Data Virtualization

DECEMBER 8, 2022

Reading Time: 3 minutes While cleaning up our archive recently, I found an old article published in 1976 about data dictionary/directory systems (DD/DS). Nowadays, we no longer use the term DD/DS, but “data catalog” or simply “metadata system”. It was written by L.

Metadata 69

Metadata IT Data Integration Publishing 69

TDAN

JULY 20, 2021

How does Spotify win against a competitor like Apple? They use data better. Using machine learning and AI, Spotify creates value for their users by providing a more personalized experience.

Metadata 119

Metadata Machine Learning Data Architecture Data Strategy 119

Rocket-Powered Data Science

MARCH 10, 2020

Ahh, that’s the topic for another article. The training data and feature sets that feed machine learning algorithms can now be immensely enriched with tags, labels, annotations, and metadata that were inferred and/or provided naturally through the transformation of your repository of data into a graph of data.

Metadata 250

Metadata Machine Learning Prescriptive Analytics ROI 250

Cloudera

JANUARY 26, 2024

In this article, we will walk you through the process of implementing fine grained access control for the data governance framework within the Cloudera platform. Case Introduction In this article we will take the example of a data governance office that wants to control access to metadata objects in the company’s central data repository.

Metadata 73

Metadata Data Governance Management Finance 73

Analytics Vidhya

SEPTEMBER 13, 2022

This article was published as a part of the Data Science Blogathon. Introduction The purpose of a data warehouse is to combine multiple sources to generate different insights that help companies make better decisions and forecasting. It consists of historical and commutative data from single or multiple sources.

Data Warehouse 357

Data Warehouse Forecasting Data Science Big Data 357

TDAN

MAY 1, 2024

Well, of course, metadata is data. Our standard definition explicitly says that metadata is data describing other data. So why would I even ask this question in the article title?

Metadata 52

Metadata Management Data Quality IT 52

TDAN

MAY 31, 2022

The main goal of creating an enterprise data fabric is not new. It is the ability to deliver the right data at the right time, in the right shape, and to the right data consumer, irrespective of how and where it is stored. Data fabric is the common “net” that stitches integrated data from multiple data […].

Metadata 98

Metadata Enterprise IT Data Architecture 98

Rocket-Powered Data Science

FEBRUARY 15, 2023

Know thy data: understand what it is (formats, types, sampling, who, what, when, where, why), encourage the use of data across the enterprise, and enrich your datasets with searchable (semantic and content-based) metadata (labels, annotations, tags). The latter is essential for Generative AI implementations. Conduct market research.

Strategy 290

Strategy Experimentation Uncertainty Machine Learning 290

Data Virtualization

JULY 28, 2023

This is where active metadata comes in. Listen to “Why is Active Metadata Management Essential?” What is Active Metadata? The post The Power of Active Metadata appeared first on Data Management Blog - Data Integration and Modern Data Management Articles, Analysis and Information. ” on Spreaker.

Metadata 52

Metadata Data Integration Management Data Science 52

TDAN

SEPTEMBER 17, 2019

In my last article I suggested that many organizations have approached Data Governance incorrectly using only centralize data governance teams and that approach is not working for many.

Data Governance 76

Data Governance Metadata Data Strategy Strategy 76

TDAN

NOVEMBER 14, 2023

The outline in the following article will help an organization manage its metadata about itself (mission, strategies, etc.) Organizations are driven by their mission and the underlying strategies to accomplish that mission. Organizations that fail to understand their mission and strategies will at best flounder and at worst fail.

Metadata 52

Metadata Strategy Management IT 52

IBM Big Data Hub

JULY 28, 2022

Metadata enrichment is about scaling the onboarding of new data into a governed data landscape by taking data and applying the appropriate business terms, data classes and quality assessments so it can be discovered, governed and utilized effectively. With public API you can now manage metadata enrichment from external tools and workflows.

Metadata 56

Metadata Machine Learning Data Quality Statistics 56

O'Reilly on Data

JANUARY 28, 2019

Almost everyone who reads this article has consented to some kind of medical procedure; did any of us have a real understanding of what the procedure was and what the risks were? Helen Nissenbaum, in an interview with Scott Berinato , articulates some of the problems.

Insurance 247

Insurance Metadata Data Collection Marketing 247

O'Reilly on Data

JULY 28, 2020

In this article, we turn our attention to the process itself: how do you bring a product to market? The development phases for an AI project map nearly 1:1 to the AI Product Pipeline we described in the second article of this series. The final article in this series will be devoted to debugging.). Identifying the problem.

Marketing 364

Marketing Experimentation Metrics Testing 364

Cloudera

MARCH 4, 2024

In this article, we will walk you through the process of implementing fine grained access control for the data governance framework within the Cloudera platform. Case Introduction In this article we will take the example of a data governance office that wants to control access to metadata objects in the company’s central data repository.

Metadata 52

Metadata Data Governance Management Finance 52

TDAN

DECEMBER 2, 2020

Ensuring data quality is an important aspect of data management and these days, DBAs are increasingly being called upon to deal with the quality of the data in their database systems more than ever before. The importance of quality data cannot be overstated. Poor data quality costs the typical company between 10% and 20% of […].

Data Quality 90

Data Quality Metadata Data Governance Management 90

TDAN

NOVEMBER 15, 2022

Common themes were the growing importance of governance metadata, especially in the areas of business value, success measurement and reduction in operational and data risk. The future lies in metadata management. Governance metadata management […].

Metadata 52

Metadata Management Data Governance Measurement 52

Ontotext

DECEMBER 15, 2022

Enter metadata. Metadata describes data and includes information such as how old data is, where it was created, who owns it, and what concepts (or other data) it relates to. As a result, leveraging metadata has become a core capability for businesses trying to extract value from their data. Knowledge (metadata) layer.

Metadata 52

Metadata Unstructured Data Structured Data Enterprise 52

erwin

DECEMBER 9, 2024

We will tackle all these burning questions and more in this article. Metadata is the basis of trust for data forensics as we answer the questions of fact or fiction when it comes to the data we see. Being that AI is comprised of more data than code, it is now more essential than ever to combine data with metadata in near real-time.

Metadata 52

Metadata Data Quality Sales Modeling 52

TDAN

DECEMBER 5, 2023

In part one of “Metadata Governance: An Outline for Success,” I discussed the steps required to implement a successful data governance environment, what data to gather to populate the environment, and how to gather the data. In part two, I will discuss the “so what” aspects of data governance — that is, what types of […]

Metadata 52

Metadata Data Governance Data Quality Business Intelligence 52

Smarten

NOVEMBER 26, 2024

Column Metadata – Provides information on the dataset’s recency, such as the last update and publication dates. Read our free article, ‘ Why Is Natural Language Processing Important To Enterprise Analytics? ’. Missing Value Analysis – Shows the analysis of the missing values across all the columns of the dataset at a glance.

Machine Learning 64

Machine Learning Data Quality Predictive Modeling Metadata 64

O'Reilly on Data

DECEMBER 12, 2023

In an article in The New Yorker , Jaron Lanier introduces the idea of data dignity, which implicitly distinguishes between training a model and generating output using a model. This fallacy was probably encouraged by another New Yorker article arguing that an LLM is like a compressed version of the web. How do we make sense of this?

Modeling 335

Modeling Sales Software Statistics 335

DataKitchen

OCTOBER 20, 2023

In this article, we explore the role of Payload DJs in addressing these complexities, illustrated with examples from industries like drug discovery and insurance. Payload DJs facilitate capturing metadata, lineage, and test results at each phase, enhancing tracking efficiency and reducing the risk of data loss.

Insurance 169

Insurance Metadata Data-driven Data Quality 169

Domino Data Lab

JULY 2, 2019

Paco Nathan ‘s latest article covers program synthesis, AutoPandas, model-driven data queries, and more. In other words, using metadata about data science work to generate code. ” BTW, that Knuth article from 1983 was probably the first time that I ever saw the word “Web” used as a computer-related meaning.

Metadata 105

Metadata Data Science Machine Learning Data-driven 105

datapine

SEPTEMBER 29, 2022

In this article, we will detail everything which is at stake when we talk about DQM: why it is essential, how to measure data quality, the pillars of good quality management, and some data quality control techniques. We will go over them in the third part of this article. 2 – Data profiling. 3 – Defining data quality.

Data Quality 241

Data Quality Metrics Data-driven Management 241

Smart Data Collective

AUGUST 28, 2021

In this article, we will explore both, unfold their key differences and discuss their usage in the context of an organization. Data lakes are much more flexible as they can store raw data, including metadata, and schemas need to be applied only when extracting data. Data Warehouses and Data Lakes in a Nutshell. Target User Group.

Data Lake 140

Data Lake Data Warehouse Unstructured Data Structured Data 140

erwin

MARCH 14, 2019

(BFSI, PHARMA, INSURANCE AND NON-PROFIT) CASE STUDIES FOR AUTOMATED METADATA-DRIVEN AUTOMATION. Additionally, a tool that leverages and draws from a single metadata repository means that mappings are dynamically linked with underlying metadata to render automated lineage views, including full transformation logic in real time.

Metadata 81

Metadata Data Governance Data-driven Digital Transformation 81

CIO Business Intelligence

APRIL 29, 2022

Preparing for an artificial intelligence (AI)-fueled future, one where we can enjoy the clear benefits the technology brings while also the mitigating risks, requires more than one article. This first article emphasizes data as the ‘foundation-stone’ of AI-based initiatives. Establishing a Data Foundation. era is upon us.

Data Governance 145

Data Governance IT Data Lake Risk 145

TDAN

MAY 17, 2022

Maybe you are one who believes that there is something called Master Data Governance, Information Governance, Metadata Governance, Big Data Governance, Customer [or insert domain name here] Data Governance, Data Governance 1.0 – 2.0 – 3.0, There is… but one… Data Governance. I know that some people will disagree with me.

Data Governance 98

Data Governance Metadata Big Data Data Strategy 98

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 124

Enterprise Metadata Knowledge Discovery Management 124

Ontotext

APRIL 15, 2020

The initial dataset consisted of 13,202 journal articles relevant to novel coronavirus research. Here, GraphDB is used for storing the ontology models, the vocabulary, the content metadata and the graphs from the PICO ontology. It contains more than 51,000 scholarly articles and is available to the global research community.

Publishing 105

Publishing Metadata Data mining Data Architecture 105

O'Reilly on Data

JULY 25, 2023

This article summarizes what I learned from that experience. The inspiration (and title) for it comes from Mike Loukides’ Radar article on Real World Programming with ChatGPT , which shares a similar spirit of digging into the potential and limits of AI tools for more realistic end-to-end programming tasks.

Consulting 246

Consulting Interactive Software Metadata 246

TDAN

DECEMBER 3, 2019

I said I thought it affected all of them pretty profoundly, but perhaps the Metadata wedge the most. Recently, I was giving a presentation and someone asked me which segment of “the DAMA wheel” did I think semantics most affected. I thought I’d spend a bit of time to reflect on the question and answer […].

Metadata 88

Metadata Data Architecture IT Data Governance 88

FineReport

FEBRUARY 5, 2020

Metadata management. Users can centrally manage metadata, including searching, extracting, processing, storing, sharing metadata, and publishing metadata externally. The metadata here is focused on the dimensions, indicators, hierarchies, measures and other data required for business analysis. of BI pages.

Metadata 98

Metadata Dashboards Informatics Visualization 98

Analytics Vidhya

MARCH 22, 2024

In this article, we will dive into the world of RAG-powered document QnA using […] The post RAG Powered Document QnA & Semantic Caching with Gemini Pro appeared first on Analytics Vidhya.

Modeling 178

Modeling Analytics Metadata 178

TDAN

AUGUST 18, 2020

Standards exist for naming conventions, abbreviations and other pertinent metadata properties. Consistent business meaning is important because distinctions between business terms are not typically well defined or documented. What are the standards for writing […].

Management 96

Management Metadata Data Architecture IT 96

Ontotext

DECEMBER 16, 2021

In this article, the second entry in our Graphs on the Ground series ( see our first article on financial services ), we will explore how knowledge graphs combat these challenges across four critical activities within the world of life sciences and pharmaceuticals. Once again, knowledge graphs lend a hand.

Metadata 98

Metadata Reporting Unstructured Data Publishing 98