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. You can apply graph optimizations or operations such as traversals and transformations.

Metadata 59

Metadata Unstructured Data Cost-Benefit Enterprise 59

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 83

Data Warehouse Cost-Benefit Metadata Data-driven 83

Alation

JUNE 2, 2022

All Machine Learning uses “algorithms,” many of which are no different from those used by statisticians and data scientists. While ML solves problems with gradient methods (optimization), classic statistical modeling is mostly mathematical equation solving. Recently, Judea Pearl said, “All ML is just curve fitting.” Conclusion.

Modeling 52

Modeling Data Governance Statistics Unstructured Data 52

Ontotext

MARCH 8, 2023

Here, I will draw upon our own experience from client projects and lessons learned to provide a selection of optimal use cases for knowledge graphs and semantic solutions along with real world examples of their applications. For many organizations, however, the question remains, “Is it the right solution for us?” million users.

Enterprise 52

Enterprise Knowledge Discovery Risk Data-driven 52

AWS Big Data

FEBRUARY 1, 2023

This introduces the need for both polling and pushing the data to access and analyze in near-real time. From an operational standpoint, we designed a new shared responsibility model for data ingestion using AWS Glue instead of internal services (REST APIs) designed on Amazon EC2 to extract the data.

Optimization 52

Optimization Forecasting Data Lake Metadata 52

DataKitchen

SEPTEMBER 21, 2023

L1 is usually the raw, unprocessed data ingested directly from various sources; L2 is an intermediate layer featuring data that has undergone some form of transformation or cleaning; and L3 contains highly processed, optimized, and typically ready for analytics and decision-making processes. What is Data in Use?

Testing 169

Testing Data Quality Predictive Modeling Metrics 169

Ontotext

MARCH 27, 2024

For example, GPS, social media, cell phone handoffs are modeled as graphs while data catalogs, data lineage and MDM tools leverage knowledge graphs for linking metadata with semantics. RDF is used extensively for data publishing and data interchange and is based on W3C and other industry standards.

Modeling 69

Modeling Metadata Data Quality Enterprise 69

Ontotext

FEBRUARY 7, 2024

However, what we usually don’t talk about when generating an asset, are the huge invisible or unplanned costs occurring at a later stage when the data needs to be made available for analysis or secondary usage. As a result, a big portion of the IT capacity in Pharma is bound by data integration. But that’s not true.

Metadata 52

Metadata Data Integration Measurement Data-driven 52

erwin

FEBRUARY 4, 2021

Points of integration. Without an accurate, high-quality, real-time enterprise data pipeline, it will be difficult to uncover the necessary intelligence to make optimal business decisions. So what’s holding organizations back from fully using their data to make better, smarter business decisions? Regulations.

Data Governance 89

Data Governance Metadata Data Quality Risk Management 89

AWS Big Data

MARCH 26, 2024

A Gartner Marketing survey found only 14% of organizations have successfully implemented a C360 solution, due to lack of consensus on what a 360-degree view means, challenges with data quality, and lack of cross-functional governance structure for customer data. Then, you transform this data into a concise format.

Data Strategy 69

Data Strategy Strategy Data Warehouse Prescriptive Analytics 69

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

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

IBM Big Data Hub

JANUARY 17, 2023

IBM Cloud Pak for Data Express solutions offer clients a simple on ramp to start realizing the business value of a modern architecture. Data governance. The data governance capability of a data fabric focuses on the collection, management and automation of an organization’s data. Data integration.

Data-driven 56

Data-driven Enterprise Data Governance Prescriptive Analytics 56

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.) But the implementation of AI is only one piece of the puzzle.

Cost-Benefit 77

Cost-Benefit Metadata Data Governance Modeling 77

AWS Big Data

JULY 20, 2023

Prior to the creation of the data lake, Orca’s data was distributed among various data silos, each owned by a different team with its own data pipelines and technology stack. Moreover, running advanced analytics and ML on disparate data sources proved challenging.

Data Lake 88

Data Lake Analytics Snapshot Optimization 88

Ontotext

FEBRUARY 26, 2024

Bad Data Tax and the Data Bill of Rights So far, our discussion has been pretty theoretical, so we need a compelling business justification for moving in this direction. In the race to become data-driven, most efforts have resulted in a tangled web of data integrations and reconciliations across a sea of data silos.

Technology 52

Technology Cost-Benefit Data-driven Metadata 52

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

AWS Big Data

JUNE 12, 2023

Acting as a bridge between producer and consumer apps, it enforces the schema, reduces the data footprint in transit, and safeguards against malformed data. AWS Glue is an ideal solution for running stream consumer applications, discovering, extracting, transforming, loading, and integrating data from multiple sources.

Management 80

Management Metadata Testing Internet of Things 80

Ontotext

MAY 30, 2019

And before we move on and look at these three in the context of the techniques Linked Data provides, here is an important reminder in case we are wondering if Linked Data is too good to be true: Linked Data is no silver bullet. 6 Linked Data, Structured Data on the Web. Linked Data and Information Retrieval.

Cost-Benefit 87

Cost-Benefit Technology Big Data Metadata 87

Ontotext

MAY 30, 2019

And before we move on and look at these three in the context of the techniques Linked Data provides, here is an important reminder in case we are wondering if Linked Data is too good to be true: Linked Data is no silver bullet. 6 Linked Data, Structured Data on the Web. Linked Data and Information Retrieval.

Cost-Benefit 75

Cost-Benefit Technology Big Data Metadata 75

Andrew White

JANUARY 9, 2022

Much as the analytics world shifted to augmented analytics, the same is happening in data management. You can find research published on the infusion of ML in data quality, and also data catalogs, data discovery, and data integration. A data fabric that can’t read or capture data would not work.

Analytics 64

Analytics Measurement Modeling Data-driven 64

Smart Data Collective

OCTOBER 13, 2021

Agile BI and Reporting, Single Customer View, Data Services, Web and Cloud Computing Integration are scenarios where Data Virtualization offers feasible and more efficient alternatives to traditional solutions. Does Data Virtualization support web data integration? In improving operational processes.

Visualization 135

Visualization Cost-Benefit Big Data Prescriptive Analytics 135

erwin

AUGUST 31, 2023

It delivers the ability to capture and unify the business and technical perspectives of data assets, enables effective collaboration between a variety of stakeholders, and delivers metadata-driven automation to accelerate the creation and maintenance of data sources on virtually any data management platform.

Data-driven 98

Data-driven Modeling Enterprise Structured Data 98

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

O'Reilly on Data

DECEMBER 19, 2018

We won’t be writing code to optimize scheduling in a manufacturing plant; we’ll be training ML algorithms to find optimum performance based on historical data. With machine learning, the challenge isn’t writing the code; the algorithms are implemented in a number of well-known and highly optimized libraries.

Machine Learning 262

Machine Learning Software Metadata Testing 262

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. ETL helps collect, optimize, and store data for better reporting and real-time analysis.

Data Warehouse 52

Data Warehouse Reporting Data Transformation Sales 52