CIO Business Intelligence

MARCH 24, 2023

The program must introduce and support standardization of enterprise data. Programs must support proactive and reactive change management activities for reference data values and the structure/use of master data and metadata.

Data Governance 142

Data Governance Management Metadata Data Quality 142

erwin

MAY 13, 2020

Metadata management is the key to managing and governing your data and drawing intelligence from it. Beyond harvesting and cataloging metadata , it also must be visualized to break down the complexity of how data is organized and what data relationships there are so that meaning is explicit to all stakeholders in the data value chain.

Data Governance 93

Data Governance Modeling Enterprise Management 93

CIO Business Intelligence

OCTOBER 26, 2023

Business intelligence (BI) analysts transform data into insights that drive business value. This is done by mining complex data using BI software and tools , comparing data to competitors and industry trends, and creating visualizations that communicate findings to others in the organization.

Business Intelligence 125

Business Intelligence Data-driven Statistics Data Warehouse 125

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

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 discoverability Unlike structured data, which is managed in well-defined rows and columns, unstructured data is stored as objects.

Data Governance 93

Data Governance Unstructured Data Metadata Data Lake 93

erwin

JANUARY 17, 2020

What is Data Modeling? 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.

Data-driven 138

Data-driven Modeling Metadata Data Governance 138

Ontotext

NOVEMBER 4, 2021

As we’ve said again and again, we believe that knowledge graphs are the next generation tool for helping businesses make critical decisions, based on harmonized knowledge models and data derived from siloed source systems. But these tasks are only part of the story. Now, let’s dive in and look into each of these webinars.

Metadata 98

Metadata Visualization Modeling Enterprise 98

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

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

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

MAY 9, 2022

These use cases provide a foundation that delivers a rich and intuitive data shopping experience. This data marketplace capability will enable organizations to efficiently deliver high quality governed data products at scale across the enterprise. Multicloud data integration. Read the eBook. Start a trial.

Management 57

Management Data Quality Metadata Data Integration 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 66

Data Quality Data Integration Metadata Cost-Benefit 66

AWS Big Data

JUNE 7, 2023

This data needs to be ingested into a data lake, transformed, and made available for analytics, machine learning (ML), and visualization. For this, Cargotec built an Amazon Simple Storage Service (Amazon S3) data lake and cataloged the data assets in AWS Glue Data Catalog.

Metadata 55

Metadata Data Lake Machine Learning Big Data 55

AWS Big Data

AUGUST 15, 2023

Athena provides the connectivity and query interface and can easily be plugged into other AWS services for downstream use cases such as interactive analysis and visualizations. Because the built-in GCS connector schema inference capability is limited, it’s recommended to create an AWS Glue database and table for your metadata.

Recreation/Entertainment 86

Recreation/Entertainment Unstructured Data Business Intelligence Data-driven 86

Alation

JUNE 2, 2022

How Data Lineage Is a Use Case in ML. Data lineage is one example of a use case in which ML can play a pivotal role. Data lineage is the process of recording and visualizing data flowing from data sources to consumption. Data lineage allows companies to troubleshoot errors in data processes.

Modeling 52

Modeling Data Governance Statistics Unstructured Data 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.

Data Governance 57

Data Governance Data Processing Data Quality Metadata 57

AWS Big Data

FEBRUARY 6, 2023

AWS Glue is a serverless data integration service that makes it simple to discover, prepare, move, and integrate data from multiple sources for analytics, machine learning (ML), and application development. MongoDB Atlas is a developer data service from AWS technology partner MongoDB, Inc. Choose Create job.

Metadata 73

Metadata Data Lake Machine Learning Management 73

Andrew White

OCTOBER 22, 2021

Data Literacy, training, coordination, collaboration 8. Data Management Infrastructure/Data Fabric 5. Data Integration tactics 4. Metadata Strategy 3. CDO (data officer) 2. Enterprise Data Manager 1. Web and Data Services Manager 1. Business Innovation with D&A 6.

IT 52

IT Data Lake Strategy Data Science 52

erwin

MAY 2, 2022

Data modeling is a serious scientific method with many rules and best practices. One must also capture the vast quantity of metadata around the OLTP business requirements that must be reflected. However, it offers two extremely valuable business benefits: reducing data redundancy and improving data integrity.

Modeling 98

Modeling Insurance Cost-Benefit Data Warehouse 98

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

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 98

Metadata Dashboards Metrics Visualization 98

AWS Big Data

SEPTEMBER 29, 2023

Analyzing XML files can help organizations gain insights into their data, allowing them to make better decisions and improve their operations. Analyzing XML files can also help in data integration, because many applications and systems use XML as a standard data format. This approach optimizes the use of your XML files.

Metadata 65

Metadata Visualization Data-driven Optimization 65

DataKitchen

SEPTEMBER 21, 2023

There are multiple locations where problems can happen in a data and analytic system. What is Data in Use? Data in Use pertains explicitly to how data is actively employed in business intelligence tools, predictive models, visualization platforms, and even during export or reverse ETL processes.

Testing 176

Testing Data Quality Predictive Modeling Metrics 176

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

OCTOBER 11, 2023

The availability of machine-readable files opens up new possibilities for data analytics, allowing organizations to analyze large amounts of pricing data. Using machine learning (ML) and data visualization tools, these datasets can be transformed into actionable insights that can inform decision-making.

Visualization 94

Visualization Dashboards Data-driven Gap analysis 94

erwin

MAY 27, 2020

And it exists across these hybrid architectures in different formats: big and unstructured and traditional structured business data may physically sit in different places. What’s desperately needed is a way to understand the relationships and interconnections between so many entities in data sets in detail. Nine Steps to Data Modeling.

Modeling 98

Modeling Metadata Data Governance Visualization 98

AWS Big Data

NOVEMBER 22, 2023

Many AWS customers adopted Apache Hudi on their data lakes built on top of Amazon S3 using AWS Glue , a serverless data integration service that makes it easier to discover, prepare, move, and integrate data from multiple sources for analytics, machine learning (ML), and application development.

Data Lake 91

Data Lake Snapshot Metadata Optimization 91

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. Each of that component has its own purpose that we will discuss in more detail while concentrating on data warehousing. Data integration.

Business Intelligence 196

Business Intelligence Data Warehouse Dashboards Visualization 196

IBM Big Data Hub

APRIL 27, 2022

Gartner defines a data fabric as “a design concept that serves as an integrated layer of data and connecting processes. The data fabric architectural approach can simplify data access in an organization and facilitate self-service data consumption at scale. 11 May 2021. . 3 March 2022.

Management 85

Management Metadata Data Architecture Data Lake 85

AWS Big Data

MARCH 26, 2024

Data ingestion You have to build ingestion pipelines based on factors like types of data sources (on-premises data stores, files, SaaS applications, third-party data), and flow of data (unbounded streams or batch data). Then, you transform this data into a concise format.

Data Strategy 67

Data Strategy Strategy Data Warehouse Prescriptive Analytics 67

Alation

SEPTEMBER 22, 2022

Alation is pleased to launch a new strategic partnership with Fivetran, the leader in modern data integration. This new partnership will unify governed, quality data into a single view, granting all stakeholders total visibility into pipelines and providing them with a superior ability to make data-driven decisions.

Metadata 52

Metadata Data-driven Data Quality Visualization 52

AWS Big Data

FEBRUARY 1, 2023

We also used AWS Lambda for data processing. To further optimize and improve the developer velocity for our data consumers, we added Amazon DynamoDB as a metadata store for different data sources landing in the data lake. Clients access this data store with an API’s.

Optimization 52

Optimization Forecasting Data Lake Metadata 52

AWS Big Data

NOVEMBER 29, 2023

With Amazon Bedrock , you can privately customize FMs for your specific use case using a small set of your own labeled data through a visual interface without writing any code. Amazon DataZone uses ML to automatically add metadata to your data catalog, making all of your data more discoverable.

Data Warehouse 109

Data Warehouse Data Lake Data Integration Dashboards 109

Cloudera

MARCH 23, 2022

Figure 1: Apache Iceberg fits the next generation data architecture by abstracting storage layer from analytics layer while introducing net new capabilities like time-travel and partition evolution. #1: Apache Iceberg enables seamless integration between different streaming and processing engines while maintaining data integrity between them.

Metadata 89

Metadata Data Architecture Machine Learning Cost-Benefit 89

AWS Big Data

NOVEMBER 29, 2023

Maximize value with comprehensive analytics and ML capabilities “Amazon Redshift is one of the most important tools we had in growing Jobcase as a company.” – Ajay Joshi, Distinguished Engineer, Jobcase With all your data integrated and available, you can easily build and run near real-time analytics to AI/ML/Generative AI applications.

Data Warehouse 99

Data Warehouse Data Lake Analytics Machine Learning 99

Ontotext

FEBRUARY 26, 2024

From a technological perspective, RED combines a sophisticated knowledge graph with large language models (LLM) for improved natural language processing (NLP), data integration, search and information discovery, built on top of the metaphactory platform. Here’s how our tool makes it work.

Data-driven 59

Data-driven Risk Modeling Risk Management 59

Jen Stirrup

MARCH 29, 2023

However, to turn data into a business problem, organizations need support to move away from technical issues to start getting value as quickly as possible. SAP Datasphere simplifies data integration, cataloging, semantic modeling, warehousing, federation, and virtualization through a unified interface. Why is this interesting?

Metadata 120

Metadata Data Warehouse Data Integration Business Intelligence 120