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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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5 great Data Strategy Resources

Data Science 101

I am putting together some of my own resources on Data Strategy. What is a Data Strategy? Building the AI-Powered Organization – while not specific to data strategy, it fits the topic. Keep watching the blog for more information around my thoughts on Data Strategy.

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The data flywheel: A better way to think about your data strategy

CIO Business Intelligence

Some are our clients—and more of them are asking our help with their data strategy. The variables seem endless: data— security , science , storage , mining , management , definition , deletion , integration , accessibility , architecture , collection , governance , and the ever-elusive, data culture.

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AI market evolution: Data and infrastructure transformation through AI

CIO Business Intelligence

AI a primary driver in IT modernization and data mobility AI’s demand for data requires businesses to have a secure and accessible data strategy. Implementing enterprise AI is a long-haul journey The journey to AI maturity is complex, with no single path or definitive approach to infrastructure decisions.

Marketing 128
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Streamline AI-driven analytics with governance: Integrating Tableau with Amazon DataZone

AWS Big Data

Leveraging AWS’s managed service was crucial for us to access business insights faster, apply standardized data definitions, and tap into generative AI potential. You can now use your tool of choice, including Tableau, to quickly derive business insights from your data while using standardized definitions and decentralized ownership.

Analytics 119
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Data’s dark secret: Why poor quality cripples AI and growth

CIO Business Intelligence

Yet, despite growing investments in advanced analytics and AI, organizations continue to grapple with a persistent and often underestimated challenge: poor data quality. Fragmented systems, inconsistent definitions, legacy infrastructure and manual workarounds introduce critical risks.

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Steps taken to build Sevita’s first enterprise data platform

CIO Business Intelligence

But because of the infrastructure, employees spent hours on manual data analysis and spreadsheet jockeying. We had plenty of reporting, but very little data insight, and no real semblance of a data strategy. This legacy situation gave us two challenges.