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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 strategy is a differentiator for universities. Here’s how to get it right

CIO Business Intelligence

Data is critical to success for universities. Data provides insights that support the overall strategy of the university. Data also lies at the heart of creating a secure, Trusted Research Environment to accelerate and improve research. The first step is to put in place a robust data strategy.

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What companies get wrong about data transformation

CIO Business Intelligence

So if funding and C-suite attention aren’t enough, what then is the key to ensuring an organization’s data transformation is successful? Companies that commit to treating data as a product and to transforming their culture are the ones that succeed, says Doug Laney, innovation fellow of data and analytics strategy at West Monroe.

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Tableau further democratizes analytics with AI-fueled features

CIO Business Intelligence

Your AI strategy is only as good as your data strategy,” Tableau CMO Elizabeth Maxon said in a press conference Monday. But to us, it’s more than just having a data strategy; it’s also about building a great foundation of a data culture.”

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Building Better Data Models to Unlock Next-Level Intelligence

Sisense

You can’t talk about data analytics without talking about data modeling. The reasons for this are simple: Before you can start analyzing data, huge datasets like data lakes must be modeled or transformed to be usable. Building the right data model is an important part of your data strategy.

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Straumann Group is transforming dentistry with data, AI

CIO Business Intelligence

“All they would have to do is just build their model and run with it,” he says. But to augment its various businesses with ML and AI, Iyengar’s team first had to break down data silos within the organization and transform the company’s data operations. For now, it operates under a centralized “hub and spokes” model.

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A step-by-step guide to setting up a data governance program

IBM Big Data Hub

In our last blog , we delved into the seven most prevalent data challenges that can be addressed with effective data governance. Today we will share our approach to developing a data governance program to drive data transformation and fuel a data-driven culture.