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8 data strategy mistakes to avoid

CIO Business Intelligence

Organizations can’t afford to mess up their data strategies, because too much is at stake in the digital economy. How enterprises gather, store, cleanse, access, and secure their data can be a major factor in their ability to meet corporate goals. Here are some data strategy mistakes IT leaders would be wise to avoid.

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Data architecture strategy for data quality

IBM Big Data Hub

Because your data architecture dictates how your data assets and data management resources are structured, it plays a critical role in how effective your organization is at performing these tasks. Meaning, data architecture is a foundational element of your business strategy for higher data quality.

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Prioritizing Data: Why a Solid Data Management Strategy Will Be Critical in 2024

Ontotext

In 2023, data leaders and enthusiasts were enamored of — and often distracted by — initiatives such as generative AI and cloud migration. Without this, organizations will continue to pay a “bad data tax” as AI/ML models will struggle to get past a proof of concept and ultimately fail to deliver on the hype.

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

CIO Business Intelligence

Payers and providers will need to create a data foundation that addresses elements such as bringing in the right data, how to classify it, and how to create a data lineage so data sources can be tracked to address potential AI hallucinations. The need for generative AI data management may seem daunting.

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Data governance in the age of generative AI

AWS Big Data

Data is your generative AI differentiator, and a successful generative AI implementation depends on a robust data strategy incorporating a comprehensive data governance approach. Data governance is a critical building block across all these approaches, and we see two emerging areas of focus.

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What is a data scientist? A key data analytics role and a lucrative career

CIO Business Intelligence

Data scientists are analytical data experts who use data science to discover insights from massive amounts of structured and unstructured data to help shape or meet specific business needs and goals. Data scientist job description. Semi-structured data falls between the two.

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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

Data science is an area of expertise that combines many disciplines such as mathematics, computer science, software engineering and statistics. It focuses on data collection and management of large-scale structured and unstructured data for various academic and business applications.