datapine

SEPTEMBER 29, 2022

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.

Data Quality 241

Data Quality Metrics Data-driven Management 241

Ontotext

JUNE 10, 2021

And yeah, the real-world relationships among the entities represented in the data had to be fudged a bit to fit in the counterintuitive model of tabular data, but, in trade, you get reliability and speed. Ironically, relational databases only imply relationships between data points by whatever row or column they exist in.

Metadata 119

Metadata Cost-Benefit OLAP Modeling 119

Rocket-Powered Data Science

JULY 19, 2023

I recently saw an informal online survey that asked users which types of data (tabular, text, images, or “other”) are being used in their organization’s analytics applications. The results showed that (among those surveyed) approximately 90% of enterprise analytics applications are being built on tabular data.

Data-driven 188

Data-driven Enterprise Analytics Machine Learning 188

erwin

OCTOBER 24, 2019

Untapped data, if mined, represents tremendous potential for your organization. While there has been a lot of talk about big data over the years, the real hero in unlocking the value of enterprise data is metadata , or the data about the data. Metadata Is the Heart of Data Intelligence.

Metadata 104

Metadata Management Data-driven Data Architecture 104

erwin

JANUARY 17, 2020

What is Data Modeling? Data modeling is a process that enables organizations to discover, design, visualize, standardize and deploy high-quality data assets through an intuitive, graphical interface. Data models provide visualization, create additional metadata and standardize data design across the enterprise.

Data-driven 138

Data-driven Modeling Metadata Data Governance 138

erwin

FEBRUARY 7, 2019

Organization’s cannot hope to make the most out of a data-driven strategy, without at least some degree of metadata-driven automation. The volume and variety of data has snowballed, and so has its velocity. So it’s safe to say that organizations can’t reap the rewards of their data without automation.

Metadata 96

Metadata Insurance Data-driven Cost-Benefit 96

Alation

MARCH 2, 2023

Instead, we got data. Lots and lots of data. It does feel, however, as if we need jet-like speed to analyze and understand our data, who is using it, how it is used, and if it is being used to drive value. This is because simply collecting data leaves it open to misinterpretation, misuse, and decay.

Metadata 52

Metadata Marketing IT Data Quality 52

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. But in many scenarios, it seems that the underlying driver of metadata collection projects is that it’s just something you do for data governance.

Metadata 84

Metadata Data Governance Digital Transformation Data Quality 84

erwin

MAY 31, 2019

It’s time to consider data-driven enterprise architecture. The traditional approach to enterprise architecture – the analysis, design, planning and implementation of IT capabilities for the successful execution of enterprise strategy – seems to be missing something … data. That’s right. This is what we call the Mezzo.

Data-driven 104

Data-driven Enterprise Metadata Strategy 104

AWS Big Data

NOVEMBER 13, 2024

This second post of a two-part series that details how Volkswagen Autoeuropa , a Volkswagen Group plant, together with AWS, built a data solution with a robust governance framework using Amazon DataZone to become a data-driven factory. Next, we detail the governance guardrails of the Volkswagen Autoeuropa data solution.

Metadata 75

Metadata Data Quality Digital Transformation Data-driven 75

AWS Big Data

OCTOBER 31, 2024

This is a joint blog post co-authored with Martin Mikoleizig from Volkswagen Autoeuropa. Volkswagen Autoeuropa aims to become a data-driven factory and has been using cutting-edge technologies to enhance digitalization efforts. The lead time to access data was often from several days to weeks.

Digital Transformation 98

Digital Transformation Metadata Manufacturing Data Quality 98

erwin

NOVEMBER 19, 2020

erwin has once again been positioned as a Leader in the Gartner “2020 Magic Quadrant for Metadata Management Solutions.”. The post erwin Positioned as a Leader in Gartner’s 2020 Magic Quadrant for Metadata Management Solutions for Second Year in a Row appeared first on erwin, Inc.

Metadata 98

Metadata Management Digital Transformation Data Governance 98

erwin

OCTOBER 4, 2021

Metadata management is essential to becoming a data-driven organization and reaping the competitive advantage your organization’s data offers. Gartner refers to metadata as data that is used to enhance the usability, comprehension, utility or functionality of any other data point.

Metadata 52

Metadata Software Management Visualization 52

erwin

NOVEMBER 6, 2019

erwin released its State of Data Governance Report in February 2018, just a few months before the General Data Protection Regulation (GDPR) took effect. Download Free GDPR Guide | Step By Step Guide to Data Governance for GDPR?. IDC Technology Spotlight, Data Intelligence in Context: Get the report (… it’s free).

Metadata 102

Metadata Data Governance Management Cost-Benefit 102

AWS Big Data

MARCH 30, 2023

An event-driven architecture is a software design pattern in which decoupled applications can asynchronously publish and subscribe to events via an event broker. By containerizing your data processing tasks, you can simply deploy them into Amazon EKS as Kubernetes jobs and use Kubernetes to manage underlying computing compute resources.

Data-driven 73

Data-driven Metadata Testing Management 73

IBM Big Data Hub

JANUARY 17, 2023

Due to the convergence of events in the data analytics and AI landscape, many organizations are at an inflection point. Furthermore, a global effort to create new data privacy laws, and the increased attention on biases in AI models, has resulted in convoluted business processes for getting data to users. Data governance.

Data-driven 56

Data-driven Enterprise Data Governance Prescriptive Analytics 56

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. But in many scenarios, it seems that the underlying driver of metadata collection projects is that it’s just something you do for data governance.

Metadata 52

Metadata Data Governance Digital Transformation Data Quality 52

IBM Big Data Hub

JUNE 8, 2022

Implementing the right data strategy spurs innovation and outstanding business outcomes by recognizing data as a critical asset that provides insights for better and more informed decision-making. By taking advantage of data, enterprises can shape business decisions, minimize risk for stakeholders, and gain competitive advantage.

Data-driven 56

Data-driven Enterprise Data Governance Data Lake 56

AWS Big Data

NOVEMBER 14, 2024

The landscape of big data management has been transformed by the rising popularity of open table formats such as Apache Iceberg, Apache Hudi, and Linux Foundation Delta Lake. These formats, designed to address the limitations of traditional data storage systems, have become essential in modern data architectures.

Metadata 98

Metadata Data Warehouse Big Data Data Lake 98

Cloudera

NOVEMBER 15, 2024

In fact, according to the Identity Theft Resource Center (ITRC) Annual Data Breach Report , there were 2,365 cyber attacks in 2023 with more than 300 million victims, and a 72% increase in data breaches since 2021. However, there is a fundamental challenge standing in the way of being successful: data.

Analytics 77

Analytics Metadata Snapshot Data-driven 77

Rocket-Powered Data Science

JULY 6, 2021

If you include the title of this blog, you were just presented with 13 examples of heteronyms in the preceding paragraphs. Specifically, in the modern era of massive data collections and exploding content repositories, we can no longer simply rely on keyword searches to be sufficient. Data catalogs are very useful and important.

Strategy 267

Strategy Machine Learning Metadata Knowledge Discovery 267

Cloudera

APRIL 18, 2024

All of that technology, though, depends on data to be successful. In those discussions, it was clear that everyone understood the need to treat data estates more cohesively as a whole—that means bringing more attention to security, data governance, and metadata management, the latter of which has become increasingly popular.

Analytics 110

Analytics Metadata Data Strategy Optimization 110

erwin

APRIL 16, 2020

We’re excited about our recognition as a March 2020 Gartner Peer Insights Customers’ Choice for Metadata Management Solutions. The solutions work in tandem to automate the processes involved in harvesting, integrating, activating and governing enterprise data according to business requirements.

Metadata 52

Metadata Management Data Governance Digital Transformation 52

Ontotext

MARCH 13, 2024

We started with our marketing content and quickly expanded that to also integrate a set of workflows for data and content management. Our goal is to generate a knowledge space where information is easy to find, reuse, and fuel knowledge-driven insights. In this way, we benefit from better SEO and semantic-driven content discovery.

Marketing 72

Marketing Knowledge Discovery Metadata Data-driven 72

Timo Elliott

JANUARY 6, 2022

Like the proverbial man looking for his keys under the streetlight , when it comes to enterprise data, if you only look at where the light is already shining, you can end up missing a lot. Remember that dark data is the data you have but don’t understand. So how do you find your dark data? Analyze your metadata.

IT 135

IT Metadata Data-driven Data Governance 135

CIO Business Intelligence

JULY 10, 2024

In today’s rapidly, and continually, evolving data landscape, maintaining the sovereignty and security of sensitive data is paramount. It has never been a more important time to make sure that data and metadata remain protected, resident within local jurisdiction, compliant, under local control, and accessible yet portable.

Metadata 98

Metadata Data-driven Marketing Measurement 98

Rocket-Powered Data Science

FEBRUARY 15, 2023

Third, any commitment to a disruptive technology (including data-intensive and AI implementations) must start with a business strategy. These changes may include requirements drift, data drift, model drift, or concept drift. I suggest that the simplest business strategy starts with answering three basic questions: What?

Strategy 290

Strategy Experimentation Uncertainty Machine Learning 290

AWS Big Data

JUNE 10, 2024

Enterprises and organizations across the globe want to harness the power of data to make better decisions by putting data at the center of every decision-making process. However, throughout history, data services have held dominion over their customers’ data.

Data Lake 116

Data Lake Metadata Snapshot Analytics 116

Ontotext

JANUARY 10, 2024

Next, I will explain how knowledge graphs help them to get a unified view to data derived from multiple sources and get richer insights in less time. This requires new tools and new systems, which results in diverse and siloed data. And each of these gains requires data integration across business lines and divisions.

Metadata 72

Metadata Slice and Dice Data Integration Enterprise 72

erwin

DECEMBER 20, 2018

The driving factors behind data governance adoption vary. Whether implemented as preventative measures (risk management and regulation) or proactive endeavors (value creation and ROI), the benefits of a data governance initiative is becoming more apparent. Defining Data Governance. www.erwin.com/blog/defining-data-governance/.

Data Governance 40

Data Governance ROI Risk Management Metadata 40

IBM Big Data Hub

JANUARY 17, 2023

A data lakehouse is an emerging data management architecture that improves efficiency and converges data warehouse and data lake capabilities driven by a need to improve efficiency and obtain critical insights faster. Let’s start with why data lakehouses are becoming increasingly important.

Data Lake 52

Data Lake Metadata Data Warehouse Data Governance 52

erwin

JUNE 26, 2020

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. Three Types of Metadata in a Data Catalog.

Metadata 132

Metadata Cost-Benefit Measurement Data-driven 132

AWS Big Data

JULY 18, 2024

Over the years, organizations have invested in creating purpose-built, cloud-based data lakes that are siloed from one another. A major challenge is enabling cross-organization discovery and access to data across these multiple data lakes, each built on different technology stacks.

Data Lake 116

Data Lake Metadata Publishing Data-driven 116

AWS Big Data

JULY 30, 2024

To help develop a data-driven culture, everyone inside an organization can use Amazon DataZone. To realize the benefits of using Amazon DataZone for governing data and making it discoverable and available across different teams for collaboration, customers integrate it with their current technology stack. Choose the Sign On tab.

Metadata 92

Metadata Dashboards Management Data-driven 92

DataKitchen

AUGUST 3, 2021

The data mesh design pattern breaks giant, monolithic enterprise data architectures into subsystems or domains, each managed by a dedicated team. DataOps helps the data mesh deliver greater business agility by enabling decentralized domains to work in concert. . But first, let’s define the data mesh design pattern.

Data Architecture 246

Data Architecture Data Lake Cost-Benefit Data Warehouse 246

Data Virtualization

SEPTEMBER 12, 2024

Reading Time: 2 minutes In today’s data-driven landscape, the integration of raw source data into usable business objects is a pivotal step in ensuring that organizations can make informed decisions and maximize the value of their data assets. To achieve these goals, a well-structured.

Data Integration 52

Data Integration Business Objectives Data-driven Management 52

Ontotext

JANUARY 29, 2024

In 2023, data leaders and enthusiasts were enamored of — and often distracted by — initiatives such as generative AI and cloud migration. I expect to see the following data and knowledge management trends emerge in 2024. However, organizations need to be aware that these may be nothing more than bolted-on Band-Aids.

Strategy 52

Strategy Management Metadata Data-driven 52