erwin

MAY 13, 2020

The role of data modeling (DM) has expanded to support enterprise data management, including data governance and intelligence efforts. Metadata management is the key to managing and governing your data and drawing intelligence from it. Types of Data Models: Conceptual, Logical and Physical.

Data Governance 93

Data Governance Modeling Enterprise Management 93

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 93

Data Lake Snapshot Metadata Data Architecture 93

Ontotext

JULY 13, 2023

Knowledge graphs have greatly helped to successfully enhance business-critical enterprise applications, especially those where high performance tagging and agile data integration is needed. How can you build knowledge graphs for enterprise applications? Want to learn more?

Enterprise 52

Enterprise Metadata Digital Transformation Software 52

Ontotext

AUGUST 22, 2023

The age of Big Data inevitably brought computationally intensive problems to the enterprise. Central to today’s efficient business operations are the activities of data capturing and storage, search, sharing, and data analytics. Get these wrong and chances are your enterprise processes and systems will suffer.

Metadata 52

Metadata Knowledge Discovery Big Data Enterprise 52

AWS Big Data

FEBRUARY 29, 2024

Data is your generative AI differentiator, and a successful generative AI implementation depends on a robust data strategy incorporating a comprehensive data governance approach. Data governance is a critical building block across all these approaches, and we see two emerging areas of focus.

Data Governance 93

Data Governance Unstructured Data Metadata Data Lake 93

CIO Business Intelligence

MARCH 24, 2023

Whereas data governance is about the roles, responsibilities, and processes for ensuring accountability for and ownership of data assets, DAMA defines data management as “an overarching term that describes the processes used to plan, specify, enable, create, acquire, maintain, use, archive, retrieve, control, and purge data.”

Data Governance 142

Data Governance Management Metadata Data Quality 142

CIO Business Intelligence

MARCH 4, 2024

Despite these benefits, the core problems that data centralization so often fails to address are the pragmatic realities of many enterprise data ecosystems. The unified need Despite these criticisms, the need for unified, integrated data solutions at the enterprise level remains.

Insurance 98

Insurance Strategy Data Governance Data Integration 98

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 86

Metadata Data Lake Visualization Data Transformation 86

Alation

DECEMBER 6, 2022

Diby Malakar joins as our new Vice President of Product Management, Cloud & Technical Metadata Platform and Jonathan Bruce is our new Vice President of Product Management, Catalog, Governance & Data Exploration Apps & Logical Metadata Platform. Oversee the connectors and lineage product areas.

Metadata 52

Metadata Enterprise Data Integration Data Quality 52

Ontotext

DECEMBER 1, 2023

Content and data management solutions based on knowledge graphs are becoming increasingly important across enterprises. ” With new business lines, leading to new tools, a lot of diverse and siloed data inevitably enters enterprise systems. Sumit started his talk by laying out the problems in today’s data landscapes.

Metadata 78

Metadata Sales Consulting Enterprise 78

AWS Big Data

APRIL 29, 2024

Addressing big data challenges – Big data comes with unique challenges, like managing large volumes of rapidly evolving data across multiple platforms. Effective permission management helps tackle these challenges by controlling how data is accessed and used, providing data integrity and minimizing the risk of data breaches.

Management 89

Management Big Data Metadata Data Warehouse 89

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. CCPA vs. GDPR: Key Differences.

Data Governance 144

Data Governance Modeling Metadata Unstructured Data 144

IBM Big Data Hub

JUNE 23, 2022

The ability to compose and re-use data services with IBM’s data fabric on IBM Cloud Pak for Data allows you to tackle a variety of use cases such as multi-cloud data integration, governance and privacy, customer 360, and MLOps and Trustworthy AI. Providing the semantic.

Enterprise 52

Enterprise Metadata Data Governance Machine Learning 52

AWS Big Data

OCTOBER 3, 2023

You can store your data as-is, without having to first structure the data and then run different types of analytics for better business insights. In-place data upgrade In an in-place data migration strategy, existing datasets are upgraded to Apache Iceberg format without first reprocessing or restating existing data.

Data Lake 100

Data Lake Metadata Snapshot Recreation/Entertainment 100

Ontotext

JUNE 27, 2019

Ontotext’s GraphDB is an enterprise-ready semantic graph database (also called RDF triplestore as it stores data in RDF triples). It provides the core infrastructure for solutions where modeling agility, data integration, relationship exploration, cross-enterprise data publishing and consumption are critical.

Metadata 40

Metadata Management Enterprise Optimization 40

Ontotext

MARCH 8, 2023

Use Case #1: Customer 360 / Enterprise 360 Customer data is typically spread across multiple applications, departments, and regions. Each team and system need to keep diverse sets of data about their customers in order to play their specific role – inadvertently leading to siloed experiences. million users.

Enterprise 52

Enterprise Knowledge Discovery Risk Data-driven 52

erwin

OCTOBER 8, 2020

By using automated and repeatable capabilities, you can quickly and safely migrate data to the cloud and govern it along the way. But transforming and migrating enterprise data to the cloud is only half the story – once there, it needs to be governed for completeness and compliance. GDPR, CCPA, HIPAA, SOX, PIC DSS).

Data Governance 145

Data Governance Metadata Testing Data Lake 145

Smarten

JANUARY 30, 2024

Business users cannot even hope to prepare data for analytics – at least not without the right tools. Gartner predicts that, ‘data preparation will be utilized in more than 70% of new data integration projects for analytics and data science.’ So, why is there so much attention paid to the task of data preparation?

Analytics 67

Analytics Data Quality Visualization Metadata 67

Octopai

OCTOBER 23, 2019

The same is true of enterprise data. Modern enterprises find themselves sitting on mountains of data. There’s value locked in that data—knowledge that can drive innovation, market insights, and strategic plans. But much of the data is separated, residing in incompatible silos. Why Go To All This Trouble?

Enterprise 43

Enterprise IT Metadata Consulting 43

Data Virtualization

MARCH 3, 2021

To remain competitive, organizations must have a data management strategy in place to effectively ingest, store, organize, and analyze data while ensuring that it is. The post Data Management Challenges for the Modern Enterprise appeared first on Data Virtualization blog.

Enterprise 75

Enterprise Management Strategy Data Lake 75

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

erwin

JANUARY 30, 2020

Digital transformation and data standards/uniformity round out the top five data governance drivers, with 37 and 36 percent, respectively. Constructing a Digital Transformation Strategy: How Data Drives Digital. However, more than 50 percent say they have deployed metadata management, data analytics, and data quality solutions.

Data Governance 145

Data Governance Metadata Digital Transformation Cost-Benefit 145

IBM Big Data Hub

JULY 6, 2022

With traditional approaches , data issues are reported by data users as they try to access and use the data and may take weeks to fix, if they’re found at all. starts at the data source, collecting data pipeline metadata across key solutions in the modern data stack like Airflow, dbt, Databricks and many more.

Metadata 57

Metadata Data Quality Snapshot Cost-Benefit 57

erwin

JANUARY 24, 2019

In most companies, an incredible amount of data flows from multiple sources in a variety of formats and is constantly being moved and federated across a changing system landscape. With an automation framework, data professionals can meet these needs at a fraction of the cost of the traditional manual way. Governing metadata.

Data Governance 71

Data Governance Metadata Data-driven Cost-Benefit 71

IBM Big Data Hub

NOVEMBER 4, 2022

Ensuring data quality is critical for data governance initiatives. Increasingly enterprise data is spread across multiple environments which contributes to inconsistent data silos that complicate data governance initiatives and create data integrity issues that could impact Business Intelligence and analytics applications.

Data Quality 57

Data Quality Metadata Data Governance Data-driven 57

Alation

DECEMBER 15, 2022

December 2012: Alation forms and goes to work creating the first enterprise data catalog. Later, in its inaugural report on data catalogs, Forrester Research recognizes that “Alation started the MLDC trend.”. May 2016: Alation named a Gartner Cool Vendor in their Data Integration and Data Quality, 2016 report.

Metadata 52

Metadata Data Governance Data Quality Marketing 52

Ontotext

SEPTEMBER 15, 2020

Added to this is the increasing demands being made on our data from event-driven and real-time requirements, the rise of business-led use and understanding of data, and the move toward automation of data integration, data and service-level management. Knowledge Graphs are the Warp and Weft of a Data Fabric.

Metadata 69

Metadata Knowledge Discovery Data Quality Strategy 69

Ontotext

NOVEMBER 4, 2021

Given all the advantages of the RDF model regarding enterprise data management, one no longer has good arguments why to bother with LPG at all. On the other hand, LPGs lack many features that are an absolute must for enterprise data management, e.g., standard schema language. The Background Story.

Metadata 98

Metadata Visualization Modeling Enterprise 98

IBM Big Data Hub

JULY 13, 2022

Data is everywhere in a hybrid and multi-cloud world. Enterprises now have more data, more data tools, and more people involved in data consumption. This data proliferation has made it harder than ever before to trust your data: knowing where it came from, how it has changed, and who is using it.

Metadata 67

Metadata Data Governance Data Quality Data Integration 67

IBM Big Data Hub

MAY 9, 2022

This architecture is agnostic to data environments, processes, utility and geography, all while integrating end-to-end data-management capabilities. A data fabric automates data discovery, governance and consumption, enabling enterprises to use data to maximize their value chain.

Management 57

Management Data Quality Metadata Data Integration 57

CIO Business Intelligence

OCTOBER 26, 2023

To earn your CBIP certification, you’ll need two or more years of full-time experience in CIS, data modeling, data planning, data definitions, metadata systems development, enterprise resource planning, systems analysis, application development, and programming or IT management.

Business Intelligence 126

Business Intelligence Data-driven Statistics Data Warehouse 126

erwin

APRIL 29, 2020

Many large organizations, in their desire to modernize with technology, have acquired several different systems with various data entry points and transformation rules for data as it moves into and across the organization. Who are the data owners? Data lineage offers proof that the data provided is reflected accurately.

Metadata 111

Metadata Data Governance Key Performance Indicator Data Quality 111

erwin

JANUARY 17, 2020

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. SQL or NoSQL?

Data-driven 138

Data-driven Modeling Metadata Data Governance 138

Octopai

JUNE 1, 2020

Implementing good data mapping practices is an important way modern enterprise organizations use advanced business metrics for strategic insight. With the rapid rise of new data regulations across the globe, capable data mapping isn’t just a tool for companies to get a leg up on the competition – it is required for legal compliance.

Metadata 52

Metadata Data Warehouse Data Integration Enterprise 52

erwin

JANUARY 17, 2019

Data governance is more important to the enterprise than ever before. It ensures everyone in the organization can discover and analyze high-quality data to quickly deliver business value. A mature and sustainable data governance initiative must include data integration. Governing metadata.

Metadata 63

Metadata Data Governance Data Quality Data-driven 63

CIO Business Intelligence

APRIL 29, 2022

Despite soundings on this from leading thinkers such as Andrew Ng , the AI community remains largely oblivious to the important data management capabilities, practices, and – importantly – the tools that ensure the success of AI development and deployment. Further, data management activities don’t end once the AI model has been developed.

Data Governance 145

Data Governance IT Risk Data Lake 145

Ontotext

JANUARY 6, 2021

That means removing errors, filling in missing information and harmonizing the various data sources so that there is consistency. Once that is done, data can be transformed and enriched with metadata to facilitate analysis. Knowledge graphs help with data analysis in a number of ways.

Metadata 59

Metadata Unstructured Data Cost-Benefit Enterprise 59