Alation

OCTOBER 18, 2022

In the modern data stack, dbt is a key tool to make data ready for analysis. Data analysts and engineers use dbt to transform, test, and document data in the cloud data warehouse. Yet every dbt transformation contains vital metadata that is not captured – until now. Improve data analysis accuracy.

Metadata 52

Metadata Metrics Recreation/Entertainment Data Quality 52

Octopai

MARCH 23, 2022

Assuming all checked out, you’d definitely feel comfortable eating that meat. Data lineage tools give you exactly that kind of transparent, x-ray vision into your data quality. Data Supervision. Having the right data intelligence tools can be a make-or-break for data responsibility success.

Data Quality 98

Data Quality Reporting Modeling Interactive 98

Ontotext

NOVEMBER 9, 2023

While everyone may subscribe to the same design decisions and agree on an ontology, there may be differences in the data quality. In such situations, data must be validated. Instead, they provide metadata about the shapes. Sometimes there is no room for error. In such an environment, most tools relied on workarounds.

Data Quality 64

Data Quality Testing Reporting Metadata 64

CIO Business Intelligence

MARCH 24, 2023

Data governance definition Data governance is a system for defining who within an organization has authority and control over data assets and how those data assets may be used. It encompasses the people, processes, and technologies required to manage and protect data assets.

Data Governance 142

Data Governance Management Metadata Data Quality 142

Jim Harris

MAY 5, 2017

Spelling, pronunciation, and examples of usage are included in the dictionary definition of a word, which is a good example of one of the many uses of metadata, namely to provide a definition, description, and context for data. In practice, I haven’t encountered a metadata dictionary that could deliver on that promise.

Metadata 61

Metadata Publishing Management IT 61

TDAN

JANUARY 3, 2024

Edwards Deming, the father of statistical quality control, said: “If you can’t describe what you are doing as a process, you don’t know what you’re doing.” When looking at the world of IT and applied to the dichotomy of software and data, Deming’s quote applies to the software part of that pair.

Statistics 52

Statistics Software Data Quality IT 52

erwin

MARCH 30, 2020

These numerous data types and data sources most definitely weren’t designed to work together. As a result, the data may be compromised, rendering faulty analyses and insights. Unraveling Data Complexities with Metadata Management. Data profiling for data assessment, metadata discovery and data validation.

Metadata 122

Metadata IT Data Governance Data Quality 122

DataKitchen

OCTOBER 11, 2021

First, you must understand the existing challenges of the data team, including the data architecture and end-to-end toolchain. Second, you must establish a definition of “done.” In DataOps, the definition of done includes more than just some working code. Definition of Done. Monitoring Job Metadata.

Testing 154

Testing Metadata Dashboards Statistics 154

AWS Big Data

DECEMBER 13, 2023

The AWS Glue Studio visual editor is a low-code environment that allows you to compose data transformation workflows, seamlessly run them on the AWS Glue Apache Spark-based serverless data integration engine, and inspect the schema and data results in each step of the job.

Metadata 104

Metadata Data Lake Visualization Data Transformation 104

Alation

APRIL 18, 2023

For any data user in an enterprise today, data profiling is a key tool for resolving data quality issues and building new data solutions. In this blog, we’ll cover the definition of data profiling, top use cases, and share important techniques and best practices for data profiling today.

IT 52

IT Data Quality Metadata Data Governance 52

erwin

DECEMBER 3, 2020

Being able to integrate all data touchpoints, including erwin DM for data modeling, Denodo for data visualization, and Jira for ticketing, has been key. Using erwin DI, customers are powering comprehensive data governance initiatives, cloud migration and other massive digital transformation projects.

Metadata 141

Metadata Data Governance Data-driven Digital Transformation 141

Alation

AUGUST 30, 2022

Centralization of metadata. A decade ago, metadata was everywhere. Consequently, useful metadata was unfindable and unusable. We had data but no data intelligence and, as a result, insights remained hidden or hard to come by. This universe of metadata represents a treasure trove of connected information.

Metadata 52

Metadata Data Quality Data Governance Machine Learning 52

Alation

MAY 31, 2023

How is the data architecture role evolving? As data governance gains importance, data architects must work with data stewards and owners to establish policies, develop governance frameworks, implement data quality programs, and provide guidance to other data professionals.

Data Governance 52

Data Governance Metadata Data Architecture Data Quality 52

erwin

MAY 27, 2020

What’s desperately needed is a way to understand the relationships and interconnections between so many entities in data sets in detail. Visualizing data from anywhere defined by its context and definition in a central model repository, as well as the rules for governing the use of those data elements, unifies enterprise data management.

Modeling 98

Modeling Metadata Data Governance Visualization 98

Rocket-Powered Data Science

FEBRUARY 15, 2023

A business-disruptive ChatGPT implementation definitely fits into this category: focus first on the MVP or MLP. Love thy data: data are never perfect, but all the data may produce value, though not immediately. Clean it, annotate it, catalog it, and bring it into the data family (connect the dots and see what happens).

Strategy 290

Strategy Experimentation Uncertainty Machine Learning 290

Alation

DECEMBER 21, 2022

Master Data Management (MDM) and data catalog growth are accelerating because organizations must integrate more systems, comply with privacy regulations, and address data quality concerns. What Is Master Data Management (MDM)? Data Catalog and Master Data Management. Assess Data Quality.

Management 52

Management Data Quality Metadata Testing 52

Alation

JANUARY 13, 2022

Spoiler alert: data fabric and data mesh are independent design concepts that are, in fact, quite complementary. Data fabric has captured most of the limelight; it focuses on the technologies required to support metadata-driven use cases across hybrid and multi-cloud environments. We’ll dig into this definition in a bit.

Data Lake 59

Data Lake Metadata Data-driven Data Governance 59

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

Alation

AUGUST 11, 2022

We chatted about industry trends, why decentralization has become a hot topic in the data world, and how metadata drives many data-centric use cases. But, through it all, Mohan says it’s critical to view everything through the same lens: gaining business value from data. Data fabric is a technology architecture.

Metadata 52

Metadata Data Warehouse Data Quality Data Lake 52

Octopai

MAY 31, 2022

The ultimate data organizational tool, letting any data user in your organization easily see what data assets you have and all the pertinent details about them, is a data catalog. Bonus tool: active metadata management. An active metadata management tool adds a maître d’ to self-serve analytics.

Management 52

Management Metadata Business Intelligence ROI 52

Alation

NOVEMBER 4, 2021

The practitioner asked me to add something to a presentation for his organization: the value of data governance for things other than data compliance and data security. Now to be honest, I immediately jumped onto data quality. Data quality is a very typical use case for data governance.

Data Governance 94

Data Governance Metadata Data Quality Enterprise 94

TDAN

SEPTEMBER 7, 2022

Fit for Purpose data has been a foundational concept of Data Governance for as long as I’ve been in the field…so that’s 10-15 years now. Most data quality definitions take Fit-for-Purpose as a given.

Data Governance 59

Data Governance Data Quality Metadata Management 59

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

MARCH 27, 2024

For example, GPS, social media, cell phone handoffs are modeled as graphs while data catalogs, data lineage and MDM tools leverage knowledge graphs for linking metadata with semantics. RDF is used extensively for data publishing and data interchange and is based on W3C and other industry standards.

Modeling 69

Modeling Metadata Data Quality Enterprise 69

Alation

NOVEMBER 30, 2022

Alation is the leading platform for data intelligence , delivering critical context about data to empower smarter use; to this end, it centralizes technical, operational, business, and behavioral metadata from a broad variety of sources. Alation Data Catalog centralizes metadata and rich context of data.

Metadata 52

Metadata Data Quality Dashboards Sales 52

Octopai

FEBRUARY 9, 2023

It’s not easy to keep up with data and metadata on the move. A comprehensive data lineage tool is the secret weapon of successful data governance managers and data stewards. Let’s take a look at four major areas where data lineage will improve your performance a hundredfold if you are in data governance.

Data Governance 52

Data Governance Metadata Data Quality Management 52

erwin

MARCH 4, 2021

Led by Frank Pörschmann of iDIGMA GmbH, an IT industry veteran and data governance strategist, it examined “ The What & Why of Data Governance.”. The What: Data Governance Defined. Data governance has no standard definition. But he cautions “this [notion] is something which is not really likely to happen.”.

Data Governance 122

Data Governance Digital Transformation Data-driven Cost-Benefit 122

Alation

FEBRUARY 13, 2020

These are all good questions and a logical place to start your data cataloging journey. This brief definition makes several points about data catalogs—data management, searching, data inventory, and data evaluation—but all depend on the central capability to provide a collection of metadata.

Metadata 64

Metadata Data Lake Recreation/Entertainment Big Data 64

Ontotext

FEBRUARY 7, 2024

We have a schema definition and every variable should have an identifier. So, on one hand, we can see our schema as a vocabulary that has different names and different standards, and, on the other, we can see it as a metadata object. The terminology sets formed by these concepts should also have identifiers.

Metadata 52

Metadata Data Integration Measurement Data-driven 52

O'Reilly on Data

MARCH 18, 2020

By contrast, AI adopters are about one-third more likely to cite problems with missing or inconsistent data. The logic in this case partakes of garbage-in, garbage out : data scientists and ML engineers need quality data to train their models. This is consistent with the results of our data quality survey.

Enterprise 314

Enterprise Deep Learning Data Governance Risk 314

Cloudera

SEPTEMBER 15, 2022

What is a data fabric? Mark: Gartner states that a data fabric “enables frictionless access and sharing of data in a distributed data environment.” The data fabric provides direct support for the first element of the architecture — linking the distributed source data into the hybrid, multi-cloud environment. .

Data Lake 81

Data Lake Data Architecture Data-driven Data Warehouse 81

erwin

APRIL 4, 2019

Some other common data governance obstacles include: Questions about where to begin and how to prioritize which data streams to govern first. Issues regarding data quality and ownership. Concerns about data lineage. Competing project and resources (time, people and funding).

Data Governance 75

Data Governance Data Quality Risk Management Data-driven 75

Alation

MARCH 11, 2022

A data catalog with a Behavioral Analysis Engine will measure human behavior around data to locate your most valuable and actionable data. This data about data, AKA metadata , is an essential layer of your new meshy fabric. Again, a data catalog can automate the tedium of compliance at scale.

Metadata 52

Metadata Data Governance ROI Data Quality 52

Alation

MAY 31, 2023

American Family Insurance: Governance by Design – Not as an Afterthought Who: Anil Kumar Kunden , Information Standards, Governance and Quality Specialist at AmFam Group When: Wednesday, June 7, at 2:45 PM Why attend: Learn how to automate and accelerate data pipeline creation and maintenance with data governance, AKA metadata normalization.

Data Governance 52

Data Governance Insurance Metadata Data-driven 52

CDW Research Hub

MAY 29, 2019

Start meeting, strategizing, and creating the justification for data governance. Agree on a definition of data governance for organization. Identify where your company currently stands in data governance, while keeping in mind your master data management plan, data quality goals, data stewardship, data lineage, and metadata.

Data Governance 40

Data Governance Data Quality ROI Metadata 40

Alation

JANUARY 25, 2022

Data Fabric: A Love Story , we defined data fabric and outlined its uses and motivations. As a reminder, here’s Gartner’s definition of data fabric: “A design concept that serves as an integrated layer (fabric) of data and connecting processes. The data catalog is a foundational layer of the data fabric.

Metadata 52

Metadata IT Metrics Data-driven 52

erwin

APRIL 15, 2020

In regard to third-party data, we should ask ourselves: How was the data created? Does the definition (description, classification, allowable range of values, etc.) of incoming, individual data elements match our internal definitions of those data elements? What formulas were used?

Data Governance 134

Data Governance Metadata Testing Reporting 134