The Governance Illusion

Boards often mistake policies, audits, and dashboards for real cyber and AI readiness. Genuine oversight requires evidence that systems can detect, contain, and recover from failures before pressure exposes the gap.

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Success in any field isn’t just about talent – it’s about adaptability. My journey into data analytics wasn’t straightforward, but each step, from journalism to IT, shaped my ability to analyze and communicate insights. Certifications, like the Certified Analytics Professional (CAP), became a key part of that growth, not as a magic bullet but as a structured way to refine my skills and prove my expertise.

For decades, analytics has lived with an uncomfortable tradeoff. If you want state-of-the-art accuracy, you must accept complexity and opacity. If you want interpretability, you must settle for weaker performance. In her keynote at the 2026 INFORMS Analytics+ Conference, Cynthia Rudin argued that this tradeoff is not only unnecessary - it is often simply wrong.

Data puns were flying during the first keynote address on the opening day of the 2026 INFORMS Analytics+ Conference at the Gaylord National Hotel in National Harbor, MD. Popularly known as the “Data Whisperer,” opening keynote speaker Scott Taylor took the stage early in the morning to deliver a lively talk about how today’s data analysts can best convey the importance of their work to members of their organizations’ C-suites.

The Certified Analytics Professional (CAP®) credential is about stepping into a role in which analytics serves as the bridge between decision-makers and data. In defense-focused operations research, where outcomes can shape missions and policy, this kind of expertise matters.

For analytics professionals, the recent Bureau of Labor Statistics jobs report revisions offer more than a headline. It serves as a valuable reminder of the responsibilities we carry when working with data in high-stakes environments – especially when our work becomes entangled with public narratives, political decisions or major economic consequences.

From Strategy to Execution

Sustainable collaboration between industry and academia does not happen by goodwill alone; it requires clear roles, reusable tools, and systems built to survive turnover, burnout, and shifting priorities.

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The Human Factor

Algorithms are only as effective as the decisions they influence, and human judgment often determines whether recommendations are accepted, adjusted, or ignored. Understanding why people deviate from algorithmic guidance can help researchers design decision tools that work better in real-world settings.

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Playing by the Framework 

Sports analytics offers a practical look at why disciplined problem framing, deployment, and life cycle management matter. The INFORMS Analytics Framework shows how a structured process can turn data into trusted, decision-ready insight.

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Before the Model

Analytics teams do not usually fail because they lack methods. They fail when the business decision is unclear before the work begins.

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Why Data Science Projects Fail

why data science projects fail conclusion

Why Data Science Projects Fail: Conclusion

The final installment of a 10-part series discusses human involvement in data science is substantial in every step of the process and materially significant, requiring the development and application of many “soft skills” that necessarily facilitate successful execution and completion of data science projects.

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Opening the Agentic AI Floodgates

Agentic AI is lowering the barrier to enterprise automation, but compute capacity, infrastructure control, proprietary data and governance will determine long-term success.

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