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Announcing Open Source DataOps Data Quality TestGen 3.0

DataKitchen

Announcing DataOps Data Quality TestGen 3.0: Open-Source, Generative Data Quality Software. It assesses your data, deploys production testing, monitors progress, and helps you build a constituency within your company for lasting change. Imagine an open-source tool thats free to download but requires minimal time and effort.

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An AI Chat Bot Wrote This Blog Post …

DataKitchen

ChatGPT> DataOps, or data operations, is a set of practices and technologies that organizations use to improve the speed, quality, and reliability of their data analytics processes. The goal of DataOps is to help organizations make better use of their data to drive business decisions and improve outcomes.

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What Is Ad Hoc Reporting? Your Guide To Definition, Meaning, Examples & Benefits

datapine

“The goal is to turn data into information, and information into insight.” – Carly Fiorina, former executive, president, HP. Digital data is all around us. quintillion bytes of data every single day, with 90% of the world’s digital insights generated in the last two years alone, according to Forbes. click to enlarge**.

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5 Ways Data Modeling Is Critical to Data Governance

erwin

Enterprises are trying to manage data chaos. They also face increasing regulatory pressure because of global data regulations , such as the European Union’s General Data Protection Regulation (GDPR) and the new California Consumer Privacy Act (CCPA), that went into effect last week on Jan. GDPR: Key Differences.

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Do I Need a Data Catalog?

erwin

If you’re serious about a data-driven strategy , you’re going to need a data catalog. Organizations need a data catalog because it enables them to create a seamless way for employees to access and consume data and business assets in an organized manner. This also diminishes the value of data as an asset.

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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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Data Observability and Monitoring with DataOps

DataKitchen

Data errors impact decision-making. Data errors infringe on work-life balance. Data errors also affect careers. If you have been in the data profession for any length of time, you probably know what it means to face a mob of stakeholders who are angry about inaccurate or late analytics.

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