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. But the attempts to standardize data across the entire enterprise haven’t produced the desired results.

Data Governance 40

Data Governance Risk Metadata Management 40

erwin

DECEMBER 12, 2019

Metadata is an important part of data governance, and as a result, most nascent data governance programs are rife with project plans for assessing and documenting metadata. Managing metadata should not be a sub-goal of data governance. The Role of Metadata in Data Governance.

Metadata 84

Metadata Data Governance Digital Transformation Data Quality 84

erwin

JANUARY 17, 2019

Data governance is more important to the enterprise than ever before. It ensures everyone in the organization can discover and analyze high-quality data to quickly deliver business value. A mature and sustainable data governance initiative must include data integration.

Metadata 63

Metadata Data Governance Data Quality Data-driven 63

erwin

DECEMBER 12, 2019

Metadata is an important part of data governance, and as a result, most nascent data governance programs are rife with project plans for assessing and documenting metadata. Managing metadata should not be a sub-goal of data governance. The Role of Metadata in Data Governance.

Metadata 52

Metadata Data Governance Digital Transformation Data Quality 52

Alation

JUNE 2, 2022

In part one of this series, I discussed how data management challenges have evolved and how data governance and security have to play in such challenges, with an eye to cloud migration and drift over time. Data management is not yet a solved problem, but modern data management is leagues ahead of prior approaches.

Modeling 52

Modeling Data Governance Statistics Unstructured Data 52

Andrew White

NOVEMBER 14, 2022

How is it that trying to be aware of all your data is the first step to working out what data (or analytic) actually needs to be governed first? Catalog are useful in a small number of data governance programs only. By documenting an organizations data issues. Or second and tenth? Maybe much later.

Data Integration 52

Data Integration Technology Data Governance Analytics 52

erwin

FEBRUARY 4, 2021

Better decision-making has now topped compliance as the primary driver of data governance. However, organizations still encounter a number of bottlenecks that may hold them back from fully realizing the value of their data in producing timely and relevant business insights. Points of integration. Sources, like IoT.

Data Governance 89

Data Governance Metadata Data Quality Risk Management 89

Ontotext

MARCH 8, 2023

However, this information is typically stored in disparate locations, often hidden within departmental documents or applications. Use Case #6: Data Quality and Governance The size and complexity of data sources and datasets is making traditional data dictionaries and Entity Relationship Diagrams (ERD) inadequate.

Enterprise 52

Enterprise Knowledge Discovery Risk Data-driven 52

Peter James Thomas

SEPTEMBER 19, 2018

The peterjamesthomas.com Data and Analytics Dictionary is an active document and I will continue to issue revised versions of it periodically. Data Asset. Data Audit. Data Classification. Data Consistency. Data Controls. Data Curation (contributor: Tenny Thomas Soman ). Data Democratisation.

Analytics 45

Analytics Testing Data Strategy Data Integration 45

Ontotext

JANUARY 10, 2024

And each of these gains requires data integration across business lines and divisions. Limiting growth by (data integration) complexity Most operational IT systems in an enterprise have been developed to serve a single business function and they use the simplest possible model for this. We call this the Bad Data Tax.

Metadata 72

Metadata Slice and Dice Data Integration Enterprise 72

IBM Big Data Hub

JANUARY 9, 2024

The entire generative AI pipeline hinges on the data pipelines that empower it, making it imperative to take the correct precautions. 4 key components to ensure reliable data ingestion Data quality and governance: Data quality means ensuring the security of data sources, maintaining holistic data and providing clear metadata.

Enterprise 77

Enterprise Data Integration Data Quality Contextual Data 77

erwin

FEBRUARY 19, 2021

It involves establishing policies and processes to ensure information can be integrated, accessed, shared, linked, analyzed and maintained across an organization. Better data quality. It harvests metadata from various data sources and maps any data element from source to target and harmonize data integration across platforms.

Metadata 110

Metadata Management Data Quality Cost-Benefit 110

erwin

FEBRUARY 6, 2020

As organizations deal with managing ever more data, the need to automate data management becomes clear. Last week erwin issued its 2020 State of Data Governance and Automation (DGA) Report. One piece of the research that stuck with me is that 70% of respondents spend 10 or more hours per week on data-related activities.

Management 144

Management Data Governance Cost-Benefit Metadata 144

CIO Business Intelligence

FEBRUARY 20, 2023

A candidate who earns the certification will have proved their ability to build bundle configurations, pricing, output documents, and renewals and amendments.

Consulting 65

Consulting B2B Cost-Benefit Sales 65

erwin

MAY 27, 2020

They should automatically generate data models , providing a simple, graphical display to visualize a wide range of enterprise data sources based on a common repository of standard data assets through a single interface. Data siloes, of course, are the enemies of data governance.

Modeling 98

Modeling Metadata Data Governance Visualization 98

IBM Big Data Hub

OCTOBER 12, 2023

Accounting for the complexities of the AI lifecycle Unfortunately, typical data storage and data governance tools fall short in the AI arena when it comes to helping an organization perform the tasks that underline efficient and responsible AI lifecycle management. And that makes sense.

Cost-Benefit 81

Cost-Benefit Metadata Data Governance Modeling 81

Andrew White

JANUARY 9, 2022

Is there a good map that shows the connections between data, advanced analytics, digital, innovation, etc. I would have to admit that there are few documents that talk about all the connections across any set of topics. Could you precise to which complementary research you mentioned when you talked about a data governance survey ?

Analytics 64

Analytics Measurement Modeling Data-driven 64

erwin

MAY 17, 2021

erwin by Quest just released the “2021 State of Data Governance and Empowerment” report. This past year also saw a major shift as the silos between data governance, data operations and data protection diminished, with enterprises seeking to understand their data and the systems they use and secure to empower smarter decision-making.

Data Governance 99

Data Governance Data Quality Snapshot Reporting 99

erwin

JUNE 17, 2021

erwin by Quest just released the “ 2021 State of Data Governance and Empowerment” report. This past year also saw a major shift as the silos between data governance, data operations and data protection diminished, with enterprises seeking to understand their data and the systems they use and secure to empower smarter decision-making.

Data Governance 59

Data Governance Data Quality Snapshot Reporting 59

datapine

FEBRUARY 22, 2022

They can govern the implementation with a documented business case and be responsible for changes in scope. IT should be involved to ensure governance, knowledge transfer, data integrity, and the actual implementation. Find a way to integrate it into the new strategy, or you will have upset employees.

Business Intelligence 218

Business Intelligence Strategy Cost-Benefit Key Performance Indicator 218

CIO Business Intelligence

NOVEMBER 7, 2023

Opting for a centralized data and reporting model rather than training and embedding analysts in individual departments has allowed us to stay nimble and responsive to meet urgent needs, and prevented us from spending valuable resources on low-value data projects which often had little organizational impact,” Higginson says.

Data Warehouse 141

Data Warehouse Digital Transformation Reporting Data-driven 141

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

erwin

AUGUST 1, 2023

Because of the ability to provide standardization and foundational stability through all these processes, data modeling is a crucial step to success, and erwin ® Data Modeler by Quest ® is celebrating its 30 th birthday by releasing its newest, most collaborative version to date. erwin Data Modeler 12.5

Modeling 52

Modeling Metadata Data Quality Data Governance 52

erwin

JULY 17, 2019

Outsourcing these data management efforts to professional services firms only delays schedules and increases costs. With automation, data quality is systemically assured. The data pipeline is seamlessly governed and operationalized to the benefit of all stakeholders. Digital Transformation Strategy: Smarter Data.

Digital Transformation 98

Digital Transformation Strategy Metadata Data-driven 98

O'Reilly on Data

DECEMBER 19, 2018

Anomaly detection is well-known in the financial industry, where it’s frequently used to detect fraudulent transactions, but it can also be used to catch and fix data quality issues automatically. We are starting to see some tools that automate data quality issues. We also see investment in new kinds of tools.

Machine Learning 261

Machine Learning Software Modeling Testing 261

Jet Global

MARCH 4, 2024

Data inconsistencies become commonplace, hindering visibility and inhibiting a holistic understanding of business operations. This lack of integration makes it difficult to track progress, measure performance, and identify potential issues. Data governance and compliance become a constant juggling act. Don’t believe us?

Management 52

Management Data Governance Reporting Software 52

Jet Global

FEBRUARY 23, 2024

Data mapping is essential for integration, migration, and transformation of different data sets; it allows you to improve your data quality by preventing duplications and redundancies in your data fields. The first step of data mapping is defining the scope of your data mapping project.

Data Warehouse 52

Data Warehouse Reporting Data Transformation Sales 52

Jet Global

MARCH 14, 2024

Jet streamlines many aspects of data administration, greatly improving data solutions built on Microsoft Fabric. It enhances analytics capabilities, streamlines migration, and enhances data integration. Through Jet’s integration with Fabric, your organization can better handle, process, and use your data.

Analytics 52

Analytics Management Reporting Enterprise 52