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7 types of tech debt that could cripple your business

CIO Business Intelligence

CIOs perennially deal with technical debts risks, costs, and complexities. While the impacts of legacy systems can be quantified, technical debt is also often embedded in subtler ways across the IT ecosystem, making it hard to account for the full list of issues and risks.

Risk 140
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Escaping POC Purgatory: Evaluation-Driven Development for AI Systems

O'Reilly on Data

The coordination tax: LLM outputs are often evaluated by nontechnical stakeholders (legal, brand, support) not just for functionality, but for tone, appropriateness, and risk. Business value : Once we have a rubric for evaluating our systems, how do we tie our macro-level business value metrics to our micro-level LLM evaluations?

Testing 168
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The Race For Data Quality in a Medallion Architecture

DataKitchen

Finally, the challenge we are addressing in this document – is how to prove the data is correct at each layer.? Similarly, downstream business metrics in the Gold layer may appear skewed due to missing segments, which can impact high-stakes decisions. How do you ensure data quality in every layer?

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Where CIOs should place their 2025 AI bets

CIO Business Intelligence

CIOs feeling the pressure will likely seek more pragmatic AI applications, platform simplifications, and risk management practices that have short-term benefits while becoming force multipliers to longer-term financial returns. CIOs should consider placing these five AI bets in 2025.

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From project to product: Architecting the future of enterprise technology

CIO Business Intelligence

Understanding and tracking the right software delivery metrics is essential to inform strategic decisions that drive continuous improvement. Documentation and diagrams transform abstract discussions into something tangible. Complex ideas that remain purely verbal often get lost or misunderstood.

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The Ultimate Guide to Modern Data Quality Management (DQM) For An Effective Data Quality Control Driven by The Right Metrics

datapine

6) Data Quality Metrics Examples. Reporting being part of an effective DQM, we will also go through some data quality metrics examples you can use to assess your efforts in the matter. The data quality analysis metrics of complete and accurate data are imperative to this step. Table of Contents. 2) Why Do You Need DQM?

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AI agents will transform business processes — and magnify risks

CIO Business Intelligence

Adding smarter AI also adds risk, of course. “At The big risk is you take the humans out of the loop when you let these into the wild.” Our goal is to analyze logs and metrics, connecting them with the source code to gain insights into code fixes, vulnerabilities, performance issues, and security concerns,” he says.

Risk 136