Remove Data Warehouse Remove Finance Remove Measurement Remove Reporting
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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. They provide the backbone for a range of use cases such as business intelligence (BI) reporting, dashboarding, and machine-learning (ML)-based predictive analytics, that enable faster decision making and insights.

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Database vs. Data Warehouse: What’s the Difference?

Jet Global

The success of any business into the next year and beyond will depend entirely on the volume, accuracy, and reportability of the data they collect—and how well the business can analyze, extract insight from, and take action on that data. All About That (Data)Base. Enter the Warehouse.

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The change management Informatica needed to overhaul its business model

CIO Business Intelligence

We built that end-to-end data model and process from scratch while we ran the old business. We still operated our core license and maintenance business, but we changed everything, including CRM, revenue recognition, reporting, and customer success to support the new cloud business operating model. Take sales territories for example.

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How Data-Driven Decisions Boost the Future of Industrial Manufacturing

Jet Global

Managing this increasing amount of data can wreak havoc on your financial teams. Can you correlate data across all departments for informed decision- making ? Or reporting across multiple manufacturing units? . By analyzing this metric, finance can help teams speed processes and improve costs. .

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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.

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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. Increase trust in AI outcomes.

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