1) MDM is the process of creating a single point of reference for highly shared types of data like customers, products, and suppliers. It links multiple data sources to ensure consistent policies for accessing, updating, and routing exceptions for master data.
2) Successful MDM requires defining business needs, setting up governance roles, designing flexible platforms, and engaging lines of business in incremental programs. Common challenges include lack of clear business cases and roadmaps.
3) Key aspects of MDM include modeling shared data, managing data quality, enabling stewardship of data, and integrating/propagating master data to operational systems in real-time or batch processes.
Gartner: Master Data Management FunctionalityGartner
Gartner will further examine key trends shaping the future MDM market during the Gartner MDM Summit 2011, 2-3 February in London. More information at www.europe.gartner.com/mdm
In business, master data management is a method used to define and manage the critical data of an organization to provide, with data integration, a single point of reference.
Reference matter data management:
Two categories of structured data :
Master data: is data associated with core business entities such as customer, product, asset, etc.
Transaction data: is the recording of business transactions such as orders in manufacturing, loan and credit card payments in banking, and product sales in retail.
Reference data: is any kind of data that is used solely to categorize other data found in a database, or solely for relating data in a database to information beyond the boundaries of the enterprise .
Master Data Management – Aligning Data, Process, and GovernanceDATAVERSITY
Master Data Management (MDM) provides organizations with an accurate and comprehensive view of their business-critical data such as customers, products, vendors, and more. While mastering these key data areas can be a complex task, the value of doing so can be tremendous – from real-time operational integration to data warehousing and analytic reporting. This webinar will provide practical strategies for gaining value from your MDM initiative, while at the same time assuring a solid architectural and governance foundation that will ensure long-term, enterprise-wide success.
Gartner: Master Data Management FunctionalityGartner
Gartner will further examine key trends shaping the future MDM market during the Gartner MDM Summit 2011, 2-3 February in London. More information at www.europe.gartner.com/mdm
In business, master data management is a method used to define and manage the critical data of an organization to provide, with data integration, a single point of reference.
Reference matter data management:
Two categories of structured data :
Master data: is data associated with core business entities such as customer, product, asset, etc.
Transaction data: is the recording of business transactions such as orders in manufacturing, loan and credit card payments in banking, and product sales in retail.
Reference data: is any kind of data that is used solely to categorize other data found in a database, or solely for relating data in a database to information beyond the boundaries of the enterprise .
Master Data Management – Aligning Data, Process, and GovernanceDATAVERSITY
Master Data Management (MDM) provides organizations with an accurate and comprehensive view of their business-critical data such as customers, products, vendors, and more. While mastering these key data areas can be a complex task, the value of doing so can be tremendous – from real-time operational integration to data warehousing and analytic reporting. This webinar will provide practical strategies for gaining value from your MDM initiative, while at the same time assuring a solid architectural and governance foundation that will ensure long-term, enterprise-wide success.
Data Architecture, Solution Architecture, Platform Architecture — What’s the ...DATAVERSITY
A solid data architecture is critical to the success of any data initiative. But what is meant by “data architecture”? Throughout the industry, there are many different “flavors” of data architecture, each with its own unique value and use cases for describing key aspects of the data landscape. Join this webinar to demystify the various architecture styles and understand how they can add value to your organization.
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
Tackling Data Quality problems requires more than a series of tactical, one-off improvement projects. By their nature, many Data Quality problems extend across and often beyond an organization. Addressing these issues requires a holistic architectural approach combining people, process, and technology. Join Nigel Turner and Donna Burbank as they provide practical ways to control Data Quality issues in your organization.
Overcoming the Challenges of your Master Data Management JourneyJean-Michel Franco
This Presentaion runs you through all the key steps of an MDM initiative. It considers and showcase the key milestones and building blocks that you will have to roll-out to make your MDM
journey
-> Please contact Talend for a dedicated interactive sessions with a storyboard by customer domain
Master Data Management's Place in the Data Governance Landscape CCG
For many organizations, Master Data Management is a necessity to ensure consistency and accuracy of essential business entities. It further plays alongside data architecture, metadata management, data quality, security & privacy, and program management in the Data Governance ecosystem.
Join CCG's data governance subject matter experts as they overview the fundamentals of Master Data Management at our Atlanta-based Data Analytics Meetup. This event will discuss how to enable components of data governance within your organization and review how to best leverage Microsoft's SQL Server Master Data Services.
Enterprise Architecture vs. Data ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how Data Architecture is a key component of an overall Enterprise Architecture for enhanced business value and success.
How to identify the correct Master Data subject areas & tooling for your MDM...Christopher Bradley
1. What are the different Master Data Management (MDM) architectures?
2. How can you identify the correct Master Data subject areas & tooling for your MDM initiative?
3. A reference architecture for MDM.
4. Selection criteria for MDM tooling.
chris.bradley@dmadvisors.co.uk
Master Data Management - Aligning Data, Process, and GovernanceDATAVERSITY
Master Data Management (MDM) can provide significant value to the organization in creating consistent key data assets such as Customer, Product, Supplier, Patient, and the list goes on. But getting MDM “right” requires a strategic mix of Data Architecture, business process, and Data Governance. Join this webinar to learn how to find the “sweet spot” between technology, design, process, and people for your MDM initiative.
The Importance of Master Data ManagementDATAVERSITY
Despite its immaterial nature, data has a tendency to pile up as time goes on, and can quickly be rendered unusable or obsolete without careful maintenance and streamlining of processes for its management. This presentation will provide you with an understanding of reference and Master Data Management (MDM), one such method for keeping mass amounts of business data organized and functional towards achieving business goals.
MDM’s guiding principles include the establishment and implementation of authoritative data sources and effective means of delivering data to various business processes, as well as increases to the quality of information used in organizational analytical functions (such as BI). To that end, attendees of this webinar will learn how to:
Structure their Data Management processes around these principles
Incorporate Data Quality engineering into the planning of reference and MDM
Understand why MDM is so critical to their organization’s overall data strategy
Discuss foundational MDM concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK)
Data Architecture Best Practices for Advanced AnalyticsDATAVERSITY
Many organizations are immature when it comes to data and analytics use. The answer lies in delivering a greater level of insight from data, straight to the point of need.
There are so many Data Architecture best practices today, accumulated from years of practice. In this webinar, William will look at some Data Architecture best practices that he believes have emerged in the past two years and are not worked into many enterprise data programs yet. These are keepers and will be required to move towards, by one means or another, so it’s best to mindfully work them into the environment.
How to Build & Sustain a Data Governance Operating Model DATUM LLC
Learn how to execute a data governance strategy through creation of a successful business case and operating model.
Originally presented to an audience of 400+ at the Master Data Management & Data Governance Summit.
Visit www.datumstrategy.com for more!
Activate Data Governance Using the Data CatalogDATAVERSITY
Data Governance programs depend on the activation of data stewards that are held formally accountable for how they manage data. The data catalog is a critical tool to enable your stewards to contribute and interact with an inventory of metadata about the data definition, production, and usage. This interaction is active Data Governance in the truest sense of the word.
In this RWDG webinar, Bob Seiner will share tips and techniques focused on activating your data stewards through a data catalog. Data Governance programs that involve stewards in daily activities are more likely to demonstrate value from their data-intensive investments.
Bob will address the following in this webinar:
- A comparison of active and passive Data Governance
- What it means to have an active Data Governance program
- How a data catalog tool can be used to activate data stewards
- The role a data catalog plays in Data Governance
- The metadata in the data catalog will not govern itself
Building a Data Strategy – Practical Steps for Aligning with Business GoalsDATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task – but it’s worth the effort. Getting your Data Strategy right can provide significant value, as data drives many of the key initiatives in today’s marketplace – from digital transformation, to marketing, to customer centricity, to population health, and more. This webinar will help demystify Data Strategy and its relationship to Data Architecture and will provide concrete, practical ways to get started.
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
Data Architecture, Solution Architecture, Platform Architecture — What’s the ...DATAVERSITY
A solid data architecture is critical to the success of any data initiative. But what is meant by “data architecture”? Throughout the industry, there are many different “flavors” of data architecture, each with its own unique value and use cases for describing key aspects of the data landscape. Join this webinar to demystify the various architecture styles and understand how they can add value to your organization.
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
Tackling Data Quality problems requires more than a series of tactical, one-off improvement projects. By their nature, many Data Quality problems extend across and often beyond an organization. Addressing these issues requires a holistic architectural approach combining people, process, and technology. Join Nigel Turner and Donna Burbank as they provide practical ways to control Data Quality issues in your organization.
Overcoming the Challenges of your Master Data Management JourneyJean-Michel Franco
This Presentaion runs you through all the key steps of an MDM initiative. It considers and showcase the key milestones and building blocks that you will have to roll-out to make your MDM
journey
-> Please contact Talend for a dedicated interactive sessions with a storyboard by customer domain
Master Data Management's Place in the Data Governance Landscape CCG
For many organizations, Master Data Management is a necessity to ensure consistency and accuracy of essential business entities. It further plays alongside data architecture, metadata management, data quality, security & privacy, and program management in the Data Governance ecosystem.
Join CCG's data governance subject matter experts as they overview the fundamentals of Master Data Management at our Atlanta-based Data Analytics Meetup. This event will discuss how to enable components of data governance within your organization and review how to best leverage Microsoft's SQL Server Master Data Services.
Enterprise Architecture vs. Data ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how Data Architecture is a key component of an overall Enterprise Architecture for enhanced business value and success.
How to identify the correct Master Data subject areas & tooling for your MDM...Christopher Bradley
1. What are the different Master Data Management (MDM) architectures?
2. How can you identify the correct Master Data subject areas & tooling for your MDM initiative?
3. A reference architecture for MDM.
4. Selection criteria for MDM tooling.
chris.bradley@dmadvisors.co.uk
Master Data Management - Aligning Data, Process, and GovernanceDATAVERSITY
Master Data Management (MDM) can provide significant value to the organization in creating consistent key data assets such as Customer, Product, Supplier, Patient, and the list goes on. But getting MDM “right” requires a strategic mix of Data Architecture, business process, and Data Governance. Join this webinar to learn how to find the “sweet spot” between technology, design, process, and people for your MDM initiative.
The Importance of Master Data ManagementDATAVERSITY
Despite its immaterial nature, data has a tendency to pile up as time goes on, and can quickly be rendered unusable or obsolete without careful maintenance and streamlining of processes for its management. This presentation will provide you with an understanding of reference and Master Data Management (MDM), one such method for keeping mass amounts of business data organized and functional towards achieving business goals.
MDM’s guiding principles include the establishment and implementation of authoritative data sources and effective means of delivering data to various business processes, as well as increases to the quality of information used in organizational analytical functions (such as BI). To that end, attendees of this webinar will learn how to:
Structure their Data Management processes around these principles
Incorporate Data Quality engineering into the planning of reference and MDM
Understand why MDM is so critical to their organization’s overall data strategy
Discuss foundational MDM concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK)
Data Architecture Best Practices for Advanced AnalyticsDATAVERSITY
Many organizations are immature when it comes to data and analytics use. The answer lies in delivering a greater level of insight from data, straight to the point of need.
There are so many Data Architecture best practices today, accumulated from years of practice. In this webinar, William will look at some Data Architecture best practices that he believes have emerged in the past two years and are not worked into many enterprise data programs yet. These are keepers and will be required to move towards, by one means or another, so it’s best to mindfully work them into the environment.
How to Build & Sustain a Data Governance Operating Model DATUM LLC
Learn how to execute a data governance strategy through creation of a successful business case and operating model.
Originally presented to an audience of 400+ at the Master Data Management & Data Governance Summit.
Visit www.datumstrategy.com for more!
Activate Data Governance Using the Data CatalogDATAVERSITY
Data Governance programs depend on the activation of data stewards that are held formally accountable for how they manage data. The data catalog is a critical tool to enable your stewards to contribute and interact with an inventory of metadata about the data definition, production, and usage. This interaction is active Data Governance in the truest sense of the word.
In this RWDG webinar, Bob Seiner will share tips and techniques focused on activating your data stewards through a data catalog. Data Governance programs that involve stewards in daily activities are more likely to demonstrate value from their data-intensive investments.
Bob will address the following in this webinar:
- A comparison of active and passive Data Governance
- What it means to have an active Data Governance program
- How a data catalog tool can be used to activate data stewards
- The role a data catalog plays in Data Governance
- The metadata in the data catalog will not govern itself
Building a Data Strategy – Practical Steps for Aligning with Business GoalsDATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task – but it’s worth the effort. Getting your Data Strategy right can provide significant value, as data drives many of the key initiatives in today’s marketplace – from digital transformation, to marketing, to customer centricity, to population health, and more. This webinar will help demystify Data Strategy and its relationship to Data Architecture and will provide concrete, practical ways to get started.
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
Slide deck from a webinar presented by Earley Information Science on "MDM - The Key to Successful Customer Experience Management." Featured speaker is EIS Director of Delivery Services, Tim Barnes.
Master Data Management: Extracting Value from Your Most Important Intangible ...FindWhitePapers
This SAP Insight explores the importance of master data and the barriers to achieving sound master data, describes the ideal master data management solution, and explains the value and benefits of effective management of master data.
Master data management (mdm) & plm in context of enterprise product managementTata Consultancy Services
The presentation discusses the classical features and advantages of Master Data Management (MDM) system along with appropriate situations to use it. How do companies apply MDM who design, manufacture and sell their products in several geographies facing challenges in making appropriate decisions on their investment in PLM & MDM space?
Another important aspect covers the comparison/relation between a MDM system (or Product Master System) and Enterprise PLM system. How can you maximize your ROI on both PLM and MDM investments? With examples from different industries the key takeaways include whether your organization requires an MDM solution or not.
The what, why, and how of master data managementMohammad Yousri
This presentation explains what MDM is, why it is important, and how to manage it, while identifying some of the key MDM patterns and best practices that are emerging. This presentation is a high-level treatment of the problem space.
The presentation is summarizing the article of Microsoft in a simple way.
https://msdn.microsoft.com/en-us/library/bb190163.aspx
Enterprise-Level Preparation for Master Data Management.pdfAmeliaWong21
Master Data Management (MDM) continues to play a foundational role in the Data Management Architecture of every 21st century enterprise. In a forward-looking organization, MDM is significant in the Enterprise Integration Hub.
The article is intended as a quick overview of what effective master data management means in today’s business context in terms of risks, challenges and opportunities for companies and decision makers. The article is structured in two main areas, which cover in turn the importance of an effective master data
management implementation and the methodology to get there.
How to Drive Better Business Insights with Strong Data GovernanceMatt Dillon
Learn why leading CMOs and CEOs are making data governance and data integration a top priority for driving revenue growth and improving profitability.
Your data is one of the most important assets you have as a business but are you taking the necessary steps to manage your data with care?
Are you using your data to improve the customer experience and make better business decisions?
Webinar includes:
- Creating a centre of excellence
- Establishing data standardization
- Developing application management plans
- Release management
- Incorporating data & systems integration strategies
- Data cleansing essentials
Data-Ed: Unlock Business Value Through Reference & MDM Data Blueprint
In order to succeed, organizations must realize what it means to utilize reference and MDM in support of business strategy. This presentation provides you with an Understanding of the goals of reference and MDM, including the establishment and implementation of authoritative data sources, more effective means of delivering data to various business processes, as well as increasing the quality of information used in organizational analytical functions, e.g. BI. We also highlight the equal importance of incorporating data quality engineering into all efforts related to reference and master data management.
Check out more of our webinars here: http://www.datablueprint.com/webinar-schedule
Data-Ed Online: Unlock Business Value through Reference & MDMDATAVERSITY
In order to succeed, organizations must realize what it means to utilize reference and MDM in support of business strategy. This presentation provides you with an understanding of the goals of reference and MDM, including the establishment and implementation of authoritative data sources, more effective means of delivering data to various business processes, as well as increasing the quality of information used in organizational analytical functions, e.g. BI. We also highlight the equal importance of incorporating data quality engineering into all efforts related to reference and master data management.
Learning objectives include:
What is Reference & MDM and why is it important?
Reference & MDM Frameworks and building blocks
Guiding principles & best practices
Understanding foundational reference & MDM concepts based on the Data Management Body of Knowledge (DMBOK)
Utilizing reference & MDM in support of business strategy
Data-Ed Webinar: Implementing the Data Management Maturity Model (DMM) - With...DATAVERSITY
The Data Management Maturity (DMM) model is a framework for the evaluation and assessment of an organization’s data management capabilities. This model—based on the Capability Maturity Model pioneered by the U.S. Department of Defense for improving software development processes—allows an organization to evaluate its current state data management capabilities, discover gaps to remediate, and identify strengths to leverage. In doing so, this assessment method reveals organizational priorities, business needs, and a clear path for rapid process improvements.
In this webinar, we will:
- Describe the DMM model, its purpose and evolution, and how it can be used as a roadmap for assessing and improving organizational data management and data management maturity
- Discuss how to get the most out of a DMM assessment, including its dependencies and requirements for use
Data matters to all of us, and business expectations are raising, everywhere in the company.
But it has not always lived up to its promises.
The conditions are in place to generalize its use and adoption, by answering these three questions:
- Organization: centralize or decentralize data management?
- Architecture: how to establish flexible and sustainable foundations?
- Governance: how to manage and encourage use and collaboration?
La data nous concerne tous, et les attentes sont considérables, partout dans l’entreprise. Mais elle n’a pas toujours tenu ses promesses.
Les conditions sont réunies pour généraliser ses usages et son adoption, en répondant à ces trois questions :
- Organisation : centraliser ou décentraliser la gestion des données ?
- Architecture : comment établir des fondations souples et pérennes ?
- Gouvernance : comment encadrer et susciter les usages et collaborations ?
Reveal the Intelligence in your Data with Talend Data FabricJean-Michel Franco
Discover the Winter'20 release of Talend Data Fabric.
Find out about the newly released product, Talend Data Inventory, and the powerful new capabilities and AI that accelerate and modernize data engineering. Find out how to:
- Ensure trusted data at first sight with Data Inventory
- Increase efficiency and productivity with Pipeline Designer
- Automate more integration tasks with AI and APIs
Découvrir la version WInter 20 de Talend Data Fabric.
Elle inclue un nouveau produit, Data Inventory, ainsi que de nouvelles et puissantes fonctionnalités de qualité des données intelligente et d'IA explicable, capables d’accélérer et de moderniser l’ingénierie de données.
Elle permet de :
- garantir des données fiables instantanément avec Data Inventory
- augmenter considérablement l’efficacité et la productivité avec Pipeline Designer
- automatiser davantage de tâches d’intégration avec l’IA et les API
3 Steps to Turning CCPA & Data Privacy into Personalized Customer ExperiencesJean-Michel Franco
Your company’s success lies in your capacity to keep your customers’ trust while offering them a personalized experience. With the right Data Privacy framework and technology for your data governance project you will maintain compliance and prosper.
CCPA isn’t the first privacy regulation to impact virtually every organization that does business in the United States – it’s simply the one starting in 2020. As these regulations continue to expand and change, what if there was a way to turn compliance into your advantage? Attend this session and learn how a strong, carefully considered data governance program can help you stay ahead of new regulations like CCPA, and also enhance customer experiences with trusted data.
Learn how a 3-step approach can help you:
Ensure regulatory compliance at scale
Deliver advanced analytics with trusted data
Enable customer personalization for more accurate business insights targeted offers, and behavioral knowledge
The reliability of data, and your company’s reputation for protecting it, have become essential to doing business in the data age. Modern data governance works at the speed of business, the scale of data, and still has a human touch so you can say “yes” and deliver trusted data.
In these presentations
, Stewart Bond, Research Director of IDC’s Data Integration and Integrity Software Service, and Talend will highlight this modern approach to data governance.
Watch now to learn how to:
Put trust and data literacy at the core of your digital transformation
Tackle the growing complexity of data management
Identify the value and ROI levers that drive success
Leverage Data Intelligence Software from discovery to enablement
To view this On Demand Webinar, please fill out the form. A Flash-based player will then open. Controls for pause/play, rewind, and sound are available at the bottom of the player.
Are you tired of saying “no” when it comes to data? IDC and Talend share insights into how you can deliver data governance with a “yes”.
The reliability of data, and your company’s reputation for protecting it, have become essential to doing business in the data age. Modern data governance works at the speed of business, the scale of data, and still has a human touch so you can say “yes” and deliver trusted data.
Les données sont partout. Fournir des données fiables à toutes les personnes qui en ont besoin est un véritable défi. Heureusement, des technologies émergeantes sont là pour vous aider. Grâce à des sémantiques intelligentes, la gestion des metadonnées, l'auto-profiling, la recherche par facettes, et l'archivage collaboratif des données, il est désormais possible d'avoir une approche de type Wikipedia pour vos données. Talend peut vous aider à operationaliser plus de données, plus rapidement et à accroître l'utilisation de ces données par tous grâce à un Data Catalog d'entreprise.
Data is everywhere, and delivering trustable data to anyone who needs it has become a challenge. But innovative technologies come to the rescue: through smart semantics, metadata management, auto-profiling, faceted search and collaborative data curation there is a way to establish a Wikipedia like approach for your data. Find out how Talend will help you to operationalize more data faster and increase data usage for everyone with an Enterprise Data Catalog
Delivering Analytics at Scale with a Governed Data LakeJean-Michel Franco
Data privacy is on everyone's mind right now. Regulations such as GDPR, as well as public sentiment, mean that governance and compliance are must-have capabilities for data lakes. Learn how to curate meaningful data from your data lake, accelerate governance and compliance, and enable your organization with searchable, trusted datasets.
Enacting the data subjects access rights for gdpr with data services and data...Jean-Michel Franco
GDPR is more than another regulation to be handled by your back office. As stated by the European Commission, “The primary objective of this new set of rules is to give citizens back control over of their personal data.” And surveys show that European citizens are eager to apply for those new fundamental rights, such as access to information, data portability, and the right to be forgotten. Will you be ready to deliver, or will you be forced to tell your customers that unfortunately, you are not yet ready to respect their rights?
Enacting the GDPR’s Data Subject Access Rights (DSAR) requires practical actions. There’s a mandate for an integrated data governance strategy to establish your data inventory, operationalize controls, foster accountability across teams and ensure compliance, and finally unleash personal data to your customers, employees, visitors, and prospects. Only a strong data governance program on top of a modern, collaborative data hub ensures that you have the policies, standards, and controls in place to enforce compliance.
This presentations outlines the practical steps to deploy governed data services that:
Know your customers and employees with a data inventory
Track and trace data using audit trails and data lineage
Manage and propagate opt-in consent across customer-facing applications
Reconcile and protect your sensitive data in a data hub with automated controls, data stewardship, and data masking
Respect the rights for your data subjects with collaborative data management and portals
The race is on for GDPR compliance, and now it is time to get hands-on with personal data. Survey highlight that the toughest operational challenges for compliance are related to Data management
This presentation shares use cases and concrete experiences on how companies are :
- Applying Metadata and Master Data Management to track, reconcile, control and trace personal data
- Establishing compliant consent mechanisms
- Enacting a privacy control center for the data subject access rights, data portability and rights to be forgotten.
Business can't wait to turn data into insights, which means they often can't wait for IT. But that increases the risk of bad data and inaccurate results. Learn how IT can engage the business to accelerate data integration, build perfect, trusted, and compliant data; and increase data usage and time-to-insight.
Delivering analytics at scale with a governed data lakeJean-Michel Franco
Data privacy is on everyone's mind right now. Regulations such as GDPR, as well as public sentiment, mean that governance and compliance are must-have capabilities for data lakes. Learn how to curate meaningful data from your data lake, accelerate governance and compliance, and enable your organization with searchable, trusted datasets.
pour accompagner les talents, gérer les compétences et assurer la conformité des données pour GDPR
Vos collaborateurs sont au cœur de la réussite de votre entreprise, mais disposez-vous d’informations précises et fiables les concernant ? Sont-elles sécurisées et en conformité avec les réglementations telles que GDPR, tout en étant facilement accessibles pour les prises de décision et activités opérationnelles ?
Lors de ce webinar à la demande, les équipes RH Orange et les consultants d’Orange Consulting partageront leur retour d’expérience dans la mise en œuvre de la vue 360° employés au sein du groupe, la méthode utilisée, les difficultés rencontrées et les résultats obtenus.
Participez à ce webinar à la demande d'une heure pour apprendre comment :
fédérer et réconcilier les 18 sources d'informations RH différents issus de plusieurs systèmes d'information ;
mieux connaitre les salariés pour répondre aux enjeux RH et business des managers de disposer d’une cartographie dynamique en temps réel des données salariés ;
mettre en place des tableaux de bord d’ indicateurs pertinents pour accompagner la réflexion stratégique et les plans d’actions RH par une vision synthétique, actualisé et multicritères des données salariés ;
anticiper la mise en application de la nouvelle règlementation européenne sur les données privées (GDPR).
As the deadline for GDPR approaches, it is time to get practical about protecting personal data.
We break down the steps for turning a data lake into a data hub with appropriate data management and governance activities: from capturing and reconciling personal data to providing for consent management, data anonymyzation, and the rights of the data subject.
A smart approach to GDPR compliance lays a foundation for personalized and profitable customer and employee relations.
Join us, as experts from MAPR and Talend show you how to:
Diagnose the maturity of your GDPR compliance
Set up milestones and priorities to reach compliance
Create a foundation to manage personal data through a data lake
Master compliance operations - from data inventory to data transfers to individual rights management
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
2. 2
Master Data Management is a
cornerstone for data-driven processes
Know Your
Customer
Know Your
Products
Know Your
Suppliers
3. 3
3
MDM DEFINITION
Master data management (MDM) is the process of creating a single point of reference for
highly shared types of data, including customer, products, suppliers, sites, organizations
and employees.
Master data management requires companies to create a single view of their shared
master data asset. It then links together multiple data sources, and ensures the
enforcement of policies for accessing and updating the master data, handling data quality
and the routing of exceptions to people.
This “data stewardship” capability allows the lines of businesses to take ownership of the
content they need for their data centric processes. Once a single view is created, that
data can be operationally applied, and eventually in real-time, to business problems and
opportunities.
MDM is a strategic initiative for data-driven organization seeking to improve business
results such as better customer service, increasing cross-sell and up-sell revenue, and
streamlining supply chains.
4. 4
The journey from Data Integration to Information
Governance
From a fully IT driven model…
…to a federated and collaborative
responsibility model
IT Lines of
Business
Evolutionpath
From Data Management… …to Information Governance
5. 5
The Business cases for MDM
M&A and
restructuring
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101101010101010101101
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0 1 0 1 0 1 0 1 0 1
360°
Views
Managed Data
Accuracy
Collaborative
Data
Governance
Information
Accessibility
Information
Accountability
MDM
Platform
Governance,
Risk Compliance
and fraud mgmt.
Just-in-time and lean
operations
Customer
centric
processes
Customer
Experience
Management
Time to market
6. 6
MDM : why change? why now? And how ?
Source : Gartner 2014 survey Enterprise Information and MDM
MDM is a hot topic
•in top 3 initiative for 50% of IT execs
There is a urgent need to refresh current
processes linked to master data
•Ratings of the current capability: 3,6 on 7 ; average for 79%;
poor for 21%
A lot of companies have engaged, but most are at
early steps
• 61% still on planning/prototyping phases
Only 49% have a clear business case
• and 31% through an ROI model
7. 7
Typical challenges during MDM planning cycle
Lack of a solid
Business Case
Lack of readiness
Unclear
Roadmap
Misalignment
between
stakeholders
Unclear
requirementsUndefined
Roadmap
Many MDM initiatives
get stuck in their
planning phase
8. 8
So Where to start your journey to data governance ?
Define your business needs and your roadmap
Set up your stewardship organization
Design the platform
Engage your
MDM programs
9. 9
Some misconceptions on MDM
Misconception Key success factor
Massive IT Project
(Think Big, Start Big)
Incremental program with
engagement from Lines of Business
MDM & integration
as separate disciplines
(Start Small, Stay Small)
Total data integration capability for
current and future needs
A standalone application
(Siloed Approach)
A real time platform to operationalize
the master data
Golden record is only based on
systems of record like CRM
(Soon to be Outdated)
There will always be new sources of
data to give you a better 360 view of
customer--- social, mobile,
clickstreams….
11. 11
Modeling your data
Key steps to consider
• Creating the data model
• Defining the business rules
• Defining Data Validation controls
• Defining the roles , and the security
Modeling
Managingthe
dataquality
Enablingstewardship
Integrating&
propagatingthedata
Operationalizing
themasterdata
12. 12
Organizing for MDM: Defining the implementation
Style
MDM
ERP
CRM
COTS
DWH
Consolidation
MDM
ERP
SFA
CRM
DWH
Centralized
MD
M
CRM
E-
Commerc
e
Marketin
g
DWH
Coexistence
MDM
ERP
SFA
CRM
DWH
RegistryLess Intrusive
Most MDM Configuration
Most ESB Configuration
Less Intrusive
Standard MDM Configuration
More Intrusive
Standard MDM Configuration
Optional ESB Configuration
Most Intrusive
Moderate MDM Configuration
Required ESB Configuration
13. 13
Modeling best practices
Functional
Engage heavily the LOBs in the designing effort
Reach consensus ASAP on the data definition of
golden record
Start at the core and keep it simple, then expand
Make the model as self explanatory as possible
for the business users, and document your
business glossary
Create your own primary key
Manage the design and validation phase
carefully, as changing a data model at run time
once the data is populated may be a tedious
exercise
Leverage views and roles for usability
Value:
➜ Establish sustainable foundations for your
MDM model
➜ Establish the cornerstone for collaboration
(Stewardship and IT integration)
Technical
Create an internal permanent key for Master
Data records
Define modeling standards and respect them
Use a graphic Case tool for the design
Establish naming rules
Reuse definition, rules and patterns
Anticipate the performance impact of
controls, enrichment and propagation rules
14. 14
Managing the Data Quality
Key steps to consider
• Data Profiling
• Collect the referential to enriching the data
• Defining parsing, standardization, validation
• Defining the matching and survivorship
• Building Address validation rules
Modeling
Managingthedata
quality
Enablestewardship
Integrating&
propagatingthedata
Operationalizing
themasterdata
15. 15
Taking care of the most precious “resource”
in a citizen community: the children
Challenge:
Need a single view of a child to provide top quality
services and value for money on a one to one basis
for the local government’s 210 000+ children and
their family
Why Talend:
• MDM masters the cross references between
public services (education, social care…) and
orchestrates data governance to effectively
match, merge and un-merge incoming records.
• Complex Data Integration and Data Quality load
routines provide sophisticated fuzzy matching.
Value:
Improved public service provided for child
protection, through a shared knowledge of each
child situation and context
* For Internal Use Only
16. 16
Data Quality best practices
Functional
Know your data before starting the design:
content, availability volume, typology, reliability,
reference data
Understand the information supply chain: who
creates, imports, update, consumes (and
when/where…)
Establish strong collaboration with stewards in
charge of manual resolution to fine tune your
matching algorithms iteratively
Define business and project metrics to be
monitored over time, in order to size the data
stewardship efforts and to show the progress
Value:
➜ Illuminate the data quality problems and its
impact for lines of business
➜ Establish clear metrics for measuring the
progress and success of the MDM program
Technical
Use a data profiling tool
Integrate the data quality rules as
gatekeepers in your data integration process
Understand the constraints and objective
that are behind the matching policies,
including performance, impact of
mismatches, cost of manual efforts…
Anticipate the need for adjustments,
including for undoing redoing data resolution
activities
17. 17
Synchronizing with the existing systems in
batch or real time
Key steps to consider
• Batch/real time, Bulk or incremental load,
propagation : defining the integration
policies
• Integrating with applications: internal, cloud
based, external
Modeling
ManagingtheDataQuality
Enablestewardship
Integrating&
propagatingthedata
Operationalizing
themasterdata
18. 18
Challenge:
Support hyper growth of members in a non profit
and highly regulated healthcare market
Re-engineering customer facing processes
Use case: Re-engineering member relationship
in a heavily regulated environment
Key capabilities need:
Start with strong Data quality and data reconciliation
capabilities
Manage external data standards and connect in real
time with exchanges in the healthcare industry
Implement workflow driven processes for customer
facing activities (on-boarding, claims, billing…)
Value:
• Compliance (with HIPAA regulations)
• Scalable processes to meet hyper growth (+250%
members acquisition rate)
• Lower TCO and automated processing
19. 19
Integration best practices
Functional
Define the integration architecture and the decision
criteria to inform data integration scenarios for each
source and targets
Design the integration layer as a moving object that
will have to evolve on a regular basis, with its own
lifecycle (new systems to connect, upgrades…)
Use design mechanisms like publish and subscribe or
Master data services to avoid dependencies
between system and have clear segregation of
duties
Value:
➜ A shared service to bring trusted data across
your IT trough a well defined and rapid to
deploy process
➜ Manage change info your MDM program and
take advantage into new sources of data and
accelerate the roll-out of new applications
Technical
Invest on productivity and change
management tools, since this makes a
substantial part of your TCO
Identify the volume now…and for the future
Identify the MDM multiple environments
Define procedures for Delivery between
environments
Integration
Services
Data Staging
MetaData
Repository
Web Layer
Hybris
TCP/IP - Kereberos
Legend
Customer Data Management – Static Architecture
Integration
Services
Batch
Adaptors
Real-time
Adaptors
Real time
data
services
File based
Master
Repository
@ComRes
ACDS
Pega
Tracs
Vision
Data Quality Services
Talend Integration Platform
Parsing
& enrichment
(Experian)
Matching
Services Batch data
services
Data Layer
Master Data
Governance
Talend
Administration
Data Quality
Dashboard
Migration
Adaptors
Standardisation
Services
IntegrationLayerActive
Directory
SOAP over JMS
GetCustomerDetailsCore
GeCustomerinteractions
CreateCustomer
UpdateCustomer
PublishCustomer
GetCustomerEngagements
GetCustomerProfile
SearchCustomer
MatchCustomer
PublishCustomerMerge
IntegrationLayer
MatchCustomerBulk
SOAP over Http
Talend ESB
20. 20
Engage your Lines of Businesses
Key steps to consider
• Organize data stewardship tasks by roles
• Managing the day to day tasks related to
master data
• Accessing and authoring the master data
• Defining the workflows for collaborative
authoring
Modeling
ManagingtheDataQuality
Enable
stewardship
Operationalize
themasterdata
Operationalize
themasterdata
21. 21
Monetizing content and increasing
ARPU in the media industry
Challenge:
Deliver 28,000 hours of multimedia content
monthly from 340 content providers targeting
75 million households
Why Talend:
• Flexibility and rapid implementation time
• Unified integration platform with
embedded data quality, ESB and Business
Process Management
Value:
Decreased costs and time for adding new
content to the movie catalog
Re-engineer the billing process to meet
compliance mandates and drastically
reduce cost and time of operations
* For Internal Use Only
22. 22
Best practices for Data Stewardship
Functional
Define and document the data governance
policies (incl inventories roles, permissions,
workflows)
Make sure that the lines of businesses are
engaged and accountable
Define clear roles & tasks for data stewards and
define their working environment and workflows
accordingly ;
Engage the data stewards early in the project,
well before the training and roll-out phase
Value:
➜ Engage the lines of business in the success of
data centric initiatives
➜ Organize for a MDM roll-out and continuous
improvement
Technical
Integrate the people driven tasks related to
data authoring, validation and correction
into the overall landscape, rather than as a
separate flow
Target the right environment for the right
roles (designers, data stewards, authors and
contributors, end users)
23. 23
To BPM or not to BPM ?
Functional
➜ Clearly identify the actors
➜ Nominate champions for roles and involve them in
the project to define the processes and activities
➜ Use agile methodologies to define the workflows
and interfaces
➜ Carefully design the users interface
➜ Leverage Business Activity Management for alerts
and continuous improvement
When to use BPM in MDM projects ?
MDM has the lead for data authoring
Lines of businesses are highly engaged
Business users are involved in the authoring
process -> need for guided procedures
There are clear links between MDM and business
processes (e.g.: onboarding a customer/employee,
referencing a product…).
Technical
Make sure you don’t transform your MDM into a
packaged app : separate data and processes in
your design
Keep it simple and anticipate frequent change
since people centric processes are subject
change and to deal with exception much more
frequently that automated processes
Don’t underestimate efforts and time related to
the user interface
Value:
• Re-engineer your processes with a data centric
approach
24. 24
Use case: getting a single view of employee in a
highly distributed organization
Challenge:
• 190000+ employees across 100 countries and
400 subsidiaries)
• No global and up to date view of the employees
at a global level in a highly decentralized
organization
Value:
• shared knowledge of employees at group
level and ability to reach them immediately,
e.g. communication in crisis situations
Key capabilities needed :
• Strong security, lineage and audit capabilities
• Integration to a disparate environment, including
employee directories)
• Workflow based authoring (e.g. : professional
transfer)
25. 25
Making MDM actionable
Key Capabilities
• Integrate Master Data Services real time into
processes
• Bring context into applications such as Big
Data, web or Mobile Applications
Modeling
ManagingtheDataQuality
Enablestewardship
Integrating&
propagatingthedata
Operationalizing
themasterdata
26. 26
Best practices for Operationalizing the Master data
Functional
Identify the touch points where you need to
integrate MDM data services, and prioritize the
roll out interactively.
Define metrics to show the business impact, e.g.
on transformation rates, click rates…
Understand the performance and availability
impact of invoking MDM real time for the
external applications
Define a small set of reusable, well documented
master data services
Connect your master data to your Big Data via
Entity Resolution to boost the relevance of your
bog data analytics
Value:
➜ 360 view are populated at the right time, right
place, when insights or actions are needed.
Technical
Closely integrate this capability into your
existing enterprise service bus capability
Define Service level agreements for the
MDM services and monitor them closely
Create sets of tests cases to industrialize and
automate the testing capabilities
MDM
Business
Applications
Mobile
Applications
Big Data
Web
applications
27. 27
Use Case Bring Actionable Customer Data
across touch points
Challenge:
Drive loyalty and customer retention in an
industry disrupted by digital transformation
Key capability needed:
• Fast & easy collection, cleansing and
reconciling of data for 15 million customers
• Definition of Master data services to bring
customer context and progressive delivery
across touch points in a real time mode
Value:
➜ Improved marketing, sales and service
through knowledge and personalization
➜ Better transformation rates, cross sell/upsell
➜ Multi-Channel consistent Customer
Experience
28. 28
Trends in MDM
Ten priorities to guide organizations into
next generation MDM
1. Multi-domain MDM
2. Multi department, multi application MDM
3. Bi-directional MDM
4. Real time MDM
5. Consolidating multiple MDM Solutions
6. Coordination with other disciplines
7. Richer Modeling
8. Beyond Enterprise Data
9. Workflow and Process Management
10.MDM solutions build atop vendor tools
and platforms
Source : TDWI next generation MDM
Key technologies challenges for next
generation MDM
1. Complex relationships
2. Mobile
3. Social
4. Big Data
5. Time-travel
6. Cloud
7. Action enablement
8. Real time
9. Extreme scalability
10.Proactive, integrated governance
Source : The MDM Institute