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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

Building a successful data strategy at scale goes beyond collecting and analyzing data,” says Ryan Swann, chief data analytics officer at financial services firm Vanguard. Overlooking these data resources is a big mistake. What are the goals for leveraging unstructured data?”

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

CIO Business Intelligence

A healthcare payer or provider must establish a data strategy to define its vision, goals, and roadmap for the organization to manage its data. Next is governance; the rules, policies, and processes to ensure data quality and integrity. The need for generative AI data management may seem daunting.

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Drive Growth with Data-Driven Strategies: Introducing Zenia Graph’s Salesforce Accelerator

Ontotext

Traditional data integration methods struggle to bridge these gaps, hampered by high costs, data quality concerns, and inconsistencies. Studies reveal that businesses lose significant time and opportunities due to missing integrations and poor data quality and accessibility.

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Why Financial Services Firms are Championing Natural Language Processing

CIO Business Intelligence

NLP solutions can be used to analyze the mountains of structured and unstructured data within companies. In large financial services organizations, this data includes everything from earnings reports to projections, contracts, social media, marketing, and investments. Intel® Technologies Move Analytics Forward.

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What is a data engineer? An analytics role in high demand

CIO Business Intelligence

What is a data engineer? Data engineers design, build, and optimize systems for data collection, storage, access, and analytics at scale. They create data pipelines used by data scientists, data-centric applications, and other data consumers. Data engineer vs. data architect.

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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.