Octopai

APRIL 19, 2021

Aptly named, metadata management is the process in which BI and Analytics teams manage metadata, which is the data that describes other data. In other words, data is the context and metadata is the content. Without metadata, BI teams are unable to understand the data’s full story.

Metadata 52

Metadata Management Business Intelligence Data Governance 52

Alation

APRIL 25, 2023

As firms mature their transformation efforts, applying Artificial Intelligence (AI), machine learning (ML) and Natural Language Processing (NLP) to the data is key to putting it into action quickly and effecitvely. Using bad data, or the incorrect data can generate devastating results. 5 common data governance mistakes 1.

Data Governance 40

Data Governance Marketing Machine Learning Sales 40

O'Reilly on Data

APRIL 2, 2019

A few years ago, we started publishing articles (see “Related resources” at the end of this post) on the challenges facing data teams as they start taking on more machine learning (ML) projects. We are still in the early days for tools supporting teams developing machine learning models. Model governance.

Machine Learning 207

Machine Learning Modeling Data Science Software 207

Cloudera

FEBRUARY 16, 2022

In this example, the Machine Learning (ML) model struggles to differentiate between a chihuahua and a muffin. Will the model correctly determine it is a muffin or get confused and think it is a chihuahua? The extent to which we can predict how the model will classify an image given a change input (e.g. Model Visibility.

Machine Learning 85

Machine Learning Modeling Metadata Recreation/Entertainment 85

TDAN

AUGUST 15, 2023

Anyone who knows me well knows that I like to build models with Legos as a diversion from my “data-ly” life. I was never fond of Legos when I was young, but started my Lego excursion when I saw a bright yellow Lego Ford Mustang kit that I had to have. Several years later, I […]

Data Governance 52

Data Governance Modeling Metadata Data Strategy 52

TDAN

APRIL 30, 2019

Roles and responsibilities are the backbone of a successful information or data governance program. To communicate effectively […].

Data Governance 79

Data Governance Data Quality IT Metadata 79

TDAN

SEPTEMBER 3, 2019

As I write this, I can almost hear you wail “No, no, we don’t have too much metadata, we don’t have nearly enough! We have several projects in flight to expand our use of metadata.” Sorry, I’m going to have to disagree with you there. You are on a fool’s errand that will just provide […].

Metadata 65

Metadata Data Governance Big Data Modeling 65

TDAN

DECEMBER 20, 2022

Over the past few months, my team in Castlebridge and I have been working with clients delivering training to business and IT teams on data management skills like data governance, data quality management, data modelling, and metadata management.

Metadata 52

Metadata Data Governance Data Quality Management 52

TDAN

JUNE 20, 2023

These days, there is much conversation about the necessity of the data model. The data model has been around for several decades now and can be classified as an artifact of an earlier day and age. But is the data model really out of date? And exactly why do we need a data model, anyway? […]

Modeling 52

Modeling Metadata Data Strategy Data Governance 52

O'Reilly on Data

MAY 21, 2019

Companies successfully adopt machine learning either by building on existing data products and services, or by modernizing existing models and algorithms. In this post, I share slides and notes from a keynote I gave at the Strata Data Conference in London earlier this year. A typical data pipeline for machine learning.

Machine Learning 244

Machine Learning Technology Deep Learning Data Science 244

TDAN

NOVEMBER 2, 2021

As proponents of Lean Thinking, we view corporations as data factories that produce information for operations, reporting, and financial modeling. We treat data as inventory, data management as manufacturing, and business output as finished goods. Anything […].

Data Governance 52

Data Governance Operational Reporting Manufacturing Risk 52

Cloudera

MARCH 22, 2023

Data observability For a platform so concerned with data and the insight it brings, knowing whether the star player — data — is up to scratch is crucial. As Barr Moses outlined in her original article , data downtime is directly related to data systems complexity and immediately impacts insight and decision making.

Metrics 88

Metrics Data Governance Cost-Benefit Dashboards 88

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. Using ML models to search more effectively brought the search space down to 102—which can run on modest hardware.

Metadata 105

Metadata Machine Learning Data Science Data-driven 105

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? You must detect when the model has become stale, and retrain it as necessary. 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.

Marketing 363

Marketing Experimentation Metrics Testing 363

IBM Big Data Hub

NOVEMBER 21, 2023

In this article, developer Michael Burgess provides an insight into the concept of schemas and schema management as a way to add value to your event-driven applications on the fully managed Kafka service, IBM Event Streams on IBM Cloud ® What is a schema? A schema describes the structure of data. What’s next?

Data Quality 96

Data Quality Metadata Data-driven Optimization 96

Cloudera

OCTOBER 7, 2021

This blog will focus more on providing a high level overview of what a data mesh architecture is and the particular CDF capabilities that can be used to enable such an architecture, rather than detailing technical implementation nuances that are beyond the scope of this article. Introduction to the Data Mesh Architecture.

Metadata 124

Metadata Cost-Benefit Enterprise Interactive 124

datapine

SEPTEMBER 29, 2022

Engineered to be the “Swiss Army Knife” of data development, these processes prepare your organization to face the challenges of digital age data, wherever and whenever they appear. Data quality refers to the assessment of the information you have, relative to its purpose and its ability to serve that purpose.

Data Quality 241

Data Quality Metrics Data-driven Management 241

Domino Data Lab

APRIL 3, 2019

Paco Nathan ‘s latest column dives into data governance. This month’s article features updates from one of the early data conferences of the year, Strata Data Conference – which was held just last week in San Francisco. Introduction. Welcome back to our monthly burst of themes and conferences.

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84

AWS Big Data

JULY 28, 2023

Data management and governance Addressing the challenges mentioned requires a combination of technical, operational, and legal measures. Organizations need to develop robust data governance practices, establish clear procedures for handling deletion requests, and maintain ongoing compliance with GDPR regulations.

Snapshot 83

Snapshot Metadata Measurement Data Warehouse 83

TDAN

AUGUST 31, 2021

Can the responsibilities for vocabulary ownership and data ownership by business stakeholders be separate? I have listened to many presentations and read many articles about data governance (or data stewardship if you prefer), but I have never come across anyone saying they can and should be. Should they be?

Data Governance 52

Data Governance IT Metadata Data Strategy 52

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

MARCH 18, 2020

According to an article in Harvard Business Review , cross-industry studies show that, on average, big enterprises actively use less than half of their structured data and sometimes about 1% of their unstructured data. Finally, they combine classical technologies like data governance and data management with modern analytics.

Enterprise 59

Enterprise B2B Unstructured Data Machine Learning 59

Domino Data Lab

AUGUST 8, 2019

Paco Nathan ‘s latest monthly article covers Sci Foo as well as why data science leaders should rethink hiring and training priorities for their data science teams. In this episode I’ll cover themes from Sci Foo and important takeaways that data science teams should be tracking. Introduction. What’s a Foo?

Data Science 71

Data Science Machine Learning Data Governance Statistics 71

TDAN

JUNE 6, 2023

In the last few months, we have seen the wave of Artificial Intelligence break on the shores of wide-scale business adoption and mainstream media coverage of Large Language Models, most famously ChatGPT.

Risk 75

Risk Modeling Technology Data Governance 75

TDAN

JANUARY 17, 2024

The “Tide” of LLMs Has Reached Our Shores As a career data professional and having spent most of the last 10 years with Collibra, Alation, and now as an independent, I’ve come to a somewhat painful, yet exciting realization that large language models (LLM) will completely disrupt and displace data cataloging as we know it! […]

Modeling 52

Modeling IT Metadata Data Governance 52

AWS Big Data

APRIL 19, 2023

A knowledge graph allows us to combine data from different sources to gain a better understanding of a specific problem domain. In Neptune, we combine the Customer product data with an additional data product: Sales Opportunity. With the AWS SDK for Pandas, we combine this data by running a query against the Neptune graph.

Technology 82

Technology Data-driven Machine Learning Sales 82

CIO Business Intelligence

JUNE 12, 2024

To help alleviate the complexity and extract insights, the foundation, using different AI models, is building an analytics layer on top of this database, having partnered with DataBricks and DataRobot. Some of the models are traditional machine learning (ML), and some, LaRovere says, are gen AI, including the new multi-modal advances.

Modeling 134

Modeling Testing Cost-Benefit IT 134

Domino Data Lab

JULY 22, 2019

data science’s emergence as an interdisciplinary field – from industry, not academia. why data governance, in the context of machine learning is no longer a “dry topic” and how the WSJ’s “global reckoning on data governance” is potentially connected to “premiums on leveraging data science teams for novel business cases”.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

Data Virtualization

JUNE 22, 2023

The Denodo Platform is a logical data management platform, powered by. The post Denodo Joins Forces with Presto appeared first on Data Management Blog - Data Integration and Modern Data Management Articles, Analysis and Information.

Data Integration 52

Data Integration Management Data Lake Metadata 52

TDAN

AUGUST 5, 2020

McKinsey research found, even before the pandemic hit, that 92% of organizations thought their business models would require a significant effort in digital transformation to remain competitive. Based on the recent and […].

Management 114

Management Digital Transformation Modeling Metadata 114

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. Scalability and elasticity.

Metadata 56

Metadata Data Quality Machine Learning Statistics 56

TDAN

DECEMBER 6, 2022

Companies are increasingly focused on finding ways to connect to new and valuable sources of data in order to enhance their analytical capabilities, enrich their models, or deliver more insight to their business units. Due to the increased demand for new data sources, companies are also looking at their internal data differently.

Modeling 52

Modeling Analytics Metadata Data Governance 52

Laminar Security

AUGUST 23, 2023

Businesses need a new kind of data security to keep pace: an agile, cloud-native approach that safeguards their sensitive data while supporting rapid innovation and constant motion. This technology collects details on typical data usage across an organization, then flags when an event falls outside this definition of “normal activity.”

Cost-Benefit 52

Cost-Benefit Risk Digital Transformation Strategy 52

Grooper

OCTOBER 23, 2019

With the increasing value of data and more tools to process and analyze information than ever before, companies with information governance and master data model programs are outpacing their peers. Simply storing information without a detailed road map for how the data can and should be used is not enough.

Metrics 59

Metrics Measurement Strategy Optimization 59

TDAN

JULY 19, 2022

In reference to the prior column on enterprise data management and high level lego framework, this column reviews in detail the foundational layer of Organization Mission, Level 1. These six lego blocks […].

Strategy 52

Strategy Enterprise Management Modeling 52

TDAN

JUNE 30, 2020

About a week ago, I was teaching a data modeling class, and an attendee asked me to explain the concept of a data catalog. However, I had recently read the book, The Data Catalog: Sherlock Holmes Data Sleuthing for Analytics by […]. Like a lot of hype-related terms in IT, there is more than one definition.

Modeling 52

Modeling Analytics Data Architecture IT 52

TDAN

OCTOBER 31, 2023

Do you love or hate organizing papers and objects in your home? For those that hate it, why do you hate it? I like the results of organizing: reduction of clutter and ease of finding things. But I hate the process of organizing. The reason usually has to do with the fact that the object […]

IT 52

IT Data Governance Metadata Big Data 52