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

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

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

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

NOVEMBER 28, 2019

Enterprise architecture tools are becoming more important than ever. The International Enterprise Architecture Institute (IEAI) defines enterprise architecture (EA) as “the analysis and documentation of an enterprise in its current and future states from an integrated strategy, business and technology perspective.”.

Enterprise 145

Enterprise Data-driven Data Governance Digital Transformation 145

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

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

erwin

SEPTEMBER 3, 2020

Why should you integrate data governance (DG) and enterprise architecture (EA)? Two of the biggest challenges in creating a successful enterprise architecture initiative are: collecting accurate information on application ecosystems and maintaining the information as application ecosystems change.

Data Governance 144

Data Governance Enterprise Risk Data Lake 144

erwin

NOVEMBER 13, 2020

The business challenges facing organizations today emphasize the value of enterprise architecture (EA) , so the future of EA is closer than you think. See also: What Is Enterprise Architecture? . Data Security & Risk Management. Data Center Consolidation. Data Governance (knowing what data you have and where it is).

Enterprise 131

Enterprise Digital Transformation Key Performance Indicator Data-driven 131

erwin

DECEMBER 19, 2019

Data has been the driving force of the decade. Many organizations have tried and failed to become truly “data-driven,” and many organizations will continue to do so. Many organizations have tried and failed to become truly “data-driven,” and many organizations will continue to do so.

Metadata 75

Metadata Data Governance Management Digital Transformation 75

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

CIO Business Intelligence

OCTOBER 26, 2023

Business intelligence (BI) analysts transform data into insights that drive business value. The role is becoming increasingly important as organizations move to capitalize on the volumes of data they collect through business intelligence strategies.

Business Intelligence 126

Business Intelligence Data-driven Statistics Data Warehouse 126

Cloudera

MARCH 3, 2022

Whether the enterprise uses dozens or hundreds of data sources for multi-function analytics, all organizations can run into data governance issues. Bad data governance practices lead to data breaches, lawsuits, and regulatory fines — and no enterprise is immune. . Everyone Fails Data Governance.

Data Governance 95

Data Governance Enterprise Data Quality Metadata 95

erwin

MARCH 17, 2020

Teams need to urgently respond to everything from massive changes in workforce access and management to what-if planning for a variety of grim scenarios, in addition to building and documenting new applications and providing fast, accurate access to data for smart decision-making. Enterprise Architecture & Business Process Modeling.

Data Governance 138

Data Governance Enterprise Modeling Metadata 138

Ontotext

APRIL 10, 2024

The data ecosystem today is crowded with dazzling buzzwords, all fighting for investment dollars. A survey in 2021 found that a data company was being funded every 45 minutes. Data ecosystems have become jungles and in spite of all the technology, data teams are struggling to create a modern data experience.

Metadata 52

Metadata Data Lake Data Warehouse Data Quality 52

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 104

Analytics Metadata Data Strategy Optimization 104

Ontotext

MAY 2, 2024

Data leaders today are faced with an almost impossible challenge. They are expected to understand the entire data landscape and generate business-moving insights while facing the voracious needs of different teams and the constraints of technology architecture and compliance.

Metadata 52

Metadata Data Lake Data-driven Enterprise 52

Octopai

APRIL 19, 2021

Aptly named, metadata management is the process in which BI and Analytics teams manage metadata, which is the data that describes other data. In other words, data is the context and metadata is the content. Without metadata, BI teams are unable to understand the data’s full story.

Metadata 52

Metadata Management Business Intelligence Data Governance 52

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

AWS Big Data

APRIL 3, 2024

Businesses are constantly evolving, and data leaders are challenged every day to meet new requirements. For many enterprises and large organizations, it is not feasible to have one processing engine or tool to deal with the various business requirements. This post is co-written with Andries Engelbrecht and Scott Teal from Snowflake.

Data Lake 91

Data Lake Snapshot Metadata Data Architecture 91

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

Octopai

AUGUST 8, 2023

Battle Creek, Michigan — July 18, 2023 — Octopai, a global leader in data lineage and business intelligence automation, and Demand Chain AI, a pioneer in AI-driven demand forecasting and supply chain optimization, have today announced a strategic partnership.

Digital Transformation 52

Digital Transformation Metadata Forecasting Data-driven 52

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 289

Strategy Experimentation Uncertainty Machine Learning 289

erwin

NOVEMBER 21, 2019

As enterprise architectures become more complex, enterprise architecture tools are becoming more important than ever. In the era of data-driven business, such perspective is critical. Enterprise architecture initially was housed within IT and therefore acted in an enterprise support role as well.

Enterprise 77

Enterprise Data-driven Data Governance Digital Transformation 77

CIO Business Intelligence

MAY 24, 2022

Just after launching a focused data management platform for retail customers in March, enterprise data management vendor Informatica has now released two more industry-specific versions of its Intelligent Data Management Cloud (IDMC) — one for financial services, and the other for health and life sciences.

Finance 140

Finance Management Metadata Data Quality 140

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

erwin

NOVEMBER 21, 2019

As enterprise architectures become more complex, enterprise architecture tools are becoming more important than ever. In the era of data-driven business, such perspective is critical. Data-Driven Business Transformation: Using data as a transformational tool to succeed in the digital age.

Enterprise 72

Enterprise Data-driven Data Governance Digital Transformation 72

AWS Big Data

DECEMBER 13, 2023

In addition to using native managed AWS services that BMS didn’t need to worry about upgrading, BMS was looking to offer an ETL service to non-technical business users that could visually compose data transformation workflows and seamlessly run them on the AWS Glue Apache Spark-based serverless data integration engine.

Metadata 84

Metadata Data Lake Visualization Data Transformation 84

CIO Business Intelligence

FEBRUARY 20, 2024

Manufacturers have long held a data-driven vision for the future of their industry. It’s one where near real-time data flows seamlessly between IT and operational technology (OT) systems. Legacy data management is holding back manufacturing transformation Until now, however, this vision has remained out of reach.

Data Architecture 98

Data Architecture Unstructured Data Manufacturing IT 98

Ontotext

MARCH 8, 2023

Gartner predicts that graph technologies will be used in 80% of data and analytics innovations by 2025, up from 10% in 2021. Use Case #1: Customer 360 / Enterprise 360 Customer data is typically spread across multiple applications, departments, and regions. Several factors are driving the adoption of knowledge graphs.

Enterprise 52

Enterprise Knowledge Discovery Risk Data-driven 52

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 133

IT Metadata Data-driven Data Governance 133

erwin

JANUARY 9, 2020

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.

Data Governance 144

Data Governance Modeling Metadata Unstructured Data 144

erwin

DECEMBER 3, 2020

As the amount of data grows exponentially, organizations turn to data intelligence to reach deeper conclusions about driving revenue, achieving regulatory compliance and accomplishing other strategic objectives. It’s no secret that data has grown in volume, variety and velocity, with 2.5

Metadata 141

Metadata Data Governance Data-driven Digital Transformation 141

IBM Big Data Hub

DECEMBER 16, 2022

An enterprise data catalog does all that a library inventory system does – namely streamlining data discovery and access across data sources – and a lot more. For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance.

Metadata 52

Metadata Data Quality Data-driven Data Governance 52

Ontotext

NOVEMBER 16, 2023

Data mesh is still in its infancy, and data personas and organizations are craving clarity and specificity. It is critical to be aware of the “why” and “what” and fully understand the role that knowledge graphs play when considering adopting a data mesh strategy. The debate on what constitutes a data mesh rages on.

Metadata 52

Metadata Data-driven Data Quality Data Architecture 52

Alation

OCTOBER 26, 2021

Today’s best-performing organizations embrace data for strategic decision-making. Because of the criticality of the data they deal with, we think that finance teams should lead the enterprise adoption of data and analytics solutions. This is because accurate data is “table stakes” for finance teams.

Finance 52

Finance Data Transformation Enterprise Metrics 52

erwin

JANUARY 14, 2021

These surveys helped IDC develop a model that describes the five stages of enterprise recovery , aligning business focus with the economic situation: When the COVID-19 crisis hit, organizations focused on business continuity. When we enter into the next normal, the future enterprise will emerge.

Digital Transformation 143

Digital Transformation Metadata Big Data Data-driven 143