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

DataKitchen

OCTOBER 11, 2021

First, you must understand the existing challenges of the data team, including the data architecture and end-to-end toolchain. Finally, when your implementation is complete, you can track and measure your process. Monitoring Job Metadata. A DataOps implementation project consists of three steps.

Testing 152

Testing Metadata Dashboards Statistics 152

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

Alation

JANUARY 9, 2023

The Australian Prudential Regulation Authority (APRA) released nonbinding standards covering data risk management. Another agency later also published a legally binding standard to strengthen risk management for financial institutions with specific language related to data architecture and IT infrastructure.

Risk Management 52

Risk Management Risk Management Metadata 52

AWS Big Data

APRIL 3, 2024

If the asset has AWS Glue Data Quality enabled, you can now quickly visualize the data quality score directly in the catalog search pane. By selecting the corresponding asset, you can understand its content through the readme, glossary terms , and technical and business metadata.

Data Quality 89

Data Quality Visualization Metadata Metrics 89

Ontotext

APRIL 15, 2020

As most of this data is relevant to specific regions, it is very important to be able to traverse sub-region relationships in order to aggregate information, discover correlations and other types of analysis. Ontotext’s knowledge graph technology is at the core of Cochrane’s data architecture developed by our partners from Data Language.

Publishing 105

Publishing Metadata Data mining Technology 105

Octopai

JULY 9, 2020

When conducted manually, however, which has tended to be the normal mode of operation before companies discovered automation – or machine learning data lineage solutions, data lineage can be extremely tedious and time-consuming for BI & Analytics teams. A key piece of legislation that emerged from that crisis was BCBS-239.

Insurance 86

Insurance Risk Management Machine Learning Metadata 86

AWS Big Data

JANUARY 22, 2024

Cost and resource efficiency – This is an area where Acast observed a reduction in data duplication, and therefore cost reduction (in some accounts, removing the copy of data 100%), by reading data across accounts while enabling scaling. In this approach, teams responsible for generating data are referred to as producers.

Data-driven 85

Data-driven Advertising Metadata Data Architecture 85

AWS Big Data

MARCH 26, 2024

Profile aggregation – When you’ve uniquely identified a customer, you can build applications in Managed Service for Apache Flink to consolidate all their metadata, from name to interaction history. Then, you transform this data into a concise format.

Data Strategy 68

Data Strategy Strategy Data Warehouse Prescriptive Analytics 68

AWS Big Data

JANUARY 8, 2024

Kinesis Data Streams has native integrations with other AWS services such as AWS Glue and Amazon EventBridge to build real-time streaming applications on AWS. Refer to Amazon Kinesis Data Streams integrations for additional details. Examples are stock prices over time, webpage clickstreams, and device logs over time.

Analytics 113

Analytics IoT Data-driven Snapshot 113

Alation

APRIL 25, 2023

More specifically, it describes the process of creating, administering, and adapting a comprehensive plan for how an organization’s data will be managed. In this way, data governance has implications for a wide range of data management disciplines, including data architecture, quality, security, metadata, and more.

Data Governance 52

Data Governance Marketing Machine Learning Sales 52

BI-Survey

FEBRUARY 6, 2020

Although a common data model consisting of aligned structures and measures is needed for consistency and governance , it will not grant business departments the freedom required to efficiently analyze and interpret data. It is crucial to simplify data consumption and access for all users to support self-service requirements.

Reporting 59

Reporting Optimization Enterprise Data Quality 59

DataKitchen

JANUARY 23, 2023

To capture a more complete picture of the data’s journey, it is important to have a DataOps Observability system in place. Data lineage is static and often lags by weeks or months. Data lineage is often considered static because it is typically based on snapshots of data and metadata taken at a specific time.

Testing 130

Testing Data Governance Data Quality Data-driven 130

Alation

APRIL 25, 2023

More specifically, it describes the process of creating, administering, and adapting a comprehensive plan for how an organization’s data will be managed. In this way, data governance has implications for a wide range of data management disciplines, including data architecture, quality, security, metadata, and more.

Data Governance 40

Data Governance Marketing Machine Learning Sales 40

Andrew White

JANUARY 9, 2022

First, how we measure emissions and carbon footprint is about data design and policy. In other words, D&A plays a key role in the foundational measuring angle. Link to item 6 on slide 27 is broken, [link] , for Dashboard to measure business impact, can you provide a current link? Sure, that can help for sure.

Analytics 64

Analytics Measurement Modeling Data-driven 64

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. Data quality Data quality is essentially the measure of data integrity.

Data Quality 63

Data Quality Data Integration Metadata Cost-Benefit 63

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

Alation

APRIL 14, 2022

In today’s AI/ML-driven world of data analytics, explainability needs a repository just as much as those doing the explaining need access to metadata, EG, information about the data being used. The Cloud Data Migration Challenge. A data governance application with the ability to connect to data and security is needed.

Metadata 59

Metadata Data Governance Modeling Data-driven 59

erwin

APRIL 16, 2024

The data mesh framework In the dynamic landscape of data management, the search for agility, scalability, and efficiency has led organizations to explore new, innovative approaches. One such innovation gaining traction is the data mesh framework. This empowers individual teams to own and manage their data.

Metadata 52

Metadata Data Quality Data Governance Modeling 52

Sisense

SEPTEMBER 3, 2019

Modeling Your Data for Performance. Data architecture. The data landscape has changed significantly over the last two decades. The volume of data being created has increased, and the storage and computational resources needed to store and analyze that data has become cheaper and more widely available.

Data Warehouse 78

Data Warehouse OLAP Statistics Cost-Benefit 78

Jet Global

MAY 1, 2023

Manage compliance through up-to-the-minute performance measures, workflow automation, and essential regulatory reports. Data Environment First off, the solutions you consider should be compatible with your current data architecture. How to measure the value. addresses). It’s important to set expectations.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52