IBM #watsonx.ai, the CE team was able to deliver some amazing results: Extraction of key data points from fund factsheets with better than 96% accuracy? Reduction in the time spent on this singular task by approximately 9,000 hours per year - a 93% benefit!?? This provides these employees with time to focus on higher value tasks, like growing their business.?? That’s the power of IBM and the value of Client Engineering. What can we do for your organization? #ibm #clientengineering #CEdeliversresults #showdonttell #technology #tech #innovation #watsonx #ai #aiforgood #fintech #financialservices? https://lnkd.in/eU_3FcKZ?

Struggling with data silos and errors? DataOps enhances communication, integration, and automation of data flows, ensuring our clients’ AI initiatives succeed. Learn how HPE Services can guide their DataOps journey.#HPEDiscover #ArubaAtmosphere

????Unified AI Governance Platform???? One of the largest telecommunications providers in the US needed to achieve a unified governance platform that integrated workflow management, model risk governance and dash-boarding reporting. With an extensive backlog of 700+ AI use cases to validate and onboard along with multiple disparate tools and processes being used, their governance team struggled with a bottleneck in the process flow causing further delays, requiring multiple teams to upkeep and slowing down AI model development. IBM's Client Engineering built a pilot showcasing how watsonx.gov and watsonx.ai can automate and improve their use case approval process, reporting, model generation, and life cycle risk management. Achieving: ??$125M in projected potential revenue generation through a 6x improvement in speed of model development ??A 90% reduction in manual reporting effort ??45% reduction on case approval and disposition monitoring effort ??Mitigating potential reparation costs in violating the EU AI Act That’s the power of IBM and the value of Client Engineering. What can we do for your organization? #ibm #clientengineering #showdonttell #technology #tech #innovation #watsonx #ai https://lnkd.in/gaVHYidB

Today, in a world that relies on data, being ahead of the game requires mastering the flow of information. Data pipelines have evolved from basic batch processing to real-time systems, revolutionizing how we handle and analyse data. Let's delve into the evolution and expansion of data pipelines! ?? Initial Stage: Basic Batch Processing: Early data pipelines moved data from one point to another, typically in batch mode. ?? Demand for Speed and Agility: Businesses required faster insights, prompting the evolution of more sophisticated data pipelines. ?? Integration of Diverse Data Sources: Modern pipelines handle structured, semi-structured, and unstructured data. ?? Complex Workflow Orchestration: Ensures data quality through cleaning, validating, and enriching processes. ?? Democratizes Data Access: Enables faster decision-making and innovation across various industries. ?? Advancements in AI/ML Integration: Promises greater automation and predictive analytics capabilities within data pipelines. ?? Focus on Data Privacy and Governance: Enhanced security measures and compliance frameworks influence pipeline designs. #dataengineering #datapipeline #DataOps #dataandAI