AWS Big Data

FEBRUARY 29, 2024

However, enterprise data generated from siloed sources combined with the lack of a data integration strategy creates challenges for provisioning the data for generative AI applications. 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

Ontotext

JULY 11, 2019

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

Statistics 40

Statistics Modeling Metadata Enterprise 40

erwin

OCTOBER 8, 2020

But even with the “need for speed” to market, new applications must be modeled and documented for compliance, transparency and stakeholder literacy. With all these diverse metadata sources, it is difficult to understand the complicated web they form much less get a simple visual flow of data lineage and impact analysis.

Data Governance 145

Data Governance Metadata Testing Data Lake 145

erwin

JANUARY 30, 2020

The results of our new research show that organizations are still trying to master data governance, including adjusting their strategies to address changing priorities and overcoming challenges related to data discovery, preparation, quality and traceability. And close to 50 percent have deployed data catalogs and business glossaries.

Data Governance 145

Data Governance Metadata Digital Transformation Cost-Benefit 145

Smart Data Collective

JULY 15, 2021

A data catalog serves the same purpose. By using metadata (or short descriptions), data catalogs help companies gather, organize, retrieve, and manage information. You can think of a data catalog as an enhanced Access database or library card catalog system. What Does a Data Catalog Consist Of?

Metadata 125

Metadata IT Data-driven Data Quality 125

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

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

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. What is Data Modeling?

Data-driven 138

Data-driven Modeling Metadata Data Governance 138

erwin

JANUARY 17, 2019

It assists in successfully meeting increasingly strict compliance requirements, such as those in the General Data Protection Regulation (GDPR). A mature and sustainable data governance initiative must include data integration. Data Governance and the System Development Lifecycle. Governing metadata.

Metadata 63

Metadata Data Governance Data Quality Data-driven 63

Octopai

JUNE 1, 2020

Data mapping is the cornerstone of many important business intelligence processes: Data Integration – Even if systems are compatible, combining two disparate data repositories still requires meticulous data mapping. Manual Metadata Management Hinders Compliance. This becomes even riskier as the stakes rise.

Metadata 52

Metadata Data Warehouse Data Integration Enterprise 52

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

Ontotext

AUGUST 5, 2020

Each of the four FAIR principles calls for data and metadata to be easily found, accessed, understood, exchanged and reused. FAIR data is data that is: Findable is such data in which data and metadata are assigned a globally unique and persistent identifier so that computers can easily find it.

Metadata 52

Metadata Insurance Manufacturing Data Integration 52

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

FEBRUARY 19, 2021

Metadata management is key to wringing all the value possible from data assets. However, most organizations don’t use all the data at their disposal to reach deeper conclusions about how to drive revenue, achieve regulatory compliance or accomplish other strategic objectives. What Is Metadata? Harvest data.

Metadata 110

Metadata Management Data Quality Cost-Benefit 110

Cloudera

AUGUST 31, 2021

Running on CDW is fully integrated with streaming, data engineering, and machine learning analytics. It has a consistent framework that secures and provides governance for all data and metadata on private clouds, multiple public clouds, or hybrid clouds. Consideration of both data & metadata in the migration.

Data Warehouse 82

Data Warehouse Cost-Benefit Metadata Data-driven 82

IBM Big Data Hub

SEPTEMBER 20, 2022

Often data scientists aren’t thrilled with the prospect of generating all the documentation necessary to meet ethical and regulatory standards. This is where technology such as IBM FactSheets , can help by reducing the manual labor needed to capture metadata and other facts about a model across stages of the AI lifecycle.

Metadata 57

Metadata Machine Learning Data-driven Modeling 57

AWS Big Data

APRIL 17, 2024

In this post, we discuss how the reimagined data flow works with OR1 instances and how it can provide high indexing throughput and durability using a new physical replication protocol. We also dive deep into some of the challenges we solved to maintain correctness and data integrity.

Optimization 111

Optimization Snapshot Metadata Cost-Benefit 111

erwin

MAY 2, 2022

One must also capture the vast quantity of metadata around the OLTP business requirements that must be reflected. I’m not proposing that business logic be held in the database, but it needs to be documented in the data model even if it will be implemented in application code. Figure 5: Adding attributes to diagram.

Modeling 98

Modeling Insurance Cost-Benefit Data Warehouse 98

Ontotext

JANUARY 10, 2024

And each of these gains requires data integration across business lines and divisions. Limiting growth by (data integration) complexity Most operational IT systems in an enterprise have been developed to serve a single business function and they use the simplest possible model for this. We call this the Bad Data Tax.

Metadata 72

Metadata Slice and Dice Data Integration Enterprise 72

Alation

JUNE 2, 2022

Data management is not yet a solved problem, but modern data management is leagues ahead of prior approaches. These include tracking, documenting, monitoring, versioning, and controlling access to AI/ML models. However, governance processes are equally important. Conclusion.

Modeling 52

Modeling Data Governance Statistics Unstructured Data 52

erwin

APRIL 27, 2022

Read more about erwin® Data Intelligence by Quest. Best Data Modeling Solution (erwin Data Modeler).

Data Governance 52

Data Governance Digital Transformation Metadata Data Warehouse 52

CIO Business Intelligence

APRIL 27, 2022

All are ideally qualified to help their customers achieve and maintain the highest standards for data integrity, including absolute control over data access, transparency and visibility into the provider’s operation, the knowledge that their information is managed appropriately, and access to VMware’s growing ecosystem of sovereign cloud solutions.

Digital Transformation 111

Digital Transformation Metadata Risk Management 111

Ontotext

NOVEMBER 23, 2023

This technique can be especially useful in data integration projects where you are combining related, potentially overlapping data from multiple sources. Remember to set up your shapes graph in a repository that has been configured from the beginning to support SHACL, as described in our documentation.

Data Quality 69

Data Quality Reporting Metadata IT 69

Octopai

JANUARY 31, 2022

If you’re a mystery lover, I’m sure you’ve read that classic tale: Sherlock Holmes and the Case of the Deceptive Data, and you know how a metadata catalog was a key plot element. In The Case of the Deceptive Data, Holmes is approached by B.I. He goes on to explain: Reasons for inaccurate data. Big data is BIG.

Metadata 77

Metadata IT Unstructured Data IoT 77

Alation

FEBRUARY 13, 2020

In the study, 75% of the 770 survey respondents indicated having difficulty in locating and accessing analytic content including data, models, and metadata. Enabling workers to find the right data is crucial to promoting self-service analytics. Alation is able to allow or restrict access to data based on role or group.

Business Intelligence 52

Business Intelligence Machine Learning Marketing Metadata 52

Ontotext

MARCH 8, 2023

Rich metadata and semantic modeling continue to drive the matching of 50K training materials to specific curricula, leading new, data-driven, audience-based marketing efforts that demonstrate how the recommender service is achieving increased engagement and performance from over 2.3 million users.

Enterprise 52

Enterprise Knowledge Discovery Risk Data-driven 52

Domino Data Lab

APRIL 3, 2019

Data governance shows up as the fourth-most-popular kind of solution that enterprise teams were adopting or evaluating during 2019. That’s a lot of priorities – especially when you group together closely related items such as data lineage and metadata management which rank nearby. Those days are long gone if they ever existed.

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84

AWS Big Data

SEPTEMBER 7, 2023

We will partition and format the server access logs with Amazon Web Services (AWS) Glue , a serverless data integration service, to generate a catalog for access logs and create dashboards for insights. Both the user data and logs buckets must be in the same AWS Region and owned by the same account.

Metadata 97

Metadata Dashboards Metrics Visualization 97

IBM Big Data Hub

JANUARY 9, 2024

The entire generative AI pipeline hinges on the data pipelines that empower it, making it imperative to take the correct precautions. 4 key components to ensure reliable data ingestion Data quality and governance: Data quality means ensuring the security of data sources, maintaining holistic data and providing clear metadata.

Enterprise 77

Enterprise Data Integration Data Quality Contextual Data 77

erwin

MAY 27, 2020

What, then, should users look for in a data modeling product to support their governance/intelligence requirements in the data-driven enterprise? Nine Steps to Data Modeling. Provide metadata and schema visualization regardless of where data is stored. naming and database standards, formatting options, and so on.

Modeling 98

Modeling Metadata Data Governance Visualization 98

O'Reilly on Data

MARCH 20, 2019

Data integrity constraints: Many databases don’t allow for strange or unrealistic combinations of input variables and this could potentially thwart watermarking attacks. Applying data integrity constraints on live, incoming data streams could have the same benefits. Disparate impact analysis: see section 1.

Modeling 226

Modeling Machine Learning Predictive Modeling Consulting 226

CIO Business Intelligence

MARCH 6, 2024

“SAP is executing on a roadmap that brings an important semantic layer to enterprise data, and creates the critical foundation for implementing AI-based use cases,” said analyst Robert Parker, SVP of industry, software, and services research at IDC. We are also seeing customers bringing in other data assets from other apps or data sources.

Unstructured Data 125

Unstructured Data Dashboards Business Intelligence Data Governance 125

erwin

FEBRUARY 4, 2021

It uncovered a number of obstacles that organizations have to overcome to improve their data operations. 1 bottleneck, according to 62 percent of respondents, was documenting complete data lineage. Automate code generation : Alleviate the need for developers to hand code connections from data sources to target schema.

Data Governance 89

Data Governance Metadata Data Quality Risk Management 89

Ontotext

JULY 29, 2021

KGs bring the Semantic Web paradigm to the enterprises, by introducing semantic metadata to drive data management and content management to new levels of efficiency and breaking silos to let them synergize with various forms of knowledge management. Take this restaurant, for example. Enterprise Knowledge Graphs and the Semantic Web.

Enterprise 124

Enterprise Metadata Knowledge Discovery Management 124

IBM Big Data Hub

OCTOBER 12, 2023

Among the tasks necessary for internal and external compliance is the ability to report on the metadata of an AI model. Metadata includes details specific to an AI model such as: The AI model’s creation (when it was created, who created it, etc.) This allows for a high degree of transparency and auditability.

Cost-Benefit 81

Cost-Benefit Metadata Data Governance Modeling 81

erwin

FEBRUARY 6, 2020

Here are our eight recommendations for how to transition from manual to automated data management: 1) Put Data Quality First: Automating and matching business terms with data assets and documenting lineage down to the column level are critical to good decision making.

Management 144

Management Data Governance Cost-Benefit Metadata 144