erwin

JANUARY 9, 2020

That’s because it’s the best way to visualize metadata , and metadata is now the heart of enterprise data management and data governance/ intelligence efforts. So here’s why data modeling is so critical to data governance. erwin Data Modeler: Where the Magic Happens.

Data Governance 144

Data Governance Modeling Metadata Unstructured Data 144

erwin

OCTOBER 8, 2020

These tools range from enterprise service bus (ESB) products, data integration tools; extract, transform and load (ETL) tools, procedural code, application program interfaces (APIs), file transfer protocol (FTP) processes, and even business intelligence (BI) reports that further aggregate and transform data.

Data Governance 145

Data Governance Metadata Testing Data Lake 145

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. They need their data mappings to fall under governance and audit controls, with instant access to dynamic impact analysis and lineage. Governing metadata.

Data Governance 71

Data Governance Metadata Data-driven Cost-Benefit 71

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 Key Performance Indicator Data Governance Data Quality 111

CIO Business Intelligence

MARCH 4, 2024

As data volumes continue to increase alongside a correlating number of business requests, modern insurance data leaders face a nuanced set of challenges. Accelerated demand in AI-enabled innovations has recently compounded these issues, prioritizing the need for new capabilities that require even more robust data foundations.

Insurance 79

Insurance Strategy Data Governance Data Integration 79

IBM Big Data Hub

NOVEMBER 4, 2022

Unlock quality data with IBM. We are excited to share that Gartner recently named IBM a Leader in the 2022 Gartner® Magic Quadrant for Data Quality Solutions. Access the full report here. It is critical to promote a common business language across the enterprise to break down these silos.

Data Quality 56

Data Quality Metadata Data Governance Data-driven 56

AWS Big Data

MARCH 29, 2024

However, to analyze trends over time, aggregate from different dimensions, and share insights across the organization, a purpose-built business intelligence (BI) tool like Amazon QuickSight may be more effective for your business. With these insights, teams have the visibility to make data integration pipelines more efficient.

Metrics 104

Metrics Visualization Dashboards Interactive 104

Octopai

FEBRUARY 24, 2021

Data and metadata are the glue that connects all these different hybrid system components. But tracking data through this type of environment can get rather… sticky. . BI system migration Regulatory compliance Changes and Impact analysis Fixing reports. Migrating Data to the Cloud. Let’s take a look at each one.

Metadata 84

Metadata Reporting Data Governance Management 84

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

IBM Big Data Hub

JANUARY 5, 2023

The right data architecture can help your organization improve data quality because it provides the framework that determines how data is collected, transported, stored, secured, used and shared for business intelligence and data science use cases.

Data Quality 52

Data Quality Data Architecture Strategy Data Lake 52

erwin

APRIL 27, 2022

In data warehousing, the data is extracted and transported from production database(s) into a data warehouse for reporting and analysis. Change Data Capture identifies and processes only the data that has changed and stores the changed data in a form so as to be of further use.

Data Governance 52

Data Governance Digital Transformation Metadata Data Warehouse 52

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. Octopai’s automated BI intelligence platform makes this process easy!

Metadata 52

Metadata Data Warehouse Data Integration Finance 52

datapine

MAY 29, 2019

BI architecture has emerged to meet those requirements, with data warehousing as the backbone of these processes. One of the BI architecture components is data warehousing. What Is Data Warehousing And Business Intelligence? A solid BI architecture framework consists of: Collection of data. Data integration.

Business Intelligence 196

Business Intelligence Data Warehouse Dashboards Visualization 196

Alation

FEBRUARY 13, 2020

This achievement is a testament not only to our legacy of helping to create the data catalog category but also to our continued innovation in improving the effectiveness of self-service analytics. A broader definition of Business Intelligence. Howard Dresner coined the term “Business Intelligence” in 1989.

Business Intelligence 52

Business Intelligence Machine Learning Marketing Metadata 52

Octopai

JANUARY 31, 2021

The particular episode we recommend looks at how WeWork struggled with understanding their data lineage so they created a metadata repository to increase visibility. Agile Data. Another podcast we think is worth a listen is Agile Data. Currently, he is in charge of the Technical Operations team at MIT Open Learning.

Data Governance 57

Data Governance Data Processing Data Quality Metadata 57

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. In short, yes.

Data Quality 63

Data Quality Data Integration Metadata Cost-Benefit 63

DataKitchen

SEPTEMBER 21, 2023

Bridging the Gap: How ‘Data in Place’ and ‘Data in Use’ Define Complete Data Observability In a world where 97% of data engineers report burnout and crisis mode seems to be the default setting for data teams, a Zen-like calm feels like an unattainable dream. What is Data in Use?

Testing 169

Testing Data Quality Predictive Modeling Metrics 169

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

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.

Modeling 98

Modeling Metadata Data Governance Visualization 98

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 107

Unstructured Data Dashboards Business Intelligence Data Governance 107

Jen Stirrup

MARCH 29, 2023

Put together, these factors help to provide the company with a competitive advantage since better data – and better use of data – leads to better strategies. Data as a product as well as a foundation The data foundation is critical for all types of reporting and analytics. Why is this interesting?

Metadata 121

Metadata Data Warehouse Data Integration Business Intelligence 121

CIO Business Intelligence

FEBRUARY 2, 2024

It consists of three separate, 90-minute exams: the Information Systems (IS) Core exam, the Data Management Core exam, and the Specialty exam. Each tests capabilities and knowledge ranging from project management and data management processes to business intelligence and IT compliance.

Management 109

Management Data Governance Cost-Benefit Testing 109

AWS Big Data

FEBRUARY 21, 2023

Loading complex multi-point datasets into a dimensional model, identifying issues, and validating data integrity of the aggregated and merged data points are the biggest challenges that clinical quality management systems face. This is one of the biggest hurdles with the data vault approach. What is a data vault?

Data Warehouse 65

Data Warehouse Data Lake Cost-Benefit Metadata 65

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.)

Cost-Benefit 76

Cost-Benefit Metadata Data Governance Modeling 76

AWS Big Data

NOVEMBER 29, 2023

In 2013, Amazon Web Services revolutionized the data warehousing industry by launching Amazon Redshift , the first fully-managed, petabyte-scale, enterprise-grade cloud data warehouse. Amazon Redshift made it simple and cost-effective to efficiently analyze large volumes of data using existing business intelligence tools.

Data Warehouse 100

Data Warehouse Data Lake Analytics Machine Learning 100

CIO Business Intelligence

SEPTEMBER 22, 2023

Data management platform definition A data management platform (DMP) is a suite of tools that helps organizations to collect and manage data from a wide array of first-, second-, and third-party sources and to create reports and build customer profiles as part of targeted personalization campaigns.

Management 99

Management Advertising Data Lake Sales 99

Alation

JANUARY 17, 2023

A typical modern data stack consists of the following: A data warehouse. Data ingestion/integration services. Data orchestration tools. Business intelligence (BI) platforms. How Did the Modern Data Stack Get Started? Today, data integration platforms like Rivery do just that.

Data Warehouse 52

Data Warehouse Cost-Benefit Data Transformation Data Science 52

CIO Business Intelligence

JUNE 9, 2022

A data management platform (DMP) is a group of tools designed to help organizations collect and manage data from a wide array of sources and to create reports that help explain what is happening in those data streams. Deploying a DMP can be a great way for companies to navigate a business world dominated by data.

Management 138

Management Advertising Data Lake Sales 138

AWS Big Data

JULY 20, 2023

These data pipelines generate valuable insights and curated data that are stored in Apache Iceberg tables for downstream usage. This data is then used by various applications for streaming analytics, business intelligence, and reporting.

Data Lake 87

Data Lake Analytics Snapshot Optimization 87

AWS Big Data

JUNE 10, 2024

This amalgamation empowers vendors with authority over a diverse range of workloads by virtue of owning the data. This authority extends across realms such as business intelligence, data engineering, and machine learning thus limiting the tools and capabilities that can be used. Here is where it can get complicated.

Data Lake 108

Data Lake Metadata Snapshot Analytics 108

Octopai

NOVEMBER 13, 2022

The modern data stack is a data management system built out of cloud-based data systems. A given modern data stack will usually include components for data ingestion from your data sources, data transformation, data storage, data analysis and reporting.

Enterprise 77

Enterprise Data Warehouse Reporting Metadata 77

O'Reilly on Data

MAY 21, 2019

We found that companies that have successfully adopted machine learning do so either by building on existing data products and services, or by modernizing existing models and algorithms. Use ML to unlock new data types—e.g., In an age of data-hungry algorithms, everything really begins with collecting and aggregating data.

Machine Learning 244

Machine Learning Technology Deep Learning Data Science 244

AWS Big Data

NOVEMBER 29, 2023

The dbt-glue adapter democratized access for dbt users to data lakes, and enabled many users to effortlessly run their transformation workloads on the cloud with the serverless data integration capability of AWS Glue. The team uses dbt-glue to build a transformed gold model optimized for business intelligence (BI).

Data Lake 102

Data Lake Management Metrics Data Warehouse 102

IBM Big Data Hub

AUGUST 4, 2023

By leveraging data services and APIs, a data fabric can also pull together data from legacy systems, data lakes, data warehouses and SQL databases, providing a holistic view into business performance. It uses knowledge graphs, semantics and AI/ML technology to discover patterns in various types of metadata.

Data Architecture 59

Data Architecture Data Lake Machine Learning Data Governance 59

erwin

NOVEMBER 6, 2020

To better explain our vision for automating data governance, let’s look at some of the different aspects of how the erwin Data Intelligence Suite (erwin DI) incorporates automation. Data Cataloging: Catalog and sync metadata with data management and governance artifacts according to business requirements in real time.

Data Governance 97

Data Governance Metadata Data-driven Visualization 97

Jet Global

FEBRUARY 23, 2024

Data mapping is essential for integration, migration, and transformation of different data sets; it allows you to improve your data quality by preventing duplications and redundancies in your data fields. Data mapping helps standardize, visualize, and understand data across different systems and applications.

Data Warehouse 52

Data Warehouse Reporting Data Transformation Sales 52

Jet Global

MAY 1, 2023

But many companies fail to achieve this goal because they struggle to provide the reporting and analytics users have come to expect. Learn how embedded analytics are different from traditional business intelligence and what analytics users expect. that gathers data from many sources. CRM, ERP, EHR/EMR) or portals (e.g.,

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52