Keynote presentation given by Ryan Androsoff (Digital Government Policy Analyst, OECD) at the 2015 EUROSAI-OLACEFS conference in Quito, Ecuador on 25 June 2015. Focus of the presentation is on Open Government Data and the opportunities for Supreme Audit Institutions presented by open data. Video of the presentation is available at: https://youtu.be/SlBfxmecJhI?t=1h50m19s
For more information on OECD's work relating to Open Government Data please see: http://www.oecd.org/gov/public-innovation/open-government-data.htm
Business Digitalization PowerPoint Presentation SlidesSlideTeam
It covers all the important concepts and has relevant templates which cater to your business needs. This complete deck has PPT slides on Business Digitalization PowerPoint Presentation Slides with well suited graphics and subject driven content. This deck consists of total of twenty slides. All templates are completely editable for your convenience. You can change the colour, text and font size of these slides. You can add or delete the content as per your requirement. Get access to this professionally designed complete deck presentation by clicking the download button below. https://bit.ly/2T1Dnh1
Architecting Agile Data Applications for ScaleDatabricks
Data analytics and reporting platforms historically have been rigid, monolithic, hard to change and have limited ability to scale up or scale down. I can’t tell you how many times I have heard a business user ask for something as simple as an additional column in a report and IT says it will take 6 months to add that column because it doesn’t exist in the datawarehouse. As a former DBA, I can tell you the countless hours I have spent “tuning” SQL queries to hit pre-established SLAs. This talk will talk about how to architect modern data and analytics platforms in the cloud to support agility and scalability. We will include topics like end to end data pipeline flow, data mesh and data catalogs, live data and streaming, performing advanced analytics, applying agile software development practices like CI/CD and testability to data applications and finally taking advantage of the cloud for infinite scalability both up and down.
Understanding big data and data analytics big dataSeta Wicaksana
Big Data helps companies to generate valuable insights. Companies use Big Data to refine their marketing campaigns and techniques. Companies use it in machine learning projects to train machines, predictive modeling, and other advanced analytics applications.
Why an AI-Powered Data Catalog Tool is Critical to Business SuccessInformatica
Imagine a fast, more efficient business thriving on trusted data-driven decisions. An intelligent data catalog can help your organization discover, organize, and inventory all data assets across the org and democratize data with the right balance of governance and flexibility. Informatica's data catalog tools are powered by AI and can automate tedious data management tasks and offer immediate recommendations based on derived business intelligence. We offer data catalog workshops globally. Visit Informatica.com to attend one near you.
Key Considerations While Rolling Out Denodo PlatformDenodo
Watch full webinar here: https://bit.ly/3zaPGLO
Our approach for data virtualization advisory takes the following 3 dimensions/areas into consideration:
- Technology / Architecture
- Business User Groups (your clients)
- IT Organization
To deliver quick results, Q-PERIOR uses a multitude of accelerators in predefined topics within these three dimensions. In our presentation we will elaborate on client examples why such an exercise makes sense before rolling out Denodo and what kind of risks you can avoid doing so.
Data Modeling, Data Governance, & Data QualityDATAVERSITY
Data Governance is often referred to as the people, processes, and policies around data and information, and these aspects are critical to the success of any data governance implementation. But just as critical is the technical infrastructure that supports the diverse data environments that run the business. Data models can be the critical link between business definitions and rules and the technical data systems that support them. Without the valuable metadata these models provide, data governance often lacks the “teeth” to be applied in operational and reporting systems.
Join Donna Burbank and her guest, Nigel Turner, as they discuss how data models & metadata-driven data governance can be applied in your organization in order to achieve improved data quality.
Business Digitalization PowerPoint Presentation SlidesSlideTeam
It covers all the important concepts and has relevant templates which cater to your business needs. This complete deck has PPT slides on Business Digitalization PowerPoint Presentation Slides with well suited graphics and subject driven content. This deck consists of total of twenty slides. All templates are completely editable for your convenience. You can change the colour, text and font size of these slides. You can add or delete the content as per your requirement. Get access to this professionally designed complete deck presentation by clicking the download button below. https://bit.ly/2T1Dnh1
Architecting Agile Data Applications for ScaleDatabricks
Data analytics and reporting platforms historically have been rigid, monolithic, hard to change and have limited ability to scale up or scale down. I can’t tell you how many times I have heard a business user ask for something as simple as an additional column in a report and IT says it will take 6 months to add that column because it doesn’t exist in the datawarehouse. As a former DBA, I can tell you the countless hours I have spent “tuning” SQL queries to hit pre-established SLAs. This talk will talk about how to architect modern data and analytics platforms in the cloud to support agility and scalability. We will include topics like end to end data pipeline flow, data mesh and data catalogs, live data and streaming, performing advanced analytics, applying agile software development practices like CI/CD and testability to data applications and finally taking advantage of the cloud for infinite scalability both up and down.
Understanding big data and data analytics big dataSeta Wicaksana
Big Data helps companies to generate valuable insights. Companies use Big Data to refine their marketing campaigns and techniques. Companies use it in machine learning projects to train machines, predictive modeling, and other advanced analytics applications.
Why an AI-Powered Data Catalog Tool is Critical to Business SuccessInformatica
Imagine a fast, more efficient business thriving on trusted data-driven decisions. An intelligent data catalog can help your organization discover, organize, and inventory all data assets across the org and democratize data with the right balance of governance and flexibility. Informatica's data catalog tools are powered by AI and can automate tedious data management tasks and offer immediate recommendations based on derived business intelligence. We offer data catalog workshops globally. Visit Informatica.com to attend one near you.
Key Considerations While Rolling Out Denodo PlatformDenodo
Watch full webinar here: https://bit.ly/3zaPGLO
Our approach for data virtualization advisory takes the following 3 dimensions/areas into consideration:
- Technology / Architecture
- Business User Groups (your clients)
- IT Organization
To deliver quick results, Q-PERIOR uses a multitude of accelerators in predefined topics within these three dimensions. In our presentation we will elaborate on client examples why such an exercise makes sense before rolling out Denodo and what kind of risks you can avoid doing so.
Data Modeling, Data Governance, & Data QualityDATAVERSITY
Data Governance is often referred to as the people, processes, and policies around data and information, and these aspects are critical to the success of any data governance implementation. But just as critical is the technical infrastructure that supports the diverse data environments that run the business. Data models can be the critical link between business definitions and rules and the technical data systems that support them. Without the valuable metadata these models provide, data governance often lacks the “teeth” to be applied in operational and reporting systems.
Join Donna Burbank and her guest, Nigel Turner, as they discuss how data models & metadata-driven data governance can be applied in your organization in order to achieve improved data quality.
Fighting financial fraud at Danske Bank with artificial intelligenceRon Bodkin
Danske Bank, the leader in mobile payments in Denmark, is innovating with AI. Danske Bank’s existing fraud detection engine is being enhanced with deep learning algorithms that can analyze potentially tens of thousands of latent features. Danske Bank’s current system is largely based on handcrafted rules created by the business, based on intuition and some light analysis. The system is effective at blocking fraud, but it has a high rate of false positives, which is expensive and inconvenient, and it has proved impractical to update and maintain as fraudsters evolve their capabilities. Moreover, the bank understands that fraud is getting worse in the near- and long-term future due to the increased digitization of banking and the prevalence of mobile banking applications and recognizes the need to use cutting-edge techniques to engage fraudsters not where they are today but where they will be tomorrow.
Application fraud is an important emerging trend, in which machines fill in transaction forms. There is evidence that criminals are employing sophisticated machine-learning techniques to attack, so it’s critical to use sophisticated machine learning to catch fraud in banking and mobile payment transactions.
Ron Bodkin and Nadeem Gulzar explore how Danske Bank uses deep learning for better fraud detection. Danske Bank’s multistep program first productionizes “classic” machine learning techniques (boosted decision trees) while in parallel developing deep learning models with TensorFlow as a “challenger” to test. The system was first tested in shadow production and then in full production in a champion-challenger setup against live transactions. Ron and Nadeem explain how the bank is integrating the models with the efforts already running, giving the bank and its investigation team the ability to adapt to new patterns faster than before and taking on complex highly varying functions not present in the training examples.
Digital Transformation And Solution ArchitectureAlan McSweeney
Digital strategy is a statement about the organisation’s digital positioning, competitors and customer and collaborator needs and behaviour to achieve a direction for innovation, communication, transaction and promotion. Digital strategy needs to be defined in the same framework structure as the proposed digital architecture platform.
Achieving the target digital organisation means deploying solutions that enable the digital architecture. Solution architecture needs to design solutions that fit into the target digital architecture framework. This requires:
• Solution architecture team operating in an integrated manner designing solutions to a set of common standards and that run on the platform
• Solution architecture team leadership ensuring solutions conform to the common standards
• Solution architecture technical leadership to develop and maintain common solution design standards
• Solution architecture updates the digital reference architecture based on solution design experience
Digital solution design requires greater discipline to create an integrated set solutions that operate within the rigour of the digital architecture framework. The solution architecture function must interact with other IT architecture disciplines to ensure the set of solutions that implement the digital framework operate together. This requires greater solution architecture team leadership. This needs to be supplemented and supported by a well-defined set of digital solution design standards.
This follows-on from the previous presentation: Digital Transformation And Enterprise Architecture
https://www.slideshare.net/alanmcsweeney/digital-transformation-and-enterprise-architecture.
AI Data Acquisition and Governance: Considerations for SuccessDatabricks
data pipeline, governance, and for growth and updating models regularly needs to be part of the AI strategy from the outset.
This session will cover:
Defining AI governance: What this means and how definitions of subjects like ethics and effectiveness can differ between organizations.
Data governance: Companies must rely on an AI governance program to ensure only high-quality, unbiased and consistent data are used in training.
AI is a growing necessity for enterprises / businesses; it provides an avenue for scaling quickly and efficiently.
Best practices / implementation: how to implement AI that meets the requirements of the organization’s defined sets of governances.
Planning the data pipeline and growing/updating the models: AI is not static in the real world; models must be frequently updated to maintain relevance and accuracy.
3 key takeaways or attendee benefits of the session:
Understand how to assess your organization’s need for AI; how to identify the opportune areas for transforming processes, interactions, scaling, cost.
How to start the implementation process. Defining data and AI governance and how to build the training data pipeline within that framework.
Best practices for maintaining AI; how to use data to evaluate models and continuously iterate on them to reflect the real world.
Denodo Data Virtualization Platform: Security (session 5 from Architect to Ar...Denodo
Everyone wants to keep their data safe from prying eyes (or even worse). The Denodo Platform has comprehensive security mechanisms to protect your data. This webinar will take a detailed look at how the Denodo Platform provides security.
Agenda:
Security Levels
Security capabilities
User and Role based Security
Security Protocols
Integration with External Security Systems
Agile & Data Modeling – How Can They Work Together?DATAVERSITY
A tenet of the Agile Manifesto is ‘Working software over comprehensive documentation’, and many have interpreted that to mean that data models are not necessary in the agile development environment. Others have seen the value of data models for achieving the other core tenets of ‘Customer Collaboration’ and ‘Responding to Change’.
This webinar will discuss how data models are being effectively used in today’s Agile development environment and the benefits that are being achieved from this approach.
LDM Webinar: Data Modeling & Metadata ManagementDATAVERSITY
Metadata management is critical for organizations looking to understand the context, definition and lineage of key data assets. Data models play a key role in metadata management, as many of the key structural and business definitions are stored within the models themselves. Can data models replace traditional metadata solutions? Or should they integrate with larger metadata management tools & initiatives? Join this webinar to discuss opportunities and challenges around:
- How data modeling fits within a larger metadata management landscape
- When can data modeling provide “just enough” metadata management
- Key data modeling artifacts for metadata
- Organization, Roles & Implementation Considerations
Data Lakehouse, Data Mesh, and Data Fabric (r1)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
Data is everywhere, and delivering trustable data to anyone who needs it has become a challenge. But innovative technologies come to the rescue: through smart semantics, metadata management, auto-profiling, faceted search and collaborative data curation there is a way to establish a Wikipedia like approach for your data. Find out how Talend will help you to operationalize more data faster and increase data usage for everyone with an Enterprise Data Catalog
Digital Transformation - Rethink The Business in The Digital Age
Digital transformation is the integration of digital technology into all areas of a business, fundamentally changing how you operate and deliver value to customers.
It's also a cultural change that requires organizations to continually challenge the status quo, experiment, and get comfortable with failure.
www.heruwijayanto.com
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.
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.
The data architecture of solutions is frequently not given the attention it deserves or needs. Frequently, too little attention is paid to designing and specifying the data architecture within individual solutions and their constituent components. This is due to the behaviours of both solution architects ad data architects.
Solution architecture tends to concern itself with functional, technology and software components of the solution
Data architecture tends not to get involved with the data aspects of technology solutions, leaving a data architecture gap. Combined with the gap where data architecture tends not to get involved with the data aspects of technology solutions, there is also frequently a solution architecture data gap. Solution architecture also frequently omits the detail of data aspects of solutions leading to a solution data architecture gap. These gaps result in a data blind spot for the organisation.
Data architecture tends to concern itself with post-individual solutions. Data architecture needs to shift left into the domain of solutions and their data and more actively engage with the data dimensions of individual solutions. Data architecture can provide the lead in sealing these data gaps through a shift-left of its scope and activities as well providing standards and common data tooling for solution data architecture
The objective of data design for solutions is the same as that for overall solution design:
• To capture sufficient information to enable the solution design to be implemented
• To unambiguously define the data requirements of the solution and to confirm and agree those requirements with the target solution consumers
• To ensure that the implemented solution meets the requirements of the solution consumers and that no deviations have taken place during the solution implementation journey
Solution data architecture avoids problems with solution operation and use:
• Poor and inconsistent data quality
• Poor performance, throughput, response times and scalability
• Poorly designed data structures can lead to long data update times leading to long response times, affecting solution usability, loss of productivity and transaction abandonment
• Poor reporting and analysis
• Poor data integration
• Poor solution serviceability and maintainability
• Manual workarounds for data integration, data extract for reporting and analysis
Data-design-related solution problems frequently become evident and manifest themselves only after the solution goes live. The benefits of solution data architecture are not always evident initially.
Fighting financial fraud at Danske Bank with artificial intelligenceRon Bodkin
Danske Bank, the leader in mobile payments in Denmark, is innovating with AI. Danske Bank’s existing fraud detection engine is being enhanced with deep learning algorithms that can analyze potentially tens of thousands of latent features. Danske Bank’s current system is largely based on handcrafted rules created by the business, based on intuition and some light analysis. The system is effective at blocking fraud, but it has a high rate of false positives, which is expensive and inconvenient, and it has proved impractical to update and maintain as fraudsters evolve their capabilities. Moreover, the bank understands that fraud is getting worse in the near- and long-term future due to the increased digitization of banking and the prevalence of mobile banking applications and recognizes the need to use cutting-edge techniques to engage fraudsters not where they are today but where they will be tomorrow.
Application fraud is an important emerging trend, in which machines fill in transaction forms. There is evidence that criminals are employing sophisticated machine-learning techniques to attack, so it’s critical to use sophisticated machine learning to catch fraud in banking and mobile payment transactions.
Ron Bodkin and Nadeem Gulzar explore how Danske Bank uses deep learning for better fraud detection. Danske Bank’s multistep program first productionizes “classic” machine learning techniques (boosted decision trees) while in parallel developing deep learning models with TensorFlow as a “challenger” to test. The system was first tested in shadow production and then in full production in a champion-challenger setup against live transactions. Ron and Nadeem explain how the bank is integrating the models with the efforts already running, giving the bank and its investigation team the ability to adapt to new patterns faster than before and taking on complex highly varying functions not present in the training examples.
Digital Transformation And Solution ArchitectureAlan McSweeney
Digital strategy is a statement about the organisation’s digital positioning, competitors and customer and collaborator needs and behaviour to achieve a direction for innovation, communication, transaction and promotion. Digital strategy needs to be defined in the same framework structure as the proposed digital architecture platform.
Achieving the target digital organisation means deploying solutions that enable the digital architecture. Solution architecture needs to design solutions that fit into the target digital architecture framework. This requires:
• Solution architecture team operating in an integrated manner designing solutions to a set of common standards and that run on the platform
• Solution architecture team leadership ensuring solutions conform to the common standards
• Solution architecture technical leadership to develop and maintain common solution design standards
• Solution architecture updates the digital reference architecture based on solution design experience
Digital solution design requires greater discipline to create an integrated set solutions that operate within the rigour of the digital architecture framework. The solution architecture function must interact with other IT architecture disciplines to ensure the set of solutions that implement the digital framework operate together. This requires greater solution architecture team leadership. This needs to be supplemented and supported by a well-defined set of digital solution design standards.
This follows-on from the previous presentation: Digital Transformation And Enterprise Architecture
https://www.slideshare.net/alanmcsweeney/digital-transformation-and-enterprise-architecture.
AI Data Acquisition and Governance: Considerations for SuccessDatabricks
data pipeline, governance, and for growth and updating models regularly needs to be part of the AI strategy from the outset.
This session will cover:
Defining AI governance: What this means and how definitions of subjects like ethics and effectiveness can differ between organizations.
Data governance: Companies must rely on an AI governance program to ensure only high-quality, unbiased and consistent data are used in training.
AI is a growing necessity for enterprises / businesses; it provides an avenue for scaling quickly and efficiently.
Best practices / implementation: how to implement AI that meets the requirements of the organization’s defined sets of governances.
Planning the data pipeline and growing/updating the models: AI is not static in the real world; models must be frequently updated to maintain relevance and accuracy.
3 key takeaways or attendee benefits of the session:
Understand how to assess your organization’s need for AI; how to identify the opportune areas for transforming processes, interactions, scaling, cost.
How to start the implementation process. Defining data and AI governance and how to build the training data pipeline within that framework.
Best practices for maintaining AI; how to use data to evaluate models and continuously iterate on them to reflect the real world.
Denodo Data Virtualization Platform: Security (session 5 from Architect to Ar...Denodo
Everyone wants to keep their data safe from prying eyes (or even worse). The Denodo Platform has comprehensive security mechanisms to protect your data. This webinar will take a detailed look at how the Denodo Platform provides security.
Agenda:
Security Levels
Security capabilities
User and Role based Security
Security Protocols
Integration with External Security Systems
Agile & Data Modeling – How Can They Work Together?DATAVERSITY
A tenet of the Agile Manifesto is ‘Working software over comprehensive documentation’, and many have interpreted that to mean that data models are not necessary in the agile development environment. Others have seen the value of data models for achieving the other core tenets of ‘Customer Collaboration’ and ‘Responding to Change’.
This webinar will discuss how data models are being effectively used in today’s Agile development environment and the benefits that are being achieved from this approach.
LDM Webinar: Data Modeling & Metadata ManagementDATAVERSITY
Metadata management is critical for organizations looking to understand the context, definition and lineage of key data assets. Data models play a key role in metadata management, as many of the key structural and business definitions are stored within the models themselves. Can data models replace traditional metadata solutions? Or should they integrate with larger metadata management tools & initiatives? Join this webinar to discuss opportunities and challenges around:
- How data modeling fits within a larger metadata management landscape
- When can data modeling provide “just enough” metadata management
- Key data modeling artifacts for metadata
- Organization, Roles & Implementation Considerations
Data Lakehouse, Data Mesh, and Data Fabric (r1)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
Data is everywhere, and delivering trustable data to anyone who needs it has become a challenge. But innovative technologies come to the rescue: through smart semantics, metadata management, auto-profiling, faceted search and collaborative data curation there is a way to establish a Wikipedia like approach for your data. Find out how Talend will help you to operationalize more data faster and increase data usage for everyone with an Enterprise Data Catalog
Digital Transformation - Rethink The Business in The Digital Age
Digital transformation is the integration of digital technology into all areas of a business, fundamentally changing how you operate and deliver value to customers.
It's also a cultural change that requires organizations to continually challenge the status quo, experiment, and get comfortable with failure.
www.heruwijayanto.com
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.
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.
The data architecture of solutions is frequently not given the attention it deserves or needs. Frequently, too little attention is paid to designing and specifying the data architecture within individual solutions and their constituent components. This is due to the behaviours of both solution architects ad data architects.
Solution architecture tends to concern itself with functional, technology and software components of the solution
Data architecture tends not to get involved with the data aspects of technology solutions, leaving a data architecture gap. Combined with the gap where data architecture tends not to get involved with the data aspects of technology solutions, there is also frequently a solution architecture data gap. Solution architecture also frequently omits the detail of data aspects of solutions leading to a solution data architecture gap. These gaps result in a data blind spot for the organisation.
Data architecture tends to concern itself with post-individual solutions. Data architecture needs to shift left into the domain of solutions and their data and more actively engage with the data dimensions of individual solutions. Data architecture can provide the lead in sealing these data gaps through a shift-left of its scope and activities as well providing standards and common data tooling for solution data architecture
The objective of data design for solutions is the same as that for overall solution design:
• To capture sufficient information to enable the solution design to be implemented
• To unambiguously define the data requirements of the solution and to confirm and agree those requirements with the target solution consumers
• To ensure that the implemented solution meets the requirements of the solution consumers and that no deviations have taken place during the solution implementation journey
Solution data architecture avoids problems with solution operation and use:
• Poor and inconsistent data quality
• Poor performance, throughput, response times and scalability
• Poorly designed data structures can lead to long data update times leading to long response times, affecting solution usability, loss of productivity and transaction abandonment
• Poor reporting and analysis
• Poor data integration
• Poor solution serviceability and maintainability
• Manual workarounds for data integration, data extract for reporting and analysis
Data-design-related solution problems frequently become evident and manifest themselves only after the solution goes live. The benefits of solution data architecture are not always evident initially.
The Value of Oracle Hyperion Data Relationship Management at Alberta Health S...Alithya
Plagued by data inconsistencies, Alberta Health Services (AHS) lacked the necessary processes and tools to manage information. It opted to implement Oracle Hyperion Data Relationship Management to address its needs. Edgewater Ranzal delivered a solution to manage information between Reporting, Oracle Hyperion Planning, and several other AHS services/groups. The solution enables financial and analytical master data management in AHS’s dynamic, fast-changing environment. The goal was to achieve a single trusted source of information, reduction in manual maintenance effort, higher data integrity, increased process transparency, improved decision-making, a decrease in operational friction, an overall reduction in costs, and improved effectiveness.
Rationalizing an Enterprise IT ArchitectureBob Rhubart
Shaun McLaurin's presentation from OTN Architect Day in Pasadena, July 9, 2009.
Find an OTN Architect Day event near you: http://www.oracle.com/technology/architect/archday.html
Interact with Architect Day presenters and participants on Oracle Mix: https://mix.oracle.com/groups/15511
Hortonworks Oracle Big Data Integration Hortonworks
Slides from joint Hortonworks and Oracle webinar on November 11, 2014. Covers the Modern Data Architecture with Apache Hadoop and Oracle Data Integration products.
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
DELSA/GOV 3rd Health meeting - Barbara UBALDIOECD Governance
This presentation by Barbara UBALDI was made at the 3rd Joint DELSA/GOV Health Meeting, Paris 24-25 April 2014. Find out more at www.oecd.org/gov/budgeting/3rdmeetingdelsagovnetworkfiscalsustainabilityofhealthsystems2014.htm
This deck of slides outlines the key aspects of the Open Data Readiness Assessment or ODRA and was presented in the consultative workshop on Rwanda Open Data Policy organized by the Ministry of Youth & ICT (GoR) and the World Bank.
Open Government Data Ecosystems: Linking Transparency for Innovation with Tra...Luigi Reggi
Presentation at IFIP EGOV 2016 Conference. September 5, 2016.
Abstract. The rhetoric of open government data (OGD) promises that data transparency will lead to multiple public benefits: economic and social innovation, civic participation, public-private collaboration, and public accountability. In reality much less has been accomplished in practice than advocates have hoped. OGD research to address this gap tends to fall into two streams – one that focuses on data publication and re-use for purposes of innovation, and one that views publication as a stimulus for civic participation and government accountability - with little attention to whether or how these two views interact. In this paper we use an ecosystem perspective to explore this question. Through an exploratory case study we show how two related cycles of influences can flow from open data publication. The first addresses transparency for innovation goals, the second addresses larger issues of data use for public engagement and greater government accountability. Together they help explain the potential and also the barriers to reaching both kinds of goals.
Digitalisation of finance activities: Challenges and opportunities - Edwin L...OECD Governance
This presentation was made by Edwin Lau, OECD, at the 40th Annual Meeting of OECD Senior Budget Officials (SBO) held in Tallinn, Estonia, on 5-6 June 2019
Open Government Partnership, Open Data and FOI – A road map towards convergencemauricemcn
A joint presentation made at the "Regional Conference on Freedom of Information Laws (FOI) in the Caribbean – Improving Management for the Environment" This presentation, delivered jointly by Dr Maurice McNaughton, Mona School of Business & Management, University of West Indies and Mrs Carole Excell, Senior Associate, World Resources Institute, sought to explore the philosophical differences and institutional synergy between the FOI and Open Data communities.
Strategic use of digital information in Government - Rwanda-CMU-2014Rajiv Ranjan
Guest talk at Carnegie Mellon University in Rwanda on Strategic use of digital information in Government delivered on October 23, 2014 to the students of M.S. in Information Technology [Strategic use of digital information in enterprises]
Intro to Open data - presentation made as part of Food and Agriculture Organization meeting with Statistician Generals from around Nigeria + other government reps. **References are in the ppt notes
The Open Data Barometer aims to uncover the true prevalence and impact of open data initiatives around the world. It analyses global trends, and provides comparative data on countries and regions via an in-depth methodology combining contextual data, technical assessments and secondary indicators to explore multiple dimensions of open data readiness, implementation and impact.
This is the second edition of the Open Data Barometer, completing a two-year pilot of the Barometer methodology and providing data for comparative research. This report is just one expression of the Barometer, for which full data is also available, supporting secondary research into the progression of open data policies and practices across the world.
The Open Data Barometer forms part of the World Wide Web Foundation’s work on common assessment methods for open data.
HLEG thematic workshop on Measurement of Well Being and Development in Africa...StatsCommunications
HLEG thematic workshop on Measurement of Well Being and Development in Africa, 12-14 November 2015, Durban, South Africa, More information at: www.oecd.org/statistics/measuring-economic-social-progress
Summary of the OECD expert meeting: Construction Risk Management in Infrastru...OECD Governance
Presented at the OECD expert meeting "Construction Risk Management in Infrastructure Procurement: The Loss of Appetite for Fixed-Price Contracts", held on 17 May 2023 at the OECD, Paris and online.
Using AI led assurance to deliver projects on time and on budget - D. Amratia...OECD Governance
Presented at the OECD expert meeting "Construction Risk Management in Infrastructure Procurement: The Loss of Appetite for Fixed-Price Contracts", held on 17 May 2023 at the OECD, Paris and online.
ECI in Sweden - A. Kadefors, KTH Royal Institute of Technology, Stockholm (SE)OECD Governance
Presented at the OECD expert meeting "Construction Risk Management in Infrastructure Procurement: The Loss of Appetite for Fixed-Price Contracts", held on 17 May 2023 at the OECD, Paris and online.
Building Client Capability to Deliver Megaprojects - J. Denicol, professor at...OECD Governance
Presented at the OECD expert meeting "Construction Risk Management in Infrastructure Procurement: The Loss of Appetite for Fixed-Price Contracts", held on 17 May 2023 at the OECD, Paris and online.
Procurement strategy in major infrastructure: The AS-IS and STEPS - D. Makovš...OECD Governance
Presented at the OECD expert meeting "Construction Risk Management in Infrastructure Procurement: The Loss of Appetite for Fixed-Price Contracts", held on 17 May 2023 at the OECD, Paris and online.
Procurement of major infrastructure projects 2017-22 - B. Hasselgren, Senior ...OECD Governance
Presented at the OECD expert meeting "Construction Risk Management in Infrastructure Procurement: The Loss of Appetite for Fixed-Price Contracts", held on 17 May 2023 at the OECD, Paris and online.
ECI Dutch Experience - A. Chao, Partner, Bird&Bird & J. de Koning, Head of Co...OECD Governance
Presented at the OECD expert meeting "Construction Risk Management in Infrastructure Procurement: The Loss of Appetite for Fixed-Price Contracts", held on 17 May 2023 at the OECD, Paris and online.
ECI in Sweden - A. Kadefors, KTH Royal Institute of Technology, StockholmOECD Governance
Presented at the OECD expert meeting "Construction Risk Management in Infrastructure Procurement: The Loss of Appetite for Fixed-Price Contracts", held on 17 May 2023 at the OECD, Paris and online.
EPEC's perception of market developments - E. Farquharson, Principal Adviser,...OECD Governance
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See: https://www.oecd.org/publication/government-at-a-glance/2023/
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Thumbnail picture is by MediaZona, you may read their report on anti-war arson attacks in Russia here: https://en.zona.media/article/2022/10/13/burn-map
Links:
Autonomous Action
http://Avtonom.org
Anarchist Black Cross Moscow
http://Avtonom.org/abc
Solidarity Zone
https://t.me/solidarity_zone
Memorial
https://memopzk.org/, https://t.me/pzk_memorial
OVD-Info
https://en.ovdinfo.org/antiwar-ovd-info-guide
RosUznik
https://rosuznik.org/
Uznik Online
http://uznikonline.tilda.ws/
Russian Reader
https://therussianreader.com/
ABC Irkutsk
https://abc38.noblogs.org/
Send mail to prisoners from abroad:
http://Prisonmail.online
YouTube: https://youtu.be/c5nSOdU48O8
Spotify: https://podcasters.spotify.com/pod/show/libertarianlifecoach/episodes/Russian-anarchist-and-anti-war-movement-in-the-third-year-of-full-scale-war-e2k8ai4
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Recordings are on YouTube and the company website.
https://www.youtube.com/@jenniferschaus/videos
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Jennifer Schaus and Associates hosts a complimentary webinar series on The FAR in 2024. Join the webinars on Wednesdays and Fridays at noon, eastern.
Recordings are on YouTube and the company website.
https://www.youtube.com/@jenniferschaus/videos
This session provides a comprehensive overview of the latest updates to the Uniform Administrative Requirements, Cost Principles, and Audit Requirements for Federal Awards (commonly known as the Uniform Guidance) outlined in the 2 CFR 200.
With a focus on the 2024 revisions issued by the Office of Management and Budget (OMB), participants will gain insight into the key changes affecting federal grant recipients. The session will delve into critical regulatory updates, providing attendees with the knowledge and tools necessary to navigate and comply with the evolving landscape of federal grant management.
Learning Objectives:
- Understand the rationale behind the 2024 updates to the Uniform Guidance outlined in 2 CFR 200, and their implications for federal grant recipients.
- Identify the key changes and revisions introduced by the Office of Management and Budget (OMB) in the 2024 edition of 2 CFR 200.
- Gain proficiency in applying the updated regulations to ensure compliance with federal grant requirements and avoid potential audit findings.
- Develop strategies for effectively implementing the new guidelines within the grant management processes of their respective organizations, fostering efficiency and accountability in federal grant administration.
Open Government Data: What it is, Where it is Going, and the Opportunities for SAIs
1. OPEN GOVERNMENT DATA:
WHAT IS IT, WHERE IT IS GOING, AND
THE OPPORTUNITIES FOR SAIS
Ryan Androsoff
OECD GOV
Quito, Ecuador
25 June 2015
2. • What is Open Government Data and why
should we care?
• Measuring the Status of Open Government
Data
– Indexes
– Trends in OECD countries
– Open Government Data in LAC
– Furthering the OECD’s work on Open Data
• SAIs and Open Data: Opportunities for
Action
Today’s Presentation
3. • Data = highest level of granularity from which information, content and
knowledge are derived.
• Public Sector Information = “information, including information
products and services, generated, created, collected, processed, preserved,
maintained, disseminated, or funded by or for a government or public
institution”
• Open Data = data that can be freely used, re-used and distributed by
anyone, only subject to (at the most) the requirement that users attribute
the data and that they make their work available to be shared as well.
• Big Data = A data-driven socio-economic model; as a phenomenon
emerged as available datasets produced by various sources have grown
larger and data users more aware of the value obtainable through linked
and combined data sets produced by different actors, both private and
public.
• Data analytics = the use of data to spot significant facts and trends to
improve policy making and service delivery (public sector intelligence).
Some Definitions
4. Public Sector Information
Visual Definition
Big Data
Open
Government
Data
Proprietary /
Internal Analytics
Apps Public / Open
Analytics
5. • Economic Value
• Growth and competitiveness in the wider economy
• Fostering innovation, efficiency and effectiveness in
government services (internal and external)
• Social Value
• Promoting citizens’ self-empowerment, social participation
and engagement
• Public Governance Value
• Improving accountability, transparency,
responsiveness and democratic control
What Value are Governments Expecting?
10. • Produced by the World Wide Web Foundation
• Measures on three dimensions, first launched in 2013
Open Data Barometer
11. • Produced by the Open Knowledge Foundation since 2013
• Index is based on 10 key datasets assessed against 9 criteria
Global Open Data Index
12. The OECD OURdata Index
The Open-Useful-Reusable Government data index
(OURdata) launched in 2015
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Total score OECD
NonationalOGDportal
Source: 2014 OECD Survey on Open Government Data
13. Measuring three components of
open government data activity
The Open-Useful-Reusable Government data index
(OURdata)
Source: 2014 OECD Survey on Open Government Data
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Data availability Data accessibility Government support to re-use
NonationalOGDportal
All three dimensions were equally weighted (33.3% each)
Note: Cronbach alpha = 0.81
14. • The “pioneers” (e.g. UK, USA, Spain)
• Devising a sustainable financial mechanism (e.g.
Denmark, the Netherlands)
• Establishing the governance framework first
(e.g. Germany, Switzerland)
• Quick followers (e.g. France and Mexico)
Emerging approaches
15. Top 5 main objectives of open data
strategies or policies
0%
29%
33%
46%
54%
63%
67%
71%
71%
71%
0% 10% 20% 30% 40% 50% 60% 70% 80%
Create economic value for the public sector
Facilitate citizens' participation in public debate
Enable citizens' engagement in decision-making processes
Improve public sector performance by strengthening
accountability for outputs/outcomes
Deliver public services more effectively and efficiently by
enabling delivery from private sector through data re-use
Deliver public services more effectively and efficiently by
improving internal operations and collaboration
Facilitate creation of new businesses
Increase transparency
Increase openness
Create economic value for the private sector
Multiple answers allowed
Percentage of respondent countries
Source: OECD Open Data in Governments Survey 2013
Transparency
vs.
Innovation
PS
Efficiency
Public
Participation
16. • Which ecosystem?
– Inside the public sector: gather, integrate, validate, release, up-date and
promote re-use of data (statistical offices, archives, sector data producers, etc.)
– Outside the public sector: sustain data re-use (media, private sector, civil
society, librarians, developers, community of practitioners, etc.)
• What activity?
• Data mining, data analytics (for policy making and service delivery), crowd-
sourcing to innovate services, social innovation, evidence-based performance,
improved financial decisions, data mash-up and data sharing, licensing,
standards, hackathon events, metadata.
• Which capacities within the organisation?
• To ensure sustainability and autonomy: data scientist, visualisation expert,
statistics and data analytics expert, computing and systems programming
skills, policy analysis expertise.
Value creation : with whom and how?
17. Creating the right ecosystem:
consulting the stakeholders
0%
10%
20%
30%
40%
50%
60%
70%
80%
Percentageofrespondentcountries
Was the central/federal OGD strategy/policy developed in consultation with stakeholders?
Source: Government at a Glance 2013
18. Involving users and knowing demand
0%
10%
20%
30%
40%
50%
60%
70%
80%
a. Yes citizens'
information needs
b. Yes, businesses'
information needs
c. Yes, other
stakeholders' needs
(e.g. non-profit
organisations)
d. None of the above
applies
Percentageofrespondentcountries
Source: Government at a Glance 2013
Does your government regularly consult users on their needs and preferences of the type of data released?
19. • Policy challenges
• Technical challenges
• Economic and financial challenges
• Organisational challenges
• Cultural challenges
• Legal challenges
Key challenges to implementation
20. Principal challenges for further
development of OGD initiatives
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
Organisational
challenges
Institutional
challenges
Funding
challenges
Policy
challenges
Technical
challenges
Context
challenges
Percentageofrespondingcountries
Source: Government at a Glance 2013 (forthcoming)
22. Yes
Central national
strategy co-exists
with line ministries'
own strategies
No, but individual line ministries /
agencies have a separate
strategies / policies in place
No OGD policies /
strategies in place
COLOMBIA - - -
COSTA RICA - - -
GUATEMALA - - -
REPUBLICA DOMINICANA - - -
MEXICO - - -
PERU - - -
CHILE - - -
URUGUAY - - -
BRAZIL - - -
EL SALVADOR - - -
PARAGUAY - - -
OGD strategies in LAC countries
28. Phase 1
• Working Paper “Open Government Data: Towards Empirical
Analysis of Open Government Data Initiatives” with full assessment
methodology [Dec 2012-May 2013]
Phase 2
• OGD survey : strategy, implementation, value generation,
challenges [Apr – Sept 2013]
Phase 3
• Pilot testing in 8 OECD countries : validate methodology, map
initiatives, collect practices, impact assessment + MENA and LAC
regions [2013 – 2014]
Now
• OGD Country Reviews: Poland, Mexico
• OURdata Index 2015
• OECD Open Government Data Expert Group
OECD OGD PROJECT 2012-15
2015 OGD Report :
Data analysis and outcome of pilot testing
29. • Improve understanding and measuring of OGD
impact on social innovation, open innovation, service
delivery innovation and public value creation
• Tackling pending issues:
• Balancing the strive for openness with privacy and security
• Resolving legal conflicts
• Harmonising definitions
• Acquiring adequate skills and capabilities in the public sector
• Avoiding new divides and focus on OD for participatory
governance
• Improving understanding of context and data demand
• Ecosystem creation
Focus of further OGD analytical work
30. • Strategy
– secure political leadership support
– institutionalise processes
– incentivize buy in across the public sector
– develop action plan
• Implementation
– build and/or strengthen capacities at all levels of governments and in society
– ensure resources to secure sustainability
– from a supply driven to a demand driven approach
– communication and awareness
• Impact
– economic, social and political value
– focus on re-use
– know demand and ecosystem
– engage the ecosystem (incld. research/academia, media, archives, statistical offices)
– monitor and evaluate
– link with access to information and transparency agendas
Key OECD Policy Messages on OGD
32. • OECD (GOV-PSI) is conducting an ongoing internationally
comparative study with 12 SAIs
• Looking at the role of SAIs in supporting better formulation,
implementation and monitoring and evaluation of policies
and programmes, including:
– the openness of government-wide strategic planning processes
– the openness of budgetary planning processes including: the
existence and/or adequacy of participative and realistic debates on
budgetary choices
– the openness and consultation of the regulatory policy process
– the accessibility and reliability of data systems for collecting, storing
and using performance information
– the compliance with access to/freedom of information laws
• OECD publication on role of SAIs in better policy making
and governance to be launched November in Brazil
The role of SAIs in Good Governance
33. • Citizen portals for accountability and
complaints
– The GEO-CGR portal: Articulation, storage,
consultation and publication of information on
investment in public works.
• Co-ordinated audits
– Country level: The Amazon Biome, Protected areas,
Co-ordinated Audit between Brazil’s TCU and 9
State Courts of Audit in the Brazilian Amazon
– International level: Co-ordinated international
audit on climate change between 14 SAIs
Examples of Using OGD Principles in
the work of SAIs
34. Effective Institutions Platform
• Multi-stakeholder alliance
of over 60 countries and
organisations established
in 2012 engaged in public
sector reforms
(government
representatives as well as
CSOs, legislators, think
tanks, etc.)
• 3 Major Pillars of work
Website:
www.effectiveinstitutions.org
35. EIP: Engaging Citizens in
Accountability Institutions
• Global commitments and regional standards recognise:
– Importance of external stakeholder engagement
– Need to go beyond traditional engagement mechanisms
– Role that development cooperation can play
• Project under the EIP initiated in 2013 to review Supreme Audit
Institutions (SAI) engagement practices and to develop practical
guidance
– Phase I: SAI and citizens engagement (32 SAIs); 2013-2014
– Phase II: checklist on engagement with other stakeholders (citizens,
parliaments and the media) with global survey and 4 case studies; 2015-
2016. Presented at XXVth OLACEFS (October 2015, Mexico)
• Steering group: Brazil, South Africa, Chile, Costa Rica, IDI, New Zealand,
Philippines, OECD
• P2P Learning Alliance in October 2014 (Paris) to discuss benefits
and risks of SAIs engagement (7 SAIs: Brazil, Costa Rica, Chile, France,
Philippines, South Africa, Zambia with CSOs and development reps.)
36. • Transparency practices
(widespread, yet uneven;
well distributed, but more
developed in stronger SAIs)
• Participatory practices
(Incipient, but promising;
more common in non-
OECD countries and
regionally concentrated in
Latin America & Asia
Pacific)
36
EIP: Mechanisms of Engagement
37. EIP: Benefits and Risks of Engaging
Citizens
Benefits
Better informed audit
activities
Strengthened audit
independence
Audits more relevant to
citizen needs
Stronger citizen demand for
enforcing audit
recommendations
Enhanced trust in SAIs
Educated citizens on the audit
process and results of
government actions
Risks
✓Undermining perceived
independence
✓Delays and increased costs
✓Work overload
✓Transparency and
participatory fatigue
✓Difficulty in measuring
progress
✓Resistance to change
➡Risk Mitigation Strategies needed
38. 1. Adding open government data programs
as an audit topic
2. Using Open Government Data as an input
into audit activities
3. Become a contributor to the Open Data
ecosystem (audit results, info about SAIs)
– Need to set common data format standards to
enable inter-jurisdictional comparison
Three areas for future exploration by
SAIs regarding OGD
39. Thank You!
For more information:
www.oecd.org/gov/public-innovation/
ryan.androsoff@oecd.org
Twitter:
@RyanAndrosoff
LinkedIn:
ca.linkedin.com/in/ryanandrosoff