erwin

FEBRUARY 28, 2024

While the essence of success in data governance is people and not technology, having the right tools at your fingertips is crucial. Technology is an enabler, and for data governance this is essentially having an excellent metadata management tool. Next to data governance, data architecture is really embedded in our DNA.

Metadata 52

Metadata Data Governance Data Quality Technology 52

TDAN

MAY 17, 2022

One of the greatest contributions to the understanding of data quality and data quality management happened in the 1980s when Stuart Madnick and Rich Wang at MIT adapted the concept of Total Quality Management (TQM) from manufacturing to Information Systems reframing it as Total Data Quality Management (TDQM).

Data Quality 52

Data Quality Manufacturing Management IT 52

CIO Business Intelligence

APRIL 24, 2024

In fact, AMA collects a huge amount of structured and unstructured data from bins, collection vehicles, facilities, and user reports, and until now, this data has remained disconnected, managed by disparate systems and interfaces, through Excel spreadsheets.

Digital Transformation 98

Digital Transformation Business Intelligence Unstructured Data Data Lake 98

AWS Big Data

NOVEMBER 9, 2023

As part of their cloud modernization initiative, they sought to migrate and modernize their legacy data platform. Data sources As part of this data platform, we are ingesting data from diverse and varied data sources, including: Transactional databases – These are active databases that store real-time data from various applications.

Data Warehouse 106

Data Warehouse Testing Data Quality Reporting 106

erwin

JULY 12, 2019

It also helps enterprises put these strategic capabilities into action by: Understanding their business, technology and data architectures and their inter-relationships, aligning them with their goals and defining the people, processes and technologies required to achieve compliance. Strengthen data security. How erwin Can Help.

Data Governance 110

Data Governance Management Metadata Risk Management 110

erwin

SEPTEMBER 13, 2022

Learn how to maximize the business impact of your data. Discover how IT, data governance teams, and business users benefit from data intelligence initiatives through automated data cataloging, data quality, data literacy, and self-service data preparation.

Data Governance 52

Data Governance Data-driven Data Architecture Data Quality 52

CIO Business Intelligence

JUNE 9, 2023

A few years ago, Gartner found that “organizations estimate the average cost of poor data quality at $12.8 million per year.’” Beyond lost revenue, data quality issues can also result in wasted resources and a damaged reputation. Learn more about data architectures in my article here.

Strategy 109

Strategy Management Key Performance Indicator ROI 109

Ontotext

NOVEMBER 16, 2023

Data mesh solves this by promoting data autonomy, allowing users to make decisions about domains without a centralized gatekeeper. It also improves development velocity with better data governance and access with improved data quality aligned with business needs. What is Data Sharing?

Metadata 52

Metadata Data-driven Data Quality Data Architecture 52

Ontotext

DECEMBER 1, 2023

Most organisations are missing this ability to connect all the data together. from Q&A with Tim Berners-Lee ) Finally, Sumit highlighted the importance of knowledge graphs to advance semantic data architecture models that allow unified data access and empower flexible data integration.

Metadata 78

Metadata Sales Consulting Enterprise 78

CIO Business Intelligence

MAY 20, 2022

That’s what some people asked of Adriana “Andi” Karaboutis, the former CIO of Dell who left the computer giant and was named National Grid’s global chief information and digital officer in 2017. I don’t want to give you exact numbers, but I had a smaller budget when I was the chief information officer at Dell.”. Her answer?

IT 97

IT Digital Transformation Internet of Things Data Architecture 97

IBM Big Data Hub

JULY 13, 2023

When we talk about data integrity, we’re referring to the overarching completeness, accuracy, consistency, accessibility, and security of an organization’s data. Together, these factors determine the reliability of the organization’s data. Data quality Data quality is essentially the measure of data integrity.

Data Quality 63

Data Quality Data Integration Metadata Cost-Benefit 63

erwin

DECEMBER 12, 2019

It’s generated every time data is captured at a source, accessed by users, moved through an organization, integrated or augmented with other data from other sources, profiled, cleansed and analyzed. It answers these important questions: What data do we have? Where did it come from? by up to 70 percent.

Metadata 84

Metadata Data Governance Digital Transformation Data Quality 84

Sisense

AUGUST 28, 2020

However, as a data team member, you know how important data integrity (and a whole host of other aspects of data management) is. In this article, we’ll dig into the core aspects of data integrity, what processes ensure it, and how to deal with data that doesn’t meet your standards.

Data Integration 101

Data Integration Testing Data Quality Data-driven 101

AWS Big Data

JANUARY 31, 2023

Data governance is increasingly top-of-mind for customers as they recognize data as one of their most important assets. Effective data governance enables better decision-making by improving data quality, reducing data management costs, and ensuring secure access to data for stakeholders.

Data Lake 83

Data Lake Data Governance Data Architecture Machine Learning 83

IBM Big Data Hub

FEBRUARY 9, 2023

Realize that a data governance program cannot exist on its own – it must solve business problems and deliver outcomes. Start by identifying business objectives, desired outcomes, key stakeholders, and the data needed to deliver these objectives. So where are you in your data governance journey?

Data Governance 76

Data Governance Business Objectives Data Quality Strategy 76

Octopai

APRIL 19, 2021

Strong metadata management enhances business intelligence which leads to more informed strategy and better performance. Donna Burbank is a Data Management Consultant and acts as the Managing Director at Global Data Strategy, Ltd. She tweets and retweets about topics such as data governance, data strategy, and data architecture.

Metadata 52

Metadata Management Business Intelligence Data Governance 52

AWS Big Data

APRIL 19, 2023

The goal of a data product is to solve the long-standing issue of data silos and data quality. Independent data products often only have value if you can connect them, join them, and correlate them to create a higher order data product that creates additional insights.

Technology 83

Technology Data-driven Machine Learning Sales 83

IBM Big Data Hub

JANUARY 25, 2023

Breaking down these silos to encourage data access, data sharing and collaboration will be an important challenge for organizations in the coming years. The right data architecture to link and gain insight across silos requires the communication and coordination of a strategic data governance program.

Data Governance 52

Data Governance Data-driven Strategy Management 52

CIO Business Intelligence

APRIL 29, 2022

The strategy should put formalized processes in place to quantify the value of different types of information, leveraging the skills of a chief data officer (CDO), who should form and chair a data governance committee.

Digital Transformation 118

Digital Transformation Strategy Data-driven Data Strategy 118

Jedox

OCTOBER 22, 2020

The term “data analytics” refers to the process of examining datasets to draw conclusions about the information they contain. Data analysis techniques enhance the ability to take raw data and uncover patterns to extract valuable insights from it.

Data Analytics 69

Data Analytics Analytics Unstructured Data Data mining 69

erwin

SEPTEMBER 17, 2020

Replace manual and recurring tasks for fast, reliable data lineage and overall data governance. It’s paramount that organizations understand the benefits of automating end-to-end data lineage. Critically, it makes it easier to get a clear view of how information is created and flows into, across and outside an enterprise.

Cost-Benefit 143

Cost-Benefit Data Governance Metadata Reporting 143

erwin

DECEMBER 12, 2019

It’s generated every time data is captured at a source, accessed by users, moved through an organization, integrated or augmented with other data from other sources, profiled, cleansed and analyzed. It answers these important questions: What data do we have? Where did it come from? by up to 70 percent.

Metadata 52

Metadata Data Governance Digital Transformation Data Quality 52

CIO Business Intelligence

MAY 21, 2024

Engineers can now ingest data from each car every second with EventStreams, analyze it with Eventhouse, and distribute it to team managers and race participants. So Morrone and his engineers no longer need to rely on drivers reading indicators on their dashboards and passing along information.

Broadcasting 104

Broadcasting Recreation/Entertainment Manufacturing Data Lake 104

Cloudera

DECEMBER 28, 2017

“We’re seeing a large increase in regulatory demand, the amount of data that we have to produce, and the accuracy of that information as well. So there’s a general explosion and growth due to more data, but more questions are being asked of the information.

Big Data 40

Big Data Data Architecture Data Quality Machine Learning 40

erwin

SEPTEMBER 19, 2019

Data sets are not important in and of themselves. Data sets become important in terms of how they are used, who uses them and what their use is – and all this information is described in the processes that generate, manipulate and use them. Will any of the above information tell us why it happened? Now a breach occurs.

Data Governance 108

Data Governance Cost-Benefit Enterprise Modeling 108

Peter James Thomas

OCTOBER 30, 2018

Where they have, I have normally found the people holding these roles to be better informed about data matters than their peers. The closing sentence of the article is probably its most revealing and informative: […] marketers must make sure they are leading [the data] agenda, or someone else will do it for them.

Marketing 51

Marketing Strategy Data Architecture Data Strategy 51

Data Virtualization

MAY 19, 2023

Reading Time: 11 minutes The post Data Strategies for Getting Greater Business Value from Distributed Data appeared first on Data Management Blog - Data Integration and Modern Data Management Articles, Analysis and Information.

Data Strategy 52

Data Strategy Strategy Data Integration Management 52

Sisense

SEPTEMBER 3, 2020

Programming and statistics are two fundamental technical skills for data analysts, as well as data wrangling and data visualization. Data analysts in one organization might be called data scientists or statisticians in another. Database Specialist.

Statistics 97

Statistics Metrics Visualization Finance 97

TDAN

SEPTEMBER 7, 2022

There are many ways to ensure high-quality information in Salesforce Sales Cloud with regular cleansing routines that keep up-to date records for each […]. Without it, you may find yourself missing out on opportunities or dissatisfaction from your customers— not what we want at all!

Sales 52

Sales Data Quality Data Architecture IT 52

Peter James Thomas

AUGUST 18, 2018

Even back then, these were used for activities such as Analytics , Dashboards , Statistical Modelling , Data Mining and Advanced Visualisation. Of course some architectures featured both paradigms as well. The key lesson is that shape of your architecture is going to be pretty similar, regardless of the technical underpinnings.

Data Lake 63

Data Lake Data Warehouse Data mining Statistics 63

IBM Big Data Hub

SEPTEMBER 7, 2022

In the last year we’ve spent a lot more time talking about data ethics. According to Harvard Business Review , data ethics is defined as “the moral obligations of gathering, protecting, and using personally identifiable information and how it affects individuals.”

Data Strategy 52

Data Strategy Strategy Data-driven Data Quality 52

Alation

MAY 31, 2023

This means that specialized roles such as data architects, which focus on modernizing data architecture to help meet business goals, are increasingly important to support data governance. What is a data architect? Their broad range of responsibilities include: Design and implement data architecture.

Data Governance 52

Data Governance Metadata Data Architecture Data Quality 52

CIO Business Intelligence

FEBRUARY 20, 2023

To earn the Salesforce Data Architect certification , candidates should be able to design and implement data solutions within the Salesforce ecosystem, such as data modelling, data integration and data governance.

Consulting 62

Consulting B2B Cost-Benefit Sales 62

CIO Business Intelligence

JANUARY 30, 2024

CSDDD is expected to make the reporting of ESG-related information a full-time job and fundamentally change how companies are structured around this reporting,” says Mentesana. What companies need more than anything is good data for ESG reporting. Data quality is key, but if we’re doing it manually there’s the potential for mistakes.

Reporting 103

Reporting Data Quality Strategy Data-driven 103

TDAN

MAY 14, 2019

Welcome to DAMA Corner, a source of information for data management professionals here in TDAN.com, an industry-leading publication for people interested in learning about data administration, data management disciplines, and best practices.

Management 40

Management Data Quality Data Architecture Metadata 40

Cloudera

OCTOBER 1, 2022

The scale of information that every company is bringing in has absolutely gotten massive logarithmic growth. People selling information. Whether that’s appreciated or not, a lot of companies are using information that they didn’t generate, that someone else did and now they have to take ownership of it.”.

Data Governance 77

Data Governance Strategy Enterprise Machine Learning 77

Cloudera

SEPTEMBER 7, 2023

Given the complexity of the datasets used to train AI systems, and factoring in the known tendency of generative AI systems to invent non-factual information, this is no small task. But these measures alone may not be sufficient to protect proprietary information. proprietary data, business strategies, methodologies, etc.

Insurance 85

Insurance Risk Data-driven Finance 85