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

CIO Business Intelligence

MARCH 24, 2023

Data governance framework Data governance may best be thought of as a function that supports an organization’s overarching data management strategy. Such a framework provides your organization with a holistic approach to collecting, managing, securing, and storing data.

Data Governance 142

Data Governance Management Metadata Data Quality 142

IBM Big Data Hub

JUNE 30, 2022

The rise of data strategy. There’s a renewed interest in reflecting on what can and should be done with data, how to accomplish those goals and how to check for data strategy alignment with business objectives. The evolution of a multi-everything landscape, and what that means for data strategy.

Data Strategy 56

Data Strategy Strategy Business Objectives Data-driven 56

CIO Business Intelligence

JANUARY 30, 2024

Birgit Fridrich, who joined Allianz as sustainability manager responsible for ESG reporting in late 2022, spends many hours validating data in the company’s Microsoft Sustainability Manager tool. Data quality is key, but if we’re doing it manually there’s the potential for mistakes.

Reporting 107

Reporting Data Quality Strategy Data-driven 107

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

erwin

JULY 12, 2019

So it’s important to understand how to use strategic data governance to manage the complexity of regulatory compliance and other business objectives … Designing and Operationalizing Regulatory Compliance Strategy. Five Steps to GDPR/CCPA Compliance. How erwin Can Help.

Data Governance 110

Data Governance Management Metadata Risk Management 110

CIO Business Intelligence

MARCH 9, 2022

They conveniently store data in a flat architecture that can be queried in aggregate and offer the speed and lower cost required for big data analytics. On the other hand, they don’t support transactions or enforce data quality. Each ETL step risks introducing failures or bugs that reduce data quality. .

Data Lake 109

Data Lake Unstructured Data Data Warehouse Data Quality 109

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

Ontotext

FEBRUARY 12, 2024

Usually, organizations will combine different domain topologies, depending on the trade-offs, and choose to focus on specific aspects of data mesh. Once accomplished, an effective implementation spurs a mindset in which organizations prioritize and value data for decision-making, formulating strategies, and day-to-day operations.

Data-driven 59

Data-driven Data Lake Data Quality Business Objectives 59

Ontotext

DECEMBER 1, 2023

Atanas Kiryakov presenting at KGF 2023 about Where Shall and Enterprise Start their Knowledge Graph Journey Only data integration through semantic metadata can drive business efficiency as “it’s the glue that turns knowledge graphs into hubs of metadata and content”.

Metadata 78

Metadata Sales Consulting Enterprise 78

AWS Big Data

JULY 20, 2023

With data becoming the driving force behind many industries today, having a modern data architecture is pivotal for organizations to be successful. Prior to the creation of the data lake, Orca’s data was distributed among various data silos, each owned by a different team with its own data pipelines and technology stack.

Data Lake 90

Data Lake Analytics Snapshot Optimization 90

Ontotext

FEBRUARY 7, 2024

They have to misallocate resources because 80% of the time the data scientists are busy doing data finding, accessing, cleansing, etc. This also results in the information loss I’ve already mentioned and severely impacts our insight creation and monetizing the data. Last but not least, we need to converge.

Metadata 52

Metadata Data Integration Measurement Data-driven 52

Cloudera

JULY 20, 2021

Once companies are able to leverage their data they’re then able to fuel machine learning and analytics models, transforming their business by embedding AI into every aspect of their business. . Build your data strategy around the convergence of software and hardware.

Manufacturing 104

Manufacturing Forecasting IoT Insurance 104

erwin

SEPTEMBER 17, 2020

Here are six benefits of automating end-to-end data lineage: Reduced Errors and Operational Costs. Data quality is crucial to every organization. Automated data capture can significantly reduce errors when compared to manual entry. However, different types of data need to be treated differently.

Cost-Benefit 143

Cost-Benefit Data Governance Metadata Reporting 143

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 85

Technology Data-driven Machine Learning Sales 85

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 84

Data Lake Data Governance Data Architecture Machine Learning 84

TDAN

MARCH 17, 2020

Business has a fundamental problem with data quality. In some places it’s merely painful, in others it’s nearly catastrophic. Why is the problem so pervasive? Why does it never seem to get fixed? I believe we’ve been thinking about the problem wrong. It’s time for a fresh look.

Data Quality 59

Data Quality Data Architecture IT Metadata 59

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

TDAN

JUNE 21, 2022

Twenty-five years ago today, I published the first issue of The Data Administration Newsletter. It only took a few months to recognize that there was an audience for an “online” publication focused on data administration. […].

Publishing 78

Publishing Data Quality Data Architecture IT 78

Andrew White

JANUARY 9, 2022

To drive a successful Data Analytics strategy do you think it is a multidisciplinary activity and if so, what additional roles would you expect to see involved. – A D&A leader of CDO or whoever will lead the development of the D&A strategy will of necessity work with business executives.

Analytics 64

Analytics Measurement Modeling Data-driven 64

Alation

JANUARY 25, 2022

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. In this blog, we will focus on the “integrated layer” part of this definition by examining each of the key layers of a comprehensive data fabric in more detail.

Metadata 52

Metadata IT Metrics Data-driven 52

TDAN

JULY 2, 2019

The third and final part of the Non-Invasive Data Governance Framework details the breakdown of components by level, providing considerations for what must be included at the intersections. The squares are completed with nouns and verbs that provide direction for meaningful discussions about how the program will be set up and operate.

Data Governance 74

Data Governance Data Quality Data Architecture Metadata 74

Alation

JULY 28, 2022

What Are the Biggest Drivers of Cloud Data Warehousing? It’s costly and time-consuming to manage on-premises data warehouses — and modern cloud data architectures can deliver business agility and innovation. Those planning their migration to a cloud data warehouse would be wise to map out a strategy.

Data Warehouse 69

Data Warehouse Cost-Benefit Data Governance Data-driven 69

IBM Big Data Hub

JANUARY 17, 2023

IBM Cloud Pak for Data Express solutions offer clients a simple on ramp to start realizing the business value of a modern architecture. Data governance. The data governance capability of a data fabric focuses on the collection, management and automation of an organization’s data. Data science and MLOps.

Data-driven 56

Data-driven Enterprise Data Governance Prescriptive Analytics 56

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

TDAN

MAY 14, 2019

Cross-Agency Priority (CAP) Goal #2 is “leverage data as a strategic asset to grow the economy, increase the effectiveness of the Federal Government, facilitate oversight, and promote transparency.” The President’s Management Agenda (PMA) lays out a long-term vision for modernizing the Federal Government.

Management 40

Management Data Quality Data Architecture Metadata 40

IBM Big Data Hub

AUGUST 4, 2023

Today, the way businesses use data is much more fluid; data literate employees use data across hundreds of apps, analyze data for better decision-making, and access data from numerous locations. It uses knowledge graphs, semantics and AI/ML technology to discover patterns in various types of metadata.

Data Architecture 62

Data Architecture Data Lake Machine Learning Data Governance 62