TopQuadrant: Data Fabric Management Webinar

Had to miss this one, of particular interest, worked with TopQuadrant : 

 ... Sorry that you were not able to attend our recent webinar: " How Metadata Management Must Evolve to Support Data Fabric".

The recording and slides from the webinar are available here: https://www.topquadrant.com/how-metadata-management-must-evolve-to-support-data-fabric/.

Should you have any follow-up questions or would like to explore all of the capabilities in TopBraid EDG (http://www.topquadrant.com/products/topbraid-enterprise-data-governance/) in more detail please contact us at edg-info@topquadrant.com ... '

Webinar: How Metadata Management Must Evolve to Support Data Fabric

Looks to be useful, plan to attend:

TopQuadrant: How Metadata Management Must Evolve to Support Data Fabric   by Irene Polikoff | Mar 8, 2022 | Metadata Management, Webinars

About This Webinar

On Thu, Mar 17, 2022 11:30 AM EDT

If you have not heard the term “data fabric” yet, you will. It is rapidly growing in popularity. Gartner identified data fabric as the top trend for data and analytics in 2021.

You can think of data fabric as a web connecting multiple locations, types, and sources of data – both on-premises and in the public cloud. It is an architectural approach designed to help organizations better deal with the growing number of available data sources and ever-changing application requirements. The backbone of data fabric design is a Knowledge Graph capturing information about data sources in RDF. This is a new type of a data catalog with semantically augmented and enriched metadata.

Join us for this webinar to:

Learn What Is Data Fabric

Understand Why Data Fabric Requires a Knowledge Graph

Get Advice on Moving from Traditional Metadata Management to Metadata Management With Knowledge Graphs

Envision How Tools Participating in Data Fabrics Will Interact With Knowledge Graphs

See these Concepts in Action  ... 

 Register:   https://register.gotowebinar.com/register/7807142108219203083 

Metadata – The Magic Behind Data Fabric

Topquadrant talks active metadata.

Metadata – The Magic Behind Data Fabric

by Irene Polikoff | Feb 7, 2022 | Blog from Topquadrant

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 and application sources and delivers it to various data consumers. 

So, what makes the data fabric approach different from previous, more traditional data integration architectures? The key differentiator of a data fabric is its fundamental reliance on metadata to accomplish this goal. Implementing a data fabric means establishing a metadata-driven architecture capable of delivering integrated and enriched data to data consumers.To emphasize this point, Gartner coined the term active metadata. 

Data fabric relies on active metadata. 

Metadata describes different aspects of data. The more comprehensive the sets of metadata we collect, the better they will be able to support our application scenarios. Traditionally, metadata categories have included:

Business metadata – provides the meaning of data through mappings to business terms.

Technical metadata – provides  information on the format and structure of the data such as physical database schemas, data types, data models.

Operational metadata – describes details of the processing and accessing of data such as data sharing rules, performance, maintenance plans, archive and retention rules.

More recently, a new category of metadata became important – Social metadata. It typically includes discussions and feedback on the data from its technical and business users. Business metadata has evolved beyond just mapping to terms to now encompassing ontologies to better assist with interpreting data’s context and meaning.

How is active metadata different from passive metadata? Gartner defines passive metadata as any metadata that is collected. Some Gartner analysts equate active metadata with metadata that is being used. By use, we mean use of the metadata by software (such as software components within the data fabric) in support of a broad range of data integration, analysis, reporting and other data processing scenarios. Other analysts push this concept further and say that active metadata is created by the data fabric by analyzing passive metadata and using the results to recommend or automate tasks.

Irrespective of the exact definition of active metadata, the underlying premise of the data fabric is that the optimal solution for the delivery of the right data in the right shape is to leverage its metadata. For example, the data fabric may use metadata to:  ... ' 

Effectively Mapping Business Terms

 Ultimately what can make such approaches work.   Have worked with TopQuadrant in the past.

WEBINAR: Automating the Mapping of Data Elements to Business Terms

Join us for this popular Webinar, offered again at more convenient time for our European and beyond audience! January 7, 2021 at 8:00 AM ET.

A business glossary is a collection of terms important to a business. It can capture valid definitions of values and business rules for data, sharing this key knowledge across the organization. A business glossary can be used to link IT assets such as data elements to terms that describe the data; thereby, connecting business and IT and enhancing organizational collaboration and productivity.

A well-defined and operationalized business glossary saves time for employees and increases consistency, standardization and data integrity for the entire enterprise. However, the process for connecting data sources to business terms presents a challenge for many organizations. With hundreds of data sources and many thousands of data elements, it is a large undertaking.

In this webinar, we will describe how TopBraid EDG can help you automate this process, including: 

Using Data Element Names to Map to Business Terms

Using Data to Map to Business Terms 

 More details about the webinar and speakers.   and Registering.

After registering, you will receive a confirmation email containing information about joining the webinar. 

Knowledge Graphs vs Property Graphs

Upcoming Webinar of interest, register at the link.

Knowledge Graphs vs. Property Graphs — A Brief Overview and Comparison
by Kristi Lee-John | Jul 29, 2020 | Webinars, Knowledge Graphs,  TopQuadrant

About This Webinar   Aug 13 @ 11:30AM ET

We are in the era of graphs. Graphs are hot. Why? Flexibility is one strong driver: heterogeneous data, integrating new data sources, and analytics all require flexibility. Graphs deliver it in spades.

Over the last few years, a number of new graph databases came to market. As we start the next decade, dare we say “the semantic twenties,” we also see vendors that never before mentioned graphs starting to position their products and solutions as graphs or graph-based.
Graph databases are one thing, but “Knowledge Graphs” are an even hotter topic. We are often asked to explain Knowledge Graphs.

Today, there are two main graph data models:
Property Graphs (also known as Labeled Property Graphs)
RDF Graphs (Resource Description Framework) aka Knowledge Graphs
Other graph data models are possible as well, but over 90% of the implementations use one of these two models. In this webinar we will cover the following:

I. A brief overview of each of the two main graph models noted above
II. Differences in Terminology and Capabilities of these models
III. Strengths and Limitations of each approach
IV. Why Knowledge Graphs provide a strong foundation for enterprise data governance and metadata management .... '

More about the Webinar, registration.

Knowledge Graphs versus Property Graphs

This came in the mail, it had been asked in an interaction recently.  Worth a look.  We worked with TopQuadrant.

New White Paper: Knowledge Graphs versus Property Graphs

We are in the era of graphs. Graphs are hot. Why? Flexibility is one strong driver: heterogeneous data, integrating new data sources, and analytics all require flexibility. Graphs deliver it in spades.

The two main graph data models are: Property Graphs and Knowledge (RDF) Graphs. People who want to take advantage of graph-based solutions for data and metadata management want to know what they are, what are their similarities and differences, and what they are each good for.

This white paper covers the following, it:
Describes the two main graph data models: Property Graphs and RDF Graphs and explains the key differences in their terminology and capabilities
Compares their strengths and limitations
Provides guidance on their respective capabilities

Other TopQuadrant resources to explore:
RECORDED WEBINARS, including this most recent one: "Getting Started with Data Governance"
WHITE PAPER COLLECTION, including: "Implementing Data Governance with Knowledge Graphs to Enable Enterprise AI"

Download Now    https://www.topquadrant.com/knowledge-assets/whitepapers/

This email sent by TopQuadrant   www.topquadrant.com

Shape of Data Implies its Uses

Recall hearing about this approach in training we took.    Making it easier to connect key data to analytics.   Shape constraint languages.

Graph Database ‘Shapes’ Data   By George Leopold in Datanami

A semantic graph database technology vendor is supporting a key specification designed to validate graph-based data against a set of conditions that specify the “shape” of data. The goal is a more agile way of analyzing larger volumes of complex, distributed data.

Franz Inc. said this week its flagship AllegoGraph platform now supports SHACL, the SHApe Constraint Language used to describe “the shape that data should have.” The spec was developed by the World Wide Web Consortium.

The language using a concept known as “triples” to describe data properties. Triples refers to the subject (the thing being described), predicate (properties or relationships of the subject) and object (an intrinsic value such as an integer or text).

SHACL also specifies the number of triples required in a repository along with metadata about the object, including a specified subject and predicate.

Oakland-based Franz said support for SHACL would improve the ability of its graph database to validate data and applications from outside sources while improving interoperability. “Adding SHACL to AllegroGraph helps our customers simplify the complexity of enterprise systems through the ability to loosely combine independent elements, while allowing the overall system to function smoothly,” said CEO Jans Aasman.

The 6.6 version of AllegroGraph includes a data-shaping validation engine used to confirm whether data conforms with desired requirements. SHACL allows a data graph, for instance, to specify the corresponding shapes graph used to describe the link between a given shape and targeted data.  .... "

Whitepaper: Data Governance, Knowledge Graphs

Continue to follow the problem of how the enterprise can most effectively support AI.   Just downloaded the following free white paper that addresses this from TopQuadrant:

https://www.topquadrant.com/knowledge-assets/whitepapers/

Are You Considering How AI May Help Your Information Management?

Then you will want to learn about the synergistic relationship between Data Governance, Knowledge Graphs, and enterprise applications of AI and Machine Learning (ML)  .... "

TopBraid and Knowledge Graphs

We actively used Semantic methods to organize our large and complex databases.

You can access a recent  update webinar recording and slides here:

Should you have any follow-up questions or would like to explore all of the capabilities in TopBraid EDG (http://www.topquadrant.com/products/topbraid-enterprise-data-governance/) in more detail please contact us at edg-info@topquadrant.com.

– TopBraid EDG as a knowledge graph for data governance
– It is designed to provide comprehensive, modular, and extendable Data Governance capabilities

“Using Knowledge Graph technologies TopBraid EDG supports integrated data governance across the ever growing numbers and types of data assets and  governance needs – because connections are important.

TopBraid EDG is an Enterprise Knowledge Graph for Data Governance
 TopBraid EDG, is a rich set of interconnected knowledge graphs expressing knowledge about how data is used and managed in the enterprise ecosystem
 These integrated knowledge graphs are ready to be enriched with your enterprise specific knowledge
 When this enrichment takes place, your enterprise is ready for implementing comprehensive data governance ... "

What are Knowledge Graphs?

Some good introductory resources from the recent Webinar:

From TopQuadrant:
Thank you for attending our webinar:   "What are Knowledge Graphs?   Why are they key to Successful Data Governance?"   We hope you enjoyed the event.  ... 

The recording and slides from the webinar are available here  (Plus other resources): 

Audio recording.
Slides.

Knowledge Graphs, Governance, Learning and Much More

Have now been involved in a number of efforts in this area, worth understanding since Google has made an impressive run at this.  The article tells a historical journey I have traveled as well, we might finally be getting to real enterprise value.  And regulations like GDPR are forcing us to take notice of the need to really understand our data. The article is long, but has good points to make.

The Semantic Zoo - Smart Data Hubs, Knowledge Graphs and Data Catalogs   By Kurt Cagle Contributor in Forbes

COGNITIVE WORLDContributor Group
Sometimes, you can enter into a technology too early. The groundwork for semantics was laid down in the late 1990s and early 2000s, with Tim Berners-Lee’s stellar Semantic Web article, debuting in Scientific American in 2004, seen by many as the movement’s birth. Yet many early participants in the field of semantics discovered a harsh reality: computer systems were too slow to handle the intense indexing requirements the technology needed, the original specifications and APIs failed to handle important edge cases, and, perhaps most importantly, the number of real world use cases where semantics made sense were simply not at a large enough scope; they could easily be met by existing approaches and technology.


Semantics faded around 2008, echoing the pattern of the Artificial Intelligence Winter of the 1970s. JSON was all the rage, then mobile apps, big data came on the scene even as Javascript underwent a radical transformation, and all of a sudden everyone wanted to be a data scientist (until they discovered the fact that data science was mostly math). Meanwhile, from the dim recesses of the troughs of despair, semantics was readying itself for its own metamorphosis. Several semantic standards, including the SPARQL query language along with a new update language began seeing implementations by 2015.  Servers became faster and cheaper, and a rise of graphics processor units (GPUs) fueled by the gaming and entertainment industry provided tools for a new class of graph databases.

Meanwhile, the Big Data initiatives that had marked the early part of the 2010s was facing some real problems. The original promise of Hadoop as a map / reduce framework had ended up creating large numbers of data lakes that aggregated content but that sat under-utilized. Data scientists struggled to deal with dirty data that was really no cleaner for having been put in data lakes. JSON databases had grown in popularity, but they were proving hard to query in a consistent fashion, and all too many Hadoop projects ended up becoming large, slow, but cheap data graveyards for regulatory data (the kind of data that must be retained for five years). ..... "

Knowledge Graphs and Governance Webinar

Will be attending.   Ultimately a crucial next step for AI and integration of Semantic data with enterprise data and metadata.

From TopQuadrant:

Join us for a webinar on February 21, 2019 at 11:30 AM ET.

Why are knowledge graphs relevant to data governance? The answer lies in key characteristics. Knowledge graphs are:

Flexible – graphs are the most flexible formal data structures
Evolvable – able to accommodate diverse data and metadata
Semantic – the meaning of the data is stored alongside the data in the graph
Intelligent – semantics of data are explicit and enable data validation and drawing conclusions and new information from the available data.

These qualities make knowledge graphs an ideal and, arguably, the only viable foundation for bridging and connecting enterprise metadata silos – the main goal of data governance.

Join us for this webinar where we will:
Provide a brief history of Knowledge Graphs
Give examples of what they are good for
Show how they provide a powerful platform for integrated data management and governance ... " 

Data Governance and Knowledge Graphs

Knowledge Graphs and Data Governance

A Note from TopQuadrant's CEO, Irene Polikoff 

As 2018 comes to a close we took a look back at key topics that were of most interest in our conversations with customers, prospects and at the many industry events we attended throughout the year.

A very popular topic continues to be Knowledge Graphs. Looking ahead, we believe that the use of knowledge graphs in the enterprise will be substantially expanded in key areas including data relationship discovery and exploration, semantic interoperability, and especially data governance. Knowledge graphs are foundational to data governance because they catalog diverse enterprise data by capturing both the technical and business context, and meaning of the data through connections across all assets in the enterprise ecosystem.

On the more technical side, we continue to receive a lot of questions about SHACL and its importance to data management. SHACL, a W3C standard for graph data modeling and validation can be used to support many capabilities needed for data governance. I cover some examples in this blog post, “How does SHACL Support Data Governance?” With the addition of SHACL to the standards that support knowledge graphs, it is possible to ensure that the knowledge remains consistent against multiple viewpoints, retains a good quality, and supports the inference of new knowledge that is not explicitly stated in the data.

For these and other reasons I can't cover in this brief note, we believe knowledge graphs are an ideal and, arguably, the only viable foundation for bridging and connecting enterprise metadata silos. For more on TopQuadrant’s views on these topics, see the newly published DATAVERSITY article: “Achieving Data Governance 2.0 with Knowledge Graph Technology.”  ... "

http://www.dataversity.net/achieving-data-governance-2-0-knowledge-graph-technology/
https://www.topquadrant.com/2017/10/25/how-does-shacl-support-data-governance/

UpComing Webinar on Knowledge Graphs

Its important to link knowledge with other forms of semantic databases.   As we use these methods they become easier to link and maintain to AI style applications.   We used TopQuadrant, I will attend.

WEBINAR: What are Knowledge Graphs? Why Are They Key to Successful Data Governance?

Join us for a webinar on February 21, 2019 at 11:30 AM ET

Robert Coyne of TopQuadrant writes:

Why are knowledge graphs relevant to data governance? The answer lies in key characteristics. Knowledge graphs are:

Flexible – graphs are the most flexible formal data structures
Evolvable – able to accommodate diverse data and metadata
Semantic – the meaning of the data is stored alongside the data in the graph
Intelligent – semantics of data are explicit and enable data validation and drawing conclusions and new information from the available data.
These qualities make knowledge graphs an ideal and, arguably, the only viable foundation for bridging and connecting enterprise metadata silos – the main goal of data governance.

Join us for this webinar where we will:

Provide a brief history of Knowledge Graphs
Give examples of what they are good for
Show how they provide a powerful platform for integrated data management and governance 

More info and registration: https://www.topquadrant.com/knowledge-graphs-webinar/ 

Semantic Ecosystems

Continuing a look at modern ontology capabilities,  We did work with Stanford's KSL to build ontologies of corporate data.

Semantic Ecosystems

Create a semantic ecosystem—evolve from disparate data to actionable knowledge.

TopQuadrant’s solutions empower people by providing them with connected, business-relevant information—an ecosystem of actionable knowledge. Using TopQuadrant’s TopBraid solutions, organizations transform diverse information into semantic assets.

Find out how TopQuadrant can help link your information assets to create a semantic ecosystem where meaningful interactions between people, applications and data occur consistently with a speed, accuracy and intelligence previously not possible.  ... " 


Reading TopQuadrant’s Blogs:
The Semantic Ecosystems Journal

Composing the Semantic Web – A TopBraid Developer’s Blog

Clean Data for Analytics

Recently been involved in the process of addressing data quality.  Key for any analytics.    A general look.  One other way to think of this is to use semantic data management tools to create precise statements of the knowledge that sits behind the data.  Examined that with Stanford KSL a while ago. That ultimately evolved to the package Protoge.   Most recently took a dive into the impressive package Top Quadrant.   What other large entities are doing this?  

Shapes Constraint Language

Brought to my attention.  Technical.  Note the mention of data quality use, using the concept of 'shape',  a recent concern.

" .... The Shapes Constraint Language (SHACL) is an evolving specification produced by the W3C RDF Data Shapes Working Group. TopQuadrant is actively supporting the development of SHACL in the W3C Working Group towards its becoming an official Standard Recommendation alongside RDF, OWL and SPARQL. ....

So what is SHACL? First and foremost SHACL is based on RDF and covers similar ground like RDF Schema and the Web Ontology Language (OWL). SHACL can be used to describe the structure of data – be it stored in RDF or JSON or similar formats. SHACL provides an RDF vocabulary for classes, properties and almost arbitrary integrity constraints that instances need to fulfil. SHACL is not limited to classes and instances, but also includes a more generic concept called “Shape” that can be overlaid on any existing data. Schemas created with SHACL can be shared on the web to communicate the intended structure of your data to other people or tools. SHACL tools can improve Data Quality. .... " 

A Semantic Ecosystem

A big challenge.  What is a semantic Ecosystem?  By Topquadrant: 

I like the thought of a combination of data, metadata and services ... via an ontology.

SEMANTIC ECOSYSTEM – WHAT’S THAT ABOUT?
A semantic ecosystem is a modern information infrastructure that uses a rich web of metadata and services to:

Add meaning to raw data artifacts
Make relationships between and within data sources explicit
Dynamically bring information together when and as needed
Enable patterns to be discovered  ... " 

Data Science Glossary

Data Science Glossary. Not a bad idea in an area you are new to, or even not that new to, as new ideas arrive.  Am about to look at some of the definitions.  My criteria have always been:  Can a non technical exec understand this?  Is it stated well for a particular business context?  For a particular enterprise's usage? It is it practically up to date, or does it imply too much capability today?

Glossary run by:   Bob DuCharme is the author of O'Reilly's Learning SPARQL and several other books, as well as a solution architect for TopQuadrant, the leading provider of software and solutions for modeling, developing and deploying semantic web applications.   Bob's blog.   Twitter: @bobdc  ... " 

Also see:  Free data science books. In DSC.

Top Quadrant Semantic Web Solutions

I mentioned Dean Allemang, Chief Scientist at Top Quadrant, and his recent book. Now exploring Semantic Web solutions:

TopQuadrant is a premier Semantic Web solutions company with innovative products and services ... We provide a full range of products, services, knowledge, training programs and methods helping organizations integrate data and processes and to harness the knowledge distributed across systems and parties ... ".

Downloading some of their solutions.