In this lecture we discuss data quality and data quality in Linked Data. This 50 minute lecture was given to masters student at Trinity College Dublin (Ireland), and had the following contents:
1) Defining Quality
2) Defining Data Quality - What, Why, Costs
3) Identifying problems early - using a simple semantic publishing process as an example
4) Assessing Linked (big) Data quality
5) Quality of LOD cloud datasets
References can be found at the end of the slides
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 (CC-BY-SA-40) International License.
Tackling data quality problems requires more than a series of tactical, one off improvement projects. By their nature, many data quality problems extend across and often beyond an organization. Addressing these issues requires a holistic architectural approach combining people, process and technology. Join Donna Burbank and Nigel Turner as they provide practical ways to control data quality issues in your organization.
How to Build & Sustain a Data Governance Operating Model DATUM LLC
Learn how to execute a data governance strategy through creation of a successful business case and operating model.
Originally presented to an audience of 400+ at the Master Data Management & Data Governance Summit.
Visit www.datumstrategy.com for more!
Tackling Data Quality problems requires more than a series of tactical, one-off improvement projects. By their nature, many Data Quality problems extend across and often beyond an organization. Addressing these issues requires a holistic architectural approach combining people, process, and technology. Join Nigel Turner and Donna Burbank as they provide practical ways to control Data Quality issues in your organization.
Good data is like good water: best served fresh, and ideally well-filtered. Data Management strategies can produce tremendous procedural improvements and increased profit margins across the board, but only if the data being managed is of a high quality. Determining how Data Quality should be engineered provides a useful framework for utilizing Data Quality management effectively in support of business strategy, which in turns allows for speedy identification of business problems, delineation between structural and practice-oriented defects in Data Management, and proactive prevention of future issues.
Over the course of this webinar, we will:
Help you understand foundational Data Quality concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK), as well as guiding principles, best practices, and steps for improving Data Quality at your organization
Demonstrate how chronic business challenges for organizations are often rooted in poor Data Quality
Share case studies illustrating the hallmarks and benefits of Data Quality success
Data-Ed Slides: Best Practices in Data Stewardship (Technical)DATAVERSITY
In order to find value in your organization's data assets, heroic data stewards are tasked with saving the day- every single day! These heroes adhere to a data governance framework and work to ensure that data is: captured right the first time, validated through automated means, and integrated into business processes. Whether its data profiling or in depth root cause analysis, data stewards can be counted on to ensure the organization's mission critical data is reliable. In this webinar we will approach this framework, and punctuate important facets of a data steward’s role.
Learning Objectives:
- Understand the business need for a data governance framework
- Learn why embedded data quality principles are an important part of system/process design
- Identify opportunities to help drive your organization to a data driven culture
Enterprise Architecture vs. Data ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how Data Architecture is a key component of an overall Enterprise Architecture for enhanced business value and success.
Tackling data quality problems requires more than a series of tactical, one off improvement projects. By their nature, many data quality problems extend across and often beyond an organization. Addressing these issues requires a holistic architectural approach combining people, process and technology. Join Donna Burbank and Nigel Turner as they provide practical ways to control data quality issues in your organization.
How to Build & Sustain a Data Governance Operating Model DATUM LLC
Learn how to execute a data governance strategy through creation of a successful business case and operating model.
Originally presented to an audience of 400+ at the Master Data Management & Data Governance Summit.
Visit www.datumstrategy.com for more!
Tackling Data Quality problems requires more than a series of tactical, one-off improvement projects. By their nature, many Data Quality problems extend across and often beyond an organization. Addressing these issues requires a holistic architectural approach combining people, process, and technology. Join Nigel Turner and Donna Burbank as they provide practical ways to control Data Quality issues in your organization.
Good data is like good water: best served fresh, and ideally well-filtered. Data Management strategies can produce tremendous procedural improvements and increased profit margins across the board, but only if the data being managed is of a high quality. Determining how Data Quality should be engineered provides a useful framework for utilizing Data Quality management effectively in support of business strategy, which in turns allows for speedy identification of business problems, delineation between structural and practice-oriented defects in Data Management, and proactive prevention of future issues.
Over the course of this webinar, we will:
Help you understand foundational Data Quality concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK), as well as guiding principles, best practices, and steps for improving Data Quality at your organization
Demonstrate how chronic business challenges for organizations are often rooted in poor Data Quality
Share case studies illustrating the hallmarks and benefits of Data Quality success
Data-Ed Slides: Best Practices in Data Stewardship (Technical)DATAVERSITY
In order to find value in your organization's data assets, heroic data stewards are tasked with saving the day- every single day! These heroes adhere to a data governance framework and work to ensure that data is: captured right the first time, validated through automated means, and integrated into business processes. Whether its data profiling or in depth root cause analysis, data stewards can be counted on to ensure the organization's mission critical data is reliable. In this webinar we will approach this framework, and punctuate important facets of a data steward’s role.
Learning Objectives:
- Understand the business need for a data governance framework
- Learn why embedded data quality principles are an important part of system/process design
- Identify opportunities to help drive your organization to a data driven culture
Enterprise Architecture vs. Data ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how Data Architecture is a key component of an overall Enterprise Architecture for enhanced business value and success.
Data Catalogs Are the Answer – What is the Question?DATAVERSITY
Organizations with governed metadata made available through their data catalog can answer questions their people have about the organization’s data. These organizations get more value from their data, protect their data better, gain improved ROI from data-centric projects and programs, and have more confidence in their most strategic data.
Join Bob Seiner for this lively webinar where he will talk about the value of a data catalog and how to build the use of the catalog into your stewards’ daily routines. Bob will share how the tool must be positioned for success and viewed as a must-have resource that is a steppingstone and catalyst to governed data across the organization.
To take a “ready, aim, fire” tactic to implement Data Governance, many organizations assess themselves against industry best practices. The process is not difficult or time-consuming and can directly assure that your activities target your specific needs. Best practices are always a strong place to start.
Join Bob Seiner for this popular RWDG topic, where he will provide the information you need to set your program in the best possible direction. Bob will walk you through the steps of conducting an assessment and share with you a set of typical results from taking this action. You may be surprised at how easy it is to organize the assessment and may hear results that stimulate the actions that you need to take.
In this webinar, Bob will share:
- The value of performing a Data Governance best practice assessment
- A practical list of industry Data Governance best practices
- Criteria to determine if a practice is best practice
- Steps to follow to complete an assessment
- Typical recommendations and actions that result from an assessment
Introduction to Data Governance
Seminar hosted by Embarcadero technologies, where Christopher Bradley presented a session on Data Governance.
Drivers for Data Governance & Benefits
Data Governance Framework
Organization & Structures
Roles & responsibilities
Policies & Processes
Programme & Implementation
Reporting & Assurance
DAS Slides: Building a Data Strategy - Practical Steps for Aligning with Busi...DATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task. The opportunity in getting it right can be significant, however, as data drives many of the key initiatives in today’s marketplace: digital transformation, marketing, customer centricity, and more. This webinar will help de-mystify Data Strategy and Data Architecture and will provide concrete, practical ways to get started.
DAS Slides: Data Governance - Combining Data Management with Organizational ...DATAVERSITY
Data Governance is both a technical and an organizational discipline, and getting Data Governance right requires a combination of Data Management fundamentals aligned with organizational change and stakeholder buy-in. Join Nigel Turner and Donna Burbank as they provide an architecture-based approach to aligning business motivation, organizational change, Metadata Management, Data Architecture and more in a concrete, practical way to achieve success in your organization.
Data Architecture, Solution Architecture, Platform Architecture — What’s the ...DATAVERSITY
A solid data architecture is critical to the success of any data initiative. But what is meant by “data architecture”? Throughout the industry, there are many different “flavors” of data architecture, each with its own unique value and use cases for describing key aspects of the data landscape. Join this webinar to demystify the various architecture styles and understand how they can add value to your organization.
Data Governance Best Practices, Assessments, and RoadmapsDATAVERSITY
When starting or evaluating the present state of your Data Governance program, it is important to focus on best practices such that you don’t take a ready, fire, aim approach. Best practices need to be practical and doable to be selected for your organization, and the program must be at risk if the best practice is not achieved.
Join Bob Seiner for an important webinar focused on industry best practice around standing up formal Data Governance. Learn how to assess your organization against the practices and deliver an effective roadmap based on the results of conducting the assessment.
In this webinar, Bob will focus on:
- Criteria to select the appropriate best practices for your organization
- How to define the best practices for ultimate impact
- Assessing against selected best practices
- Focusing the recommendations on program success
- Delivering a roadmap for your Data Governance program
Data Architecture - The Foundation for Enterprise Architecture and GovernanceDATAVERSITY
Organizations are faced with an increasingly complex data landscape, finding themselves unable to cope with exponentially increasing data volumes, compounded by additional regulatory requirements with increased fines for non-compliance. Enterprise architecture and data governance are often discussed at length, but often with different stakeholder audiences. This can result in complementary and sometimes conflicting initiatives rather than a focused, integrated approach. Data governance requires a solid data architecture foundation in order to support the pillars of enterprise architecture. In this session, IDERA’s Ron Huizenga will discuss a practical, integrated approach to effectively understand, define and implement an cohesive enterprise architecture and data governance discipline with integrated modeling and metadata management.
Data Management, Metadata Management, and Data Governance – Working TogetherDATAVERSITY
The data disciplines listed in the title must work together. The key to success requires understanding the boundaries and overlaps between the disciplines. Wouldn’t it be great to be able to present the relationships between the disciplines in a simple all-in diagram? At the end of this webinar, you will be able to do just that.
This new RWDG webinar with Bob Seiner will outline how Data Management, Metadata Management, and Data Governance can be optimized to work together. Bob will share a diagram that has successfully communicated the relationship between these disciplines to leadership resulting in the disciplines working in harmony and delivering success.
Bob will share the following in this webinar:
- Categories of disciplines focused on managing data as an asset
- A definition of Data Management that embraces numerous data disciplines
- The importance of Metadata -Management to all data disciplines
- Why data and metadata require formal governance
- A graphic that effectively exhibits the relationship between the disciplines
Data Verification and Validation - Melissa Data helps you in analyzing, cleansing & match data quality, data standardization and data quality management services for your organization.
Strategic Business Requirements for Master Data Management SystemsBoris Otto
This presentation describes strategic business requirements of master data management (MDM) systems. The requirements were developed in a consortium research approach by the Institute of Information Management at the University of St. Gallen, Switzerland, and 20 multinational enterprises.
The presentation was given at the 17th Amercias Conference on Information Systems (AMCIS 2011) in Detroit, MI.
The research paper on which this presentation is based on can be found here: http://www.alexandria.unisg.ch/Publikationen/Zitation/Boris_Otto/177697
• History of Data Management
• Business Drivers for implementation of data governance • Building Data Strategy & Governance Framework
• Data Management Maturity Models
• Data Quality Management
• Metadata and Governance
• Metadata Management
• Data Governance Stakeholder Communication Strategy
Review existing data management maturity models to identify core set of characteristics of an effective data maturity model:
DMBOK (Data Management Book of Knowledge) from DAMA (Data Management Association)
MIKE2.0 (Method for an Integrated Knowledge Environment) Information Maturity Model (IMM)
IBM Data Governance Council Maturity Model
Enterprise Data Management Council Data Management Maturity Model
Improving Data Literacy Around Data ArchitectureDATAVERSITY
Data Literacy is an increasing concern, as organizations look to become more data-driven. As the rise of the citizen data scientist and self-service data analytics becomes increasingly common, the need for business users to understand core Data Management fundamentals is more important than ever. At the same time, technical roles need a strong foundation in Data Architecture principles and best practices. Join this webinar to understand the key components of Data Literacy, and practical ways to implement a Data Literacy program in your organization.
Data Governance Takes a Village (So Why is Everyone Hiding?)DATAVERSITY
Data governance represents both an obstacle and opportunity for enterprises everywhere. And many individuals may hesitate to embrace the change. Yet if led well, a governance initiative has the potential to launch a data community that drives innovation and data-driven decision-making for the wider business. (And yes, it can even be fun!). So how do you build a roadmap to success?
This session will gather four governance experts, including Mary Williams, Associate Director, Enterprise Data Governance at Exact Sciences, and Bob Seiner, author of Non-Invasive Data Governance, for a roundtable discussion about the challenges and opportunities of leading a governance initiative that people embrace. Join this webinar to learn:
- How to build an internal case for data governance and a data catalog
- Tips for picking a use case that builds confidence in your program
- How to mature your program and build your data community
Data-Ed Webinar: Data Quality Success StoriesDATAVERSITY
Organizations must realize what it means to utilize data quality management in support of business strategy. This webinar will demonstrate how chronic business challenges can often be attributed to the root problem of poor data quality. Showing how data quality should be engineered provides a useful framework in which to develop an effective approach. Establishing this framework allows organizations to more efficiently identify business and data problems caused by structural issues versus practice-oriented defects; giving them the skillset to prevent these problems from re-occurring.
Learning Objectives:
Understanding foundational data quality concepts based on the DAMA DMBOK
Utilizing data quality engineering in support of business strategy
Case Studies illustrating data quality success
Data quality guiding principles & best practices
Steps for improving data quality at your organization
Engaging Information Professionals in the Process of Authoritative Interlinki...Lucy McKenna
Through the use of Linked Data (LD), Libraries, Archives and Museums (LAMs) have the potential to expose their collections to a larger audience and to allow for more efficient user searches. Despite this, relatively few LAMs have invested in LD projects and the majority of these display limited interlinking across datasets and institutions. A survey was conducted to understand Information Professionals' (IPs') position with regards to LD, with a particular focus on the interlinking problem. The survey was completed by 185 librarians, archivists, metadata cataloguers and researchers. Results indicated that, when interlinking, IPs find the process of ontology and property selection to be particularly challenging, and LD tooling to be technologically complex and unsuitable for their needs.
Our research is focused on developing an authoritative interlinking framework for LAMs with a view to increasing IP engagement in the linking process. Our framework will provide a set of standards to facilitate IPs in the selection of link types, specifically when linking local resources to authorities. The framework will include guidelines for authority, ontology and property selection, and for adding provenance data. A user-interface will be developed which will direct IPs through the resource interlinking process as per our framework. Although there are existing tools in this domain, our framework differs in that it will be designed with the needs and expertise of IPs in mind. This will be achieved by involving IPs in the design and evaluation of the framework. A mock-up of the interface has already been tested and adjustments have been made based on results. We are currently working on developing a minimal viable product so as to allow for further testing of the framework. We will present our updated framework, interface, and proposed interlinking solutions.
Data Catalogs Are the Answer – What is the Question?DATAVERSITY
Organizations with governed metadata made available through their data catalog can answer questions their people have about the organization’s data. These organizations get more value from their data, protect their data better, gain improved ROI from data-centric projects and programs, and have more confidence in their most strategic data.
Join Bob Seiner for this lively webinar where he will talk about the value of a data catalog and how to build the use of the catalog into your stewards’ daily routines. Bob will share how the tool must be positioned for success and viewed as a must-have resource that is a steppingstone and catalyst to governed data across the organization.
To take a “ready, aim, fire” tactic to implement Data Governance, many organizations assess themselves against industry best practices. The process is not difficult or time-consuming and can directly assure that your activities target your specific needs. Best practices are always a strong place to start.
Join Bob Seiner for this popular RWDG topic, where he will provide the information you need to set your program in the best possible direction. Bob will walk you through the steps of conducting an assessment and share with you a set of typical results from taking this action. You may be surprised at how easy it is to organize the assessment and may hear results that stimulate the actions that you need to take.
In this webinar, Bob will share:
- The value of performing a Data Governance best practice assessment
- A practical list of industry Data Governance best practices
- Criteria to determine if a practice is best practice
- Steps to follow to complete an assessment
- Typical recommendations and actions that result from an assessment
Introduction to Data Governance
Seminar hosted by Embarcadero technologies, where Christopher Bradley presented a session on Data Governance.
Drivers for Data Governance & Benefits
Data Governance Framework
Organization & Structures
Roles & responsibilities
Policies & Processes
Programme & Implementation
Reporting & Assurance
DAS Slides: Building a Data Strategy - Practical Steps for Aligning with Busi...DATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task. The opportunity in getting it right can be significant, however, as data drives many of the key initiatives in today’s marketplace: digital transformation, marketing, customer centricity, and more. This webinar will help de-mystify Data Strategy and Data Architecture and will provide concrete, practical ways to get started.
DAS Slides: Data Governance - Combining Data Management with Organizational ...DATAVERSITY
Data Governance is both a technical and an organizational discipline, and getting Data Governance right requires a combination of Data Management fundamentals aligned with organizational change and stakeholder buy-in. Join Nigel Turner and Donna Burbank as they provide an architecture-based approach to aligning business motivation, organizational change, Metadata Management, Data Architecture and more in a concrete, practical way to achieve success in your organization.
Data Architecture, Solution Architecture, Platform Architecture — What’s the ...DATAVERSITY
A solid data architecture is critical to the success of any data initiative. But what is meant by “data architecture”? Throughout the industry, there are many different “flavors” of data architecture, each with its own unique value and use cases for describing key aspects of the data landscape. Join this webinar to demystify the various architecture styles and understand how they can add value to your organization.
Data Governance Best Practices, Assessments, and RoadmapsDATAVERSITY
When starting or evaluating the present state of your Data Governance program, it is important to focus on best practices such that you don’t take a ready, fire, aim approach. Best practices need to be practical and doable to be selected for your organization, and the program must be at risk if the best practice is not achieved.
Join Bob Seiner for an important webinar focused on industry best practice around standing up formal Data Governance. Learn how to assess your organization against the practices and deliver an effective roadmap based on the results of conducting the assessment.
In this webinar, Bob will focus on:
- Criteria to select the appropriate best practices for your organization
- How to define the best practices for ultimate impact
- Assessing against selected best practices
- Focusing the recommendations on program success
- Delivering a roadmap for your Data Governance program
Data Architecture - The Foundation for Enterprise Architecture and GovernanceDATAVERSITY
Organizations are faced with an increasingly complex data landscape, finding themselves unable to cope with exponentially increasing data volumes, compounded by additional regulatory requirements with increased fines for non-compliance. Enterprise architecture and data governance are often discussed at length, but often with different stakeholder audiences. This can result in complementary and sometimes conflicting initiatives rather than a focused, integrated approach. Data governance requires a solid data architecture foundation in order to support the pillars of enterprise architecture. In this session, IDERA’s Ron Huizenga will discuss a practical, integrated approach to effectively understand, define and implement an cohesive enterprise architecture and data governance discipline with integrated modeling and metadata management.
Data Management, Metadata Management, and Data Governance – Working TogetherDATAVERSITY
The data disciplines listed in the title must work together. The key to success requires understanding the boundaries and overlaps between the disciplines. Wouldn’t it be great to be able to present the relationships between the disciplines in a simple all-in diagram? At the end of this webinar, you will be able to do just that.
This new RWDG webinar with Bob Seiner will outline how Data Management, Metadata Management, and Data Governance can be optimized to work together. Bob will share a diagram that has successfully communicated the relationship between these disciplines to leadership resulting in the disciplines working in harmony and delivering success.
Bob will share the following in this webinar:
- Categories of disciplines focused on managing data as an asset
- A definition of Data Management that embraces numerous data disciplines
- The importance of Metadata -Management to all data disciplines
- Why data and metadata require formal governance
- A graphic that effectively exhibits the relationship between the disciplines
Data Verification and Validation - Melissa Data helps you in analyzing, cleansing & match data quality, data standardization and data quality management services for your organization.
Strategic Business Requirements for Master Data Management SystemsBoris Otto
This presentation describes strategic business requirements of master data management (MDM) systems. The requirements were developed in a consortium research approach by the Institute of Information Management at the University of St. Gallen, Switzerland, and 20 multinational enterprises.
The presentation was given at the 17th Amercias Conference on Information Systems (AMCIS 2011) in Detroit, MI.
The research paper on which this presentation is based on can be found here: http://www.alexandria.unisg.ch/Publikationen/Zitation/Boris_Otto/177697
• History of Data Management
• Business Drivers for implementation of data governance • Building Data Strategy & Governance Framework
• Data Management Maturity Models
• Data Quality Management
• Metadata and Governance
• Metadata Management
• Data Governance Stakeholder Communication Strategy
Review existing data management maturity models to identify core set of characteristics of an effective data maturity model:
DMBOK (Data Management Book of Knowledge) from DAMA (Data Management Association)
MIKE2.0 (Method for an Integrated Knowledge Environment) Information Maturity Model (IMM)
IBM Data Governance Council Maturity Model
Enterprise Data Management Council Data Management Maturity Model
Improving Data Literacy Around Data ArchitectureDATAVERSITY
Data Literacy is an increasing concern, as organizations look to become more data-driven. As the rise of the citizen data scientist and self-service data analytics becomes increasingly common, the need for business users to understand core Data Management fundamentals is more important than ever. At the same time, technical roles need a strong foundation in Data Architecture principles and best practices. Join this webinar to understand the key components of Data Literacy, and practical ways to implement a Data Literacy program in your organization.
Data Governance Takes a Village (So Why is Everyone Hiding?)DATAVERSITY
Data governance represents both an obstacle and opportunity for enterprises everywhere. And many individuals may hesitate to embrace the change. Yet if led well, a governance initiative has the potential to launch a data community that drives innovation and data-driven decision-making for the wider business. (And yes, it can even be fun!). So how do you build a roadmap to success?
This session will gather four governance experts, including Mary Williams, Associate Director, Enterprise Data Governance at Exact Sciences, and Bob Seiner, author of Non-Invasive Data Governance, for a roundtable discussion about the challenges and opportunities of leading a governance initiative that people embrace. Join this webinar to learn:
- How to build an internal case for data governance and a data catalog
- Tips for picking a use case that builds confidence in your program
- How to mature your program and build your data community
Data-Ed Webinar: Data Quality Success StoriesDATAVERSITY
Organizations must realize what it means to utilize data quality management in support of business strategy. This webinar will demonstrate how chronic business challenges can often be attributed to the root problem of poor data quality. Showing how data quality should be engineered provides a useful framework in which to develop an effective approach. Establishing this framework allows organizations to more efficiently identify business and data problems caused by structural issues versus practice-oriented defects; giving them the skillset to prevent these problems from re-occurring.
Learning Objectives:
Understanding foundational data quality concepts based on the DAMA DMBOK
Utilizing data quality engineering in support of business strategy
Case Studies illustrating data quality success
Data quality guiding principles & best practices
Steps for improving data quality at your organization
Engaging Information Professionals in the Process of Authoritative Interlinki...Lucy McKenna
Through the use of Linked Data (LD), Libraries, Archives and Museums (LAMs) have the potential to expose their collections to a larger audience and to allow for more efficient user searches. Despite this, relatively few LAMs have invested in LD projects and the majority of these display limited interlinking across datasets and institutions. A survey was conducted to understand Information Professionals' (IPs') position with regards to LD, with a particular focus on the interlinking problem. The survey was completed by 185 librarians, archivists, metadata cataloguers and researchers. Results indicated that, when interlinking, IPs find the process of ontology and property selection to be particularly challenging, and LD tooling to be technologically complex and unsuitable for their needs.
Our research is focused on developing an authoritative interlinking framework for LAMs with a view to increasing IP engagement in the linking process. Our framework will provide a set of standards to facilitate IPs in the selection of link types, specifically when linking local resources to authorities. The framework will include guidelines for authority, ontology and property selection, and for adding provenance data. A user-interface will be developed which will direct IPs through the resource interlinking process as per our framework. Although there are existing tools in this domain, our framework differs in that it will be designed with the needs and expertise of IPs in mind. This will be achieved by involving IPs in the design and evaluation of the framework. A mock-up of the interface has already been tested and adjustments have been made based on results. We are currently working on developing a minimal viable product so as to allow for further testing of the framework. We will present our updated framework, interface, and proposed interlinking solutions.
Blackboard Learn Deployment: A Detailed Update of Managed Hosting and SaaS De...Blackboard APAC
Blackboard has deployed cloud solutions for well over a decade and is very excited to launch our new SaaS offering at the Teaching and Learning conference. The session will explore Blackboard’s continued commitment to managed hosting, partnership with IBM/AWS and next generation SaaS offerings that offer institutions unique control over their innovation journey.
Driving Enterprise Adoption: Tragedies, Triumphs and Our NEXTDataWorks Summit
Standard Bank is a leading South African bank with a vision to be the leading financial services organization in and for Africa. We will share our vision, greatest challenges, and most valuable lessons learned on our journey towards enterprise adoption of a big data strategy.
This includes our implementation of: a multi-tenant enterprise data lake, a real time streaming capability, appropriate data management and governance principles, a data science workbench, and a process for model productionisation to support data science teams across the Group and across Africa and Europe.
Speakers
Zakeera Mahomen, Standard Bank, Big Data Practice Lead
Kristel Sampson, Standard Bank, Platform Lead
Presentation to IASSIST 2013, in the session Expanding Scholarship: Research Journals and Data Linkages. Describes PREPARDE workshop on repository accreditation for data publication and invites comments on guidelines.
Semantic Similarity and Selection of Resources Published According to Linked ...Riccardo Albertoni
The position paper aims at discussing the potential of exploiting linked data best practice to provide metadata documenting domain specific resources created through verbose acquisition-processing pipelines. It argues that resource selection, namely the process engaged to choose a set of resources suitable for a given analysis/design purpose, must be supported by a deep comparison of their metadata. The semantic similarity proposed in our previous works is discussed for this purpose and the main issues to make it scale up to the web of data are introduced. Discussed issues contribute beyond the re-engineering of our similarity since they largely apply to every tool which is going to exploit information made available as linked data. A research plan and an exploratory phase facing the presented issues are described remarking the lessons we have learnt so far.
Linked Data Quality Assessment – daQ and Luzzujerdeb
Presentation at the Ontology Engineering Group at UPM related to Linked Data Quality and the work done in the Enterprise Information System Group at Universität Bonn
Rabobank - There is something about DataBigDataExpo
Technologische mogelijkheden en GDPR, een continue clash? En hoe staat het met de het ethisch (her)gebruik van data? Leer in deze sessie van Rabobank’s Big Data journey en krijg inzicht in: organisatorische keuzes, data Lab technologie visie & data strategie, als enabler en accelerator van digitale innovatie en transformatie.
This presentation was provided by Dr. Paul Burton of the University of Bristol during the NISO Symposium, Privacy Implications of Research Data, held on September 11, 2016, in conjunction with the International Data Week in Denver, Colorado.
Easy Analytics on AWS with Amazon Redshift, Amazon QuickSight, and Amazon Mac...Amazon Web Services
AWS has a large and growing portfolio of big data management and analytics services, designed to be integrated into solution architectures that meet the needs of your business. In this session, we look at analytics through the eyes of a business intelligence analyst, a data scientist, and an application developer, and we explore how to quickly leverage Amazon Redshift, Amazon QuickSight, RStudio, and Amazon Machine Learning to create powerful, yet straightforward, business solutions.
Architect’s Open-Source Guide for a Data Mesh ArchitectureDatabricks
Data Mesh is an innovative concept addressing many data challenges from an architectural, cultural, and organizational perspective. But is the world ready to implement Data Mesh?
In this session, we will review the importance of core Data Mesh principles, what they can offer, and when it is a good idea to try a Data Mesh architecture. We will discuss common challenges with implementation of Data Mesh systems and focus on the role of open-source projects for it. Projects like Apache Spark can play a key part in standardized infrastructure platform implementation of Data Mesh. We will examine the landscape of useful data engineering open-source projects to utilize in several areas of a Data Mesh system in practice, along with an architectural example. We will touch on what work (culture, tools, mindset) needs to be done to ensure Data Mesh is more accessible for engineers in the industry.
The audience will leave with a good understanding of the benefits of Data Mesh architecture, common challenges, and the role of Apache Spark and other open-source projects for its implementation in real systems.
This session is targeted for architects, decision-makers, data-engineers, and system designers.
RWE & Patient Analytics Leveraging Databricks – A Use CaseDatabricks
Gaining insights and knowledge from real-world health data (RWD), i.e., data acquired outside the context of randomized clinical trials, has been an area of continued opportunity for pharma organizations.
What is real-world data and real-world evidence – how it is generated, what value it drives for life sciences in general and what kind of analytics are performed.
What are some considerations and challenges related to data security, privacy, and industrialization of a big data platform hosted in the cloud.
How we leveraged Databricks to perform big data ingestion – advantages over native AWS Batch/Glue Examples of some of the advanced analytics use cases downstream that leveraged DB for RWE.
Note: This solution and one of the use case leveraging the solution won the 2020 Gartner Eye for Innovation award.
https://www.gartner.com/en/newsroom/press-releases/2020-11-17-gartner-announces-winners-of-the-2020-gartner-healthcare-and-life-sciences-eye-on-innovation-awardIn this
Qiagram is a collaborative visual data exploration environment that enables investigator-initiated, hypothesis-driven data exploration, allowing business users as well as IT professionals to easily ask complex questions against complex data sets.
A BASILar Approach for Building Web APIs on top of SPARQL EndpointsEnrico Daga
Presented at #SALAD2015
The heterogeneity of methods and technologies to publish open data is still an issue to develop distributed systems on the Web. On the one hand, Web APIs, the most popular approach to offer data services, implement REST principles, which focus on addressing loose coupling and interoperability issues. On the other hand, Linked Data, available through SPARQL endpoints, focus on data integration between distributed data sources. We proposes BASIL, an approach to build Web APIs on top of SPARQL endpoints, in order to benefit of the advantages from both Web APIs and Linked Data approaches. Compared to similar solution, BASIL aims on minimising the learning curve for users to promote its adoption. The main feature of BASIL is a simple API that does not introduce new specifications, formalisms and technologies for users that belong to both Web APIs and Linked Data communities.
LOP – Capturing and Linking Open Provenance on LOD Cyclerogers.rj
Presentation of the paper "LOP – Capturing and Linking Open Provenance on LOD Cycle" at 5th Internacional Workshop on Semantic Web Information Management (SWIM 2013). New York, USA – June 23, 2013
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
FIDO Alliance Osaka Seminar: Passkeys at Amazon.pdf
Data Quality
1. Data Quality
Jeremy Debattista
ADAPT Centre, Trinity College Dublin
This research has received funding from the Irish Research Council Government of Ireland Postdoctoral Fellowship award (GOIPD/2017/1204)
and the ADAPT Centre for Digital Content Technology, funded under the SFI Research Centres Programme (Grant 13/RC/2106) and co-funded by
theEuropeanRegionalDevelopmentFund.
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How many of you...
… check product review before purchasing?
Image and Reviews taken from
https://www.amazon.co.uk/Echo-Dot-Smart-Speaker-Alexa/dp/B0792KWK57/
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Quality: A definition from a Personal Perspective
Crowd Image by James Cridland, taken from https://www.flickr.com/photos/jamescridland/613445810/. Licensed under CC-BY 2.0
What does quality mean to you?
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Quality: A definition – Pirsig’s Perspective
Robert Pirsig
… the result of care
Zen and the Art of Motorcycle Maintenance (1974)
Photo taken from: https://www.goodreads.com
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Quality: A definition – Juran’s Perspective
… fitness for use
Quality Control Handbook (1974)
Joseph Juran
Photo taken from: https://www.toolshero.com
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Quality: A definition – Crosby’s Perspective
… conformance to
requirements
Quality is Free : The Art of Making Quality
Certain. Mentor book. (1979)
Phillip Crosby
Photo taken from: https://ceopedia.org
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Quality in terms of data is:
• Multi-dimensional concept
• Characterise quality for a particular task
• Variety of quality measures, Subjective or Objective for different
tasks
• e.g. Accessibility, Trustworthiness, Consistency
High quality data = data that fits for its intended use.
Data Quality Definition
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Data Quality – A Strategy for Organisations
• Data Quality is expensive
• Data Quality is not just about assessing but also about improving.
Figure from Ismael Caballero, Jorge Merino, Manuel Serrano, Mario Piattini, Data Quality for Big Data: Addressing Veracity and Value, 2016
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Data Quality – Identify problems early!
A simplistic view of the semantic publishing process
(Un/semi-)structured
data sources
Processing/Uplifting
Schemas
Mapping
Transform
Fusion
Semantic
(Knowledge) Graph
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Data Quality – Identify problems early!
A simplistic view of the semantic publishing process
(Un/semi-)structured
data sources
Processing/Uplifting
Schemas
Mapping
Transform
Fusion
Semantic
(Knowledge) Graph
• Potentially external data
• No structure and context to the data
• Certification of quality?
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Data Quality – Identify problems early!
A simplistic view of the semantic publishing process
(Un/semi-)structured
data sources
Processing/Uplifting
Schemas
Mapping
Transform
Fusion
Semantic
(Knowledge) Graph
• Gives context to raw data
• Drives the resulting knowledge graphs
• Should be free of contradictions and incorrect definitions
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Data Quality – Identify problems early!
A simplistic view of the semantic publishing process
(Un/semi-)structured
data sources
Processing/Uplifting
Schemas
Mapping
Transform
Fusion
Semantic
(Knowledge) Graph
• Incorrect/Incomplete mappings (e.g. typos)
• Catch errors here, as otherwise errors in your KG will multiply
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Data Quality – Identify problems early!
A simplistic view of the semantic publishing process
(Un/semi-)structured
data sources
Processing/Uplifting
Schemas
Mapping
Transform
Fusion
Semantic
(Knowledge) Graph
• Are external data sources fit for the task at hand?
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Data Quality – Identify problems early!
A simplistic view of the semantic publishing process
(Un/semi-)structured
data sources
Processing/Uplifting
Schemas
Mapping
Transform
Fusion
Semantic
(Knowledge) Graph
• Any quality issues not dealt with before will definitely be here
• Big data, time consuming, more expensive to clean
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Linked Data Quality Metrics
Figure from: A. J. Zaveri, A. Rula, A. Maurino, R. Pietrobon, J. Lehmann, and S. Auer. Quality Assessment for Linked Data: A survey.
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Linked Data Quality Metrics - Accessibility
Are Linked Data resources readily available to be re-used in
different applications/context?
Example Metrics:
• Availability of SPARQL endpoints and RDF Data Dumps
• Dereferenceability of resources
• Indication of machine/human readable license
• Links to external datasets
• Correct usage of hash/slash URIs
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Linked Data Quality Metrics - Intrinsic
Measures metrics that are related to the correctness and
coherence of the data, independent of the user’s context
Example Metrics:
• Syntactic valid dataset
• Incorrect datatype specification (e.g. “23.42”^^xsd:integer)
• Outlier detection
• Correct domain and range definition
• Data conciseness
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Linked Data Quality Metrics - Contextual
Measures metrics dependent on the task at hand.
Example Metrics:
• Trustworthiness of data
• Identification of timely data
• Provenance information
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Linked Data Quality Metrics - Representational
How well is the data represented in terms of common best
practices and guidelines?
Example Metrics:
• Re-using existing vocabularies
• Usage of undefined classes/properties
• Provide different serialisation formats for the data
• Use of multiple languages
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ISO/IEC 25012 Standard
• Every metric identified in the
research was mapped to the
ISO/IEC 25012 Model:
§ The Inherent Category –
measures intrinsic quality
characteristics.
§ The System Category –
measures the degree of quality
when the system is used.
§ The Inherent-System
Category – which includes
metrics covering both aspects.
http://iso25000.com/index.php/en/iso-25000-standards/iso-25012
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Problems with Assessing the Quality of Big Datasets
• Metrics classified in Zaveri et al. did not take into consideration time
and space complexity
• Efficient computation of impractical quality metrics when assessing
big datasets
• Solving intractable problems?
• Trade-off? Faster computation time against metric’s value precision
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Quality Assessment – A Conceptual Methodology
1. Identify Quality Measures for the task at hand
• What are the important characteristics of my task?
2. Re-use or define quality metrics
3. Prepare the quality assessment
a) Access point of dataset in question
b) External Resources such as gold standard
4. Running the quality assessment
5. Assessment representation
a) Immediate use
b) Mid-to-long term use
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Linked Data Quality Frameworks – Over the Years
Flemming LinkQA Sieve RDF Unit Triple
Check
Mate
LiQuate TRELLIS tRDF/tSP
ARQL
WIQA Luzzu
Scalability X ✓ ✓ ✓ N/A N/A N/A ✓ N/A ✓
Extensibility X Java XML SPARQL X Bayesian
Rules
X tSPARQL
Rules
WIQA PL Java or
LQML
Quality
Metadata
X X ✓
(Optional)
✓
(DQV)
X X X X X ✓(daQ)
Quality
Report
HTML HTML X HTML or
RDF
X X X X X RDF
Collaboration X X X X ✓ X ✓ X X X
Cleaning
Support
X X ✓ X X X X X X X
Last Update 2010 2011 2014 2017 2013 2014 2005 2014 2009 2018
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Luzzu – A Quality Assessment Framework for Linked
Data
• Four Principles:
1. Extensibility
2. Scalability
3. Interoperability
4. Customisability
Luzzu
Thread Pool
Metrics Identification
List Metrics Impl. Library
Metric 1
Metric 2
Metric 3
…
Metric n
Dataset /
SPARQL Endpoint
Stream Processing
<s,p,o>
Quality Metadata
Quality Problem
Report
Try it out:
http://www.github.com/Luzzu/Framework
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Luzzu – A Quality Assessment Framework for Linked
Data
• Four Principles:
1. Extensibility
2. Scalability
3. Interoperability
4. Customisability
Luzzu
Thread Pool
Metrics Identification
List Metrics Impl. Library
Metric 1
Metric 2
Metric 3
…
Metric n
Dataset /
SPARQL Endpoint
Stream Processing
<s,p,o>
Quality Metadata
Quality Problem
Report
Try it out:
http://www.github.com/Luzzu/Framework
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W3C Data Quality Vocabulary (DQV)
• Policies: Express policies or agreements a dataset follows defined by some
data quality concerns
• Annotations: Providing rating, certificates, feedback etc…
• Feedback: Comments from data consumers on a dataset (imagine
comments in Trip Advisor)
https://www.w3.org/TR/vocab-dqv/
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Web of Data Quality – Accessibility Category
Accessibility Category:
Examples: Availability of Resources,
Licensing, Server Performance
Lessons Learned:
• Average Conformance: 30%
• Standard Deviation: 19%
• Low usage of Machine-Readable
Licences (17 out of 131 datasets)
and Human-Readable Licences (11
out of 131 datasets)
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Web of Data Quality – Contextual Category
Contextual Category:
Examples: Provenance of Data, Human
Comprehensibility
Lessons Learned:
• Average Conformance: 13%
• Standard Deviation: 13%
• Poor conformance w.r.t. basic
provenance information (e.g.
creator of dataset), and
traceability of data (predicates
defining origin of data)
• More effort towards human
labelling and description of
resources by publishers
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Web of Data Quality – Intrinsic Category
Intrinsic Category:
Examples: Syntactic Validity,
Consistency, Conciseness
Lessons Learned:
• Average Conformance: 77%
• Standard Deviation: 13%
• Overall high conformance for
almost all metrics
• Conformance towards the usage of
correct domain or range datatypes
should be improved (average
conformance ≈ 60%)
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Web of Data Quality – Representational Category
Representational Category:
Examples: Interoperability, Versatility,
Interpretability, Data Representation
Lessons Learned:
• Average Conformance: 63%
• Standard Deviation: 14%
• Data publishers should re-use
more existing terms (average
conformance ≈ 34%)
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Conclusions
Quality is different
for everyone
Cost vs need for
assessment
Detect quality issues
earlier!
SoTA evolved to meet
the consumers need
to characterise
fitness for intended
use
The quality of the
Web of Data is not
bad – but needs to
improve
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References
• J. Debattista, S. Auer, C. Lange. Luzzu - A Methodology and Framework for Linked Data Quality
Assessment. In ACM Journal of Data Information Quality. V8 I1, November 2016
• J. Debattista, S. Londoño, C. Lange, S. Auer. Quality Assessment of Linked Datasets using
Probabilistic Approximation. In 12th European Semantic Web Conference Proceedings 2015, 221-
236, Springer
• J. Debattista. Scalable Quality Assessment of Linked Data. (Thesis) Universitäts-und
Landesbibliothek Bonn 2017
• A. J. Zaveri, A. Rula, A. Maurino, R. Pietrobon, J. Lehmann, and S. Auer. Quality Assessment for
Linked Data: A survey. Semantic Web Journal, 2015
• J. Debattista, C. Lange, S. Auer. Representing dataset quality metadata using multi-dimensional
views. In Proceedings of the 10th International Conference on Semantic Systems (SEMANTiCS
’14), 92-99, ACM
• S. McGurk, J. Debattista, C. Abela. Towards Ontology Quality Assessment. 4th Workshop on
Linked Data Quality (LDQ)
