Octopai

JUNE 19, 2023

Data Catalogs also allow for Improved Collaboration by serving as a central repository for enterprise data, a data catalog facilitates collaboration among different teams. Everyone has access to the same data and the same understanding of what the data represents, reducing miscommunications and discrepancies.

Metadata 88

Metadata Data Governance Measurement Risk Management 88

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. And there’s control of that landscape to facilitate insight and collaboration and limit risk.

Data Governance 40

Data Governance Risk Metadata Management 40

erwin

JUNE 26, 2020

These fragmented data environments make data governance a challenge since business stakeholders, data analysts and other users are unable to discover data or run queries across an entire data set. This also diminishes the value of data as an asset. Three Types of Metadata in a Data Catalog.

Metadata 132

Metadata Cost-Benefit Measurement Data-driven 132

CDW Research Hub

MAY 29, 2019

Data governance helps organizations manage their information and answer questions about business performance, allowing them to better understand data, and govern it to mitigate compliance risks and empower information stakeholders. Checklist: Building an Enterprise Data Governance Program.

Data Governance 40

Data Governance Data Quality ROI Metadata 40

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.

Data Governance 40

Data Governance ROI Risk Management Metadata 40

IBM Big Data Hub

APRIL 10, 2023

A well-designed data architecture should support business intelligence and analysis, automation, and AI—all of which can help organizations to quickly seize market opportunities, build customer value, drive major efficiencies, and respond to risks such as supply chain disruptions.

Strategy 86

Strategy Data Architecture Cost-Benefit Reporting 86

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

O'Reilly on Data

JULY 28, 2020

These measures are commonly referred to as guardrail metrics , and they ensure that the product analytics aren’t giving decision-makers the wrong signal about what’s actually important to the business. When a measure becomes a target, it ceases to be a good measure ( Goodhart’s Law ). Any metric can and will be abused.

Marketing 363

Marketing Experimentation Metrics Testing 363

Alation

MARCH 14, 2023

All critical data elements (CDEs) should be collated and inventoried with relevant metadata, then classified into relevant categories and curated as we further define below. Store Where individual departments have their own databases for metadata management, data will be siloed, meaning it can’t be shared and used business-wide.

Data Strategy 52

Data Strategy Strategy Metadata Data Quality 52

Octopai

JULY 9, 2020

When conducted manually, however, which has tended to be the normal mode of operation before companies discovered automation – or machine learning data lineage solutions, data lineage can be extremely tedious and time-consuming for BI & Analytics teams. Automated Data Lineage Enables Data Teams to Deliver Faster Results.

Insurance 86

Insurance Risk Management Machine Learning Metadata 86

Alation

MARCH 22, 2022

DataOps requires an array of technology to automate the design, development, deployment, and management of data delivery, with governance sprinkled on for good measure. So, how can you quickly take advantage of the DataOps opportunity while avoiding the risk and costs of DIY? Transparency is key.

Metadata 52

Metadata Cost-Benefit Data Quality Data Lake 52

Alation

JANUARY 19, 2023

The words “ data governance ” and “fun” are seldom spoken together. The term data governance conjures images of restrictions and control that result in an uphill challenge for most programs and organizations from the beginning. Or they are spending too much time preparing the data for proper use.

Data Governance 94

Data Governance IT Metadata Recreation/Entertainment 94

Domino Data Lab

APRIL 3, 2019

Paco Nathan ‘s latest column dives into data governance. This month’s article features updates from one of the early data conferences of the year, Strata Data Conference – which was held just last week in San Francisco. In particular, here’s my Strata SF talk “Overview of Data Governance” presented in article form.

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84

Cloudera

NOVEMBER 12, 2020

Cloud has given us hope, with public clouds at our disposal we now have virtually infinite resources, but they come at a different cost – using the cloud means we may be creating yet another series of silos, which also creates unmeasurable new risks in security and traceability of our data. Key areas of concern are: .

Data Warehouse 104

Data Warehouse Reporting Risk Cost-Benefit 104

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. Data Governance Bottlenecks. Regulations.

Data Governance 89

Data Governance Metadata Data Quality Risk Management 89

Cloudera

JUNE 29, 2023

To ensure the stability of the US financial system, the implementation of advanced liquidity risk models and stress testing using (MI/AI) could potentially serve as a protective measure. To improve the way they model and manage risk, institutions must modernize their data management and data governance practices.

Data Architecture 85

Data Architecture Risk Management Risk Management 85

Octopai

DECEMBER 12, 2021

What enables you to use all those gigabytes and terabytes of data you’ve collected? Metadata is the pertinent, practical details about data assets: what they are, what to use them for, what to use them with. Without metadata, data is just a heap of numbers and letters collecting dust. Where does metadata come from?

Metadata 93

Metadata Management Data Quality Data Governance 93

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

Laminar Security

SEPTEMBER 18, 2023

Do you know the data risks of your regular backup practices? I’ve previously discussed the risk of shadow data (those data stores you didn’t even know you had), and the team specifically dove into how versioning (a feature used to protect from accidental deletion) can unintentionally create shadow data.

Risk 128

Risk Testing Data Governance Metadata 128

IBM Big Data Hub

APRIL 11, 2024

Key themes and concerns are emerging, however government agencies must get ahead of the game by evaluating their agency-specific priorities and processes. Compliance with official policies through auditing tools and other measures is merely the final step. ” Academic and scientific perspectives are also essential.

Risk 74

Risk Consulting Modeling Data Processing 74

AWS Big Data

JULY 28, 2023

Data dependencies – Personal data can be interconnected and intertwined with other data systems, making it challenging to remove specific data without impacting the integrity of functionality of other systems or processes. Ensuring the complete removal of data from all these copies and backups can be challenging.

Snapshot 84

Snapshot Metadata Measurement Data Warehouse 84

Alation

APRIL 14, 2022

Semantics, context, and how data is tracked and used mean even more as you stretch to reach post-migration goals. This is why, when data moves, it’s imperative for organizations to prioritize data discovery. Data discovery is also critical for data governance , which, when ineffective, can actually hinder organizational growth.

Metadata 59

Metadata Data Governance Modeling Data-driven 59

IBM Big Data Hub

OCTOBER 12, 2023

They’re built on machine learning algorithms that create outputs based on an organization’s data or other third-party big data sources. Among the tasks necessary for internal and external compliance is the ability to report on the metadata of an AI model. And that makes sense.

Cost-Benefit 77

Cost-Benefit Metadata Data Governance Modeling 77

IBM Big Data Hub

JANUARY 9, 2024

Companies still often accept the risk of using internal data when exploring large language models (LLMs) because this contextual data is what enables LLMs to change from general-purpose to domain-specific knowledge. Increased variance: Variance measures consistency.

Enterprise 72

Enterprise Data Integration Data Quality Contextual Data 72

erwin

APRIL 21, 2020

By combining physical system catalogs, critical data elements, and key performance measures with clearly defined product and sales goals, you can manage the effectiveness of your business and ensure you understand what critical systems are for business continuity and measuring corporate performance.

Measurement 97

Measurement Sales Data Governance Metadata 97

erwin

APRIL 21, 2020

One of the biggest lessons we’re learning from the global COVID-19 pandemic is the importance of data, specifically using a data catalog to comply, collaborate and innovate to crisis-proof our businesses. So one of the biggest lessons we’re learning from COVID-19 is the need for data collection, management and governance.

Measurement 96

Measurement Sales Data Governance Metadata 96

AWS Big Data

JANUARY 22, 2024

Discussions with users showed they were happier to have faster access to data in a simpler way, a more structured data organization, and a clear mapping of who the producer is. A lot of progress has been made to advance their data-driven culture (data literacy, data sharing, and collaboration across business units).

Data-driven 86

Data-driven Advertising Metadata Data Architecture 86

AWS Big Data

MARCH 26, 2024

In this post, we discuss how you can use purpose-built AWS services to create an end-to-end data strategy for C360 to unify and govern customer data that address these challenges. Then, you transform this data into a concise format. Enrichment typically involves adding demographic, behavioral, and geolocation data.

Data Strategy 69

Data Strategy Strategy Data Warehouse Prescriptive Analytics 69

Domino Data Lab

AUGUST 8, 2019

In this episode I’ll cover themes from Sci Foo and important takeaways that data science teams should be tracking. First and foremost: there’s substantial overlap between what the scientific community is working toward for scholarly infrastructure and some of the current needs of data governance in industry.

Data Science 71

Data Science Machine Learning Data Governance Statistics 71

Andrew White

JANUARY 9, 2022

First, how we measure emissions and carbon footprint is about data design and policy. In other words, D&A plays a key role in the foundational measuring angle. Link to item 6 on slide 27 is broken, [link] , for Dashboard to measure business impact, can you provide a current link? This was from 2020.

Analytics 64

Analytics Measurement Modeling Data-driven 64

Grooper

OCTOBER 23, 2019

By measuring performance, they drive improvements through necessary process and workflow changes. It simply takes the right resources and a measured approach to get there. Most companies today are more reactive to information governance. As Peter Drucker so famously said, “If you can’t measure it, you can’t improve it.”

Metrics 59

Metrics Measurement Strategy Optimization 59

Alation

DECEMBER 15, 2021

Yet high-volume collection makes keeping that foundation sound a challenge, as the amount of data collected by businesses is greater than ever before. An effective data governance strategy is critical for unlocking the full benefits of this information. Data governance requires a system.

Data Governance 52

Data Governance Strategy Metadata Data Strategy 52

Alation

NOVEMBER 11, 2021

As IT leaders oversee migration, it’s critical they do not overlook data governance. Data governance is essential because it ensures people can access useful, high-quality data. Therefore, the question is not if a business should implement cloud data management and governance, but which framework is best for them.

Data Governance 52

Data Governance Data Quality Metrics Metadata 52

erwin

APRIL 16, 2024

In this blog, we’ll delve into the critical role of governance and data modeling tools in supporting a seamless data mesh implementation and explore how erwin tools can be used in that role. erwin also provides data governance, metadata management and data lineage software called erwin Data Intelligence by Quest.

Metadata 52

Metadata Data Quality Data Governance Modeling 52

Alation

NOVEMBER 22, 2022

What Is Data Governance In The Public Sector? Effective data governance for the public sector enables entities to ensure data quality, enhance security, protect privacy, and meet compliance requirements. With so much focus on compliance, democratizing data for self-service analytics can present a challenge.

Data Governance 52

Data Governance Metadata Data-driven Unstructured Data 52

Alation

DECEMBER 8, 2022

Data governance is growing in urgency and prominence. As regulations grow more complex (and compliance fines more onerous) organizations aren’t just adapting data governance frameworks to drive compliance – they’re leveraging governance to fuel a growing range of use cases, from collaboration to stewardship, discovery, and more.

Data Governance 52

Data Governance Data-driven Strategy Reporting 52

AWS Big Data

AUGUST 14, 2023

In the same way, overly restrictive data governance practices that either prevent data products from taking root at all, or pare them back too aggressively (deforestation), can over time create “data deserts” that drive both the producers and consumers of data within an organization to look elsewhere for their data needs.

Management 80

Management Risk Measurement Optimization 80