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What Stands Between IT and Business Success? Data Complexity

CIO Business Intelligence

A real-time data technology stack has to shrink this innovation gap for the business. . Analysts and data scientists need flexibility when working with data; experimentation fuels the development of analytics and machine learning models. Innovation at integration points.

IT 125
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Customer Experience and Emerging Technologies: My CXChat Summary on Artificial Intelligence, Machine Learning and the Customer

Business Over Broadway

According to Gartner, companies need to adopt these practices: build culture of collaboration and experimentation; start with a 3-way partnership among executives leading digital initiative, line of business and IT. Also, loyalty leaders infuse analytics into CX programs, including machine learning, data science and data integration.

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Four starting points to transform your organization into a data-driven enterprise

IBM Big Data Hub

This capability will provide data users with visibility into origin, transformations, and destination of data as it is used to build products. The result is more useful data for decision-making, less hassle and better compliance. Data integration. Data science and MLOps. AI is no longer experimental.

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The DataOps Vendor Landscape, 2021

DataKitchen

RightData – A self-service suite of applications that help you achieve Data Quality Assurance, Data Integrity Audit and Continuous Data Quality Control with automated validation and reconciliation capabilities. QuerySurge – Continuously detect data issues in your delivery pipelines. Production Monitoring Only.

Testing 300
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How to choose the best AI platform

IBM Big Data Hub

Decision optimization: Streamline the selection and deployment of optimization models and enable the creation of dashboards to share results, enhance collaboration and recommend optimal action plans. AutoAI automates data preparation, model development, feature engineering and hyperparameter optimization.