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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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Getting started with AWS Glue Data Quality from the AWS Glue Data Catalog

AWS Big Data

Data consumers lose trust in data if it isn’t accurate and recent, making data quality essential for undertaking optimal and correct decisions. Evaluation of the accuracy and freshness of data is a common task for engineers. Currently, various tools are available to evaluate data quality.

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5 surefire ways to derail a digital transformation (without knowing it)

CIO Business Intelligence

Worse is when prioritized initiatives don’t have a documented shared vision, including a definition of the customer, targeted value propositions, and achievable success criteria. But there are common pitfalls , such as selecting the wrong KPIs , monitoring too many metrics, or not addressing poor data quality.

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Sport analytics leverage AI and ML to improve the game

CIO Business Intelligence

Digital Athlete draws data from players’ radio frequency identification (RFID) tags, 38 5K optical tracking cameras placed around the field capturing 60 frames per second, and other data such as weather, equipment, and play type. million video frames and documents about 100 million locations and positions of players on the field.

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9 Habits of Data Fluent Organizations — and How to Learn Them

Juice Analytics

At Juice, we are working everyday to create these habits and we wanted to share how we are building a data-first mindset and where we look for inspiration. Habit 1: Define shared metrics Data fluency requires getting everyone on the same page as to what matters most. How do we track “first success” for a user?

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Accomplish Agile Business Intelligence & Analytics For Your Business

datapine

It’s necessary to say that these processes are recurrent and require continuous evolution of reports, online data visualization , dashboards, and new functionalities to adapt current processes and develop new ones. Working software over comprehensive documentation. Discover the available data sources.

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Create a modern data platform using the Data Build Tool (dbt) in the AWS Cloud

AWS Big Data

As part of their cloud modernization initiative, they sought to migrate and modernize their legacy data platform. This popular open-source tool for data warehouse transformations won out over other ETL tools for several reasons. The tool also offered desirable out-of-the-box features like data lineage, documentation, and unit testing.