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5 misconceptions about cloud data warehouses

IBM Big Data Hub

In today’s world, data warehouses are a critical component of any organization’s technology ecosystem. The rise of cloud has allowed data warehouses to provide new capabilities such as cost-effective data storage at petabyte scale, highly scalable compute and storage, pay-as-you-go pricing and fully managed service delivery.

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The change management Informatica needed to overhaul its business model

CIO Business Intelligence

Then at the other end, we did a fantastic job involving the sales operations, finance, and marketing teams in the testing and design, and we did a great job training people. The metrics you use to measure a cloud company are different than those you use to measure an enterprise license and maintenance company.

Modeling 112
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4 ways to ensure CEO support for your digital strategy

CIO Business Intelligence

It’s then up to the CIO to sound less like a technical guru and more like the finance, marketing, and payroll people, making it clear that every activity takes place on the IT infrastructure. Data is one of the most important levers the CIO can use to have an effective dialogue with the CEO. Software is invisible.

Strategy 114
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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

1) What Is Data Quality Management? 4) Data Quality Best Practices. 5) How Do You Measure Data Quality? 6) Data Quality Metrics Examples. 7) Data Quality Control: Use Case. 8) The Consequences Of Bad Data Quality. 9) 3 Sources Of Low-Quality Data. 10) Data Quality Solutions: Key Attributes.

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Your Data Won’t Speak Unless You Ask It The Right Data Analysis Questions

datapine

The questions to ask when analyzing data will be the framework, the lens, that allows you to focus on specific aspects of your business reality. Once you have your data analytics questions, you need to have some standard KPIs that you can use to measure them. As Data Dan reminded us, “did the best” is too vague to be useful.

IT 317
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How to use foundation models and trusted governance to manage AI workflow risk

IBM Big Data Hub

Getting started with foundation models An AI development studio can train, validate, tune and deploy foundation models and build AI applications quickly, requiring only a fraction of the data previously needed. Such datasets are measured by how many “tokens” (words or word parts) they include.

Risk 75
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7 enterprise data strategy trends

CIO Business Intelligence

As a result, a growing number of IT leaders are looking for data strategies that will allow them to manage the massive amounts of disparate data located in silos without introducing new risk and compliance challenges. Data collection and management shouldn’t be classified as just another project, Gusher notes.