Are you tired of struggling with data inconsistencies and standards in your organization? Do you want to streamline your processes and save time and resources? Look no further.
Our Data Consistency and Data Standards Knowledge Base is the solution you′ve been searching for.
This comprehensive dataset consists of 1512 prioritized requirements, solutions, benefits, results, and real-life case studies focused on data consistency and standards.
It is designed to provide professionals like you with all the necessary tools and information to effectively manage your data and achieve optimal results.
Compared to its competitors and alternatives, our Data Consistency and Data Standards Knowledge Base is unparalleled in its depth and scope.
It covers urgent and important questions that need to be asked, ensuring that your data-related issues are addressed promptly and efficiently.
Whether you are a data expert or a novice, this knowledge base is suitable for all levels of expertise.
Our product is not only affordable but also easy to use.
With our DIY approach, you have the freedom to access the information you need at any time.
No more waiting for expensive consultants or relying on outdated information.
Our detailed and comprehensive specifications make it user-friendly and accessible to anyone in your organization.
Our Data Consistency and Data Standards Knowledge Base offers numerous benefits.
It provides you with proven strategies and solutions to maintain consistent and standardized data, leading to improved accuracy and reliability.
By investing in our product, you are investing in the success and growth of your organization.
Extensive research has gone into developing this Knowledge Base, making it a valuable resource for any business looking to improve their data management processes.
It is specifically tailored for businesses of all sizes and industries, ensuring that you have all the necessary information to tackle your data challenges.
Some products may claim to offer similar solutions, but none match the cost-effectiveness and effectiveness of our Data Consistency and Data Standards Knowledge Base.
By investing in our product, you will see a significant return on investment through increased efficiency and productivity.
We understand that no product is perfect, which is why we provide you with a comprehensive overview of both the pros and cons of our Data Consistency and Data Standards Knowledge Base.
This way, you can make an informed decision and have a realistic expectation of what our product can do for your organization.
In a nutshell, our Data Consistency and Data Standards Knowledge Base is a game-changer for any organization looking to improve their data management processes.
It offers a professional, affordable, and easy-to-use alternative to expensive consultants and outdated information.
Say goodbye to data inconsistencies and embrace the power of our Knowledge Base today.
Your organization′s success and growth depend on it.
Don′t hesitate, get yours now!
Is it comparable and compatible with other data, does it have useful groupings and classifications?
Data Consistency
Data consistency refers to the reliability and coherence of data, ensuring that it is standardized and can be easily compared and combined with other relevant data.
1. Use established data standards to ensure consistency across different databases.
- This allows for easy comparison and compatibility between different datasets.
2. Implement automated data quality checks to identify and correct inconsistent data.
- This helps maintain the accuracy and reliability of the data over time.
3. Create clear guidelines and procedures for data entry, storage, and management.
- This ensures that data is consistently handled and organized in a standardized manner.
4. Employ data validation techniques to ensure that data conforms to specific rules and formats.
- This helps prevent incorrect or incomplete data from being entered into the system.
5. Regularly review and update data standards to reflect current practices and technologies.
- This ensures that the data standards remain relevant and effective in maintaining consistency.
6. Provide training and support to data users on how to properly adhere to data standards.
- This promotes a culture of data consistency and helps prevent data discrepancies.
7. Utilize master data management (MDM) tools to establish a single source of truth for data.
- This centralizes data management and reduces the chances of inconsistencies across multiple systems.
8. Perform regular data audits to identify and resolve any data inconsistencies.
- This helps maintain data integrity and ensures that data standards are being followed.
9. Collaborate with other organizations to adopt and adhere to common data standards.
- This allows for seamless data exchange and promotes consistency across different systems.
10. Utilize data governance structures to monitor and enforce compliance with data standards.
- This helps maintain data consistency by ensuring that all data-related activities adhere to the established standards.
CONTROL QUESTION: Is it comparable and compatible with other data, does it have useful groupings and classifications?
Big Hairy Audacious Goal (BHAG) for 10 years from now:
By 2030, our goal for data consistency is to achieve a universally accepted standard for data compatibility and comparability across all industries and sectors. This standard will ensure that data can seamlessly flow between different platforms and systems, regardless of the source or destination.
Furthermore, our goal is to establish a comprehensive system for grouping and classifying data, allowing for efficient and accurate analysis and decision making. This system will be continuously updated and improved upon to accommodate the ever-changing landscape of data.
Through this achievement, we envision a world where data is consistently reliable and accurate, enabling businesses, governments, and individuals to make informed and impactful decisions. This will lead to increased efficiency, innovation, and overall progress in society.
To accomplish this goal, we will collaborate with organizations and experts in various industries, as well as continuously invest in research and development. We recognize that this is a bold and ambitious goal, but we believe it is essential for the future of data and ultimately, for the betterment of society.
"I`m blown away by the value this dataset provides. The prioritized recommendations are incredibly useful, and the download process was seamless. A must-have for data enthusiasts!"
Introduction:
Data consistency is an essential aspect of any data management strategy. It refers to the quality and accuracy of data across different systems and applications. The need for consistent data has become increasingly important with the rise of big data and the need for businesses to make informed decisions based on accurate and reliable information. This case study will examine the data consistency practices of a global technology company and evaluate whether their data is comparable and compatible with other data and if it has useful groupings and classifications. The study will also highlight the consulting methodology used to assess and improve data consistency, the deliverables achieved, implementation challenges faced, key performance indicators (KPIs) used, and other management considerations.
Client Situation:
The client in this case study is a large multinational technology company that produces a wide range of hardware and software products. The company has a global presence and operates in multiple industries, including consumer electronics, enterprise solutions, and cloud services. They have an extensive customer base and handle vast amounts of data from various sources, including sales, marketing, customer support, and product development. With such a diverse data landscape, data consistency had become a significant challenge for the company.
Consulting Methodology:
To address the data consistency issues faced by the client, a consulting firm was engaged to provide a thorough assessment of their data management processes. The consulting methodology used consisted of four phases - Discovery, Analysis, Implementation, and Continuous Improvement.
Discovery Phase:
The initial stage involved gathering information about the client′s current data management practices. This included identifying data sources, data flows, and data management tools used. The consulting team also conducted interviews with key stakeholders to understand their data requirements and pain points. During this phase, it was discovered that the lack of consistent data was causing discrepancies in reporting and leading to incorrect decision-making.
Analysis Phase:
In the second phase, the consulting team conducted a comprehensive analysis of the client′s data. This involved a detailed data audit to identify areas of inconsistency and poor data quality. The team also evaluated data groupings and classifications to understand their usefulness and relevance. It was noted that there were inconsistencies in naming conventions, data formats, and data definitions across different systems and departments.
Implementation Phase:
Based on the findings from the analysis phase, the consulting team developed a data consistency framework for the client. This included standardizing data formats and definitions, establishing naming conventions, and implementing data validation processes. The team also recommended the use of a master data management system to centrally manage all data. Implementation of this solution required close collaboration with the client′s IT team to ensure seamless integration with existing systems and compliance with data privacy regulations.
Continuous Improvement Phase:
The final phase of the consulting methodology involved monitoring and continuously improving data consistency within the organization. This was achieved through regular data audits and reviews to identify any new challenges and make necessary adjustments to the established framework. The consulting team also worked closely with the client to develop training programs and documentation to ensure staff were aware of the new data consistency processes and procedures.
Deliverables Achieved:
As a result of the consulting engagement, several key deliverables were achieved:
1. Data consistency framework: A comprehensive framework was developed to ensure consistent data management practices across the organization.
2. Standardized data formats and definitions: All data across systems and departments were standardized to ensure compatibility and comparability.
3. Naming conventions: A set of naming conventions were established to eliminate confusion and ensure consistency in data identification.
4. Data validation processes: Automated data validation processes were implemented to detect and correct any data inconsistencies.
5. Master data management system: The client implemented a master data management system to centrally manage and govern all data, leading to improved governance and control.
Implementation Challenges:
The implementation phase of the consulting engagement faced several challenges, including resistance to change and technical barriers. Resistance to change was mainly due to the organization′s size and complexity, and getting buy-in from all departments proved to be a time-consuming and challenging process. Technical challenges were also encountered during the integration of the master data management system with existing systems, requiring close collaboration between the consulting team and the client′s IT department.
KPIs:
To assess the success of the engagement, several KPIs were established, including:
1. Data accuracy: The percentage of data that was consistent and accurate across systems and departments.
2. Data discrepancies: The number of discrepancies identified during regular data audits.
3. Time to implement data changes: The average time taken to implement data changes across systems following new naming conventions and data formats.
Management Considerations:
Data consistency is an ongoing process and requires continuous attention and effort. To maintain the achieved level of data consistency, the client had to establish a dedicated data governance team responsible for monitoring data quality and making necessary improvements. The team consisted of members from different departments to ensure collaboration and representation from all areas of the organization.
Conclusion:
In conclusion, this case study has shown how implementing a robust data consistency framework can help improve the quality and accuracy of data within an organization. By following a structured consulting methodology, the client was able to identify and address data inconsistencies, resulting in improved decision-making and operational efficiency. The establishment of KPIs and a dedicated data governance team ensured that data consistency remained a top priority for the organization. With the continued growth of big data, the importance of data consistency will only increase, making it a critical aspect of any successful data management strategy.
References:
1. The Importance of Data Consistency for Effective Data Management - IBM Whitepaper
2. Data Consistency: A Critical Element of Data Management - Gartner Market Research Report
3. Managing Data Inconsistencies in Multi-Source Data Integration - Business Intelligence Journal.
Our Data Consistency and Data Standards Knowledge Base is the solution you′ve been searching for.
This comprehensive dataset consists of 1512 prioritized requirements, solutions, benefits, results, and real-life case studies focused on data consistency and standards.
It is designed to provide professionals like you with all the necessary tools and information to effectively manage your data and achieve optimal results.
Compared to its competitors and alternatives, our Data Consistency and Data Standards Knowledge Base is unparalleled in its depth and scope.
It covers urgent and important questions that need to be asked, ensuring that your data-related issues are addressed promptly and efficiently.
Whether you are a data expert or a novice, this knowledge base is suitable for all levels of expertise.
Our product is not only affordable but also easy to use.
With our DIY approach, you have the freedom to access the information you need at any time.
No more waiting for expensive consultants or relying on outdated information.
Our detailed and comprehensive specifications make it user-friendly and accessible to anyone in your organization.
Our Data Consistency and Data Standards Knowledge Base offers numerous benefits.
It provides you with proven strategies and solutions to maintain consistent and standardized data, leading to improved accuracy and reliability.
By investing in our product, you are investing in the success and growth of your organization.
Extensive research has gone into developing this Knowledge Base, making it a valuable resource for any business looking to improve their data management processes.
It is specifically tailored for businesses of all sizes and industries, ensuring that you have all the necessary information to tackle your data challenges.
Some products may claim to offer similar solutions, but none match the cost-effectiveness and effectiveness of our Data Consistency and Data Standards Knowledge Base.
By investing in our product, you will see a significant return on investment through increased efficiency and productivity.
We understand that no product is perfect, which is why we provide you with a comprehensive overview of both the pros and cons of our Data Consistency and Data Standards Knowledge Base.
This way, you can make an informed decision and have a realistic expectation of what our product can do for your organization.
In a nutshell, our Data Consistency and Data Standards Knowledge Base is a game-changer for any organization looking to improve their data management processes.
It offers a professional, affordable, and easy-to-use alternative to expensive consultants and outdated information.
Say goodbye to data inconsistencies and embrace the power of our Knowledge Base today.
Your organization′s success and growth depend on it.
Don′t hesitate, get yours now!
Discover Insights, Make Informed Decisions, and Stay Ahead of the Curve:
Key Features:
Comprehensive set of 1512 prioritized Data Consistency requirements. - Extensive coverage of 170 Data Consistency topic scopes.
- In-depth analysis of 170 Data Consistency step-by-step solutions, benefits, BHAGs.
- Detailed examination of 170 Data Consistency case studies and use cases.
- Digital download upon purchase.
- Enjoy lifetime document updates included with your purchase.
- Benefit from a fully editable and customizable Excel format.
- Trusted and utilized by over 10,000 organizations.
- Covering: Data Retention, Data Management Certification, Standardization Implementation, Data Reconciliation, Data Transparency, Data Mapping, Business Process Redesign, Data Compliance Standards, Data Breach Response, Technical Standards, Spend Analysis, Data Validation, User Data Standards, Consistency Checks, Data Visualization, Data Clustering, Data Audit, Data Strategy, Data Governance Framework, Data Ownership Agreements, Development Roadmap, Application Development, Operational Change, Custom Dashboards, Data Cleansing Processes, Blockchain Technology, Data Regulation, Contract Approval, Data Integrity, Enterprise Data Management, Data Transmission, XBRL Standards, Data Classification, Data Breach Prevention, Data Governance Training, Data Classification Schemes, Data Stewardship, Data Standardization Framework, Data Quality Framework, Data Governance Industry Standards, Continuous Improvement Culture, Customer Service Standards, Data Standards Training, Vendor Relationship Management, Resource Bottlenecks, Manipulation Of Information, Data Profiling, API Standards, Data Sharing, Data Dissemination, Standardization Process, Regulatory Compliance, Data Decay, Research Activities, Data Storage, Data Warehousing, Open Data Standards, Data Normalization, Data Ownership, Specific Aims, Data Standard Adoption, Metadata Standards, Board Diversity Standards, Roadmap Execution, Data Ethics, AI Standards, Data Harmonization, Data Standardization, Service Standardization, EHR Interoperability, Material Sorting, Data Governance Committees, Data Collection, Data Sharing Agreements, Continuous Improvement, Data Management Policies, Data Visualization Techniques, Linked Data, Data Archiving, Data Standards, Technology Strategies, Time Delays, Data Standardization Tools, Data Usage Policies, Data Consistency, Data Privacy Regulations, Asset Management Industry, Data Management System, Website Governance, Customer Data Management, Backup Standards, Interoperability Standards, Metadata Integration, Data Sovereignty, Data Governance Awareness, Industry Standards, Data Verification, Inorganic Growth, Data Protection Laws, Data Governance Responsibility, Data Migration, Data Ownership Rights, Data Reporting Standards, Geospatial Analysis, Data Governance, Data Exchange, Evolving Standards, Version Control, Data Interoperability, Legal Standards, Data Access Control, Data Loss Prevention, Data Standards Benchmarks, Data Cleanup, Data Retention Standards, Collaborative Monitoring, Data Governance Principles, Data Privacy Policies, Master Data Management, Data Quality, Resource Deployment, Data Governance Education, Management Systems, Data Privacy, Quality Assurance Standards, Maintenance Budget, Data Architecture, Operational Technology Security, Low Hierarchy, Data Security, Change Enablement, Data Accessibility, Web Standards, Data Standardisation, Data Curation, Master Data Maintenance, Data Dictionary, Data Modeling, Data Discovery, Process Standardization Plan, Metadata Management, Data Governance Processes, Data Legislation, Real Time Systems, IT Rationalization, Procurement Standards, Data Sharing Protocols, Data Integration, Digital Rights Management, Data Management Best Practices, Data Transmission Protocols, Data Quality Profiling, Data Protection Standards, Performance Incentives, Data Interchange, Software Integration, Data Management, Data Center Security, Cloud Storage Standards, Semantic Interoperability, Service Delivery, Data Standard Implementation, Digital Preservation Standards, Data Lifecycle Management, Data Security Measures, Data Formats, Release Standards, Data Compliance, Intellectual Property Rights, Asset Hierarchy
Data Consistency Assessment Dataset - Utilization, Solutions, Advantages, BHAG (Big Hairy Audacious Goal):
Data Consistency
Data consistency refers to the reliability and coherence of data, ensuring that it is standardized and can be easily compared and combined with other relevant data.
1. Use established data standards to ensure consistency across different databases.
- This allows for easy comparison and compatibility between different datasets.
2. Implement automated data quality checks to identify and correct inconsistent data.
- This helps maintain the accuracy and reliability of the data over time.
3. Create clear guidelines and procedures for data entry, storage, and management.
- This ensures that data is consistently handled and organized in a standardized manner.
4. Employ data validation techniques to ensure that data conforms to specific rules and formats.
- This helps prevent incorrect or incomplete data from being entered into the system.
5. Regularly review and update data standards to reflect current practices and technologies.
- This ensures that the data standards remain relevant and effective in maintaining consistency.
6. Provide training and support to data users on how to properly adhere to data standards.
- This promotes a culture of data consistency and helps prevent data discrepancies.
7. Utilize master data management (MDM) tools to establish a single source of truth for data.
- This centralizes data management and reduces the chances of inconsistencies across multiple systems.
8. Perform regular data audits to identify and resolve any data inconsistencies.
- This helps maintain data integrity and ensures that data standards are being followed.
9. Collaborate with other organizations to adopt and adhere to common data standards.
- This allows for seamless data exchange and promotes consistency across different systems.
10. Utilize data governance structures to monitor and enforce compliance with data standards.
- This helps maintain data consistency by ensuring that all data-related activities adhere to the established standards.
CONTROL QUESTION: Is it comparable and compatible with other data, does it have useful groupings and classifications?
Big Hairy Audacious Goal (BHAG) for 10 years from now:
By 2030, our goal for data consistency is to achieve a universally accepted standard for data compatibility and comparability across all industries and sectors. This standard will ensure that data can seamlessly flow between different platforms and systems, regardless of the source or destination.
Furthermore, our goal is to establish a comprehensive system for grouping and classifying data, allowing for efficient and accurate analysis and decision making. This system will be continuously updated and improved upon to accommodate the ever-changing landscape of data.
Through this achievement, we envision a world where data is consistently reliable and accurate, enabling businesses, governments, and individuals to make informed and impactful decisions. This will lead to increased efficiency, innovation, and overall progress in society.
To accomplish this goal, we will collaborate with organizations and experts in various industries, as well as continuously invest in research and development. We recognize that this is a bold and ambitious goal, but we believe it is essential for the future of data and ultimately, for the betterment of society.
Customer Testimonials:
"I`m blown away by the value this dataset provides. The prioritized recommendations are incredibly useful, and the download process was seamless. A must-have for data enthusiasts!"
"The prioritized recommendations in this dataset have exceeded my expectations. It`s evident that the creators understand the needs of their users. I`ve already seen a positive impact on my results!"
"I`ve been searching for a dataset that provides reliable prioritized recommendations, and I finally found it. The accuracy and depth of insights have exceeded my expectations. A must-have for professionals!"
Data Consistency Case Study/Use Case example - How to use:
Introduction:
Data consistency is an essential aspect of any data management strategy. It refers to the quality and accuracy of data across different systems and applications. The need for consistent data has become increasingly important with the rise of big data and the need for businesses to make informed decisions based on accurate and reliable information. This case study will examine the data consistency practices of a global technology company and evaluate whether their data is comparable and compatible with other data and if it has useful groupings and classifications. The study will also highlight the consulting methodology used to assess and improve data consistency, the deliverables achieved, implementation challenges faced, key performance indicators (KPIs) used, and other management considerations.
Client Situation:
The client in this case study is a large multinational technology company that produces a wide range of hardware and software products. The company has a global presence and operates in multiple industries, including consumer electronics, enterprise solutions, and cloud services. They have an extensive customer base and handle vast amounts of data from various sources, including sales, marketing, customer support, and product development. With such a diverse data landscape, data consistency had become a significant challenge for the company.
Consulting Methodology:
To address the data consistency issues faced by the client, a consulting firm was engaged to provide a thorough assessment of their data management processes. The consulting methodology used consisted of four phases - Discovery, Analysis, Implementation, and Continuous Improvement.
Discovery Phase:
The initial stage involved gathering information about the client′s current data management practices. This included identifying data sources, data flows, and data management tools used. The consulting team also conducted interviews with key stakeholders to understand their data requirements and pain points. During this phase, it was discovered that the lack of consistent data was causing discrepancies in reporting and leading to incorrect decision-making.
Analysis Phase:
In the second phase, the consulting team conducted a comprehensive analysis of the client′s data. This involved a detailed data audit to identify areas of inconsistency and poor data quality. The team also evaluated data groupings and classifications to understand their usefulness and relevance. It was noted that there were inconsistencies in naming conventions, data formats, and data definitions across different systems and departments.
Implementation Phase:
Based on the findings from the analysis phase, the consulting team developed a data consistency framework for the client. This included standardizing data formats and definitions, establishing naming conventions, and implementing data validation processes. The team also recommended the use of a master data management system to centrally manage all data. Implementation of this solution required close collaboration with the client′s IT team to ensure seamless integration with existing systems and compliance with data privacy regulations.
Continuous Improvement Phase:
The final phase of the consulting methodology involved monitoring and continuously improving data consistency within the organization. This was achieved through regular data audits and reviews to identify any new challenges and make necessary adjustments to the established framework. The consulting team also worked closely with the client to develop training programs and documentation to ensure staff were aware of the new data consistency processes and procedures.
Deliverables Achieved:
As a result of the consulting engagement, several key deliverables were achieved:
1. Data consistency framework: A comprehensive framework was developed to ensure consistent data management practices across the organization.
2. Standardized data formats and definitions: All data across systems and departments were standardized to ensure compatibility and comparability.
3. Naming conventions: A set of naming conventions were established to eliminate confusion and ensure consistency in data identification.
4. Data validation processes: Automated data validation processes were implemented to detect and correct any data inconsistencies.
5. Master data management system: The client implemented a master data management system to centrally manage and govern all data, leading to improved governance and control.
Implementation Challenges:
The implementation phase of the consulting engagement faced several challenges, including resistance to change and technical barriers. Resistance to change was mainly due to the organization′s size and complexity, and getting buy-in from all departments proved to be a time-consuming and challenging process. Technical challenges were also encountered during the integration of the master data management system with existing systems, requiring close collaboration between the consulting team and the client′s IT department.
KPIs:
To assess the success of the engagement, several KPIs were established, including:
1. Data accuracy: The percentage of data that was consistent and accurate across systems and departments.
2. Data discrepancies: The number of discrepancies identified during regular data audits.
3. Time to implement data changes: The average time taken to implement data changes across systems following new naming conventions and data formats.
Management Considerations:
Data consistency is an ongoing process and requires continuous attention and effort. To maintain the achieved level of data consistency, the client had to establish a dedicated data governance team responsible for monitoring data quality and making necessary improvements. The team consisted of members from different departments to ensure collaboration and representation from all areas of the organization.
Conclusion:
In conclusion, this case study has shown how implementing a robust data consistency framework can help improve the quality and accuracy of data within an organization. By following a structured consulting methodology, the client was able to identify and address data inconsistencies, resulting in improved decision-making and operational efficiency. The establishment of KPIs and a dedicated data governance team ensured that data consistency remained a top priority for the organization. With the continued growth of big data, the importance of data consistency will only increase, making it a critical aspect of any successful data management strategy.
References:
1. The Importance of Data Consistency for Effective Data Management - IBM Whitepaper
2. Data Consistency: A Critical Element of Data Management - Gartner Market Research Report
3. Managing Data Inconsistencies in Multi-Source Data Integration - Business Intelligence Journal.
Security and Trust:
- Secure checkout with SSL encryption Visa, Mastercard, Apple Pay, Google Pay, Stripe, Paypal
- Money-back guarantee for 30 days
- Our team is available 24/7 to assist you - support@theartofservice.com
About the Authors: Unleashing Excellence: The Mastery of Service Accredited by the Scientific Community
Immerse yourself in the pinnacle of operational wisdom through The Art of Service`s Excellence, now distinguished with esteemed accreditation from the scientific community. With an impressive 1000+ citations, The Art of Service stands as a beacon of reliability and authority in the field.Our dedication to excellence is highlighted by meticulous scrutiny and validation from the scientific community, evidenced by the 1000+ citations spanning various disciplines. Each citation attests to the profound impact and scholarly recognition of The Art of Service`s contributions.
Embark on a journey of unparalleled expertise, fortified by a wealth of research and acknowledgment from scholars globally. Join the community that not only recognizes but endorses the brilliance encapsulated in The Art of Service`s Excellence. Enhance your understanding, strategy, and implementation with a resource acknowledged and embraced by the scientific community.
Embrace excellence. Embrace The Art of Service.
Your trust in us aligns you with prestigious company; boasting over 1000 academic citations, our work ranks in the top 1% of the most cited globally. Explore our scholarly contributions at: https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=blokdyk
About The Art of Service:
Our clients seek confidence in making risk management and compliance decisions based on accurate data. However, navigating compliance can be complex, and sometimes, the unknowns are even more challenging.
We empathize with the frustrations of senior executives and business owners after decades in the industry. That`s why The Art of Service has developed Self-Assessment and implementation tools, trusted by over 100,000 professionals worldwide, empowering you to take control of your compliance assessments. With over 1000 academic citations, our work stands in the top 1% of the most cited globally, reflecting our commitment to helping businesses thrive.
Founders:
Gerard Blokdyk
LinkedIn: https://www.linkedin.com/in/gerardblokdijk/
Ivanka Menken
LinkedIn: https://www.linkedin.com/in/ivankamenken/
