Remove Business Objectives Remove Data Quality Remove Measurement Remove Modeling
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Data architecture strategy for data quality

IBM Big Data Hub

Poor data quality is one of the top barriers faced by organizations aspiring to be more data-driven. Ill-timed business decisions and misinformed business processes, missed revenue opportunities, failed business initiatives and complex data systems can all stem from data quality issues.

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Automating Model Risk Compliance: Model Validation

DataRobot Blog

Last time , we discussed the steps that a modeler must pay attention to when building out ML models to be utilized within the financial institution. In summary, to ensure that they have built a robust model, modelers must make certain that they have designed the model in a way that is backed by research and industry-adopted practices.

Risk 52
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Business Strategies for Deploying Disruptive Tech: Generative AI and ChatGPT

Rocket-Powered Data Science

While generative AI has been around for several years , the arrival of ChatGPT (a conversational AI tool for all business occasions, built and trained from large language models) has been like a brilliant torch brought into a dark room, illuminating many previously unseen opportunities.

Strategy 289
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A step-by-step guide to setting up a data governance program

IBM Big Data Hub

The primary goal of any data governance program is to deliver against prioritized business objectives and unlock the value of your data across your organization. Realize that a data governance program cannot exist on its own – it must solve business problems and deliver outcomes.

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Bringing an AI Product to Market

O'Reilly on Data

These measures are commonly referred to as guardrail metrics , and they ensure that the product analytics aren’t giving decision-makers the wrong signal about what’s actually important to the business. When a measure becomes a target, it ceases to be a good measure ( Goodhart’s Law ). Any metric can and will be abused.

Marketing 362
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6 strategic imperatives for your next data strategy

CIO Business Intelligence

According to the MIT Technology Review Insights Survey, an enterprise data strategy supports vital business objectives including expanding sales, improving operational efficiency, and reducing time to market. The problem is today, just 13% of organizations excel at delivering on their data strategy.

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10 digital transformation roadblocks — and 5 tips for overcoming them

CIO Business Intelligence

Failure to align technology capabilities with business goals can result in a wasted investment in technology that doesn’t support business objectives. Transformational leaders must ensure their organizations have the right systems and processes in place to collect, store, and analyze data effectively.