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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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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.

Analytics 118
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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?

Metrics 119
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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.

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How gaming companies can use Amazon Redshift Serverless to build scalable analytical applications faster and easier

AWS Big Data

ETL (extract, transform, and load) technologies, streaming services, APIs, and data exchange interfaces are the core components of this pillar. Unlike ingestion processes, data can be transformed as per business rules before loading. You can apply technical or business data quality rules and load raw data as well.

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Top 10 Reasons for Alation with Snowflake: Reduce Risk with Active Data Governance

Alation

In the next section, let’s take a deeper look into how these key attributes help data scientists and analysts make faster, more informed decisions, while supporting stewards in their quest to scale governance policies on the Data Cloud easily. Find Trusted Data. Verifying quality is time consuming.