A short presentation to the Defence IT 2012 conference to provide them with a view of how data quality underpins new regulations in Financial Services, e.g. Solvency II. Data quality has a raised profile in financial services due to mandates from the European Union that companies must demonstrate good understanding and management of data quality.
Pragmatics Driven Issues in Data and Process Integrity in EnterprisesAmit Sheth
Keynote/Invited Talk
IFIP TC-11 First Working Conference on
Keynote/Invited Talk at the IFIP TC-11 First Working Conference on
Integrity and Internal Control in Information Systems
Zurich, Switzerland, December 4-5, 1997
The document describes several potential metadata use cases, including reporting/analytics, desktop accessibility of metadata definitions, and governance workflows. It provides examples of actors, system interactions, and sample data for each use case. The use cases are presented to demonstrate how they can address common challenges with metadata solutions projects.
Introduction To Strait & Associates 2011kmichaelo
Strait & Associates provides records and information management (RIM) consulting services. They help clients define RIM solutions, reduce costs and complexity, and maintain compliance. Their services include RIM assessments, program development, policy creation, and training. Representative projects include developing a global RIM program, performing a RIM assessment, and designing data disposition processes. The document provides contact information for the company.
Auditing Active Directory to Comply with State and Federal RegulationsNetwrix Corporation
The State of Maine implemented NetWrix Active Directory Change Reporter to meet state and federal auditing and compliance regulations. The solution provided automated auditing of all Active Directory changes without impacting performance or requiring infrastructure changes. It generated real-time alerts and detailed reports on who made what changes. This streamlined the auditing process and helped the State of Maine maintain the necessary visibility to ensure compliance with guidelines.
This document summarizes Barry Williams' presentation on establishing an enterprise data quality strategy. It discusses identifying infrastructure needs, setting quality control initiatives, and developing plans to improve data quality. Specific topics covered include defining data quality, assessing current and desired states, establishing roles and responsibilities, learning from past experiences, and choosing data quality tools and vendors.
The document discusses data quality management processes and concepts. It describes establishing a data quality management environment, scoping data quality projects and implementation plans, implementing projects using define, measure, analyze, improve steps, and evaluating methods. It also discusses misconceptions around data quality such as the idea that data can simply be fixed or that it is solely an IT problem. Quality is a company-wide issue requiring business and IT collaboration.
The document discusses the new age of data quality and challenges of ensuring high quality data. It notes that traditional batch-based approaches are no longer sufficient and real-time validation of large, diverse datasets is now needed. Additionally, business users require more control over data rules rather than rules being centrally managed. Effective data quality requires balancing standards-governance with collaboration and giving users self-service functionality. Ensuring quality in big data also requires addressing completeness, conformity, accuracy and other metrics.
This use case describes a metadata governance workflow where an authorized user can create a new business term, submit it for approval, and approvers can then review and approve the term to publish it for other users. The system tracks the status of business terms and only approved terms are visible to general users. Notifications are sent during the approval process.
Pragmatics Driven Issues in Data and Process Integrity in EnterprisesAmit Sheth
Keynote/Invited Talk
IFIP TC-11 First Working Conference on
Keynote/Invited Talk at the IFIP TC-11 First Working Conference on
Integrity and Internal Control in Information Systems
Zurich, Switzerland, December 4-5, 1997
The document describes several potential metadata use cases, including reporting/analytics, desktop accessibility of metadata definitions, and governance workflows. It provides examples of actors, system interactions, and sample data for each use case. The use cases are presented to demonstrate how they can address common challenges with metadata solutions projects.
Introduction To Strait & Associates 2011kmichaelo
Strait & Associates provides records and information management (RIM) consulting services. They help clients define RIM solutions, reduce costs and complexity, and maintain compliance. Their services include RIM assessments, program development, policy creation, and training. Representative projects include developing a global RIM program, performing a RIM assessment, and designing data disposition processes. The document provides contact information for the company.
Auditing Active Directory to Comply with State and Federal RegulationsNetwrix Corporation
The State of Maine implemented NetWrix Active Directory Change Reporter to meet state and federal auditing and compliance regulations. The solution provided automated auditing of all Active Directory changes without impacting performance or requiring infrastructure changes. It generated real-time alerts and detailed reports on who made what changes. This streamlined the auditing process and helped the State of Maine maintain the necessary visibility to ensure compliance with guidelines.
This document summarizes Barry Williams' presentation on establishing an enterprise data quality strategy. It discusses identifying infrastructure needs, setting quality control initiatives, and developing plans to improve data quality. Specific topics covered include defining data quality, assessing current and desired states, establishing roles and responsibilities, learning from past experiences, and choosing data quality tools and vendors.
The document discusses data quality management processes and concepts. It describes establishing a data quality management environment, scoping data quality projects and implementation plans, implementing projects using define, measure, analyze, improve steps, and evaluating methods. It also discusses misconceptions around data quality such as the idea that data can simply be fixed or that it is solely an IT problem. Quality is a company-wide issue requiring business and IT collaboration.
The document discusses the new age of data quality and challenges of ensuring high quality data. It notes that traditional batch-based approaches are no longer sufficient and real-time validation of large, diverse datasets is now needed. Additionally, business users require more control over data rules rather than rules being centrally managed. Effective data quality requires balancing standards-governance with collaboration and giving users self-service functionality. Ensuring quality in big data also requires addressing completeness, conformity, accuracy and other metrics.
This use case describes a metadata governance workflow where an authorized user can create a new business term, submit it for approval, and approvers can then review and approve the term to publish it for other users. The system tracks the status of business terms and only approved terms are visible to general users. Notifications are sent during the approval process.
The document discusses six governance processes for data and business intelligence: data lifecycle, data models, data quality, data security, data warehousing, and metadata. For each process, it provides an overview of why governance is important in that area, and what the governance process will do to manage issues and ensure requirements are met. The governance processes aim to balance various factors, control changes, and provide oversight and accountability for data management.
Oracle Application User Group sponsored Collaborate 2009 Presentation 'Building a Practical Strategy for Managing Data Quality' by Alex Fiteni CPA, CMA
The document summarizes a presentation on data quality and master data management. It discusses:
- The development of ISO 8000 and ISO 8000-100 which provide standards for data quality and master data quality management.
- The importance of common concept encoding and terminology mapping across supply chains and applications to ensure data portability and system interoperability.
- How data quality must support defined business functions and provide transparency through consistent identification of entities like customers, suppliers, materials using metadata.
- The need for organizations to publish quality data specifications to their suppliers and customers to improve visibility and differentiation.
The document discusses data quality management (DQM) concepts and activities. It describes the DQM approach as a continuous cycle of planning, deployment, monitoring, and acting. Key activities include developing data quality awareness, defining requirements, profiling/assessing data, defining metrics/rules, testing requirements, setting service levels, continuously measuring/monitoring quality, and managing issues. DQM aims to ensure data meets fitness-for-use expectations and business needs.
Chapter 12: Data Quality ManagementAhmed Alorage
This document discusses data quality management (DQM). It covers DQM concepts and activities, including developing data quality awareness, defining data quality requirements, profiling and assessing data quality, and defining metrics. The key DQM approach is the Deming cycle of planning, deploying, monitoring, and acting to continuously improve data quality. Data quality requirements are identified by reviewing business policies and rules to understand dimensions like accuracy, completeness, consistency and more.
Developing A Universal Approach to Cleansing Customer and Product DataFindWhitePapers
Take a look at this review of current industry problems concerning data quality, and learn more about how companies are addressing quality problems with customer, product, and other types of corporate data. Read about products and use cases from SAP to see how vendors are supporting data cleansing.
This document summarizes the role and work of NEHTA, an organization established by the Australian government to facilitate e-health initiatives. It discusses NEHTA's work on healthcare identifiers and developing a data quality strategy, including defining data quality dimensions, standards, policies, and a maturity model. It also provides advice on prioritizing data quality and ensuring senior management support for these efforts.
This document outlines the process of clinical data management. It discusses the key steps and technologies involved in digitization, electronic data capture, data analytics, document management, data standardization, and infrastructure/security. The main stages described are feasibility analysis, system selection, design and implementation, data collection, quality control, and regulatory submission preparation. Technologies mentioned include EDC tools like Oracle Clinical, data standards like CDISC SDTM, and document management solutions from vendors such as Documentum.
Akili provides data integration and management services for oil and gas companies. They leverage over 25 years of experience and experts in SAP, BI platforms, financial systems, and oil and gas data. Akili helps customers address challenges around data quality, reliability, disparate systems and gaining a single view of data. They provide predefined solutions and accelerators using industry standards from PPDM (Professional Petroleum Data Management). Akili's approach involves assessing an organization's data maturity, developing a data integration strategy, addressing governance, master data and tools to integrate data from multiple sources and systems into meaningful business information.
The data services marketplace is enabled by a data abstraction layer that supports rapid development of operational applications and single data view portals. In this presentation yo will learn services-based reference architecture, modality, and latency of data access.
- Reference architecture for enterprise data services marketplace
- Modality and latency of data access
- Customer use cases and demo
This presentation is part of the Denodo Educational Seminar , and you can watch the video here goo.gl/vycYmZ.
Migrating data without proper tools and methodology can lead to significant risks and issues. The document recommends best practices for data migration including using tools and methodologies, having an experienced team, and conducting assessments. It outlines a recommended process including scoping, assessments, core migration, and decommissioning to help identify and mitigate risks.
PCTY 2012, Risk Based Access Control v. Pat WardropIBM Danmark
Risk-based access uses contextual information like device, location, identity, and behavior to dynamically determine access to resources, complementing traditional static access controls. It considers changing business environments with mobile, cloud, and social aspects. Common context sources are endpoints, identity, environment, resource/action, and behavior. Enforcement can occur at intermediary, container, or application levels. Use cases include enabling secure BYOD access to business applications from any location and providing access based on risk instead of just authentication.
¿En qué se parece el Gobierno del Dato a un parque de atracciones?Denodo
Watch full webinar here: https://bit.ly/3Ab9gYq
Imagina llegar a un parque de atracciones con tu familia y comenzar tu día sin el típico plano que te permitirá planificarte para saber qué espectáculos ver, a qué atracciones ir, donde pueden o no pueden montar los niños… Posiblemente, no podrás sacar el máximo partido a tu día y te habrás perdido muchas cosas. Hay personas que les gusta ir a la aventura e ir descubriendo poco a poco, pero cuando hablamos de negocios, ir a la aventura puede ser fatídico...
En la era de la explosión de la información repartida en distintas fuentes, el gobierno de datos es clave para garantizar la disponibilidad, usabilidad, integridad y seguridad de esa información. Asimismo, el conjunto de procesos, roles y políticas que define permite que las organizaciones alcancen sus objetivos asegurando el uso eficiente de sus datos.
La virtualización de datos, herramienta estratégica para implementar y optimizar el gobierno del dato, permite a las empresas crear una visión 360º de sus datos y establecer controles de seguridad y políticas de acceso sobre toda la infraestructura, independientemente del formato o de su ubicación. De ese modo, reúne múltiples fuentes de datos, las hace accesibles desde una sola capa y proporciona capacidades de trazabilidad para supervisar los cambios en los datos.
En este webinar aprenderás a:
- Acelerar la integración de datos provenientes de fuentes de datos fragmentados en los sistemas internos y externos y obtener una vista integral de la información.
- Activar en toda la empresa una sola capa de acceso a los datos con medidas de protección.
- Cómo la virtualización de datos proporciona los pilares para cumplir con las normativas actuales de protección de datos mediante auditoría, catálogo y seguridad de datos.
Bi 4.0 Migration Strategy and Best PracticesEric Molner
The document discusses best practices for migrating to SAP BusinessObjects BI 4.0 including conducting an assessment of the current BI environment and existing content, developing a migration plan and roadmap, installing and configuring the new BI 4.0 software, migrating and converting existing reports and content where needed, testing the new system, and providing training to users. It also outlines Analytics8's methodology for assisting with the various phases of a BI migration project from assessment to deployment.
This document summarizes a presentation about managing enterprise data quality using SAP Information Steward. It discusses:
1) How data quality challenges can arise within a business intelligence information pipeline as data moves between systems.
2) The role of Information Steward in providing visibility into data quality issues across systems and addressing those issues.
3) Best practices for implementing a data quality tool, such as defining roles and responsibilities, and using the tool to monitor quality and detect issues.
Sharing a presentation highlighting some key aspects to be taken into consideration while harnessing your Digital Transformation projects as a Digital Intelligence enabler for your enterprise
• Democratizing data access and maturing feedback loops between producers and consumers is central to unlocking the potential of DoD’s data to deliver decision advantage
• We are building the framework for DoD’s data ecosystem – including
people, process and technology
• Quality data is the foundation for trusted analytics and AI
• DoD cannot address all data challenges at once – priorities:
• Foundational enterprise data: personnel, logistics, and finance
• End-user facing capability for marquee customers: OSD & CCMDs
Check out this SlideShare to understand the challenges of BCBS 239 and learn ways to collect, measure, monitor and report on data to achieve better data integrity and data quality. Both G-SIBs and D-SIBS will learn how to help better govern their data.
This is a slide deck that was assembled as a result of months of Project work at a Global Multinational. Collaboration with some incredibly smart people resulted in content that I wish I had come across prior to having to have assembled this.
Implementar una estrategia eficiente de gobierno y seguridad del dato con la ...Denodo
Watch full webinar here: https://bit.ly/3lSwLyU
En la era de la explosión de la información repartida en distintas fuentes, el gobierno de datos es un componente clave para garantizar la disponibilidad, usabilidad, integridad y seguridad de la información. Asimismo, el conjunto de procesos, roles y políticas que define permite que las organizaciones alcancen sus objetivos asegurando el uso eficiente de sus datos.
La virtualización de datos forma parte de las herramientas estratégica para implementar y optimizar el gobierno de datos. Esta tecnología permite a las empresas crear una visión 360º de sus datos y establecer controles de seguridad y políticas de acceso sobre toda la infraestructura, independientemente del formato o de su ubicación. De ese modo, reúne múltiples fuentes de datos, las hace accesibles desde una sola capa y proporciona capacidades de trazabilidad para supervisar los cambios en los datos.
Le invitamos a participar en este webinar para aprender:
- Cómo acelerar la integración de datos provenientes de fuentes de datos fragmentados en los sistemas internos y externos y obtener una vista integral de la información.
- Cómo activar en toda la empresa una sola capa de acceso a los datos con medidas de protección.
- Cómo la virtualización de datos proporciona los pilares para cumplir con las normativas actuales de protección de datos mediante auditoría, catálogo y seguridad de datos.
Brian Fitzsimmons on the Business Strategy and Content Flywheel of Barstool S...Neil Horowitz
On episode 272 of the Digital and Social Media Sports Podcast, Neil chatted with Brian Fitzsimmons, Director of Licensing and Business Development for Barstool Sports.
What follows is a collection of snippets from the podcast. To hear the full interview and more, check out the podcast on all podcast platforms and at www.dsmsports.net
The document discusses six governance processes for data and business intelligence: data lifecycle, data models, data quality, data security, data warehousing, and metadata. For each process, it provides an overview of why governance is important in that area, and what the governance process will do to manage issues and ensure requirements are met. The governance processes aim to balance various factors, control changes, and provide oversight and accountability for data management.
Oracle Application User Group sponsored Collaborate 2009 Presentation 'Building a Practical Strategy for Managing Data Quality' by Alex Fiteni CPA, CMA
The document summarizes a presentation on data quality and master data management. It discusses:
- The development of ISO 8000 and ISO 8000-100 which provide standards for data quality and master data quality management.
- The importance of common concept encoding and terminology mapping across supply chains and applications to ensure data portability and system interoperability.
- How data quality must support defined business functions and provide transparency through consistent identification of entities like customers, suppliers, materials using metadata.
- The need for organizations to publish quality data specifications to their suppliers and customers to improve visibility and differentiation.
The document discusses data quality management (DQM) concepts and activities. It describes the DQM approach as a continuous cycle of planning, deployment, monitoring, and acting. Key activities include developing data quality awareness, defining requirements, profiling/assessing data, defining metrics/rules, testing requirements, setting service levels, continuously measuring/monitoring quality, and managing issues. DQM aims to ensure data meets fitness-for-use expectations and business needs.
Chapter 12: Data Quality ManagementAhmed Alorage
This document discusses data quality management (DQM). It covers DQM concepts and activities, including developing data quality awareness, defining data quality requirements, profiling and assessing data quality, and defining metrics. The key DQM approach is the Deming cycle of planning, deploying, monitoring, and acting to continuously improve data quality. Data quality requirements are identified by reviewing business policies and rules to understand dimensions like accuracy, completeness, consistency and more.
Developing A Universal Approach to Cleansing Customer and Product DataFindWhitePapers
Take a look at this review of current industry problems concerning data quality, and learn more about how companies are addressing quality problems with customer, product, and other types of corporate data. Read about products and use cases from SAP to see how vendors are supporting data cleansing.
This document summarizes the role and work of NEHTA, an organization established by the Australian government to facilitate e-health initiatives. It discusses NEHTA's work on healthcare identifiers and developing a data quality strategy, including defining data quality dimensions, standards, policies, and a maturity model. It also provides advice on prioritizing data quality and ensuring senior management support for these efforts.
This document outlines the process of clinical data management. It discusses the key steps and technologies involved in digitization, electronic data capture, data analytics, document management, data standardization, and infrastructure/security. The main stages described are feasibility analysis, system selection, design and implementation, data collection, quality control, and regulatory submission preparation. Technologies mentioned include EDC tools like Oracle Clinical, data standards like CDISC SDTM, and document management solutions from vendors such as Documentum.
Akili provides data integration and management services for oil and gas companies. They leverage over 25 years of experience and experts in SAP, BI platforms, financial systems, and oil and gas data. Akili helps customers address challenges around data quality, reliability, disparate systems and gaining a single view of data. They provide predefined solutions and accelerators using industry standards from PPDM (Professional Petroleum Data Management). Akili's approach involves assessing an organization's data maturity, developing a data integration strategy, addressing governance, master data and tools to integrate data from multiple sources and systems into meaningful business information.
The data services marketplace is enabled by a data abstraction layer that supports rapid development of operational applications and single data view portals. In this presentation yo will learn services-based reference architecture, modality, and latency of data access.
- Reference architecture for enterprise data services marketplace
- Modality and latency of data access
- Customer use cases and demo
This presentation is part of the Denodo Educational Seminar , and you can watch the video here goo.gl/vycYmZ.
Migrating data without proper tools and methodology can lead to significant risks and issues. The document recommends best practices for data migration including using tools and methodologies, having an experienced team, and conducting assessments. It outlines a recommended process including scoping, assessments, core migration, and decommissioning to help identify and mitigate risks.
PCTY 2012, Risk Based Access Control v. Pat WardropIBM Danmark
Risk-based access uses contextual information like device, location, identity, and behavior to dynamically determine access to resources, complementing traditional static access controls. It considers changing business environments with mobile, cloud, and social aspects. Common context sources are endpoints, identity, environment, resource/action, and behavior. Enforcement can occur at intermediary, container, or application levels. Use cases include enabling secure BYOD access to business applications from any location and providing access based on risk instead of just authentication.
¿En qué se parece el Gobierno del Dato a un parque de atracciones?Denodo
Watch full webinar here: https://bit.ly/3Ab9gYq
Imagina llegar a un parque de atracciones con tu familia y comenzar tu día sin el típico plano que te permitirá planificarte para saber qué espectáculos ver, a qué atracciones ir, donde pueden o no pueden montar los niños… Posiblemente, no podrás sacar el máximo partido a tu día y te habrás perdido muchas cosas. Hay personas que les gusta ir a la aventura e ir descubriendo poco a poco, pero cuando hablamos de negocios, ir a la aventura puede ser fatídico...
En la era de la explosión de la información repartida en distintas fuentes, el gobierno de datos es clave para garantizar la disponibilidad, usabilidad, integridad y seguridad de esa información. Asimismo, el conjunto de procesos, roles y políticas que define permite que las organizaciones alcancen sus objetivos asegurando el uso eficiente de sus datos.
La virtualización de datos, herramienta estratégica para implementar y optimizar el gobierno del dato, permite a las empresas crear una visión 360º de sus datos y establecer controles de seguridad y políticas de acceso sobre toda la infraestructura, independientemente del formato o de su ubicación. De ese modo, reúne múltiples fuentes de datos, las hace accesibles desde una sola capa y proporciona capacidades de trazabilidad para supervisar los cambios en los datos.
En este webinar aprenderás a:
- Acelerar la integración de datos provenientes de fuentes de datos fragmentados en los sistemas internos y externos y obtener una vista integral de la información.
- Activar en toda la empresa una sola capa de acceso a los datos con medidas de protección.
- Cómo la virtualización de datos proporciona los pilares para cumplir con las normativas actuales de protección de datos mediante auditoría, catálogo y seguridad de datos.
Bi 4.0 Migration Strategy and Best PracticesEric Molner
The document discusses best practices for migrating to SAP BusinessObjects BI 4.0 including conducting an assessment of the current BI environment and existing content, developing a migration plan and roadmap, installing and configuring the new BI 4.0 software, migrating and converting existing reports and content where needed, testing the new system, and providing training to users. It also outlines Analytics8's methodology for assisting with the various phases of a BI migration project from assessment to deployment.
This document summarizes a presentation about managing enterprise data quality using SAP Information Steward. It discusses:
1) How data quality challenges can arise within a business intelligence information pipeline as data moves between systems.
2) The role of Information Steward in providing visibility into data quality issues across systems and addressing those issues.
3) Best practices for implementing a data quality tool, such as defining roles and responsibilities, and using the tool to monitor quality and detect issues.
Sharing a presentation highlighting some key aspects to be taken into consideration while harnessing your Digital Transformation projects as a Digital Intelligence enabler for your enterprise
• Democratizing data access and maturing feedback loops between producers and consumers is central to unlocking the potential of DoD’s data to deliver decision advantage
• We are building the framework for DoD’s data ecosystem – including
people, process and technology
• Quality data is the foundation for trusted analytics and AI
• DoD cannot address all data challenges at once – priorities:
• Foundational enterprise data: personnel, logistics, and finance
• End-user facing capability for marquee customers: OSD & CCMDs
Check out this SlideShare to understand the challenges of BCBS 239 and learn ways to collect, measure, monitor and report on data to achieve better data integrity and data quality. Both G-SIBs and D-SIBS will learn how to help better govern their data.
This is a slide deck that was assembled as a result of months of Project work at a Global Multinational. Collaboration with some incredibly smart people resulted in content that I wish I had come across prior to having to have assembled this.
Implementar una estrategia eficiente de gobierno y seguridad del dato con la ...Denodo
Watch full webinar here: https://bit.ly/3lSwLyU
En la era de la explosión de la información repartida en distintas fuentes, el gobierno de datos es un componente clave para garantizar la disponibilidad, usabilidad, integridad y seguridad de la información. Asimismo, el conjunto de procesos, roles y políticas que define permite que las organizaciones alcancen sus objetivos asegurando el uso eficiente de sus datos.
La virtualización de datos forma parte de las herramientas estratégica para implementar y optimizar el gobierno de datos. Esta tecnología permite a las empresas crear una visión 360º de sus datos y establecer controles de seguridad y políticas de acceso sobre toda la infraestructura, independientemente del formato o de su ubicación. De ese modo, reúne múltiples fuentes de datos, las hace accesibles desde una sola capa y proporciona capacidades de trazabilidad para supervisar los cambios en los datos.
Le invitamos a participar en este webinar para aprender:
- Cómo acelerar la integración de datos provenientes de fuentes de datos fragmentados en los sistemas internos y externos y obtener una vista integral de la información.
- Cómo activar en toda la empresa una sola capa de acceso a los datos con medidas de protección.
- Cómo la virtualización de datos proporciona los pilares para cumplir con las normativas actuales de protección de datos mediante auditoría, catálogo y seguridad de datos.
Similar to Defence IT 2012 - Data Quality and Financial Services - Solvency II (20)
Brian Fitzsimmons on the Business Strategy and Content Flywheel of Barstool S...Neil Horowitz
On episode 272 of the Digital and Social Media Sports Podcast, Neil chatted with Brian Fitzsimmons, Director of Licensing and Business Development for Barstool Sports.
What follows is a collection of snippets from the podcast. To hear the full interview and more, check out the podcast on all podcast platforms and at www.dsmsports.net
Profiles of Iconic Fashion Personalities.pdfTTop Threads
The fashion industry is dynamic and ever-changing, continuously sculpted by trailblazing visionaries who challenge norms and redefine beauty. This document delves into the profiles of some of the most iconic fashion personalities whose impact has left a lasting impression on the industry. From timeless designers to modern-day influencers, each individual has uniquely woven their thread into the rich fabric of fashion history, contributing to its ongoing evolution.
The Steadfast and Reliable Bull: Taurus Zodiac Signmy Pandit
Explore the steadfast and reliable nature of the Taurus Zodiac Sign. Discover the personality traits, key dates, and horoscope insights that define the determined and practical Taurus, and learn how their grounded nature makes them the anchor of the zodiac.
Best Competitive Marble Pricing in Dubai - ☎ 9928909666Stone Art Hub
Stone Art Hub offers the best competitive Marble Pricing in Dubai, ensuring affordability without compromising quality. With a wide range of exquisite marble options to choose from, you can enhance your spaces with elegance and sophistication. For inquiries or orders, contact us at ☎ 9928909666. Experience luxury at unbeatable prices.
Top mailing list providers in the USA.pptxJeremyPeirce1
Discover the top mailing list providers in the USA, offering targeted lists, segmentation, and analytics to optimize your marketing campaigns and drive engagement.
3 Simple Steps To Buy Verified Payoneer Account In 2024SEOSMMEARTH
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Buy Verified Payoneer Account With 100% secure documents, [ USA, UK, CA ]. Are you looking for a reliable and safe way to receive payments online? Then you need buy verified Payoneer account ! Payoneer is a global payment platform that allows businesses and individuals to send and receive money in over 200 countries.
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Top 10 Free Accounting and Bookkeeping Apps for Small BusinessesYourLegal Accounting
Maintaining a proper record of your money is important for any business whether it is small or large. It helps you stay one step ahead in the financial race and be aware of your earnings and any tax obligations.
However, managing finances without an entire accounting staff can be challenging for small businesses.
Accounting apps can help with that! They resemble your private money manager.
They organize all of your transactions automatically as soon as you link them to your corporate bank account. Additionally, they are compatible with your phone, allowing you to monitor your finances from anywhere. Cool, right?
Thus, we’ll be looking at several fantastic accounting apps in this blog that will help you develop your business and save time.
[To download this presentation, visit:
https://www.oeconsulting.com.sg/training-presentations]
This PowerPoint compilation offers a comprehensive overview of 20 leading innovation management frameworks and methodologies, selected for their broad applicability across various industries and organizational contexts. These frameworks are valuable resources for a wide range of users, including business professionals, educators, and consultants.
Each framework is presented with visually engaging diagrams and templates, ensuring the content is both informative and appealing. While this compilation is thorough, please note that the slides are intended as supplementary resources and may not be sufficient for standalone instructional purposes.
This compilation is ideal for anyone looking to enhance their understanding of innovation management and drive meaningful change within their organization. Whether you aim to improve product development processes, enhance customer experiences, or drive digital transformation, these frameworks offer valuable insights and tools to help you achieve your goals.
INCLUDED FRAMEWORKS/MODELS:
1. Stanford’s Design Thinking
2. IDEO’s Human-Centered Design
3. Strategyzer’s Business Model Innovation
4. Lean Startup Methodology
5. Agile Innovation Framework
6. Doblin’s Ten Types of Innovation
7. McKinsey’s Three Horizons of Growth
8. Customer Journey Map
9. Christensen’s Disruptive Innovation Theory
10. Blue Ocean Strategy
11. Strategyn’s Jobs-To-Be-Done (JTBD) Framework with Job Map
12. Design Sprint Framework
13. The Double Diamond
14. Lean Six Sigma DMAIC
15. TRIZ Problem-Solving Framework
16. Edward de Bono’s Six Thinking Hats
17. Stage-Gate Model
18. Toyota’s Six Steps of Kaizen
19. Microsoft’s Digital Transformation Framework
20. Design for Six Sigma (DFSS)
To download this presentation, visit:
https://www.oeconsulting.com.sg/training-presentations
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IMPACT Silver is a pure silver zinc producer with over $260 million in revenue since 2008 and a large 100% owned 210km Mexico land package - 2024 catalysts includes new 14% grade zinc Plomosas mine and 20,000m of fully funded exploration drilling.
How are Lilac French Bulldogs Beauty Charming the World and Capturing Hearts....Lacey Max
“After being the most listed dog breed in the United States for 31
years in a row, the Labrador Retriever has dropped to second place
in the American Kennel Club's annual survey of the country's most
popular canines. The French Bulldog is the new top dog in the
United States as of 2022. The stylish puppy has ascended the
rankings in rapid time despite having health concerns and limited
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Defence IT 2012 - Data Quality and Financial Services - Solvency II
1. Data Quality Dimension of
European Union Regulation -
Solvency II
David Twaddell
AGENDA
- Regulatory Requirements
- Corporate Strategy
- Data Governance
- Defining Data Quality
- Data Quality Risk Assessment
- Data Quality Management solution
- Sample Dashboard
2. Understand the quality of data used in the
calculation of technical provisions and in
the internal model (completeness,
accuracy and appropriateness - L2DIM
Solvency II
Art 14 and 220) Data Quality
Requirements
Understand the quality of Solvency II data
using other indicators (Consistency,
Transparency, Credibility, Comparability,
Similarity – L2DIM Art various)
Provide evidence of effective data
governance (L2DIM Art 246)
04/19/12 (c) 2012 Inpreci – www.inpreci.com 2
3. Vision:
Global Insurer: • Business ownership of data
• >50k employees worldwide • Efficient data quality procedures along
• >$50B Gross Written Premium data lifecycle
• >1,000 disparate applications • Trusted DQ operational and
• Low confidence in data quality management information
• Global solution
• High confidence in data quality
Strategy: Data
• New Data Governance Framework
(+ clear Procedures and DQ standards +
Governance &
communication strategy)
• Central management of metadata
Quality
(including definition, lineage, ownership, etc.) Strategy
• New data quality controls, re-using existing controls
where possible.
• Powerful data tools
04/19/12 (c) 2012 Inpreci – www.inpreci.com 3
4. Overall management and control
Responsibilities and reporting lines
Governance, Policies,
Consistency across the enterprise Standards and Procedures
Clear policies, standards and
procedures
Education important
4
(c) 2012 Inpreci – www.inpreci.com
5. Defining Data Quality #1 – for example
Completeness
2. Reconcile data received against data expected
3. Process to assess if data is available for all relevant model variables and risk
modules
Accuracy
6. Compare directly against the source (if available).
7. Check internal consistency and coherence of the received/output data against
expected properties of the data such as age-range, standard deviation, number
of outliers, and mean.
8. Compare with other data derived from the same source, or sources which are
correlated.
Appropriateness
11. Check consistency and reasonableness to identify outliers and gaps through
comparison against known trends, historic data and external independent
sources.
12. A definition and consistent application of the rules that govern the amount and
nature of data used in the internal model.
04/19/12 (c) 2012 Inpreci – www.inpreci.com 5
6. Data Quality Risk Assessment
– where to put controls?
Define Materiality
Actuaries attach a materiality level to data terms within data sets, based on how the
data would affect the internal model, and define quantitative and qualitative
tolerances for data quality.
Document Lineage
Document the target business process and the data flow from source to internal
model for each dataset:
Check Data Controls
Identify existing control points that can be re-used. For each control point, document
actual controls (i.e. governance controls and data quality checks applied.). Link
controls to data quality indicators (completeness, accuracy, etc).
Risk Assessment
Documented procedure to assess risk along lineage. Assess effectiveness of
controls. May recommend additional control points and additional governance and
quality checks.
04/19/12 (c) 2012 Inpreci – www.inpreci.com 6
7. Component/Building
Summary of capabilities
Block Name
~ Define business terms
~ Associate terms to Data Domain/Owner
Data Definitions ~ Associate terms to Source, Uses, Characteristics
~ Associate terms to DQ rules, weights and metrics
~ Describe business processes that relate to the flow
of data into SII.
Data flows ~ Describe points of data governance
~ Describe risk assessment of business processes, as
it relates to data quality
~ Stores data quality business rules
DQ Rule Repository ~ Stores data control technical rules
~ Extract, transform, load data
DQ Rules Engine ~ Apply data quality rules at appropriate points
~ Write out data quality measurements
Data Quality
~ Allow business people to log data governance activities
DQ Metrics Collector
~ Allow create/modify/delete/read of governance data
~ Provide only relevant 'questions' to specific people
Management
~ Maintain history of governance data
~ Qualitative and Quantitative assessments and metrics
Architecture
~ Define Key Quality Indicators
~ Relate quality rules/measurements to KQIs
DQ Aggregator/Scoring ~ Define data quality scoring methodology
~ Aggregate data quality measurements for reporting
~ Logical data models
Components
DQ Storage ~ Physical data models
~ Physical storage
~ Present KQI dashboard
DQ Dashboard and Reports ~ Drill-down to more detailed reports
(Delivery) ~ Slide/dice by agreed dimensions
~ Provide views for Operations, Governance, Stewardship
~ Record data defects, prioritise
7
DQ Defect Manager ~ Track defect resolution
~ Interface with data quality measurements (c) 2012 Inpreci – www.inpreci.com
9. - Data Risk Assessment
- Data Quality
- Data Security
- Data Governance
- Data Infrastructure
- Data Complexity
- Metadata
- Policy, Standards & Procedures
- Solutions
Editor's Notes
Solvency II IT & Data have analysed primary requirements for data governance and quality by referring to the legal Directive and the associated implementation and guidance material. New guidance is provided by regulators from time-to-time and will continue to appear into 2012.
Solvency II IT & Data have analysed primary requirements for data governance and quality by referring to the legal Directive and the associated implementation and guidance material. New guidance is provided by regulators from time-to-time and will continue to appear into 2012.