Analyst field reports on top 15 MDM solutions - Aaron Zornes (NYC 2021)Aaron Zornes
The document provides field reports on various Master Data Management (MDM) solutions. It lists the top evaluation criteria for MDM solutions and the "Top 15" MDM vendors. For several vendors, it summarizes the strengths and caveats of their solutions based on recent versions. It also lists notable customer references for some vendors.
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 Governance Precisely
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.
The document discusses strategies for managing master data through a Master Data Management (MDM) solution. It outlines challenges with current data management practices and goals for an improved MDM approach. Key considerations for implementing an effective MDM strategy include identifying initial data domains, use cases, source systems, consumers, and the appropriate MDM patterns to address business needs.
Reference data is something we often encounter in our projects. In our experience, it is often underestimated and does not get enough attention. In the webinar, we want to make you aware of some interesting aspects of ‘reference data’ such as how it relates to MDM, which it’s often mixed with.
There’s growing recognition in the analyst community that reference data is a form of master data that requires its own governance. Locations, currency codes, financial accounts, and organizational hierarchies are so widely used in an organization that mismatches can result in: reconciliation issues, poor quality analytics or even transactional failures.
While it’s easy to see how poor reference data management (RDM) can cause problems, many companies struggle with determining how to get started. Multiple questions arise: What’s the scope? How should one choose between RDM solutions? How do I compute ROI? To answer these questions and more, Orchestra Networks teamed up with Aaron Zornes, Chief Research Office of the MDM Institute and Godfather of MDM, for: Everything you ever wanted to know about Reference Data (but were afraid to ask).
In this hour long webcast featuring Aaron Zornes (MDM Institute) and Conrad Chuang (Orchestra Networks) you will learn the:
Characteristics of reference data,
Key features of a reference data management (RDM) solution,
Lessons learned RDM implementations,
and more
DAS Slides: Data Modeling Case Study — Business Data Modeling at KiewitDATAVERSITY
The document discusses data modeling efforts at Kiewit, a large construction company. It describes how Kiewit created conceptual data models to gain clarity on key business data assets and drive their data strategy. The models were organized by business capability and resonated well with stakeholders. This allowed Kiewit to identify data issues and gain insights that informed activities like data literacy training and system decommissioning planning. The modeling process highlighted differences between IT and business views of data and helped improve communication.
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.
Analyst field reports on top 15 MDM solutions - Aaron Zornes (NYC 2021)Aaron Zornes
The document provides field reports on various Master Data Management (MDM) solutions. It lists the top evaluation criteria for MDM solutions and the "Top 15" MDM vendors. For several vendors, it summarizes the strengths and caveats of their solutions based on recent versions. It also lists notable customer references for some vendors.
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 Governance Precisely
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.
The document discusses strategies for managing master data through a Master Data Management (MDM) solution. It outlines challenges with current data management practices and goals for an improved MDM approach. Key considerations for implementing an effective MDM strategy include identifying initial data domains, use cases, source systems, consumers, and the appropriate MDM patterns to address business needs.
Reference data is something we often encounter in our projects. In our experience, it is often underestimated and does not get enough attention. In the webinar, we want to make you aware of some interesting aspects of ‘reference data’ such as how it relates to MDM, which it’s often mixed with.
There’s growing recognition in the analyst community that reference data is a form of master data that requires its own governance. Locations, currency codes, financial accounts, and organizational hierarchies are so widely used in an organization that mismatches can result in: reconciliation issues, poor quality analytics or even transactional failures.
While it’s easy to see how poor reference data management (RDM) can cause problems, many companies struggle with determining how to get started. Multiple questions arise: What’s the scope? How should one choose between RDM solutions? How do I compute ROI? To answer these questions and more, Orchestra Networks teamed up with Aaron Zornes, Chief Research Office of the MDM Institute and Godfather of MDM, for: Everything you ever wanted to know about Reference Data (but were afraid to ask).
In this hour long webcast featuring Aaron Zornes (MDM Institute) and Conrad Chuang (Orchestra Networks) you will learn the:
Characteristics of reference data,
Key features of a reference data management (RDM) solution,
Lessons learned RDM implementations,
and more
DAS Slides: Data Modeling Case Study — Business Data Modeling at KiewitDATAVERSITY
The document discusses data modeling efforts at Kiewit, a large construction company. It describes how Kiewit created conceptual data models to gain clarity on key business data assets and drive their data strategy. The models were organized by business capability and resonated well with stakeholders. This allowed Kiewit to identify data issues and gain insights that informed activities like data literacy training and system decommissioning planning. The modeling process highlighted differences between IT and business views of data and helped improve communication.
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.
Informatica Presents: 10 Best Practices for Successful MDM Implementations fr...DATAVERSITY
This document outlines the top 10 best practices for successful master data management (MDM) implementations according to MDM experts. It discusses supporting multiple business data domains, automatically generating web services and user interfaces, starting small and scaling the implementation, creating a single best version of truth, and ensuring the MDM solution supports reference data needs. The document is presented by speakers from The MDM Institute and an MDM product marketing company.
3 Keys To Successful Master Data Management - Final PresentationJames Chi
This document discusses keys to successful master data management including process, governance, and architecture. It summarizes a survey finding that while many companies see data as an asset, only around 20% have implemented master data management. Successful MDM requires alignment with business objectives, clear governance models, and comprehensive solution architectures. The document advocates establishing policies, procedures, standards, governance, and tools to create and maintain high-quality shared reference data.
Straight Talk to Demystify Data LineageDATAVERSITY
Are you sure you trust the data you just used for that $10 million decision? To trust data authenticity we must first understand its lineage. However, the term "Data Lineage" itself is ambiguous since it is used in different contexts. "Business Lineage" links metadata constructs to specific terms in a business glossary. This approach is used by numerous Data Governance solutions. This approach alone comes up short, since it doesn't trace the real flow of information through an organization. "Technical Lineage" traces data's journey through different systems and data stores, providing an audit trail of the changes along the way. True "Data Lineage" combines both aspects, providing context to fully understand the data life cycle. Every step in data's journey is a potential source for introduction of error that could compromise Data Quality, and hence, business decisions. In this session, Ron Huizenga offers a comprehensive discussion of data lineage and associated Data Quality remediation approaches that are essential to build a foundation for Data Governance.
Henry Peyret Presentation - Data Governance 2.0.
Based on the analysis of Digital Transformation and Values Transformation, Forrester gives its insight and orientations in terms of Data Governance 2.0 and Data Citizenship.
The document discusses different techniques for building a Customer Data Hub (CDH), including registry, co-existence, and transactional techniques. It outlines the CDH build methodology, including data analysis, defining the data model and business logic, participation models, governance, and deliverables. An example enterprise customer data model is also shown using a hybrid-party model with relationships, hierarchies, and extended attributes.
DAS Slides: 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 & analytic reporting. This webinar provides 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
MDM solutions require tightly integrated capabilities including data modeling, integration, synchronization, propagation, flexible architecture, granular and packaged services, performance, availability, analysis, information quality management, and security. These capabilities allow organizations to extend data models, integrate and synchronize data in real-time and batch processes across systems, measure ROI and data quality, and securely manage the MDM solution.
Data mesh is a decentralized approach to managing and accessing analytical data at scale. It distributes responsibility for data pipelines and quality to domain experts. The key principles are domain-centric ownership, treating data as a product, and using a common self-service infrastructure platform. Snowflake is well-suited for implementing a data mesh with its capabilities for sharing data and functions securely across accounts and clouds, with built-in governance and a data marketplace for discovery. A data mesh implemented on Snowflake's data cloud can support truly global and multi-cloud data sharing and management according to data mesh principles.
Enabling a Data Mesh Architecture with Data VirtualizationDenodo
Watch full webinar here: https://bit.ly/3rwWhyv
The Data Mesh architectural design was first proposed in 2019 by Zhamak Dehghani, principal technology consultant at Thoughtworks, a technology company that is closely associated with the development of distributed agile methodology. A data mesh is a distributed, de-centralized data infrastructure in which multiple autonomous domains manage and expose their own data, called “data products,” to the rest of the organization.
Organizations leverage data mesh architecture when they experience shortcomings in highly centralized architectures, such as the lack domain-specific expertise in data teams, the inflexibility of centralized data repositories in meeting the specific needs of different departments within large organizations, and the slow nature of centralized data infrastructures in provisioning data and responding to changes.
In this session, Pablo Alvarez, Global Director of Product Management at Denodo, explains how data virtualization is your best bet for implementing an effective data mesh architecture.
You will learn:
- How data mesh architecture not only enables better performance and agility, but also self-service data access
- The requirements for “data products” in the data mesh world, and how data virtualization supports them
- How data virtualization enables domains in a data mesh to be truly autonomous
- Why a data lake is not automatically a data mesh
- How to implement a simple, functional data mesh architecture using data virtualization
Lessons in Data Modeling: Data Modeling & MDMDATAVERSITY
Master Data Management (MDM) can create a 360 view of core business assets such as Customer, Product, Vendor, and more. Data modeling is a core component of MDM in both creating the technical integration between disparate systems and, perhaps more importantly, aligning business definitions & rules.
Join this webcast to learn how to effectively apply a data model in your MDM implementation.
A hybrid cloud combines private and public clouds to provide flexibility, agility and cost control. However, operational silos, complex application management and lack of portability limit its effectiveness. To address these challenges, enterprises should unify infrastructure management across clouds with a single control plane. This allows monitoring, managing and orchestrating all environments with the same tools. Choosing a solution like unified cloud management or a unified platform like Kubernetes can provide the necessary abstraction and standardization to improve hybrid cloud operations.
The document discusses challenges in building a data pipeline including making it highly scalable, available with low latency and zero data loss while supporting multiple data sources. It covers expectations for real-time vs batch processing and explores stream and batch architectures using tools like Apache Storm, Spark and Kafka. Challenges of data replication, schema detection and transformations with NoSQL are also examined. Effective implementations should include monitoring, security and replay mechanisms. Finally, lambda and kappa architectures for combining stream and batch processing are presented.
This is a slide deck that was assembled as a result of months of Project work at a Global Multinational. Collaboration with some incredibly smart people resulted in content that I wish I had come across prior to having to have assembled this.
There are many questions on what are the best steps and ways to migrate to the cloud better. Enterprises need to have specific steps to follow when migrating to the cloud.
In this solution, we identify those specific steps and processes and how it can be adapted best.
To know more, please get in touch with us at info@blazeclan.com
Enterprise Cloud Governance: A Frictionless ApproachRightScale
As enterprise IT teams become a broker of cloud services, they need to embrace a new approach to cloud governance. Frictionless governance embeds and automates necessary controls to drive delays to zero by offering developers and business units cloud resources as quickly as teams can obtain them directly from cloud providers.
The document discusses how data modeling and data governance are related. It defines key terms like data modeling, data governance, and data stewardship. Data modeling requires business involvement, formal accountability, and attention to metadata - which are also traits of solid data governance programs. Therefore, data modeling can be considered a form of data governance. The document also outlines the role of the data modeler in a governance program and how data modeling best practices align with governance best practices. Finally, it discusses how the data model itself can be leveraged as a governance artifact.
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
A Comparative Study of Data Management Maturity ModelsData Crossroads
The document presents the results of a comparative analysis of leading data management maturity models. It aims to help data professionals choose the right approach for measuring maturity in their organization. The analysis compares several maturity models, identifying commonalities and differences in their scope, levels, domains, dimensions and artifacts. A framework is presented to evaluate and compare the different models in a consistent way. The goal is to define a maturity model tailored for small and medium enterprises.
This document summarizes Navisite's cloud assessment services, which provide comprehensive guidance for customers migrating to the cloud. The assessment includes discovery of current infrastructure and applications, cloud readiness evaluation, optimization recommendations, migration planning, and cost analysis. The process involves automated data collection, interviews, analysis of application dependencies and performance, and deliverables such as architecture design, cost projections, and a phased migration roadmap. An example case study outlines how these services helped an airline reduce data centers and implement a scalable cloud solution.
MDM Institute: Why is Reference data mission critical now?Orchestra Networks
The document discusses reference data management (RDM) and why it has become mission critical. It finds that errors in reference data can ripple through other systems and affect quality across domains. As enterprise data relies on clean reference data, RDM is becoming a starting point for many organizations' master data management and data governance efforts. The document also summarizes the results of a survey on RDM that found over 50% of respondents plan to invest in RDM within two years and that RDM projects have enterprise-level accountability and budgets.
The document provides an overview of master data management (MDM) solutions and tools. It defines MDM solutions as software that supports linking and synchronizing master data across systems, creates a central system of record, and enables a single view of data for stakeholders. The document compares features of MDM tools from IBM, Informatica, Siperian and Oracle, such as data modeling capabilities, integration support, and scalability. It also outlines guidance for implementing MDM solutions, including starting with quick wins, defining data governance strategies, and engaging business stakeholders.
Informatica Presents: 10 Best Practices for Successful MDM Implementations fr...DATAVERSITY
This document outlines the top 10 best practices for successful master data management (MDM) implementations according to MDM experts. It discusses supporting multiple business data domains, automatically generating web services and user interfaces, starting small and scaling the implementation, creating a single best version of truth, and ensuring the MDM solution supports reference data needs. The document is presented by speakers from The MDM Institute and an MDM product marketing company.
3 Keys To Successful Master Data Management - Final PresentationJames Chi
This document discusses keys to successful master data management including process, governance, and architecture. It summarizes a survey finding that while many companies see data as an asset, only around 20% have implemented master data management. Successful MDM requires alignment with business objectives, clear governance models, and comprehensive solution architectures. The document advocates establishing policies, procedures, standards, governance, and tools to create and maintain high-quality shared reference data.
Straight Talk to Demystify Data LineageDATAVERSITY
Are you sure you trust the data you just used for that $10 million decision? To trust data authenticity we must first understand its lineage. However, the term "Data Lineage" itself is ambiguous since it is used in different contexts. "Business Lineage" links metadata constructs to specific terms in a business glossary. This approach is used by numerous Data Governance solutions. This approach alone comes up short, since it doesn't trace the real flow of information through an organization. "Technical Lineage" traces data's journey through different systems and data stores, providing an audit trail of the changes along the way. True "Data Lineage" combines both aspects, providing context to fully understand the data life cycle. Every step in data's journey is a potential source for introduction of error that could compromise Data Quality, and hence, business decisions. In this session, Ron Huizenga offers a comprehensive discussion of data lineage and associated Data Quality remediation approaches that are essential to build a foundation for Data Governance.
Henry Peyret Presentation - Data Governance 2.0.
Based on the analysis of Digital Transformation and Values Transformation, Forrester gives its insight and orientations in terms of Data Governance 2.0 and Data Citizenship.
The document discusses different techniques for building a Customer Data Hub (CDH), including registry, co-existence, and transactional techniques. It outlines the CDH build methodology, including data analysis, defining the data model and business logic, participation models, governance, and deliverables. An example enterprise customer data model is also shown using a hybrid-party model with relationships, hierarchies, and extended attributes.
DAS Slides: 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 & analytic reporting. This webinar provides 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
MDM solutions require tightly integrated capabilities including data modeling, integration, synchronization, propagation, flexible architecture, granular and packaged services, performance, availability, analysis, information quality management, and security. These capabilities allow organizations to extend data models, integrate and synchronize data in real-time and batch processes across systems, measure ROI and data quality, and securely manage the MDM solution.
Data mesh is a decentralized approach to managing and accessing analytical data at scale. It distributes responsibility for data pipelines and quality to domain experts. The key principles are domain-centric ownership, treating data as a product, and using a common self-service infrastructure platform. Snowflake is well-suited for implementing a data mesh with its capabilities for sharing data and functions securely across accounts and clouds, with built-in governance and a data marketplace for discovery. A data mesh implemented on Snowflake's data cloud can support truly global and multi-cloud data sharing and management according to data mesh principles.
Enabling a Data Mesh Architecture with Data VirtualizationDenodo
Watch full webinar here: https://bit.ly/3rwWhyv
The Data Mesh architectural design was first proposed in 2019 by Zhamak Dehghani, principal technology consultant at Thoughtworks, a technology company that is closely associated with the development of distributed agile methodology. A data mesh is a distributed, de-centralized data infrastructure in which multiple autonomous domains manage and expose their own data, called “data products,” to the rest of the organization.
Organizations leverage data mesh architecture when they experience shortcomings in highly centralized architectures, such as the lack domain-specific expertise in data teams, the inflexibility of centralized data repositories in meeting the specific needs of different departments within large organizations, and the slow nature of centralized data infrastructures in provisioning data and responding to changes.
In this session, Pablo Alvarez, Global Director of Product Management at Denodo, explains how data virtualization is your best bet for implementing an effective data mesh architecture.
You will learn:
- How data mesh architecture not only enables better performance and agility, but also self-service data access
- The requirements for “data products” in the data mesh world, and how data virtualization supports them
- How data virtualization enables domains in a data mesh to be truly autonomous
- Why a data lake is not automatically a data mesh
- How to implement a simple, functional data mesh architecture using data virtualization
Lessons in Data Modeling: Data Modeling & MDMDATAVERSITY
Master Data Management (MDM) can create a 360 view of core business assets such as Customer, Product, Vendor, and more. Data modeling is a core component of MDM in both creating the technical integration between disparate systems and, perhaps more importantly, aligning business definitions & rules.
Join this webcast to learn how to effectively apply a data model in your MDM implementation.
A hybrid cloud combines private and public clouds to provide flexibility, agility and cost control. However, operational silos, complex application management and lack of portability limit its effectiveness. To address these challenges, enterprises should unify infrastructure management across clouds with a single control plane. This allows monitoring, managing and orchestrating all environments with the same tools. Choosing a solution like unified cloud management or a unified platform like Kubernetes can provide the necessary abstraction and standardization to improve hybrid cloud operations.
The document discusses challenges in building a data pipeline including making it highly scalable, available with low latency and zero data loss while supporting multiple data sources. It covers expectations for real-time vs batch processing and explores stream and batch architectures using tools like Apache Storm, Spark and Kafka. Challenges of data replication, schema detection and transformations with NoSQL are also examined. Effective implementations should include monitoring, security and replay mechanisms. Finally, lambda and kappa architectures for combining stream and batch processing are presented.
This is a slide deck that was assembled as a result of months of Project work at a Global Multinational. Collaboration with some incredibly smart people resulted in content that I wish I had come across prior to having to have assembled this.
There are many questions on what are the best steps and ways to migrate to the cloud better. Enterprises need to have specific steps to follow when migrating to the cloud.
In this solution, we identify those specific steps and processes and how it can be adapted best.
To know more, please get in touch with us at info@blazeclan.com
Enterprise Cloud Governance: A Frictionless ApproachRightScale
As enterprise IT teams become a broker of cloud services, they need to embrace a new approach to cloud governance. Frictionless governance embeds and automates necessary controls to drive delays to zero by offering developers and business units cloud resources as quickly as teams can obtain them directly from cloud providers.
The document discusses how data modeling and data governance are related. It defines key terms like data modeling, data governance, and data stewardship. Data modeling requires business involvement, formal accountability, and attention to metadata - which are also traits of solid data governance programs. Therefore, data modeling can be considered a form of data governance. The document also outlines the role of the data modeler in a governance program and how data modeling best practices align with governance best practices. Finally, it discusses how the data model itself can be leveraged as a governance artifact.
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
A Comparative Study of Data Management Maturity ModelsData Crossroads
The document presents the results of a comparative analysis of leading data management maturity models. It aims to help data professionals choose the right approach for measuring maturity in their organization. The analysis compares several maturity models, identifying commonalities and differences in their scope, levels, domains, dimensions and artifacts. A framework is presented to evaluate and compare the different models in a consistent way. The goal is to define a maturity model tailored for small and medium enterprises.
This document summarizes Navisite's cloud assessment services, which provide comprehensive guidance for customers migrating to the cloud. The assessment includes discovery of current infrastructure and applications, cloud readiness evaluation, optimization recommendations, migration planning, and cost analysis. The process involves automated data collection, interviews, analysis of application dependencies and performance, and deliverables such as architecture design, cost projections, and a phased migration roadmap. An example case study outlines how these services helped an airline reduce data centers and implement a scalable cloud solution.
MDM Institute: Why is Reference data mission critical now?Orchestra Networks
The document discusses reference data management (RDM) and why it has become mission critical. It finds that errors in reference data can ripple through other systems and affect quality across domains. As enterprise data relies on clean reference data, RDM is becoming a starting point for many organizations' master data management and data governance efforts. The document also summarizes the results of a survey on RDM that found over 50% of respondents plan to invest in RDM within two years and that RDM projects have enterprise-level accountability and budgets.
The document provides an overview of master data management (MDM) solutions and tools. It defines MDM solutions as software that supports linking and synchronizing master data across systems, creates a central system of record, and enables a single view of data for stakeholders. The document compares features of MDM tools from IBM, Informatica, Siperian and Oracle, such as data modeling capabilities, integration support, and scalability. It also outlines guidance for implementing MDM solutions, including starting with quick wins, defining data governance strategies, and engaging business stakeholders.
Conference Chairman Keynote & Welcome
Capitalizing on MDM in Times of Crisis
Aaron Zornes, Founder & Chief Research Officer, The MDM Institute
--------------------------------------------------------------------------------
MDM is particularly important in today’s increasingly complex and harsh global business landscape – in part due to increasingly demanding suppliers, trading partners, customers … as well as financial challenges and government regulations. Despite the current economic crisis, analyst firms have declared MDM to be “recession proof” as businesses strive to dramatically reduce costs, meet compliance reporting mandates, deliver increased sales and marketing effectiveness, and provide superior service to customers and suppliers. MDM and its variants – customer data integration (CDI), product information management (PIM), and data governance – all significantly contribute to these tactical business priorities.
Research analysts at the MDM Institute annually produce a set of twelve milestones for their MDM Road Map to help Global 5000 enterprises focus efforts for their own large-scale, mission-critical MDM projects. This keynote will focus on this set of strategic planning assumptions and present an enlightening view of the key trends and issues facing IT organizations during 2009-10 and beyond by highlighting:
Understanding the impact of MDM market momentum, maturation, and consolidation
Coping with the skills shortage for data governance, MDM project leadership, & enterprise architecture
Identifying the essential (vs. desirable) features of an enterprise-strength MDM solution
Analyst field reports on top 20 multi domain MDM solutions - Aaron Zornes (NY...Aaron Zornes
“Top 10” MDM Evaluation Criteria
Data model
Business services
Identity resolution
Data governance
Architecture
Data management
Infrastructure
Analytics
Developer productivity
Vendor integrity
This document provides summaries of field reports on major master data management (MDM) solutions. It evaluates the strengths and weaknesses of various MDM vendors and products. Key information included in the summaries are the vendors' target industries, reference customers, integration capabilities, data governance strategies, and growth momentum. In total, field reports are provided on 15 different MDM solutions.
The document discusses master data management (MDM) including its definition, need, and implementation process. MDM aims to create and maintain consistent and accurate master data across systems. It discusses key aspects like the different types of data, MDM architecture styles, and domains. The implementation involves identifying data sources, developing data models, deploying tools, and maintaining processes to manage master data effectively.
Fast Data Strategy Houston Roadshow PresentationDenodo
Fast Data Strategy Houston Roadshow focused on the next industrial revolution on the horizon, driven by the application of big data, IoT and Cloud technologies.
• Denodo’s innovative customer, Anadarko, elaborated on how data virtualization serves as the key component in their prescriptive and predictive analytics initiatives, driven by multi-structured data ranging from customer data to equipment data.
• Denodo’s session, Unleashing the Power of Data, described the complexity of the modern data ecosystem and how to overcome challenges and successfully harness insights.
• Our Partner Noah Consulting, an expert analytics solutions provider in the energy industry, explained how your peers are innovating using new business models and reducing cost in areas such as Asset Management and Operations by leveraging Data Virtualization and Prescriptive and Predictive Analytics.
For more information on upcoming roadshows near you, follow this link: https://goo.gl/WBDHiE
Peopleware. Introduction to Enterprise DataMashupsJusto Hidalgo
1. The document discusses enterprise data mashups, which aggregate and enrich data from multiple sources to provide a unified view for business users and applications.
2. It provides an overview of Denodo, a company that provides an enterprise data mashup platform, and compares their capabilities to traditional web mashups. Their platform is designed for enterprise needs like security, performance, and integration.
3. Examples are given of how mashups can be used for applications like customer analytics, sales automation by combining internal and external data sources.
Master Data Management: An Enterprise’s Key Asset to Bring Clean Corporate Ma...garry thomos
Over the last several decades, IT landscapes have proliferated into complex arrays of different systems, applications and technologies. Eventually, this fragmented environment has created significant data problems.
The document discusses master data management (MDM), which aims to integrate tools, people and practices to organize an enterprise view of key business information like customers, suppliers, products, and employees. MDM seeks to consolidate common data concepts, subject that data to analysis to benefit the organization. It allows organizations to clearly define business concepts, integrate related data sets, and make the data available across the organization. The document outlines the typical technical capabilities of MDM, including a core master data hub, data integration, master data services, integration and delivery, access control, synchronization, and data governance. It provides advice for evaluating MDM software and transitioning to an MDM program.
This document provides an overview of Master Data Management (MDM) and Complex Event Processing (CEP) capabilities in SQL Server 2008 R2 and how they can be used with BizTalk Server to create actionable data solutions. It discusses how MDM with SQL Server Master Data Services can be used to cleanse, govern and manage master data, while BizTalk Server provides application integration and process automation capabilities. It also overview how CEP with SQL Server StreamInsight can be used to analyze event streams in real-time and how BizTalk Server can automate actions from event processing.
Kaizentric is a Data Analytics firm, based in Chennai, India. Statistical Analysis is performed on a well-built client specific data warehouse, supported by Data Mining.
Business Process Re-engineering (BPR) How to fight Maverick Rebel. A too frequently overlooked cause preventing process optimization. This is one of the causes why too big companies are having issues which must be addressed DATA QUALITY ASSURANCE - DQA a typically overlooked domain.
The document discusses Master Data Management (MDM). It defines MDM as a framework for creating and maintaining authoritative, reliable, accurate and secure master data across an enterprise. The key points covered are:
- MDM is needed to resolve data uncertainty and have a single version of truth. It identifies master data items and manages them.
- MDM implementation involves identifying master data sources, appointing data stewards, developing a data model, choosing tools, and designing infrastructure to generate and test master data.
- MDM provides benefits like a single version of truth, increased consistency, data governance and facilitates multiple domains and data analysis across departments.
G10.2012 magic quadrant for master data management (mdm) of customer data s...Satya Harish
This document provides a summary of a Gartner report on master data management of customer data solutions. It positions relevant technology providers on the basis of their completeness of vision and ability to execute. IBM's InfoSphere MDM Advanced Edition is highlighted as having a broad information management strategy, strong market momentum as the clear market leader in revenue, and providing a powerful combination of IBM's two prior MDM products through convergence.
The document discusses master data management (MDM) and introduces EDR-MDS as a potential solution. It defines MDM and describes challenges with existing approaches. EDR-MDS is proposed as a service-oriented MDM approach that uses an Enterprise Domain Repository (EDR) to master disjoint business objects across systems. The EDR allows standard software to coexist with SOA by providing a simple, inexpensive strategy to control data redundancy and enforce governance through dynamic rules and automatic updates across sources.
This white paper discusses the importance of data quality for successful Master Data Management (MDM) systems. It defines MDM as consolidating all relevant company data from different systems into a single version of truth. High quality meta data and content data are critical for MDM success. The paper describes how data profiling can analyze meta data quality issues across sources. It also discusses challenges in keeping data quality high as MDM systems operate continuously with live data updates and entries. The paper proposes a Data Quality Life Cycle approach including identifying data sources, initial data cleansing, real-time data validation, and ongoing monitoring to help maintain high quality master data.
Analyst field reports on top 20 MDM and Data Governance implementation partne...Aaron Zornes
(1) Determining the evaluation criteria for selecting implementation partners for MDM, RDM and Data Governance projects
(2) Identifying which partners are market leaders in your industry & your chosen software technologies
(3) Managing the partner relationship – esp. avoiding “brain drain” & inflationary “blended rates”
- Credit Suisse is a global financial services company providing banking services to companies, institutional clients, high-net-worth individuals, and retail clients in Switzerland. It has over 48,000 employees across over 50 countries.
- Reference data is foundational data used across business transactions, such as client, product, and legal entity data. Consistent reference data is important for accurate reporting and analysis. However, Credit Suisse currently faces challenges of inconsistent views of reference data across applications.
- Credit Suisse's vision is to implement a multi-domain reference data management strategy using a central platform to provide consistent, validated reference data across the organization and reduce complexity.
Similaire à Analyst field reports on top 10 RDM solutions - Aaron Zornes (NYC 2021) (20)
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Project Management Semester Long Project - Acuityjpupo2018
Acuity is an innovative learning app designed to transform the way you engage with knowledge. Powered by AI technology, Acuity takes complex topics and distills them into concise, interactive summaries that are easy to read & understand. Whether you're exploring the depths of quantum mechanics or seeking insight into historical events, Acuity provides the key information you need without the burden of lengthy texts.
Ocean lotus Threat actors project by John Sitima 2024 (1).pptxSitimaJohn
Ocean Lotus cyber threat actors represent a sophisticated, persistent, and politically motivated group that poses a significant risk to organizations and individuals in the Southeast Asian region. Their continuous evolution and adaptability underscore the need for robust cybersecurity measures and international cooperation to identify and mitigate the threats posed by such advanced persistent threat groups.
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceIndexBug
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
Digital Marketing Trends in 2024 | Guide for Staying AheadWask
https://www.wask.co/ebooks/digital-marketing-trends-in-2024
Feeling lost in the digital marketing whirlwind of 2024? Technology is changing, consumer habits are evolving, and staying ahead of the curve feels like a never-ending pursuit. This e-book is your compass. Dive into actionable insights to handle the complexities of modern marketing. From hyper-personalization to the power of user-generated content, learn how to build long-term relationships with your audience and unlock the secrets to success in the ever-shifting digital landscape.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Analyst field reports on top 10 RDM solutions - Aaron Zornes (NYC 2021)
1. Field Reports
for the ‘Top 10’ RDM Solutions
16th Annual MDM & Data Governance Summit
2021 date t.b.d. NYC
Aaron Zornes
Chief Research Officer
The MDM Institute
aaron.zornes@the-MDM-Institute.com
www.linkedin.com/in/aaronzornes
@azornes
+1 650.743.2278