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 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. Creating a High-Quality Data Pipeline.

Data Governance 40

Data Governance Risk Metadata Management 40

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

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 107

Digital Transformation Metadata Risk Management 107

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. Data management can never be a pure, complete process. However, governance processes are equally important.

Modeling 52

Modeling Data Governance Statistics Unstructured Data 52

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

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 113

Optimization Snapshot Metadata Cost-Benefit 113

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

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 222

Modeling Machine Learning Predictive Modeling Consulting 222

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

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

Ontotext

SEPTEMBER 2, 2020

There must be a strategy and a practice for preserving the knowledge, to create institutional memory. There are a multitude of recommendations such as creating internal wikis to record policy and procedures, document templates, exit interviews, job shadowing, digitizing employee training programs, etc. Preserving Knowledge.

Insurance 72

Insurance Metadata Publishing Unstructured Data 72

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. Let’s have a quick look under the bonnet.

Data-driven 59

Data-driven Risk Modeling Risk Management 59

Andrew White

JANUARY 9, 2022

To drive a successful Data Analytics strategy do you think it is a multidisciplinary activity and if so, what additional roles would you expect to see involved. – A D&A leader of CDO or whoever will lead the development of the D&A strategy will of necessity work with business executives. – Yes and no.

Analytics 64

Analytics Measurement Modeling Data-driven 64

Alation

JANUARY 17, 2023

What if, experts asked, you could load raw data into a warehouse, and then empower people to transform it for their own unique needs? Today, data integration platforms like Rivery do just that. By pushing the T to the last step in the process, such products have revolutionized how data is understood and analyzed.

Data Warehouse 52

Data Warehouse Cost-Benefit Data Transformation Data Science 52

AWS Big Data

NOVEMBER 29, 2023

It enables data engineers, data scientists, and analytics engineers to define the business logic with SQL select statements and eliminates the need to write boilerplate data manipulation language (DML) and data definition language (DDL) expressions. Data engineers define dbt models for their data representations.

Data Lake 102

Data Lake Management Metrics Data Warehouse 102

Alation

FEBRUARY 10, 2022

Let’s discuss what data classification is, the processes for classifying data, data types, and the steps to follow for data classification: What is Data Classification? Either completed manually or using automation, the data classification process is based on the data’s context, content, and user discretion.

Data Governance 52

Data Governance Risk Insurance Strategy 52

erwin

JULY 17, 2019

Having a clearly defined digital transformation strategy is an essential best practice for successful digital transformation. But what makes a viable digital transformation strategy? Constructing A Digital Transformation Strategy: Data Enablement. With automation, data quality is systemically assured.

Digital Transformation 98

Digital Transformation Strategy Metadata Data-driven 98

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

MARCH 14, 2024

Jet streamlines many aspects of data administration, greatly improving data solutions built on Microsoft Fabric. It enhances analytics capabilities, streamlines migration, and enhances data integration. Through Jet’s integration with Fabric, your organization can better handle, process, and use your data.

Analytics 52

Analytics Management Reporting Enterprise 52

Jet Global

MAY 1, 2023

Other money-making strategies include adding users in a per-seat structure or achieving price dominance in the market due. This strategy will ultimately increase sales, and prove a competitive advantage. Modern Data Sources Painlessly connect with modern data such as streaming, search, big data, NoSQL, cloud, document-based sources.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52