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CIO metrics are failing digital transformation; it’s time to radically rethink success

CIO Business Intelligence

As digital transformation becomes a critical driver of business success, many organizations still measure CIO performance based on traditional IT values rather than transformative outcomes. This creates a disconnect between the strategic role that CIOs are increasingly expected to play and how their success is measured.

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

CIO Business Intelligence

These strategies, such as investing in AI-powered cleansing tools and adopting federated governance models, not only address the current data quality challenges but also pave the way for improved decision-making, operational efficiency and customer satisfaction. When customer records are duplicated or incomplete, personalization fails.

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Digital KPIs: The secret to measuring transformational success

CIO Business Intelligence

Regardless of where organizations are in their digital transformation, CIOs must provide their board of directors, executive committees, and employees definitions of successful outcomes and measurable key performance indicators (KPIs). He suggests, “Choose what you measure carefully to achieve the desired results.

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Writing Business Cases: 6 Things You Should Know About Identifying Project Benefits

BA Learnings

While some companies identify business benefits with the sole intention of getting business cases approved, more mature companies tend to devote their resources to tracking and measuring these business benefits after the projects have been concluded. This is particularly central to fostering continuous improvement.

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Managing machine learning in the enterprise: Lessons from banking and health care

O'Reilly on Data

In recent posts, we described requisite foundational technologies needed to sustain machine learning practices within organizations, and specialized tools for model development, model governance, and model operations/testing/monitoring. Sources of model risk. Model risk management. Image by Ben Lorica.

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AI Product Management After Deployment

O'Reilly on Data

Similarly, in “ Building Machine Learning Powered Applications: Going from Idea to Product ,” Emmanuel Ameisen states: “Indeed, exposing a model to users in production comes with a set of challenges that mirrors the ones that come with debugging a model.”.

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Using Analytics to Maximize Revenue with a SaaS Business Model

Smart Data Collective

Data analytics technology is becoming a more important aspect of business models in all industries. The importance of customer loyalty and customer service has become increasingly well-known and companies have needed to adapt their business models accordingly to gain a competitive edge. This is a key stage for customer retention.

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