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10 ways to kill your IT culture

CIO

Without a strong IT culture, inspiring IT teams to extend beyond their “run the business” responsibilities into areas requiring collaboration between business colleagues, data scientists, and partners is challenging. Additionally, 84% of leaders believed their organizations had strong teamwork compared to 60% of team members.

Culture 211
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Case in point: taking stock of the CrowdStrike outages

CIO

Last summer, a faulty CrowdStrike software update took down millions of computers, caused billions in damages, and underscored that companies are still not able to manage third-party risks, or respond quickly and efficiently to disruptions. It was an interesting case study of global cyber impact, says Charles Clancy, CTO at Mitre.

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CIOs must mind their own data confidence gap

CIO

Moreover, 68% of vice presidents in charge of AI or data management already see their companies making decisions based on bad data all or most of the time, versus 47% of C-level IT leaders. Directors are often more accurate in their confidence assessments, because theyre swimming in the systems, not just reviewing summaries.

Data 195
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5 tips for better business value from gen AI

CIO

According to AI at Wartons report on navigating gen AIs early years, 72% of enterprises predict gen AI budget growth over the next 12 months but slower increases over the next two to five years. CIOs should speak to sales leaders to identify areas where sales metrics are underperforming and where gen AI-driven improvements can drive revenue.

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Static vs Dynamic Code Analysis: How to Choose Between Them

OverOps

What’s the difference between static and dynamic code analysis, and how do you know which to use? Static code analysis is analogous to practicing your baseball swing with a practice net and a pitching machine. Static Code Analysis. This is usually done by analyzing the code against a given set of rules or coding standards.

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The future of data: A 5-pillar approach to modern data management

CIO

By modern, I refer to an engineering-driven methodology that fully capitalizes on automation and software engineering best practices. This approach is repeatable, minimizes dependence on manual controls, harnesses technology and AI for data management and integrates seamlessly into the digital product development process.

Data 167
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Simple sabotage for software

Erik Bernhardsson

You can of course make a series of obviously bad decisions, but you'd get fired quickly. Make sure production environment differs from developer environments in as many ways as possible. Leverage any production issue as a reason to “pull the brakes” Introduce very complex processes for code change and common workflows.