CIO Business Intelligence

MAY 10, 2023

AI relies upon large sets of data fed into it to help create output but is limited by the quality of data that is consumed by the model. This was on display during the initial test releases of Google Bard, where it provided a factually inaccurate answer on the James Webb Space Telescope based on reference data it ingested.

Metadata 96

Metadata Data Governance Modeling Technology 96

CIO Business Intelligence

OCTOBER 13, 2023

Application data architect: The application data architect designs and implements data models for specific software applications. Information/data governance architect: These individuals establish and enforce data governance policies and procedures.

Data Architecture 127

Data Architecture Data Warehouse Statistics Visualization 127

erwin

JANUARY 11, 2019

Organizations have spent a lot of time and money trying to harmonize data across diverse platforms , including cleansing, uploading metadata, converting code, defining business glossaries, tracking data transformations and so on. And there’s control of that landscape to facilitate insight and collaboration and limit risk.

Data Governance 40

Data Governance Risk Metadata Management 40

erwin

AUGUST 7, 2019

A data catalog benefits organizations in a myriad of ways. With the right data catalog tool, organizations can automate enterprise metadata management – including data cataloging, data mapping, data quality and code generation for faster time to value and greater accuracy for data movement and/or deployment projects.

Metadata 90

Metadata Data Governance Data Quality Data Warehouse 90

Ontotext

FEBRUARY 12, 2024

Determine ownership by making sure all teams involved in the data mesh own the quality of their domain data, ensure service-level agreements are met, and share that data with data contracts. Domain teams should continually monitor for data errors with data validation checks and incorporate data lineage to track usage.

Data Quality 69

Data Quality Data-driven Data Lake Data Governance 69

Alation

APRIL 18, 2023

Gartner defines data profiling as: A technology for discovering and investigating data quality issues, such as duplication, lack of consistency, and lack of accuracy and completeness. The tools provide data statistics, such as degree of duplication and ratios of attribute values, both in tabular and graphical formats.

IT 52

IT Metadata Data Quality Data Governance 52

datapine

SEPTEMBER 29, 2022

This person (or group of individuals) ensures that the theory behind data quality is communicated to the development team. 2 – Data profiling. Data profiling is an essential process in the DQM lifecycle. These processes could include reports, campaigns, or financial documentation. date, month, and year).

Data Quality 241

Data Quality Metrics Data-driven Management 241

Laminar Security

SEPTEMBER 18, 2023

I’ve previously discussed the risk of shadow data (those data stores you didn’t even know you had), and the team specifically dove into how versioning (a feature used to protect from accidental deletion) can unintentionally create shadow data. There is another class of shadow data that’s caused intentionally by data backups.

Risk 128

Risk Testing Data Governance Metadata 128

Alation

DECEMBER 21, 2022

Transactional systems and data warehouses can then use the golden records as the entity’s most current, trusted representation. Data Catalog and Master Data Management. Early on, analysts used data catalogs to find and understand data more quickly. Having good data is crucial to creating golden records.

Management 52

Management Data Quality Metadata Testing 52

O'Reilly on Data

DECEMBER 19, 2018

have a large body of tools to choose from: IDEs, CI/CD tools, automated testing tools, and so on. are only starting to exist; one big task over the next two years is developing the IDEs for machine learning, plus other tools for data management, pipeline management, data cleaning, data provenance, and data lineage.

Machine Learning 261

Machine Learning Software Metadata Testing 261

O'Reilly on Data

JULY 28, 2020

Product Managers are responsible for the successful development, testing, release, and adoption of a product, and for leading the team that implements those milestones. Some of the best lessons are captured in Ron Kohavi, Diane Tang, and Ya Xu’s book: Trustworthy Online Controlled Experiments : A Practical Guide to A/B Testing.

Marketing 362

Marketing Experimentation Metrics Testing 362

AWS Big Data

JUNE 9, 2023

Data in customers’ data lakes is used to fulfil a multitude of use cases, from real-time fraud detection for financial services companies, inventory and real-time marketing campaigns for retailers, or flight and hotel room availability for the hospitality industry. This post is not intended to provide detailed technical guidance (e.g.

Data Lake 117

Data Lake Metadata Optimization Statistics 117

erwin

FEBRUARY 19, 2021

Metadata management is key to wringing all the value possible from data assets. However, most organizations don’t use all the data at their disposal to reach deeper conclusions about how to drive revenue, achieve regulatory compliance or accomplish other strategic objectives. What Is Metadata? Harvest data.

Metadata 110

Metadata Management Data Quality Cost-Benefit 110

Domino Data Lab

JULY 2, 2019

In other words, using metadata about data science work to generate code. In this case, code gets generated for data preparation, where so much of the “time and labor” in data science work is concentrated. Less data gets decompressed, deserialized, loaded into memory, run through the processing, etc.

Metadata 105

Metadata Machine Learning Data Science Data-driven 105

Alation

NOVEMBER 15, 2021

This highly respected standards & training organization was “created to elevate the practice of data management as a business and operational priority.” The Data Management Capability Assessment Method (DCAM) framework is used to pressure-test data management procedures and demonstrate good practices to regulators.

Management 52

Management Testing Metadata Consulting 52

Domino Data Lab

APRIL 3, 2019

Paco Nathan ‘s latest column dives into data governance. This month’s article features updates from one of the early data conferences of the year, Strata Data Conference – which was held just last week in San Francisco. In particular, here’s my Strata SF talk “Overview of Data Governance” presented in article form.

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84

DataKitchen

FEBRUARY 2, 2023

DataOps Automation (Orchestration, Environment Management, Deployment Automation) DataOps Observability (Monitoring, Test Automation) Data Governance (Catalogs, Lineage, Stewardship) Data Privacy (Access and Compliance) Data Team Management (Projects, Tickets, Documentation, Value Stream Management) What are the drivers of this consolidation?

Metadata 130

Metadata Testing Risk Data Science 130

Domino Data Lab

AUGUST 8, 2019

In this episode I’ll cover themes from Sci Foo and important takeaways that data science teams should be tracking. First and foremost: there’s substantial overlap between what the scientific community is working toward for scholarly infrastructure and some of the current needs of data governance in industry. Sci Foo 2019.

Data Science 71

Data Science Machine Learning Data Governance Statistics 71

IBM Big Data Hub

FEBRUARY 26, 2024

The financial services industry has been in the process of modernizing its data governance for more than a decade. But as we inch closer to global economic downturn, the need for top-notch governance has become increasingly urgent. Download the Gartner® Market Guide for Active Metadata Management 1.

Cost-Benefit 98

Cost-Benefit Metadata Data Governance Reporting 98

AWS Big Data

AUGUST 14, 2023

In the same way, overly restrictive data governance practices that either prevent data products from taking root at all, or pare them back too aggressively (deforestation), can over time create “data deserts” that drive both the producers and consumers of data within an organization to look elsewhere for their data needs.

Management 81

Management Risk Measurement Optimization 81

Alation

APRIL 14, 2022

Semantics, context, and how data is tracked and used mean even more as you stretch to reach post-migration goals. This is why, when data moves, it’s imperative for organizations to prioritize data discovery. Data discovery is also critical for data governance , which, when ineffective, can actually hinder organizational growth.

Metadata 59

Metadata Data Governance Modeling Data-driven 59

Grooper

OCTOBER 23, 2019

Lack of competitiveness or business losses seem to be the main drivers to becoming more proactive with data governance and management. What you’re shooting for is enterprise-wide information governance. Information governance is more than just ‘store everything!’ Want to test your governance level?

Metrics 59

Metrics Measurement Strategy Optimization 59

erwin

SEPTEMBER 25, 2020

Understanding the benefits of data modeling is more important than ever. Data modeling is the process of creating a data model to communicate data requirements, documenting data structures and entity types. It has many benefits. Successfully design and implement databases. Foster successful cloud adoption.

Modeling 111

Modeling Cost-Benefit Visualization Metadata 111

IBM Big Data Hub

APRIL 11, 2024

Though these principles aligned with the themes above–including stating that AI tools “should be tested before deployment”–the AI-powered chatbot that the city rolled out to answer questions about starting and operating a business gave answers that encouraged users to break the law.

Risk 77

Risk Consulting Modeling Data Processing 77

Alation

SEPTEMBER 6, 2022

Ten years ago, there was no modern data catalog. So I was excited to sit down recently with Michael Ting, Senior Software Engineer on the Logical Metadata Infrastructure Team. I worked with biological databases, pulling information and running experiments to synthesize something, test it, and see if you can build a tool.

Data-driven 52

Data-driven Software Testing Metadata 52

Alation

JANUARY 17, 2023

Data literacy — Employees can interpret and analyze data to draw logical conclusions; they can also identify subject matter experts best equipped to educate on specific data assets. Data governance is a key use case of the modern data stack. Who Can Adopt the Modern Data Stack?

Data Warehouse 52

Data Warehouse Cost-Benefit Data Transformation Data Science 52

Alation

FEBRUARY 17, 2023

This past week, I had the pleasure of hosting Data Governance for Dummies author Jonathan Reichental for a fireside chat , along with Denise Swanson , Data Governance lead at Alation. Can you have proper data management without establishing a formal data governance program?

Data Governance 52

Data Governance Data Quality Metadata Cost-Benefit 52

Cloudera

OCTOBER 7, 2021

Application Logic: Application logic refers to the type of data processing, and can be anything from analytical or operational systems to data pipelines that ingest data inputs, apply transformations based on some business logic and produce data outputs.

Metadata 122

Metadata Cost-Benefit Enterprise Interactive 122

erwin

NOVEMBER 6, 2020

Prashant Parikh, erwin’s Senior Vice President of Software Engineering, talks about erwin’s vision to automate every aspect of the data governance journey to increase speed to insights. Although AI and ML are massive fields with tremendous value, erwin’s approach to data governance automation is much broader.

Data Governance 97

Data Governance Metadata Data-driven Visualization 97

DataKitchen

JANUARY 23, 2023

Data lineage can also be used for compliance, auditing, and data governance purposes. DataOps Observability Five on data lineage: Data lineage traces data’s origin, history, and movement through various processing, storage, and analysis stages. What is missing in data lineage?

Testing 130

Testing Data Governance Data Quality Data-driven 130

Jet Global

FEBRUARY 23, 2024

Source-to-target mapping integration tasks vary in complexity, depending on data hierarchy and structure. Business applications use metadata and semantic rules to ensure seamless data transfer without loss. Finally, test and automate your data mapping process.

Data Warehouse 52

Data Warehouse Reporting Data Transformation Sales 52