Presented on PHPID Online Learning 35.
Komunitas PHP Indonesia
Title: Enabling Data Governance - The Journey through Data Trust, Ethics, and Quality
Eryk B. Pratama
Global IT & Cybersecurity Advisor
Becoming a Data-Driven Organization - Aligning Business & Data StrategyDATAVERSITY
More organizations are aspiring to become ‘data driven businesses’. But all too often this aim fails, as business goals and IT & data realities are misaligned, with IT lagging behind rapidly changing business needs. So how do you get the perfect fit where data strategy is driven by and underpins business strategy? This webinar will show you how by de-mystifying the building blocks of a global data strategy and highlighting a number of real world success stories. Topics include:
•How to align data strategy with business motivation and drivers
•Why business & data strategies often become misaligned & the impact
•Defining the core building blocks of a successful data strategy
•The role of business and IT
•Success stories in implementing global data strategies
This material was presented at Orang Siber Indonesia regular webinar.
Content:
> Understanding privacy management
> Global privacy news
> Understanding privacy regulations and frameworks
> Data Privacy Program Management practices
Data Governance Roles as the Backbone of Your ProgramDATAVERSITY
The method you follow to form your Data Governance roles and responsibilities will impact the success of your program. There are industry-standard roles that require adjustment to fit the culture of your organization when getting started, gaining acceptance, and demonstrating sustained value. Roles are the backbone of a productive Data Governance program.
Bob Seiner will share his updated operating model of roles and responsibilities in this topical RWDG webinar. The model Bob uses is meant to overlay your present organizational structure rather than requiring you to try and plug your organization into someone else’s model. This webinar will provide everything you need to know about Data Governance roles.
Bob will address the following in this webinar:
• An operating model of Data Governance roles and responsibilities
• How to customize the model to mimic your existing structure
• The meaning behind the oft-used “roles pyramid”
• Detailed responsibilities at each level of the organization
• Using the model to influence Data Governance acceptance
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
Creating a clearly articulated data strategy—a roadmap of technology-driven capability investments prioritized to deliver value—helps ensure from the get-go that you are focusing on the right things, so that your work with data has a business impact. In this presentation, the experts at Silicon Valley Data Science share their approach for crafting an actionable and flexible data strategy to maximize business value.
Privacy-ready Data Protection Program ImplementationEryk Budi Pratama
Presented at CDEF 16th Meetup at 18 August 2022.
Title:
Privacy-ready Data Protection Program Implementation
Topics:
- Why data protection is important
- Data Privacy Program Domain
- Operationalize Data Privacy Program
- Privacy-aligned Information Security Framework
- Roadmap to Protect Personal Data
- Privacy Management Technology
Becoming a Data-Driven Organization - Aligning Business & Data StrategyDATAVERSITY
More organizations are aspiring to become ‘data driven businesses’. But all too often this aim fails, as business goals and IT & data realities are misaligned, with IT lagging behind rapidly changing business needs. So how do you get the perfect fit where data strategy is driven by and underpins business strategy? This webinar will show you how by de-mystifying the building blocks of a global data strategy and highlighting a number of real world success stories. Topics include:
•How to align data strategy with business motivation and drivers
•Why business & data strategies often become misaligned & the impact
•Defining the core building blocks of a successful data strategy
•The role of business and IT
•Success stories in implementing global data strategies
This material was presented at Orang Siber Indonesia regular webinar.
Content:
> Understanding privacy management
> Global privacy news
> Understanding privacy regulations and frameworks
> Data Privacy Program Management practices
Data Governance Roles as the Backbone of Your ProgramDATAVERSITY
The method you follow to form your Data Governance roles and responsibilities will impact the success of your program. There are industry-standard roles that require adjustment to fit the culture of your organization when getting started, gaining acceptance, and demonstrating sustained value. Roles are the backbone of a productive Data Governance program.
Bob Seiner will share his updated operating model of roles and responsibilities in this topical RWDG webinar. The model Bob uses is meant to overlay your present organizational structure rather than requiring you to try and plug your organization into someone else’s model. This webinar will provide everything you need to know about Data Governance roles.
Bob will address the following in this webinar:
• An operating model of Data Governance roles and responsibilities
• How to customize the model to mimic your existing structure
• The meaning behind the oft-used “roles pyramid”
• Detailed responsibilities at each level of the organization
• Using the model to influence Data Governance acceptance
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
Creating a clearly articulated data strategy—a roadmap of technology-driven capability investments prioritized to deliver value—helps ensure from the get-go that you are focusing on the right things, so that your work with data has a business impact. In this presentation, the experts at Silicon Valley Data Science share their approach for crafting an actionable and flexible data strategy to maximize business value.
Privacy-ready Data Protection Program ImplementationEryk Budi Pratama
Presented at CDEF 16th Meetup at 18 August 2022.
Title:
Privacy-ready Data Protection Program Implementation
Topics:
- Why data protection is important
- Data Privacy Program Domain
- Operationalize Data Privacy Program
- Privacy-aligned Information Security Framework
- Roadmap to Protect Personal Data
- Privacy Management Technology
To take a “ready, aim, fire” tactic to implement Data Governance, many organizations assess themselves against industry best practices. The process is not difficult or time-consuming and can directly assure that your activities target your specific needs. Best practices are always a strong place to start.
Join Bob Seiner for this popular RWDG topic, where he will provide the information you need to set your program in the best possible direction. Bob will walk you through the steps of conducting an assessment and share with you a set of typical results from taking this action. You may be surprised at how easy it is to organize the assessment and may hear results that stimulate the actions that you need to take.
In this webinar, Bob will share:
- The value of performing a Data Governance best practice assessment
- A practical list of industry Data Governance best practices
- Criteria to determine if a practice is best practice
- Steps to follow to complete an assessment
- Typical recommendations and actions that result from an assessment
Legal obligations and responsibilities of data processors and controllers und...IT Governance Ltd
This webinar covers:
-The definitions of ‘data controller’ and ‘data processor’ under the GDPR.
-The responsibilities and obligations of controllers and processors.
-The data breach reporting responsibilities of controllers and processors.
-The liability of, and penalties that may be imposed on, data processors and controllers.
-The appointment of joint controllers and subcontracting processors
The webinar can be found here https://www.youtube.com/watch?v=cyUPGGD3iVg&t=8s
What has changed in DMBok V2?
We have been working with DMBoK V1 for may years and it is great to finally get to read and study the changes. Did a quikc comparison between the 2 versions.
You Need a Data Catalog. Do You Know Why?Precisely
The data catalog has become a popular discussion topic within data management and data governance circles. A data catalog is a central repository that contains metadata for describing data sets, how they are defined, and where to find them. TDWI research indicates that implementing a data catalog is a top priority among organizations we survey. The data catalog can also play an important part in the governance process. It provides features that help ensure data quality, compliance, and that trusted data is used for analysis. Without an in-depth knowledge of data and associated metadata, organizations cannot truly safeguard and govern their data.
Join this on-demand webinar to learn more about the data catalog and its role in data governance efforts.
Topics include:
· Data management challenges and priorities
· The modern data catalog – what it is and why it is important
· The role of the modern data catalog in your data quality and governance programs
· The kinds of information that should be in your data catalog and why
Data Management and Data Governance are the same thing! Aren’t they? Most people would say that this line of thinking is absurd – or even worse. There is NO WAY that they are the same thing. Or are they?
Join Bob Seiner and Anthony Algmin for a lively, interactive, and entertaining discussion targeted at providing attendees ways to consider relating these two disciplines. You’ve never attended a session like this.
In this session, Bob and Anthony will discuss:
- The similarities between Data Management and Data Governance
- The differences between the two
- How to use Data Management to sell Data Governance … and the other way around
- Deciding if the two disciplines are the same … or different
Tackling data quality problems requires more than a series of tactical, one off improvement projects. By their nature, many data quality problems extend across and often beyond an organization. Addressing these issues requires a holistic architectural approach combining people, process and technology. Join Donna Burbank and Nigel Turner as they provide practical ways to control data quality issues in your organization.
Data governance Program PowerPoint Presentation Slides SlideTeam
Presenting this set of slides with name - Data Governance Program Powerpoint Presentation Slides. Our creatively crafted slides come with apt research and planning. This exclusive deck with twenty-five slides is here to help you to strategize, plan, analyze, or segment the topic with clear understanding and apprehension. Utilize ready to use presentation slides on Data Governance Program Powerpoint Presentation Slides with all sorts of editable templates, charts and graphs, overviews, analysis templates. PPT slides are accessible in both widescreen and standard format. PowerPoint templates are compatible with Google Slides. Quick and risk-free downloading process. It can be easily converted into JPG or PDF format
This introduction to data governance presentation covers the inter-related DM foundational disciplines (Data Integration / DWH, Business Intelligence and Data Governance). Some of the pitfalls and success factors for data governance.
• IM Foundational Disciplines
• Cross-functional Workflow Exchange
• Key Objectives of the Data Governance Framework
• Components of a Data Governance Framework
• Key Roles in Data Governance
• Data Governance Committee (DGC)
• 4 Data Governance Policy Areas
• 3 Challenges to Implementing Data Governance
• Data Governance Success Factors
Slides zum Impuls-Vortrag "Data Strategy & Governance" - BI or DIE LEVEL UP 2022
Aufzeichnung des Vortrags: https://www.youtube.com/watch?v=705DfyfF5-M
Key Data Privacy Roles Explained: Data Protection Officer, Information Securi...PECB
Key Data Privacy Roles Explained: Data Protection Officer, Information Security Manager, and Information Security Auditor
In this session, we will go through the roles and responsibilities of the main actors responsible for protecting data in an organization: the Data Protection Officer, Information Security Manager, and Information Security Auditor.
The webinar will cover:
• What are the roles and responsibilities of the main actors responsible for protecting data in an organization?
• How can an organization find out if they are required to designate a DPO role or not?
• Can the roles of a DPO and Information Security Manager be covered by the same individual?
• What organizations are required to do to have the DPO perform its role and responsivities independently?
Presenter:
Our first presenter for this webinar is Peter Geelen, director and managing consultant at CyberMinute and Owner of Quest for Security, Belgium. Over more than 20 years, Peter has built strong experience in enterprise security & architecture, Identity & Access management, but also privacy, information & data protection, cyber- and cloud security. Last few years, the focus is on ISO/IEC 27001 and other ISO certification mechanisms. Peter is accredited Lead Auditor for ISO/IEC 27001, ISO 9001, PECB Trainer and Fellow in Privacy. Committed to continuous learning, Peter holds renowned security certificates as certified ISO/IEC 27701 lead implementer and lead auditor, ISO/IEC 27001 Master, Sr. Lead Cybersecurity Manager, ISO/IEC 27002 lead manager, ISO/IEC 27701 Lead Implementer, cDPO, Risk management, Lead Incident Mgr., Disaster Recovery, and many more.
Our second presenter is Stefan Mathuvis, owner & senior consultant at Quality Management & Auditing BV, Zonhoven, Belgium. With over 20 years of experience, Stefan built strong experience in quality management systems, Information Security management systems, GDPR, data privacy & data protection. Stefan is accredited ISO/IEC 27001 Lead Auditor and operates as a third party auditor for DQS Belgium. Dividing his time between consultancy, training & third party auditing on an international scale, Stefan remains in touch with the issues of today allowing him to assist clients in their needs for Information Security and Data Privacy.
Recorded webinar: https://www.youtube.com/watch?v=Y0hnv1laxAw&feature=youtu.be
Strategic Business Requirements for Master Data Management SystemsBoris Otto
This presentation describes strategic business requirements of master data management (MDM) systems. The requirements were developed in a consortium research approach by the Institute of Information Management at the University of St. Gallen, Switzerland, and 20 multinational enterprises.
The presentation was given at the 17th Amercias Conference on Information Systems (AMCIS 2011) in Detroit, MI.
The research paper on which this presentation is based on can be found here: http://www.alexandria.unisg.ch/Publikationen/Zitation/Boris_Otto/177697
Metadata is hotter than ever, according a number of recent DATAVERSITY surveys. More and more organizations are realizing that in order to drive business value from data, robust metadata is needed to gain the necessary context and lineage around key data assets. At the same time, industry regulations are driving the need for better transparency and understanding of information.
While metadata has been managed for decades, new strategies & approaches have been developed to support the ever-evolving data landscape, and provide more innovative ways to drive business value from metadata. This webinar will provide an overview of metadata strategies & technologies available to today’s organization, and provide insights into building successful business strategies for metadata adoption & use.
How to Strengthen Enterprise Data Governance with Data QualityDATAVERSITY
If your organization is in a highly-regulated industry – or relies on data for competitive advantage – data governance is undoubtedly a top priority. Whether you’re focused on “defensive” data governance (supporting regulatory compliance and risk management) or “offensive” data governance (extracting the maximum value from your data assets, and minimizing the cost of bad data), data quality plays a critical role in ensuring success.
Join our webinar to learn how enterprise data quality drives stronger data governance, including:
The overlaps between data governance and data quality
The “data” dependencies of data governance – and how data quality addresses them
Key considerations for deploying data quality for data governance
Are you ready for Big Data? This assessment review from Data Management Advisors will provide pragmatic recommendations & actionable transition steps to help you achieve your Big Data goals & deliver actionable insights.
info@dmadvisors.co.uk
An examination of the ethical considerations involved in data analyticsUncodemy
Data analytics can be used for various purposes, including marketing, product development, and customer service. One of the primary benefits of data analytics is that it can help you identify patterns in your data that you might not have been able to see with other methods.
To take a “ready, aim, fire” tactic to implement Data Governance, many organizations assess themselves against industry best practices. The process is not difficult or time-consuming and can directly assure that your activities target your specific needs. Best practices are always a strong place to start.
Join Bob Seiner for this popular RWDG topic, where he will provide the information you need to set your program in the best possible direction. Bob will walk you through the steps of conducting an assessment and share with you a set of typical results from taking this action. You may be surprised at how easy it is to organize the assessment and may hear results that stimulate the actions that you need to take.
In this webinar, Bob will share:
- The value of performing a Data Governance best practice assessment
- A practical list of industry Data Governance best practices
- Criteria to determine if a practice is best practice
- Steps to follow to complete an assessment
- Typical recommendations and actions that result from an assessment
Legal obligations and responsibilities of data processors and controllers und...IT Governance Ltd
This webinar covers:
-The definitions of ‘data controller’ and ‘data processor’ under the GDPR.
-The responsibilities and obligations of controllers and processors.
-The data breach reporting responsibilities of controllers and processors.
-The liability of, and penalties that may be imposed on, data processors and controllers.
-The appointment of joint controllers and subcontracting processors
The webinar can be found here https://www.youtube.com/watch?v=cyUPGGD3iVg&t=8s
What has changed in DMBok V2?
We have been working with DMBoK V1 for may years and it is great to finally get to read and study the changes. Did a quikc comparison between the 2 versions.
You Need a Data Catalog. Do You Know Why?Precisely
The data catalog has become a popular discussion topic within data management and data governance circles. A data catalog is a central repository that contains metadata for describing data sets, how they are defined, and where to find them. TDWI research indicates that implementing a data catalog is a top priority among organizations we survey. The data catalog can also play an important part in the governance process. It provides features that help ensure data quality, compliance, and that trusted data is used for analysis. Without an in-depth knowledge of data and associated metadata, organizations cannot truly safeguard and govern their data.
Join this on-demand webinar to learn more about the data catalog and its role in data governance efforts.
Topics include:
· Data management challenges and priorities
· The modern data catalog – what it is and why it is important
· The role of the modern data catalog in your data quality and governance programs
· The kinds of information that should be in your data catalog and why
Data Management and Data Governance are the same thing! Aren’t they? Most people would say that this line of thinking is absurd – or even worse. There is NO WAY that they are the same thing. Or are they?
Join Bob Seiner and Anthony Algmin for a lively, interactive, and entertaining discussion targeted at providing attendees ways to consider relating these two disciplines. You’ve never attended a session like this.
In this session, Bob and Anthony will discuss:
- The similarities between Data Management and Data Governance
- The differences between the two
- How to use Data Management to sell Data Governance … and the other way around
- Deciding if the two disciplines are the same … or different
Tackling data quality problems requires more than a series of tactical, one off improvement projects. By their nature, many data quality problems extend across and often beyond an organization. Addressing these issues requires a holistic architectural approach combining people, process and technology. Join Donna Burbank and Nigel Turner as they provide practical ways to control data quality issues in your organization.
Data governance Program PowerPoint Presentation Slides SlideTeam
Presenting this set of slides with name - Data Governance Program Powerpoint Presentation Slides. Our creatively crafted slides come with apt research and planning. This exclusive deck with twenty-five slides is here to help you to strategize, plan, analyze, or segment the topic with clear understanding and apprehension. Utilize ready to use presentation slides on Data Governance Program Powerpoint Presentation Slides with all sorts of editable templates, charts and graphs, overviews, analysis templates. PPT slides are accessible in both widescreen and standard format. PowerPoint templates are compatible with Google Slides. Quick and risk-free downloading process. It can be easily converted into JPG or PDF format
This introduction to data governance presentation covers the inter-related DM foundational disciplines (Data Integration / DWH, Business Intelligence and Data Governance). Some of the pitfalls and success factors for data governance.
• IM Foundational Disciplines
• Cross-functional Workflow Exchange
• Key Objectives of the Data Governance Framework
• Components of a Data Governance Framework
• Key Roles in Data Governance
• Data Governance Committee (DGC)
• 4 Data Governance Policy Areas
• 3 Challenges to Implementing Data Governance
• Data Governance Success Factors
Slides zum Impuls-Vortrag "Data Strategy & Governance" - BI or DIE LEVEL UP 2022
Aufzeichnung des Vortrags: https://www.youtube.com/watch?v=705DfyfF5-M
Key Data Privacy Roles Explained: Data Protection Officer, Information Securi...PECB
Key Data Privacy Roles Explained: Data Protection Officer, Information Security Manager, and Information Security Auditor
In this session, we will go through the roles and responsibilities of the main actors responsible for protecting data in an organization: the Data Protection Officer, Information Security Manager, and Information Security Auditor.
The webinar will cover:
• What are the roles and responsibilities of the main actors responsible for protecting data in an organization?
• How can an organization find out if they are required to designate a DPO role or not?
• Can the roles of a DPO and Information Security Manager be covered by the same individual?
• What organizations are required to do to have the DPO perform its role and responsivities independently?
Presenter:
Our first presenter for this webinar is Peter Geelen, director and managing consultant at CyberMinute and Owner of Quest for Security, Belgium. Over more than 20 years, Peter has built strong experience in enterprise security & architecture, Identity & Access management, but also privacy, information & data protection, cyber- and cloud security. Last few years, the focus is on ISO/IEC 27001 and other ISO certification mechanisms. Peter is accredited Lead Auditor for ISO/IEC 27001, ISO 9001, PECB Trainer and Fellow in Privacy. Committed to continuous learning, Peter holds renowned security certificates as certified ISO/IEC 27701 lead implementer and lead auditor, ISO/IEC 27001 Master, Sr. Lead Cybersecurity Manager, ISO/IEC 27002 lead manager, ISO/IEC 27701 Lead Implementer, cDPO, Risk management, Lead Incident Mgr., Disaster Recovery, and many more.
Our second presenter is Stefan Mathuvis, owner & senior consultant at Quality Management & Auditing BV, Zonhoven, Belgium. With over 20 years of experience, Stefan built strong experience in quality management systems, Information Security management systems, GDPR, data privacy & data protection. Stefan is accredited ISO/IEC 27001 Lead Auditor and operates as a third party auditor for DQS Belgium. Dividing his time between consultancy, training & third party auditing on an international scale, Stefan remains in touch with the issues of today allowing him to assist clients in their needs for Information Security and Data Privacy.
Recorded webinar: https://www.youtube.com/watch?v=Y0hnv1laxAw&feature=youtu.be
Strategic Business Requirements for Master Data Management SystemsBoris Otto
This presentation describes strategic business requirements of master data management (MDM) systems. The requirements were developed in a consortium research approach by the Institute of Information Management at the University of St. Gallen, Switzerland, and 20 multinational enterprises.
The presentation was given at the 17th Amercias Conference on Information Systems (AMCIS 2011) in Detroit, MI.
The research paper on which this presentation is based on can be found here: http://www.alexandria.unisg.ch/Publikationen/Zitation/Boris_Otto/177697
Metadata is hotter than ever, according a number of recent DATAVERSITY surveys. More and more organizations are realizing that in order to drive business value from data, robust metadata is needed to gain the necessary context and lineage around key data assets. At the same time, industry regulations are driving the need for better transparency and understanding of information.
While metadata has been managed for decades, new strategies & approaches have been developed to support the ever-evolving data landscape, and provide more innovative ways to drive business value from metadata. This webinar will provide an overview of metadata strategies & technologies available to today’s organization, and provide insights into building successful business strategies for metadata adoption & use.
How to Strengthen Enterprise Data Governance with Data QualityDATAVERSITY
If your organization is in a highly-regulated industry – or relies on data for competitive advantage – data governance is undoubtedly a top priority. Whether you’re focused on “defensive” data governance (supporting regulatory compliance and risk management) or “offensive” data governance (extracting the maximum value from your data assets, and minimizing the cost of bad data), data quality plays a critical role in ensuring success.
Join our webinar to learn how enterprise data quality drives stronger data governance, including:
The overlaps between data governance and data quality
The “data” dependencies of data governance – and how data quality addresses them
Key considerations for deploying data quality for data governance
Are you ready for Big Data? This assessment review from Data Management Advisors will provide pragmatic recommendations & actionable transition steps to help you achieve your Big Data goals & deliver actionable insights.
info@dmadvisors.co.uk
An examination of the ethical considerations involved in data analyticsUncodemy
Data analytics can be used for various purposes, including marketing, product development, and customer service. One of the primary benefits of data analytics is that it can help you identify patterns in your data that you might not have been able to see with other methods.
Data Analytics Ethics Issues and Questions
Presented at the University of Chicago Booth Big Data & Analytics Roundtable, April 2018
Presenter:
Arnie Aronoff, Ph.D.
Instructor, MScA in Data Analytics
Instructor, School of Social Services Administration
The University of Chicago
Group Concept OD
Organizational Development and Training
(312) 259-4544
aaronoff33@gmail.com
Presented by
My keynote speech at the ISACA IIA Belgium software watch day in October 2014 in Brussels on the value of big data and data analytics for auditors and other assurance professionals
Ethical Considerations in Data Analysis_ Balancing Power, Privacy, and Respon...Soumodeep Nanee Kundu
The explosion of data and the increasing capabilities of data analysis have transformed various aspects of our lives. From healthcare and finance to marketing and law enforcement, data analysis has become an essential tool for decision-making and problem-solving. However, with great power comes great responsibility. Ethical considerations in data analysis are more critical than ever as data professionals grapple with questions related to privacy, fairness, transparency, and accountability. In this article, we will delve into the ethical challenges that data analysts and organizations face and explore strategies to address them.
Navigating the Complex Terrain of Data Governance in Data Analysis.pdfSoumodeep Nanee Kundu
Data governance is a critical framework in the world of data analysis. This essay delves into the concept of data governance, exploring its fundamental principles, components, and significance in data analysis. We discuss the importance of data governance in ensuring data quality, security, compliance, and transparency, as well as its role in fostering a data-driven culture within organizations. This comprehensive examination illuminates the intricate web of data governance and its pivotal role in effective and responsible data analysis.
In the digital age, data is often referred to as the "new oil." Its value is undeniable, driving insights, innovation, and informed decision-making across various domains. However, the efficient and responsible utilization of data depends on a critical foundation: data governance. In the realm of data analysis, data governance plays a central role in ensuring the quality, security, compliance, and transparency of data, while also fostering a data-driven culture within organizations. This essay delves into the concept of data governance, elucidating its principles, components, and significance in the context of data analysis.
Closing the Governance Gap - Enabling Governed Self-Service AnalyticsPrivacera
Data democratization and data protection are conflicting forces that both need to be addressed through data governance and security by defining, deploying, and auditing data access control policies. Yet there is a latent “governance gap”: the individuals in the organization accountable for articulating and specifying data policies do not have enough knowledge of the systems to understand how policies are to be implemented, and the technologists who understand the system are not familiar enough with data policy drivers to appropriately define and deploy data protection policies.
This webinar is a must for personnel with an analytics and technology mandate to learn about the root causes of this governance gap and consider ideas for closing the gap.
On-Demand here: https://tdwi.org/webcasts/2021/07/arch-all-closing-the-governance-gap-enabling-governed-self-service-analytics.aspx
Learn about:
- Different roles tasked with managing data policies
- Root causes of the governance gap
- Establishing bridges among the different personas - privacy and compliance teams, data stewards, security teams, IT teams, data users
- Simplifying data policy governance
- Governed self-service analytics and data sharing
- Definitions of data sources and data assets and how to enable delegated policy administration
Ethical Considerations in Data Analyticsarchijain931
The age of data analytics has ushered in a wealth of opportunities for organizations and individuals to derive valuable insights from data. However, with great power comes great responsibility. Ethical considerations in data analytics have become increasingly important as the potential for misuse and privacy breaches has grown. In this article, we will explore the ethical challenges and principles that guide responsible data analytics, emphasizing the need for transparency, fairness, and accountability.
How to Strengthen Enterprise Data Governance with Data QualityPrecisely
If your organization is in a highly-regulated industry – or relies on data for competitive advantage – data governance is undoubtedly a top priority. Whether you’re focused on “defensive” data governance (supporting regulatory compliance and risk management) or “offensive” data governance (extracting the maximum value from your data assets, and minimizing the cost of bad data), data quality plays a critical role in ensuring success.
View this webinar on-demand to learn how enterprise data quality drives stronger data governance, including:
• The overlaps between data governance and data quality
• The “data” dependencies of data governance – and how data quality addresses them
• Key considerations for deploying data quality for data governance
In the rapidly evolving field of data analytics, ethical considerations are more critical than ever. Responsible data analytics involves not only extracting insights but also respecting privacy, ensuring fairness, and being transparent and accountable for data practices. Data professionals, organizations, and policymakers must collaborate to establish ethical guidelines and regulations that protect individuals' rights and promote responsible data use.
Perspectives on Ethical Big Data GovernanceCloudera, Inc.
Enterprise data governance is a critical, yet challenging, business process, and the rapidly expanding universe of data volumes and types make it a more significant undertaking, particularly for public sector organizations. In this session, attendees will learn how to bring comprehensive data governance to their organizations to ensure data collected and managed is handled and protected as required. Discover practical information on how to use the components and frameworks of the Hadoop stack to support your requirements for data auditing, lineage, metadata management, and policy enforcement, and hear recommendations on how to get started with measuring the progress of ethical big data usage--including what’s legal and what’s right. Bring your questions and join this lively, interactive dialogue.
Today’s organizations give predominant importance to increased privacy regulations, stakeholder’s profitability demands and the ever so changing consumer privacy expectations. As a result, the emphasis on personal data is growing and the companies are facing complicated reputational, regulatory and data privacy risk environment. It’s a sad fact that the frequency of critical data breaches are increasing and as a result the management administration and the IT departments focus on safeguarding their data systems more than ever before. Our experienced and expertise data security, privacy and information governance experts in UAE helps you to reduce the risks associated with various privacy compliance frameworks along with recognizing the value of your personal data.
Ringkasan Standar Kompetensi Data Protection Officer | Agustus 2023 | IODTIEryk Budi Pratama
UU No 27 Tahun 2022 tentang Pelindungan Data Pribadi (“UU PDP”) telah disahkan pada bulan Oktober 2022 dan saat ini telah memasuki masa tenggang. Ketiadaan peraturan teknis / turunan membuat banyak organisasi masih ragu dalam menetapkan arah dan mengimplementasikan UU PDP sesuai dengan peraturan perundang-undangan yang berlaku. Salah satu aspek penting dalam UU PDP adalah terkait penunjukan Pejabat/Petugas yang melaksanakan fungsi Pelindungan Data Pribadi (PPDP) atau Data Protection Officer (DPO) seperti yang diamanatkan oleh UU PDP Pasal 53 dan 54.
Melalui Keputusan Menteri Ketenagakerjaan Republik Indonesia Nomor 103 Tahun 2023 tentang Penetapan Standar Kompetensi Kerja Nasional Indonesia Kategori Informasi dan Komunikasi Golongan Pokok Aktivitas Pemrograman, Konsultasi Komputer dan Kegiatan yang Berhubungan dengan Itu (YBDI) Bidang Keahlian Pelindungan Data Pribadi yang ditetapkan pada tanggal 23 Juni 2023, maka standar kompetensi PPDP/DPO telah sah untuk dapat dijadikan rujukan dalam menentukan kompetensi SDM, kebutuhan rekrutmen, pelatihan, dan sertifikasi terkait dengan Pelindungan Data Pribadi.
Ringkasan Standar Kompentensi / SKKNI Pelindungan Data Pribadi ini disusun untuk memudahkan masyarakat dalam memahami secara ringkas 4 Fungsi Kunci, 8 Fungsi Utama, dan 19 Fungsi Dasar yang telah disusun oleh Tim Perumus dan Kementerian Komunikasi dan Informatika Republik Indonesia, serta disahkan oleh Menteri Ketenagakerjaan Republik Indonesia. Semoga ringkasan SKKNI PDP ini dapat bermanfaat dan memberikan panduan secara ringkas tidak hanya perihal kompetensi PPDP/DPO, namun juga hal-hal yang dapat dilakukan oleh organisasi dalam menerapkan Program Pelindungan Data Pribadi.
Salam,
Eryk Budi Pratama, CIPM, CIPP/E, FIP
Chairman - Institute of Digital Trust Indonesia (IODTI)
Tim Perumus SKKNI Pelindungan Data Pribadi
Tim Perumus Rancangan Peraturan Pemerintah Pelindungan Data Pribadi (“RPP PDP”)
eryk@digitaltrustid.org
Implikasi UU PDP terhadap Tata Kelola Data Sektor Kesehatan - Rangkuman UU Pe...Eryk Budi Pratama
Sosialisasi UU Pelindungan Data Pribadi untuk sektor kesehatan.
Webinar Serial TIK I-2022
Diselenggarakan oleh:
*INDOHCF - KREKI - IODTI - FORKOMTIKNAS - Z-COURSE*
TOPIK:
*Implikasi UU PDP (Perlindungan Data Pribadi) Terhadap Tata Kelola Data di Sektor Kesehatan*
Rancangan Undang - Undang (RUU) Perlindungan Data Pribadi (PDP) telah resmi disahkan menjadi Undang-Undang (UU) dalam Rapat Paripurna DPR RI pada tanggal 20 Sept 2022. Sambil menunggu peraturan pelaksanaannya, maka perlu lebih mencermati isi regulasi tsb dan mendiskusikan bagaimana implikasinya bagi sektor kesehatan baik Faskes, BPJS, Masyarakat dan stakeholder kesehatan lainnya
Modern IT Service Management Transformation - ITIL IndonesiaEryk Budi Pratama
Presented at Online ITIL Indonesia Webinar #5.
Content:
> Setting up the context
> Understanding holistic IT Management point of view
> IT Service Management Transformation
> Key Performance Indicator (KPI)
> IT Service Catalogue
> IT Sourcing
> Agile Incident Management
Data Protection Indonesia: Basic Regulation and Technical Aspects_ErykEryk Budi Pratama
Presented at Orang Siber Indonesia webinar.
11 July 2020
Topic: Data Protection: Basic Regulation and Technical Aspects
This presentation covers:
> Indonesia Data Protection Bill
> Data Masking
> Identity & Access Management
> Data Loss Prevention
Join us (for Indonesian):
t.me/orangsiber
t.me/dataprotectionid
Data Loss Prevention (DLP) - Fundamental Concept - ErykEryk Budi Pratama
Presented at APTIKNAS (Indonesia ICT Business Association) DKI Jakarta regular webinar.
Title:Data Loss Prevention: Fundamental Concept in Enabling DLP System
2 July 2020
Presented at National Webinar of ISACA Student Group, Universitas Kristen Satya Wacana, indonesia.
Title: Cyber Resilience: Post COVID-19 - Welcoming New Normal
2 July 2020
Guardians of Trust: Building Trust in Data & AnalyticsEryk Budi Pratama
Presented at Absolut Data Event, 17 Dec 2019, at GoWork Kuningan.
Event URL: https://www.eventbrite.com/e/panel-discussion-what-will-you-prepare-with-data-in-2020-tickets-84851546259
My presentation summarized the two of KPMG publication related to Trust in Data & Analytics. The focus of this event was panel discussion.
Ref 1 : https://assets.kpmg/content/dam/kpmg/xx/pdf/2016/10/building-trust-in-analytics.pdf
Ref 2: https://assets.kpmg/content/dam/kpmg/xx/pdf/2018/02/guardians-of-trust.pdf
Presented at ISACA Indonesia Monthly Technical Meeting, 11 Dec 2019 at Telkom Landmark.
Key takeaways from my presentation:
1. Cloud customers have to understand the share responsibilities between customer and cloud provider
2. Different cloud service model (IaaS, PaaS, SaaS) has different audit methodology
3. Customer’s IT Auditor have to be trained to have the skills needed to audit the cloud service
4. Understanding IAM in Cloud is very important. Each Cloud Service Provider has different IAM mechanism
5. Understanding different type of audit logs in cloud platform is important for IT Auditor
Emerging Technology Risk Series - Internet of Things (IoT)Eryk Budi Pratama
Presented at Indonesia Honeynet Project (IHP) meetup. This presentation covering:
1. Overview of Industry 4.0
2. IoT Security Model
3. How to Secure IoT
4. Research in IoT
Other emerging technology risk area that will be covered in my professional services:
1. Cloud
2. Mobile
3. Artificial Intelligence / Intelligent Automation
4. Data & Analytics
Protecting Agile Transformation through Secure DevOps (DevSecOps)Eryk Budi Pratama
Respresenting Cyber Defense Community (cdef.id) to present and share my view on Secure DevOps / DevSecOps. Through this presentation, I shared several insights about:
1. How to balance the risk and controls in the "great shift left" paradigm (agile)
2. DevOps activities
3. How to seamlessly integrate security into DevOps
4. How to "shift left" the security"
5. Get started with Secure DevOps / DevSecOps
6. Case Study about DevSecOps implementation
For further discussion, especially how to secure digital and agile transformation in your organization, don't hesitate to contact me :)
IT Governance - Capability Assessment using COBIT 5Eryk Budi Pratama
(re-upload)
Capability assessment of IT Governance using COBIT 5 Process Assessment Model (PAM). Presented for Information System Department, Universitas Bakrie - Indonesia
Adjusting OpenMP PageRank : SHORT REPORT / NOTESSubhajit Sahu
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Enabling Data Governance - Data Trust, Data Ethics, Data Quality
1. 11
ENABLING DATA GOVERNANCE
Eryk B. Pratama
IT Advisory & Cyber Security Consultant at Global Consulting Firm
20 June 2020 | 19:00
PHPID-OL#35
The Journey through Data Trust, Ethics, and Quality
2. About Me
q Global IT Advisory & Cyber Security Professional
q Community Enthusiast
q Blogger / Writer
q Knowledge Hunter
q https://medium.com/@proferyk
q https://www.slideshare.net/proferyk
https://www.linkedin.com/in/erykbudipratama/
You can subscribe to my telegram channel.
§ IT Advisory & Risk (t.me/itadvindonesia)
§ Data Privacy & Protection (t.me/dataprivid)
§ Komunitas Data Privacy & Protection (t.me/dataprotectionid)
5. “ Data is the new oil “
“ Data is the new gold “
6. Data, Information, Knowledge and Wisdom (DIKW) Hierarchy
Introduction
Wisdom
Knowledge
Information
Data
DIKW Hierarchy
Data is the foundation of the pyramid, information is the next layer,
then knowledge, and, finally, wisdom is the apex. DIKW is a model or
construct that has been used widely within Information Science and
Knowledge Management. Some theoreticians in library and
information science have used DIKW to offer an account of logico-
conceptual constructions of interest to them, particularly concepts
relating to knowledge and epistemology.
Source: COBIT 5
7. Data/Information Lifecycle
Introduction
Source: ISACA – Getting Started with Data Governance with COBIT 5
It is important to plan the life cycle of data along with their placement within the governance structure. As practices
operate, the data supporting or underlying them reach the various levels of their natural life cycles. Data is planned,
designed, acquired, used, monitored and disposed of.
Critical information security control
Store | Data at Rest Share | Data in Motion Use | Data in Use
8. Data
Security
Data
Quality
Data
Governance
Data Governance DNA
Introduction
ProcessPeopleTechnology
•Data Policies
•Data Standards
•Business Data Ownership
•Data Workflow
•Data Quality Rules & Policies
•Data Cleansing Standards
•Compliance Rules
•Compliance & Security Policies
•Local, National & International
Laws
•Data Stewards
•Business Data Owners
•Data Mgmt Committee
•Data Administration
•Data Quality Services Team
•Data Governance
•DBAs
•Corporate Security
•Auditors
•Compliance Dept.
•Data Rules Library
•Automated Notifications
(Workflow)
•Data Profiling, Quality &
Monitoring Tools
•ETL Tools
•Audit Reports
•Security Software
•Access Rights Management
•Data Audit Trails
9. Data Governance Organization (Simple)
Introduction
Sponsor
Data Steward
Data OwnerData OwnerData Analyst
Data
Consumer
Data
Consumer
Data
Consumer
Data
Consumer
Technical
Support
Technical
Support
10. Data Governance Organization (Recommended)
Introduction
Corporate Governance
Committee
Data Management Lead
Database
Administrators
Data ArchitectsData Stewards
Business SMEs
Provide corporate governance to strategic data management decisions
such as new subject areas, new data sources, new business problems
solved with the data management, and new expenditures for processors
and storage
• Business Data Owner
• Understand data
• Define rules
• Identify errors
• Set thresholds for acceptable
levels of data quality
Oversee the organizations’ data stewardship, data administration and data
quality programs
Change
Management
• Resolve data integration issues
• Determine data security
• Document data definitions, calculations
and summarizations
• Maintain/update business rules
• Analyze and improve data quality
• Define mandatory data elements that
need to be measured
• Define and review metrics for measuring
data quality
• Translate Business Rules into
Data Models
• Maintain Conceptual, Logical
and Physical Data Models
• Assist in Data Integration
Resolution
• Maintain Metadata Repository
• Generate Physical Database
Schema
• Performance Database Tuning
• Create Database Backups
• Plan for Database Capacity
• Implement Data Security
Requirements
• Provide education on the
importance of data quality to the
company
• Communicate data quality
improvements to all employees
13. … trigger the rise of Four Anchors to make analytics more trusted
Data Trust
Does it perform as intended?Are the inputs and the
development process high
quality?
Is its use considered
acceptable?
Is its long term operation
optimised?
Percentage of respondents who reported being very confident in their D&A insights
14. Four Dimension of Data Trust
Data Trust
Quality
Effectiveness
Organizations need to ensure that the input and output can be in accordance with the context in the information /
insight will be used.
Effectiveness in this case is the extent to the output can achieve the expected results and provide value to the decision
makers who use the information.
Integrity
In this context, integrity refers to the use of data that is ethical and acceptable to related parties and complies with
existing regulations (for example data privacy).
In this context, resilience means how to ensure that the data source and output can be optimized for the long term.
Resilience
Does it perform as intended?Are the inputs and the development
process high quality?
Is its use considered acceptable? Is its long term operation
optimised?
15. Data sourcing is the key trust in stage of the analytics lifecycle
Data Trust
18. Ethics in Data Processing
Data Ethics
In the context of personal data, data represent the characteristics of individuals that can later be used
to determine decisions that can affect the life of the individual. For example health data / medical
records. What is the impact if a medical record is leaked? Unauthorized and irresponsible people can
exploit it for financial needs, for example by selling medical records to companies that need the data.
Impact on
People
Abuse
Potential
The
economic
value of data
Misuse of data can have a negative impact on individuals. For example when we register a credit card at
the mall. Mostly, there will be offers from either other credit card providers or other advertisements
that we would ask from where or whom this sales person obtain our number. Another example is the
leak of permanent voter list (which the KPU said that those data indeed opened for public). What can
you do with that data? We can sell those data to certain parties. For criminals, this information can be
used for fraud activities.
Proper data processing will provide economic value. The ethics of the data owner can determine how
this value is obtained and who may take economic value from the data.
19. Ethical Decision Point
Data Ethics
Source: https://www.accenture.com/us-en/blogs/blogs-new-data-ethics-guidelines-organizations-digital-trust
20. Implementation of Data Ethics
Data Ethics
Vision
Vision really determines the direction / goals of the organization. In this context, the organization
needs to determine what ethical data usage is in the organization. The vision can be adopted from
data ethics principles chosen by Management.
Strategy
Strategies are arranged to achieve the vision. In this case, organizations need to develop strategies
so that data ethics can be applied and carried out consistently as part of the organization's culture.
Governance
To "force" stakeholders to carry out data ethics practices, organizations need to develop effective
policies and procedures and ensure that each related party has clearly defined responsibilities.
Infrastructure & Architecture
Managing complex data (especially for large organizations) will certainly be easier and integrated if
the organization has visibility of all data and is outlined in architecture (for example Enterprise
Architecture) and supported by systems and infrastructure that are qualified and reliable.
Data Insight
The use of insight to support clear and accurate data results is certainly very necessary. Use of tools
(such as dashboards) can help organizations monitor and provide early warnings of potential ethical
data violations.
Training & Development
People are the main factor in the context of data ethics. Organizations need to conduct training
related to ethics in the use (and misuse) of data. Of course this can be done when the organization
conducts socialization or training related to Data Privacy and Personal Data Protection, because data
ethics is attached to both
https://medium.com/@proferyk
Source: https://home.kpmg/pl/en/home/insights/2018/01/report-building-trust-in-analytics.html
21. RUU Perlindungan Data Pribadi
Data Ethics
Key Highlight
§ Explicit Consent is required from the data owner for
personal data processing.
§ Responding timelines for Data subject rights have been
separately called out in the RUU PDP.
§ Data controller to notify the data owner and the Minister
within 3 days of data breach.
§ Penalties for non-compliance may range from Rp 20 Billion
to Rp 70 Billion or Imprisonment ranging from 2 to 7 years
Data Owner Data Controller Data Processor Data Protection Officer
22. Data Privacy Framework
Data Ethics
NIST Privacy KPMG PrivacyISO/IEC 27701
§ Information Lifecycle
Management
§ Governance and Operating
Model
§ Inventory/Data Mapping
§ Regulatory Management
§ Risk and Control
§ Policies
§ Processes, Procedures and
Technology
§ Security for Privacy
§ Third Party Oversight
§ Training and Awareness
§ Monitoring
§ Incident Management
§ Inventory and Mapping
§ Data Processing Ecosystem Risk
Management
§ Governance Policies, Processes,
and Procedures
§ Awareness and Training
§ Monitoring and Review
§ Data Processing Management
§ Communication
§ Data Security
§ Protective Technology
§ Detection Processes
§ Respond Processes
§ Recovery Processes
§ Conditions for collection and
processing
§ Obligations to PII principals
§ Privacy by design and privacy by
default
§ PII sharing, transfer, and
disclosure
§ PIMS-specific requirements
related to ISO/IEC 27001
§ PIMS-specific requirements
related to ISO/IEC 27002
§ Additional ISO/IEC 27002
guidance for PII controllers
§ Additional ISO/IEC 27002
guidance for PII processors
Data Privacy Framework
Further Discussion
§ Data Privacy & Protection News (t.me/dataprivid)
§ Komunitas Data Privacy & Protection (t.me/dataprotectionid)
24. Common Questions
Data Quality
qHow can poor quality data impact our decisions?
qHow can we decrease costs connected with poor
quality data?
qHow can we improve the success of data related
projects?
qWhich approach should we choose to
continuously improve data?
qHow can we set up efficient and sustainable data
governance?
Source: ISACA – Getting Started with Data Governance with COBIT 5
Does our data fit the purpose we use it for?
25. Basic Data Quality Criteria
Data Quality
Accuracy Completeness
Consistency Timeliness
Accuracy Completeness
Consistency
26. Purpose of Data Quality Management
Data Quality
Develop an approach that managed appropriately to make data "fit for
purpose" based on the needs of customer data.
Define standards, needs, and specifications for quality control
purposes as part of the data lifecycle.
Define and implement processes to measure, monitor, and report
levels of data quality.
Identify opportunities to improve data quality through improved
processes and systems.
27. Data Quality Assessment
Data Quality
Some recommended steps are as follows.
1. Define the purpose of the assessment.
2. Identifying data to be assessed; focus on small data first or on specific problems.
3. Identify data usage and who will use the data.
4. Identify risks from data to be assessed, including their impact on business processes.
5. Check data in accordance with predetermined rules.
6. Document the issues found.
7. Conduct further analysis to quantify findings, prioritize issues based on business impact, and
develop hypotheses for root causes of the issues found.
8. Meet with Data Stewards / Owners, Subject Matter Experts, and data users to confirm issues and
priorities for improvement.
9. Use the assessment findings to improvement of data quality management processes.
30. Data Quality Check (example)
Data Quality
To properly analyze certain data set, a look how it is stored and what are the examples of the records inside it
should be always taken. It helps to understand the provided unprocessed information and gives an idea what
are the characteristics of an ideal record plus it’s meaning.
Basic Checks
fill percentage
number of
duplicates
distinct and
unique values
31. Basic Data Quality Checks
Data Quality
As we can see on the above graph values of the data can be divided into several categories:
§ Empty records or the one with "Null“ value are grouped into “Null“ category
§ The other ones (”Not Null”) can be divided further into duplicates and distinct content.
If the percentage of the missing data is too big, we needs to take into account replacing them by specific
numbers or exclude the whole column/attribute from the set.
Duplicate repeated more than once in the given data.
Distinct non-null values that are different from each other.
Unique values that have no duplicates.
Non-
Unique
number of values that have at
least one duplicate in the list.
ILLUSTRATIVES
32. Case Example
Data Quality
Having given below data, a fast quality check can be done. Let’s assume that it is an analysis of phone numbers of 20 clients.
Having an output for “Phone” column:
§ We can see that out of 20 samples all of the records are non-empty and 1 of
them is a duplicate.
§ Taking into account that phone numbers have unique values that probably
means that two of our clients share the same number or the same client is
in the system twice (e.g. with a typo in his name).
§ The minimum value consists only zeros, therefore here we can also
distinguish wrongly typed phone number.
33. Case Example
Data Quality
The minimum value consists only zeros, therefore here we can also distinguish wrongly typed phone number.
34. Case Example
Data Quality
From the below shown mask analysis it might be observed that there are two records which are not in the correct format (9
digit number). First one has only 3 digits and the second one has a letter on the 6th place.
The quantile analysis in this case doesn’t bring much useful information, since the phone numbers are not scalable.