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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

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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The Rise of Unstructured Data

Cloudera

Here we mostly focus on structured vs unstructured data. In terms of representation, data can be broadly classified into two types: structured and unstructured. Structured data can be defined as data that can be stored in relational databases, and unstructured data as everything else.

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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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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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3 key digital transformation priorities for 2024

CIO Business Intelligence

This year’s technology darling and other machine learning investments have already impacted digital transformation strategies in 2023 , and boards will expect CIOs to update their AI transformation strategies frequently. Luckily, many are expanding budgets to do so. “94%

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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.