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Data architecture strategy for data quality

IBM Big Data Hub

Poor data quality is one of the top barriers faced by organizations aspiring to be more data-driven. Ill-timed business decisions and misinformed business processes, missed revenue opportunities, failed business initiatives and complex data systems can all stem from data quality issues.

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8 data strategy mistakes to avoid

CIO Business Intelligence

“Similar to disaster recovery, business continuity, and information security, data strategy needs to be well thought out and defined to inform the rest, while providing a foundation from which to build a strong business.” Overlooking these data resources is a big mistake. It will not be something they can ignore.

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3 key digital transformation priorities for 2024

CIO Business Intelligence

Improving search capabilities and addressing unstructured data processing challenges are key gaps for CIOs who want to deliver generative AI capabilities. But 99% also report technical challenges, listing integration (68%), data volume and cleansing (59%), and managing unstructured data (55% ) as the top three.

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Healthcare organizations must create a strong data foundation to fully benefit from generative AI

CIO Business Intelligence

Key elements of this foundation are data strategy, data governance, and data engineering. A healthcare payer or provider must establish a data strategy to define its vision, goals, and roadmap for the organization to manage its data. The need for generative AI data management may seem daunting.

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The state of data quality in 2020

O'Reilly on Data

We suspected that data quality was a topic brimming with interest. The responses show a surfeit of concerns around data quality and some uncertainty about how best to address those concerns. Key survey results: The C-suite is engaged with data quality. Data quality might get worse before it gets better.

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Straumann Group is transforming dentistry with data, AI

CIO Business Intelligence

The Basel, Switzerland-based company, which operates in more than 100 countries, has petabytes of data, including highly structured customer data, data about treatments and lab requests, operational data, and a massive, growing volume of unstructured data, particularly imaging data.

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Five benefits of a data catalog

IBM Big Data Hub

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 data quality and data privacy and compliance.