Ontotext

NOVEMBER 16, 2023

In the first part of this series of technological posts, we talked about what SHACL is and how you can set up validation for your data. Tacking the data quality issue — bit by bit or incrementally There are two main approaches to validating your data, which would be dependent on the specific implementation.

Data Quality 69

Data Quality Reporting Testing Technology 69

AWS Big Data

JUNE 6, 2023

Data consumers lose trust in data if it isn’t accurate and recent, making data quality essential for undertaking optimal and correct decisions. Evaluation of the accuracy and freshness of data is a common task for engineers. Currently, various tools are available to evaluate data quality.

Data Quality 75

Data Quality Data-driven Data Lake Metrics 75

DataKitchen

MAY 7, 2024

DataKitchen Training And Certification Offerings For Individual contributors with a background in Data Analytics/Science/Engineering Overall Ideas and Principles of DataOps DataOps Cookbook (200 page book over 30,000 readers, free): DataOps Certificatio n (3 hours, online, free, signup online): DataOps Manifesto (over 30,000 signatures) One (..)

Data Quality 164

Data Quality Testing Consulting Metrics 164

Ontotext

NOVEMBER 23, 2023

This technique can be especially useful in data integration projects where you are combining related, potentially overlapping data from multiple sources. Remember to set up your shapes graph in a repository that has been configured from the beginning to support SHACL, as described in our documentation.

Data Quality 69

Data Quality Reporting Metadata IT 69

Ontotext

NOVEMBER 9, 2023

While everyone may subscribe to the same design decisions and agree on an ontology, there may be differences in the data quality. In such situations, data must be validated. Meanwhile, you can download GraphDB and use the documentation to get started doing SHACL validation yourself! Sometimes there is no room for error.

Data Quality 64

Data Quality Testing Reporting Metadata 64

Ontotext

MAY 26, 2021

In natural language processing (NLP) and computational linguistics the Gold Standard typically represents a corpus of text or a set of documents, annotated or tagged with the desired results for the analysis – be it designation of the corresponding part of speech, syntactic parsing, concept or relationship.

Data Quality 87

Data Quality Machine Learning Measurement Metadata 87

CIO Business Intelligence

SEPTEMBER 12, 2024

An aircraft engine provider uses AI to manage thousands of technical documents required for engine certification, reducing administration time from 3-6 months to a few weeks. Assess and address data quality Once your data is centralized and cataloged, assessing and addressing data quality standards is crucial.

Unstructured Data 119

Unstructured Data Data Quality Structured Data Modeling 119

O'Reilly on Data

NOVEMBER 19, 2024

Here’s a simple rough sketch of RAG: Start with a collection of documents about a domain. Split each document into chunks. One more embellishment is to use a graph neural network (GNN) trained on the documents. Chunk your documents from unstructured data sources, as usual in GraphRAG. at Facebook—both from 2020.

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Unstructured Data Structured Data Modeling Statistics 252

CIO Business Intelligence

NOVEMBER 4, 2024

McKinsey estimates that leveraging internal data for sales and marketing insights can result in above-average market growth and increases of 15 to 25% in EBITDA. LLMs offer a new and unique way to extract this value, and training them on proprietary data to achieve specific business objectives could transform many companies.

Data Quality 126

Data Quality Business Objectives Sales Data-driven 126

CIO Business Intelligence

NOVEMBER 27, 2024

Once the province of the data warehouse team, data management has increasingly become a C-suite priority, with data quality seen as key for both customer experience and business performance. But along with siloed data and compliance concerns , poor data quality is holding back enterprise AI projects.

Enterprise 140

Enterprise Data Quality Modeling Structured Data 140

DataKitchen

FEBRUARY 23, 2024

Webinar: Beyond Data Observability: Personalization DataKitchen DataOps Observability Problem Statement White Paper: ‘Taming Chaos’ Technical Product Overview Four-minute online demo Detailed Product: Documentation Webinar: Data Observability Demo Day DataKitchen DataOps TestGen Problem Statement White Paper: ‘Mystery Box Full Of Data Errors’ (..)

Testing 118

Testing Data-driven Metrics Data Quality 118

CIO Business Intelligence

SEPTEMBER 10, 2024

This AI-augmented approach ensures that no critical feature falls through the cracks and that accurate requirements documents reduce the likelihood of defects. Invest in data quality: GenAI models are only as good as the data they’re trained on -with GenAI, mistakes can be amplified at speed. Result: 80% less rework.

Software 121

Software Digital Transformation Testing Advertising 121

CIO Business Intelligence

SEPTEMBER 12, 2024

At Gartner’s London Data and Analytics Summit earlier this year, Senior Principal Analyst Wilco Van Ginkel predicted that at least 30% of genAI projects would be abandoned after proof of concept through 2025, with poor data quality listed as one of the primary reasons.

Unstructured Data 111

Unstructured Data Data Quality Enterprise Data Governance 111

CIO Business Intelligence

JUNE 5, 2024

Crucial data resides in hundreds of emails sent and received every day, on spreadsheets, in PowerPoint presentations, on videos, in pictures, in reports with graphs, in text documents, on web pages, in purchase orders, in utility bills, and on PDFs. That data is free flowing and does not reside in one place.

Management 142

Management Strategy Data Strategy Marketing 142

AWS Big Data

FEBRUARY 29, 2024

Data governance is a critical building block across all these approaches, and we see two emerging areas of focus. First, many LLM use cases rely on enterprise knowledge that needs to be drawn from unstructured data such as documents, transcripts, and images, in addition to structured data from data warehouses.

Data Governance 102

Data Governance Unstructured Data Metadata Data Lake 102

CIO Business Intelligence

JANUARY 15, 2024

With seven operating centers, nine research facilities, and more than 18,000 staff, the agency continually generates an overwhelming amount of data, which it stores in more than 30 science data repositories across five topical areas — astrophysics, heliophysics, biological science, physical science, earth science, and planetary science.

Informatics 131

Informatics Metadata Data mining Digital Transformation 131

CIO Business Intelligence

MARCH 24, 2023

The Business Application Research Center (BARC) warns that data governance is a highly complex, ongoing program, not a “big bang initiative,” and it runs the risk of participants losing trust and interest over time. Data-related decisions, processes, and controls subject to data governance must be auditable.

Data Governance 142

Data Governance Management Metadata Data Quality 142

Smart Data Collective

AUGUST 11, 2022

How Artificial Intelligence is Impacting Data Quality. Artificial intelligence has the potential to combat human error by taking up the tasking responsibilities associated with the analysis, drilling, and dissection of large volumes of data. Data quality is crucial in the age of artificial intelligence. Conclusion.

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Data Quality Machine Learning Digital Transformation Big Data 133

CIO Business Intelligence

SEPTEMBER 19, 2024

It will do this, it said, with bidirectional integration between its platform and Salesforce’s to seamlessly delivers data governance and end-to-end lineage within Salesforce Data Cloud. We look at the entire landscape of information that an enterprise has,” Sangani said. “As

Data Governance 123

Data Governance Metadata Unstructured Data Structured Data 123

CIO Business Intelligence

APRIL 4, 2024

In total, it took the CIO’s team and agency a little over two years to convert 160 million documents into a transformed, revamped, and people-centric system, built on the Salesforce CRM, that tells their stories and focuses on people outcomes, not case outcomes.

Digital Transformation 111

Digital Transformation Data Lake ROI Modeling 111

AWS Big Data

NOVEMBER 13, 2024

Reuse of consumer-based data saves cost in extract, transform, and load (ETL) implementation and system maintenance. In addition to the preceding steps, the team established a data quality framework to improve the quality of the data product registered in the data solution.

Metadata 87

Metadata Data Quality Digital Transformation Data-driven 87

CIO Business Intelligence

MAY 2, 2023

Worse is when prioritized initiatives don’t have a documented shared vision, including a definition of the customer, targeted value propositions, and achievable success criteria. But there are common pitfalls , such as selecting the wrong KPIs , monitoring too many metrics, or not addressing poor data quality.

Digital Transformation 141

Digital Transformation IT Data-driven Metrics 141

Octopai

JUNE 19, 2024

By having these elements clearly defined, data producers can ensure they are providing data that meets the needs of the consumers, while consumers can trust the data they receive, knowing it adheres to the agreed-upon standards.

Data Quality 52

Data Quality Data-driven Data Integration Risk 52

Octopai

JUNE 19, 2024

By having these elements clearly defined, data producers can ensure they are providing data that meets the needs of the consumers, while consumers can trust the data they receive, knowing it adheres to the agreed-upon standards.

Data Quality 52

Data Quality Data-driven Data Integration Risk 52

CIO Business Intelligence

JUNE 12, 2024

Most enterprise data is unstructured and semi-structured documents and code, as well as images and video. For example, gen AI can be used to extract metadata from documents, create indexes of information and knowledge graphs, and to query, summarize, and analyze this data.

Modeling 140

Modeling Testing Cost-Benefit Metadata 140

erwin

APRIL 29, 2020

In the event of a change in data expectations, data lineage provides a way to determine which downstream applications and processes are affected by the change and helps in planning for application updates. Business terms and data policies should be implemented through standardized and documented business rules.

Key Performance Indicator 111

Key Performance Indicator Metadata Data Governance Data Quality 111

erwin

FEBRUARY 19, 2021

A strong data management strategy and supporting technology enables the data quality the business requires, including data cataloging (integration of data sets from various sources), mapping, versioning, business rules and glossaries maintenance and metadata management (associations and lineage).

Metadata 110

Metadata Management Data Quality Cost-Benefit 110

CIO Business Intelligence

OCTOBER 2, 2024

Companies need to establish clear guidelines for how its data is collected, stored and used, and ensure compliance with data protection regulations like GDPR in the EU, CCPA in California, LGPD in Brazil, PIPL in China and AI regulations such as EU AI Act.

Gap analysis 119

Gap analysis Risk Manufacturing Data Governance 119

datapine

APRIL 15, 2020

Working software over comprehensive documentation. The agile BI implementation methodology starts with light documentation: you don’t have to heavily map this out. But before production, you need to develop documentation, test driven design (TDD), and implement these important steps: Actively involve key stakeholders once again.

Business Intelligence 276

Business Intelligence Analytics Testing Dashboards 276

Ontotext

FEBRUARY 12, 2024

Domain teams should continually monitor for data errors with data validation checks and incorporate data lineage to track usage. Establish and enforce data governance by ensuring all data used is accurate, complete, and compliant with regulations. This calls for additional planning, documentation, and testing.

Data Quality 69

Data Quality Data-driven Data Lake Data Governance 69

Grooper

APRIL 9, 2020

The heart of digital transformation is data, and organizations without a clear strategy for data curation and enrichment from documents are at risk of failed transformation and missing out on massive revenue opportunities. The way we’ve thought about curating data from documents over the years is fundamentally flawed.

Digital Transformation 65

Digital Transformation Data Quality Enterprise Risk 65

O'Reilly on Data

JUNE 18, 2019

As model building become easier, the problem of high-quality data becomes more evident than ever. Even with advances in building robust models, the reality is that noisy data and incomplete data remain the biggest hurdles to effective end-to-end solutions. Data integration and cleaning.

Machine Learning 263

Machine Learning Data Quality Statistics Modeling 263

Smart Data Collective

SEPTEMBER 26, 2021

A NoSQl database can use documents for the storage and retrieval of data. The central concept is the idea of a document. Documents encompass and encode data (or information) in a standard format. A document is susceptible to change. The documents can be in PDF format. Speaking of which.

Metadata 103

Metadata Visualization Unstructured Data Data mining 103

AWS Big Data

NOVEMBER 22, 2024

This makes sure your data models are well-documented, versioned, and straightforward to manage within a collaborative environment. Compliance and data governance – For organizations managing sensitive or regulated data, you can use Athena and the adapter to enforce data governance rules.

Data Lake 77

Data Lake Data Warehouse Cost-Benefit Data Transformation 77

AWS Big Data

SEPTEMBER 11, 2024

This also includes building an industry standard integrated data repository as a single source of truth, operational reporting through real time metrics, data quality monitoring, 24/7 helpdesk, and revenue forecasting through financial projections and supply availability projections.

Data Architecture 89

Data Architecture Optimization Data Warehouse Metadata 89

DataRobot Blog

MAY 10, 2022

The regulatory guidance presented in these documents laid the foundation for evaluating and managing model risk for financial institutions across the United States. The first step would be to make sure that the data used at the beginning of the model development process is thoroughly vetted, so that it is appropriate for the use case at hand.

Risk 64

Risk Modeling Machine Learning Data Quality 64

CIO Business Intelligence

AUGUST 14, 2024

“They’re more able to connect to a diverse set of data sources, build more complex context around queries going through the model, and do retrieval augmented generation,” he adds. For most of our customers, Azure is where their identity is, where people’s credentials are, where people’s data already is,” he says.

Enterprise 136

Enterprise Cost-Benefit Experimentation Modeling 136