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Product lifecycle management for data-driven organizations 

IBM Big Data Hub

In a world where every company is now a technology company, all enterprises must become well-versed in managing their digital products to remain competitive. In other words, they need a robust digital product lifecycle management (PLM) strategy. In addition, there is a downstream team that owns product transactional data.

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Enabling a data-driven IT modernization strategy

CIO Business Intelligence

The big picture : In the midst of a rush to technology modernization, it’s critical to ensure the organization’s data assets are not overlooked. Why it matters:  Data-driven business decisions must factor prominently in modernization efforts. The bottom line:  Don’t leave data behind.

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How to build a successful talent management strategy

IBM Big Data Hub

An organization is only as strong as its talent pool, so organizations have increasingly embraced talent management as a core component of their overarching strategy. Thus, organizations are prioritizing talent management more than ever. Talent management needs to become a holistic part of the organization.

Strategy 108
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Data Driven Customer Retention Strategies

TDAN

Data driven marketing strategies supported by customer insights can significantly boost the customer experience, leading to higher customer retention rates.

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MLOps 101: The Foundation for Your AI Strategy

Machine Learning Operations (MLOps) allows organizations to alleviate many of the issues on the path to AI with ROI by providing a technological backbone for managing the machine learning lifecycle through automation and scalability. Why do AI-driven organizations need it? Download this comprehensive guide to learn: What is MLOps?

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Streamlining supply chain management: Strategies for the future

IBM Big Data Hub

In today’s complex global business environment, effective supply chain management (SCM) is crucial for maintaining a competitive advantage. Here’s how companies are using different strategies to address supply chain management and meet their business goals.

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The Ultimate Guide to Modern Data Quality Management (DQM) For An Effective Data Quality Control Driven by The Right Metrics

datapine

1) What Is Data Quality Management? 4) Data Quality Best Practices. 5) How Do You Measure Data Quality? 6) Data Quality Metrics Examples. 7) Data Quality Control: Use Case. 8) The Consequences Of Bad Data Quality. 9) 3 Sources Of Low-Quality Data. 10) Data Quality Solutions: Key Attributes.