Remove Business Intelligence Remove Data Quality Remove Reporting
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Get The Most Out Of Smart Business Intelligence Reporting

datapine

Big data plays a crucial role in online data analysis , business information, and intelligent reporting. Companies must adjust to the ambiguity of data, and act accordingly. So, what is BI reporting advancing in a business? Exclusive Bonus Content: Do you need to create your own BI report?

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The state of data quality in 2020

O'Reilly on Data

We suspected that data quality was a topic brimming with interest. The responses show a surfeit of concerns around data quality and some uncertainty about how best to address those concerns. Key survey results: The C-suite is engaged with data quality. Data quality might get worse before it gets better.

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Accomplish Agile Business Intelligence & Analytics For Your Business

datapine

When encouraging these BI best practices what we are really doing is advocating for agile business intelligence and analytics. Therefore, we will walk you through this beginner’s guide on agile business intelligence and analytics to help you understand how they work and the methodology behind them.

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A Guide to the Six Types of Data Quality Dashboards

DataKitchen

A Guide to the Six Types of Data Quality Dashboards Poor-quality data can derail operations, misguide strategies, and erode the trust of both customers and stakeholders. However, not all data quality dashboards are created equal. These dimensions provide a best practice grouping for assessing data quality.

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Looker Simplifies Business Intelligence in the Cloud

David Menninger's Analyst Perspectives

Organizations face various challenges with analytics and business intelligence processes, including data curation and modeling across disparate sources and data warehouses, maintaining data quality and ensuring security and governance.

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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.

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The Race For Data Quality in a Medallion Architecture

DataKitchen

The Race For Data Quality In A Medallion Architecture The Medallion architecture pattern is gaining traction among data teams. It is a layered approach to managing and transforming data. It sounds great, but how do you prove the data is correct at each layer? How do you ensure data quality in every layer ?