Andrew White

DECEMBER 4, 2019

I argued that one vendors’ book on data quality was really about data governance; I argued that another vendors’ marketing message was totally upside down; and I argued that some approaches to achieving single source of truth were different from traditional approaches. See Salesforce acquisition of Tableau – What does it mean?

Digital Transformation 64

Digital Transformation Data Integration Data Quality Data Warehouse 64

erwin

JANUARY 11, 2019

It’s only when companies take their first stab at manually cataloging and documenting operational systems, processes and the associated data, both at rest and in motion, that they realize how time-consuming the entire data prepping and mapping effort is, and why that work is sure to be compounded by human error and data quality issues.

Data Governance 40

Data Governance Risk Metadata Management 40

Ontotext

MARCH 8, 2023

With the size of data and dropping attention spans of online users, digital personalization has become one of the top priorities for companies’ business models. Graph-based solutions further leverage the relationships among the entities involved to create a semantically enhanced machine learning model. million users.

Enterprise 52

Enterprise Knowledge Discovery Risk Data-driven 52

Cloudera

AUGUST 31, 2021

While this “data tsunami” may pose a new set of challenges, it also opens up opportunities for a wide variety of high value business intelligence (BI) and other analytics use cases that most companies are eager to deploy. . Traditional data warehouse vendors may have maturity in data storage, modeling, and high-performance analysis.

Data Warehouse 82

Data Warehouse Cost-Benefit Metadata Data-driven 82

Ontotext

JANUARY 6, 2021

A knowledge graph provides centralized information control for enterprises at a time when it is no longer possible to integrate all transactional information due to its extreme volume, velocity and variety. Reuse of knowledge from third party data providers and establishing data quality principles to populate it.

Metadata 59

Metadata Unstructured Data Cost-Benefit Enterprise 59

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. Knowledge graphs model knowledge of a domain as a graph with a network of entities and relationships.

Modeling 69

Modeling Metadata Data Quality Enterprise 69

FineReport

APRIL 26, 2023

For example, a computer manufacturing company could develop new models or add features to products that are in high demand. E-commerce giants like Alibaba and Amazon extensively use big data to understand the market. Unlike traditional databases, processing large data volumes can be quite challenging. Easy implementation.

Big Data 52

Big Data Data Analytics Analytics Cost-Benefit 52

DataKitchen

APRIL 13, 2021

Because it is such a new category, both overly narrow and overly broad definitions of DataOps abound. DataOps needs a directed graph-based workflow that contains all the data access, integration, model and visualization steps in the data analytic production process. OwlDQ — Predictive data quality.

Testing 300

Testing Machine Learning Consulting Data Quality 300

erwin

MAY 27, 2020

Data modeling supports collaboration among business stakeholders – with different job roles and skills – to coordinate with business objectives. Data resides everywhere in a business , on-premise and in private or public clouds. A single source of data truth helps companies begin to leverage data as a strategic asset.

Modeling 98

Modeling Metadata Data Governance Visualization 98

Ontotext

FEBRUARY 19, 2020

Although there is still no single, universally accepted definition, there have been various attempts at it – such as in Towards a Definition of Knowledge Graphs. We hope it will bring some clarity to the topic and will help you get a better understanding of what it takes to craft a knowledge graph the semantic data modeling way.

Modeling 90

Modeling Knowledge Discovery Data Quality Visualization 90

DataKitchen

MAY 10, 2021

That’s a fair point, and it places emphasis on what is most important – what best practices should data teams employ to apply observability to data analytics. We see data observability as a component of DataOps. In our definition of data observability, we put the focus on the important goal of eliminating data errors.

Testing 214

Testing Manufacturing Data Quality Statistics 214

erwin

FEBRUARY 19, 2021

It involves establishing policies and processes to ensure information can be integrated, accessed, shared, linked, analyzed and maintained across an organization. Connect physical metadata to specific data models, business terms, definitions and reusable design standards. Map data flows. Govern data.

Metadata 110

Metadata Management Data Quality Cost-Benefit 110

AWS Big Data

JULY 13, 2023

To reflect the needs of their customers spread across different geographies and industries, Altron has organized their operating model across individual Operating Companies (OpCos). Data quality for account and customer data – Altron wanted to enable data quality and data governance best practices.

Optimization 91

Optimization B2B Data Quality Sales 91

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.

Metadata 52

Metadata Data Integration Measurement Data-driven 52

Ontotext

JUNE 29, 2023

This standardizes your data processing across different teams and departments, reducing manual effort and improving data quality. The straightforward answer to this question is that knowledge graphs put data into context to provide a smart framework for data integration, unification, analytics and sharing.

Cost-Benefit 59

Cost-Benefit Data-driven Data Quality Enterprise 59

Alation

JANUARY 13, 2022

But make no mistake: A data catalog addresses many of the underlying needs of this self-serve data platform, including the need to empower users with self-serve discovery and exploration of data products. In this blog series, we’ll offer deep definitions of data fabric and data mesh, and the motivations for each. (We

Data Lake 59

Data Lake Metadata Data-driven Data Governance 59

Alation

JULY 28, 2022

If you don’t have a well-defined business problem, your analytics or data science project will be an expensive failure. Where the old data warehouse model was driven by feeding data into it, the new model is about providing a view across lots of data with a specific purpose. Data quality /wrangling.

Data Warehouse 69

Data Warehouse Cost-Benefit Data Governance Data-driven 69

Ontotext

JULY 29, 2021

The Semantic Web started in the late 90’s as a fascinating vision for a web of data, which is easy to interpret by both humans and machines. One of its pillars are ontologies that represent explicit formal conceptual models, used to describe semantically both unstructured content and databases.

Enterprise 124

Enterprise Metadata Knowledge Discovery Management 124

Andrew White

JANUARY 9, 2022

Data mesh seems to mean a network of systems and hubs connected to help cope with silos. Perhaps a data mesh is what a pure architecture might design. There are many tactics to employ that might help – including data literacy; storytelling; use of our value pyramid/business value model; use of our decision intelligence model.

Analytics 64

Analytics Measurement Modeling Data-driven 64

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 Volume.

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 Volume.

Cost-Benefit 75

Cost-Benefit Technology Big Data Metadata 75

Alation

JANUARY 25, 2022

Data fabric is now on the minds of most data management leaders. In our previous blog, Data Mesh vs. Data Fabric: A Love Story , we defined data fabric and outlined its uses and motivations. The data catalog is a foundational layer of the data fabric. Alation Data Catalog for the data fabric.

Metadata 52

Metadata IT Metrics Data-driven 52

erwin

AUGUST 31, 2023

Q: Is data modeling cool again? In today’s fast-paced digital landscape, data reigns supreme. The data-driven enterprise relies on accurate, accessible, and actionable information to make strategic decisions and drive innovation. A: It always was and is getting cooler!!

Data-driven 98

Data-driven Modeling Enterprise Structured Data 98

datapine

NOVEMBER 27, 2019

Companies are no longer wondering if data visualizations improve analyses but what is the best way to tell each data-story. 2020 will be the year of data quality management and data discovery: clean and secure data combined with a simple and powerful presentation. 1) Data Quality Management (DQM).

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

datapine

MAY 17, 2021

Fundamentally, this is a term that describes the process through which businesses collect data from a variety of sources and apply it practically to generate real business value. Being a data-driven organization starts with understanding your data. So, what is data discovery? What is a data discovery platform?

Visualization 161

Visualization Data-driven Business Intelligence Metrics 161

erwin

JULY 17, 2019

Part Two of the Digital Transformation Journey … In our last blog on driving digital transformation , we explored how enterprise architecture (EA) and business process (BP) modeling are pivotal factors in a viable digital transformation strategy. With automation, data quality is systemically assured.

Digital Transformation 98

Digital Transformation Strategy Metadata Data-driven 98

Jet Global

FEBRUARY 23, 2024

The quick and dirty definition of data mapping is the process of connecting different types of data from various data sources. Data mapping is a crucial step in data modeling and can help organizations achieve their business goals by enabling data integration, migration, transformation, and quality.

Data Warehouse 52

Data Warehouse Reporting Data Transformation Sales 52