This document outlines best practices for implementing a Master Data Management (MDM) solution to improve data quality. MDM can help by providing a single source of trusted customer, product, and partner data across systems. When implementing MDM, organizations should follow best practices like establishing formal data governance policies, using architecture consistent with existing IT systems, demonstrating a clear business case and ROI, taking a phased approach while making MDM a long-term program, and ensuring active vendor support. Following these practices can help organizations realize the benefits of MDM like increased revenue, cost savings, and regulatory compliance.
Scan any number of financial industry news publications, and stories regarding Wall Street’s hunger for new data sets to improve alpha abound. While this might seem like a new trend, monetizing data - or the ability to turn corporate data into revenue streams - has existed for decades. But both the supply side and the demand side have changed. On the supply side, the extreme variety of data that now exists (location-based, geospatial, socio-demographic, online search trends, pricing, etc.) combines with high computing power and new digital requirements to create a fertile data market environment. On the demand side, to remain competitive, companies in a wide variety of industries, not just the financial sector, are leveraging data in all forms to maintain an edge or be disruptive.
During this session we’ll explore what data monetization is and the forms it can take; characteristics of data that could make it more valuable to external parties; and key considerations in making data products available to external parties. Intellectual property, data privacy, and contractual issues will also be explored.
Data Quality Management: Cleaner Data, Better Reportingaccenture
This document discusses Accenture's regulatory reporting framework and offerings around data quality management. It provides an overview of Accenture's high-performance financial reporting framework, which aims to consolidate frameworks, processes, and technology to create efficiencies across reporting functions. It also summarizes Accenture's regulatory reporting offerings, including data quality management, capability design, target operating models, and regulatory reporting vendor implementation support. Finally, it covers key aspects of data quality management, such as issue classification, management processes, governance structures, root cause analysis, and issue prioritization. The goal is to help financial institutions improve data quality, reporting accuracy and efficiency.
Customer Event Hub - the modern Customer 360° viewGuido Schmutz
Today, companies are using various channels to communicate with their customers. As a consequence, a lot of data is created, more and more also outside of the traditional IT infrastructure of an enterprise. This data often does not have a common format and they are continuously created with ever increasing volume. With Internet of Things (IoT) and their sensors, the volume as well as the velocity of data just gets more extreme.
To achieve a complete and consistent view of a customer, all these customer-related information has to be included in a 360 degree view in a real-time or near-real-time fashion. By that, the Customer Hub will become the Customer Event Hub. It constantly shows the actual view of a customer over all his interaction channels and provides an enterprise the basis for a substantial and effective customer relation.
In this presentation the value of such a platform is shown and how it can be implemented.
Managed services involves outsourcing the day-to-day management responsibilities of an organization's IT infrastructure to improve efficiency. It provides a more cost-effective alternative to traditional IT management and large outsourcing agreements. A managed services provider can help lower costs, reduce risks, and maintain control through services like desktop management, server hosting, network management, and security management. These services involve tasks like asset tracking, backup and recovery, patching, monitoring, and help desk support. Managed services implementations generally involve an initial IT audit and assessment, followed by remediation if needed, ongoing performance monitoring, IT support, and operational maintenance outsourced to the provider.
Honeywell has implemented several initiatives to improve customer master data governance across its business units, including establishing a governance council, standardizing processes, and developing a Customer Data on Demand (CD2) tool. The CD2 tool provides a single view of customer data, facilitates improved data quality, and enables greater collaboration and growth opportunities across Honeywell. Future focus areas include further incorporating lean principles, maturing data quality metrics, and driving increased business ownership of customer data.
Data governance Program PowerPoint Presentation Slides SlideTeam
The document discusses the need for data governance programs in companies. It outlines why companies suffer without effective data governance, such as applications being unable to communicate and inconsistencies in data leading to increased costs. The document then compares manual and automated approaches to data governance. It provides details on key aspects of building a data governance program, including establishing a framework, defining roles and responsibilities, and outlining a roadmap for improving data governance over time.
Linking Data Governance to Business GoalsPrecisely
This document discusses linking data governance to business goals. It begins with an example of a typical governance program that loses business support over time. It then advocates taking a business-first approach to accelerate programs and increase ROI. Successful programs link governance to business goals, outcomes, stakeholders and capabilities. The document provides examples of how different business goals map to governance objectives and capabilities. It emphasizes quantifying value at strategic, operational and tactical levels. Finally, it discusses Jean-Paulotte Group's Chief Data Officer implementing a working approach driven by business value through an iterative process between a Data Management Committee and Working Groups.
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.
Scan any number of financial industry news publications, and stories regarding Wall Street’s hunger for new data sets to improve alpha abound. While this might seem like a new trend, monetizing data - or the ability to turn corporate data into revenue streams - has existed for decades. But both the supply side and the demand side have changed. On the supply side, the extreme variety of data that now exists (location-based, geospatial, socio-demographic, online search trends, pricing, etc.) combines with high computing power and new digital requirements to create a fertile data market environment. On the demand side, to remain competitive, companies in a wide variety of industries, not just the financial sector, are leveraging data in all forms to maintain an edge or be disruptive.
During this session we’ll explore what data monetization is and the forms it can take; characteristics of data that could make it more valuable to external parties; and key considerations in making data products available to external parties. Intellectual property, data privacy, and contractual issues will also be explored.
Data Quality Management: Cleaner Data, Better Reportingaccenture
This document discusses Accenture's regulatory reporting framework and offerings around data quality management. It provides an overview of Accenture's high-performance financial reporting framework, which aims to consolidate frameworks, processes, and technology to create efficiencies across reporting functions. It also summarizes Accenture's regulatory reporting offerings, including data quality management, capability design, target operating models, and regulatory reporting vendor implementation support. Finally, it covers key aspects of data quality management, such as issue classification, management processes, governance structures, root cause analysis, and issue prioritization. The goal is to help financial institutions improve data quality, reporting accuracy and efficiency.
Customer Event Hub - the modern Customer 360° viewGuido Schmutz
Today, companies are using various channels to communicate with their customers. As a consequence, a lot of data is created, more and more also outside of the traditional IT infrastructure of an enterprise. This data often does not have a common format and they are continuously created with ever increasing volume. With Internet of Things (IoT) and their sensors, the volume as well as the velocity of data just gets more extreme.
To achieve a complete and consistent view of a customer, all these customer-related information has to be included in a 360 degree view in a real-time or near-real-time fashion. By that, the Customer Hub will become the Customer Event Hub. It constantly shows the actual view of a customer over all his interaction channels and provides an enterprise the basis for a substantial and effective customer relation.
In this presentation the value of such a platform is shown and how it can be implemented.
Managed services involves outsourcing the day-to-day management responsibilities of an organization's IT infrastructure to improve efficiency. It provides a more cost-effective alternative to traditional IT management and large outsourcing agreements. A managed services provider can help lower costs, reduce risks, and maintain control through services like desktop management, server hosting, network management, and security management. These services involve tasks like asset tracking, backup and recovery, patching, monitoring, and help desk support. Managed services implementations generally involve an initial IT audit and assessment, followed by remediation if needed, ongoing performance monitoring, IT support, and operational maintenance outsourced to the provider.
Honeywell has implemented several initiatives to improve customer master data governance across its business units, including establishing a governance council, standardizing processes, and developing a Customer Data on Demand (CD2) tool. The CD2 tool provides a single view of customer data, facilitates improved data quality, and enables greater collaboration and growth opportunities across Honeywell. Future focus areas include further incorporating lean principles, maturing data quality metrics, and driving increased business ownership of customer data.
Data governance Program PowerPoint Presentation Slides SlideTeam
The document discusses the need for data governance programs in companies. It outlines why companies suffer without effective data governance, such as applications being unable to communicate and inconsistencies in data leading to increased costs. The document then compares manual and automated approaches to data governance. It provides details on key aspects of building a data governance program, including establishing a framework, defining roles and responsibilities, and outlining a roadmap for improving data governance over time.
Linking Data Governance to Business GoalsPrecisely
This document discusses linking data governance to business goals. It begins with an example of a typical governance program that loses business support over time. It then advocates taking a business-first approach to accelerate programs and increase ROI. Successful programs link governance to business goals, outcomes, stakeholders and capabilities. The document provides examples of how different business goals map to governance objectives and capabilities. It emphasizes quantifying value at strategic, operational and tactical levels. Finally, it discusses Jean-Paulotte Group's Chief Data Officer implementing a working approach driven by business value through an iterative process between a Data Management Committee and Working Groups.
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.
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
Rajesh Sharma is a SAP certified consultant specializing in SAP Activate, SAP Sourcing & Procurement, SAP EWM, and SAP ERP. The document provides an overview of ERP systems and SAP ERP in particular. It defines ERP as integrated software that manages core business processes. SAP is one of the leading ERP providers, with over 60% market share. The document outlines the key modules in SAP ERP including finance, human resources, sales, procurement, and production. It also describes the roles and responsibilities of an SAP materials management consultant in implementation, support, and training.
Presentation about IT managed services and solutions being offered by IISGL .
At IISGL, we have a fully consultative approach. We want
to understand your business, its pain points and
ambitions. We can then utilize that knowledge,
dovetailing with our years of extensive experience of
the technologies available, to provide you with a custom
solution.
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.
Suresh Menon, Vice President, Product Management - Information Quality Solutions at Informatica, shares how to master your data and your business from the 2015 Informatica Government Summit.
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 .
The document discusses modern data architectures. It presents conceptual models for data ingestion, storage, processing, and insights/actions. It compares traditional vs modern architectures. The modern architecture uses a data lake for storage and allows for on-demand analysis. It provides an example of how this could be implemented on Microsoft Azure using services like Azure Data Lake Storage, Azure Data Bricks, and Azure Data Warehouse. It also outlines common data management functions such as data governance, architecture, development, operations, and security.
Data Modeling Best Practices - Business & Technical ApproachesDATAVERSITY
Data Modeling is hotter than ever, according to a number of recent surveys. Part of the appeal of data models lies in their ability to translate complex data concepts in an intuitive, visual way to both business and technical stakeholders. This webinar provides real-world best practices in using Data Modeling for both business and technical teams.
This document provides an overview of moving to SAP S/4HANA. It discusses the major changes that come with S/4HANA including the transition from SAP EasyAccess to SAP Fiori and the backend changes involving HANA. It describes the different types of Fiori applications and the new Fiori V3 launchpad. The benefits of Fiori and HANA are highlighted. Considerations for on-premise versus public cloud deployments are discussed including customization options, release cycles, and localization. Finally, it covers the various paths for upgrading to S/4HANA such as system conversion and new implementation.
Gartner: Seven Building Blocks of Master Data ManagementGartner
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.
Capgemini & SAP: Fast Digital 4 Discrete Industries for AutomotiveCapgemini
This document summarizes Capgemini and SAP's Fast Digital 4 Discrete Industries (FD4) program for the automotive industry. FD4 aims to drive innovation through co-development of solutions on SAP technologies. It outlines three SAP S4 solution options for automotive suppliers: 1) A public cloud release in mid-2019, 2) A private cloud solution using Capgemini's pre-built AutoPath templates, and 3) Custom on-premise implementations. It then provides details on the AutoPath solution capabilities and benefits delivered through previous client implementations.
The document discusses SAP HANA Cloud Platform, which is SAP's platform-as-a-service offering. It provides everything needed to build enterprise applications in the cloud, including integration, APIs, analytics, user experience, IoT, security, collaboration, development and operations capabilities. It allows customers to increase business speed and agility by extending SAP solutions to hybrid landscapes. Example use cases and customer stories are also presented to illustrate how Walmart and EnterpriseJungle have leveraged SAP HANA Cloud Platform.
Data Architecture Best Practices for Today’s Rapidly Changing Data LandscapeDATAVERSITY
With the rise of the data-driven organization, the pace of innovation in data-centric technologies has been tremendous. New tools and techniques are emerging at an exponential rate, and it is difficult to keep track of the array of technological choices available to today’s data management professional.
At the same time, core fundamentals such as data quality and metadata management remain critical in order for organizations to obtain true business value from their data. This webinar will help demystify the options available: from data lake to data warehouse, to graph database, to NoSQL, and more, and how to integrate these new technologies with core architectural fundamentals that will help your organization benefit from the quick wins that are possible from these exciting technologies, while at the same time build a longer-term sustainable architecture that will support the inevitable change that will continue in the industry.
This document outlines an MDM architecture using SAP components, including SAP MDG for the master data repository, SAP Info Steward for metadata management, and SAP Data Services for data integration and quality. It recommends using Sybase PowerDesigner for data modeling, profiling data with SAP Info Steward, and leveraging SAP HANA for faster processing. The architecture utilizes SAP components for presentation, persistence, integration and processing of master data.
Building a Data Lake for Your Enterprise, ft. Sysco (STG309) - AWS re:Invent ...Amazon Web Services
Data lakes are transforming the way enterprises store, analyze, and learn insights from their data. While data lakes are a relatively new concept, many enterprises have already generated significant business value from the insights gleaned. In this session, AWS experts and technology leaders from Sysco, a Fortune 50 company and leader in food distribution and marketing, explain why Sysco decided to evolve its data management capabilities to include data lakes and how they customized them to support diverse querying capabilities and data science use cases. They also discuss how to architect different aspects of a data lake—ingestion from disparate sources, data consumption, and usability layers—and how to track data ingestion and consumption, monitor associated costs, enforce wanted levels of user access, manage data file formats, synchronize production and non-production environments, and maintain data integrity. Services to be discussed include Amazon S3 and S3 Select, Amazon Athena, Amazon EMR, Amazon EC2, and Amazon Redshift Spectrum.
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.
DMBOK 2.0 and other frameworks including TOGAF & COBIT - keynote from DAMA Au...Christopher Bradley
This document provides biographical information about Christopher Bradley, an expert in information management. It outlines his 36 years of experience in the field working with major organizations. He is the president of DAMA UK and author of sections of the DAMA DMBoK 2. It also lists his recent presentations and publications, which cover topics such as data governance, master data management, and information strategy. The document promotes training courses he provides on information management fundamentals and data modeling.
This webinar from Gartner provided seven building blocks for a successful master data management (MDM) plan: vision, strategy, metrics, information governance, organization and roles, information lifecycle, and enabling infrastructure. The presentation emphasized the importance of establishing an MDM vision aligned with business goals, assessing the organization's current MDM maturity, defining metrics to measure success, establishing governance, and considering organizational roles and responsibilities. It also stressed understanding the information lifecycle and having the right technology infrastructure.
Accenture Cloud Platform: Control, Manage and Govern the Enterprise Cloudaccenture
The Accenture Cloud Platform is a multi-cloud management platform that enables organizations to manage all of their enterprise cloud
resources—public and private—and automate and accelerate solution delivery.
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.
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
The document discusses product information management (PIM) for HP Printing and Personal Systems. It outlines the challenges of managing vast amounts of product data across departments and systems. It then describes how a PIM solution could address these challenges by providing a single source of truth for product information through capabilities like data integration, governance and a centralized repository. The paper also provides details on how HP could implement a PIM architecture using a transactional hub model to manage master product data.
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
Rajesh Sharma is a SAP certified consultant specializing in SAP Activate, SAP Sourcing & Procurement, SAP EWM, and SAP ERP. The document provides an overview of ERP systems and SAP ERP in particular. It defines ERP as integrated software that manages core business processes. SAP is one of the leading ERP providers, with over 60% market share. The document outlines the key modules in SAP ERP including finance, human resources, sales, procurement, and production. It also describes the roles and responsibilities of an SAP materials management consultant in implementation, support, and training.
Presentation about IT managed services and solutions being offered by IISGL .
At IISGL, we have a fully consultative approach. We want
to understand your business, its pain points and
ambitions. We can then utilize that knowledge,
dovetailing with our years of extensive experience of
the technologies available, to provide you with a custom
solution.
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.
Suresh Menon, Vice President, Product Management - Information Quality Solutions at Informatica, shares how to master your data and your business from the 2015 Informatica Government Summit.
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 .
The document discusses modern data architectures. It presents conceptual models for data ingestion, storage, processing, and insights/actions. It compares traditional vs modern architectures. The modern architecture uses a data lake for storage and allows for on-demand analysis. It provides an example of how this could be implemented on Microsoft Azure using services like Azure Data Lake Storage, Azure Data Bricks, and Azure Data Warehouse. It also outlines common data management functions such as data governance, architecture, development, operations, and security.
Data Modeling Best Practices - Business & Technical ApproachesDATAVERSITY
Data Modeling is hotter than ever, according to a number of recent surveys. Part of the appeal of data models lies in their ability to translate complex data concepts in an intuitive, visual way to both business and technical stakeholders. This webinar provides real-world best practices in using Data Modeling for both business and technical teams.
This document provides an overview of moving to SAP S/4HANA. It discusses the major changes that come with S/4HANA including the transition from SAP EasyAccess to SAP Fiori and the backend changes involving HANA. It describes the different types of Fiori applications and the new Fiori V3 launchpad. The benefits of Fiori and HANA are highlighted. Considerations for on-premise versus public cloud deployments are discussed including customization options, release cycles, and localization. Finally, it covers the various paths for upgrading to S/4HANA such as system conversion and new implementation.
Gartner: Seven Building Blocks of Master Data ManagementGartner
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.
Capgemini & SAP: Fast Digital 4 Discrete Industries for AutomotiveCapgemini
This document summarizes Capgemini and SAP's Fast Digital 4 Discrete Industries (FD4) program for the automotive industry. FD4 aims to drive innovation through co-development of solutions on SAP technologies. It outlines three SAP S4 solution options for automotive suppliers: 1) A public cloud release in mid-2019, 2) A private cloud solution using Capgemini's pre-built AutoPath templates, and 3) Custom on-premise implementations. It then provides details on the AutoPath solution capabilities and benefits delivered through previous client implementations.
The document discusses SAP HANA Cloud Platform, which is SAP's platform-as-a-service offering. It provides everything needed to build enterprise applications in the cloud, including integration, APIs, analytics, user experience, IoT, security, collaboration, development and operations capabilities. It allows customers to increase business speed and agility by extending SAP solutions to hybrid landscapes. Example use cases and customer stories are also presented to illustrate how Walmart and EnterpriseJungle have leveraged SAP HANA Cloud Platform.
Data Architecture Best Practices for Today’s Rapidly Changing Data LandscapeDATAVERSITY
With the rise of the data-driven organization, the pace of innovation in data-centric technologies has been tremendous. New tools and techniques are emerging at an exponential rate, and it is difficult to keep track of the array of technological choices available to today’s data management professional.
At the same time, core fundamentals such as data quality and metadata management remain critical in order for organizations to obtain true business value from their data. This webinar will help demystify the options available: from data lake to data warehouse, to graph database, to NoSQL, and more, and how to integrate these new technologies with core architectural fundamentals that will help your organization benefit from the quick wins that are possible from these exciting technologies, while at the same time build a longer-term sustainable architecture that will support the inevitable change that will continue in the industry.
This document outlines an MDM architecture using SAP components, including SAP MDG for the master data repository, SAP Info Steward for metadata management, and SAP Data Services for data integration and quality. It recommends using Sybase PowerDesigner for data modeling, profiling data with SAP Info Steward, and leveraging SAP HANA for faster processing. The architecture utilizes SAP components for presentation, persistence, integration and processing of master data.
Building a Data Lake for Your Enterprise, ft. Sysco (STG309) - AWS re:Invent ...Amazon Web Services
Data lakes are transforming the way enterprises store, analyze, and learn insights from their data. While data lakes are a relatively new concept, many enterprises have already generated significant business value from the insights gleaned. In this session, AWS experts and technology leaders from Sysco, a Fortune 50 company and leader in food distribution and marketing, explain why Sysco decided to evolve its data management capabilities to include data lakes and how they customized them to support diverse querying capabilities and data science use cases. They also discuss how to architect different aspects of a data lake—ingestion from disparate sources, data consumption, and usability layers—and how to track data ingestion and consumption, monitor associated costs, enforce wanted levels of user access, manage data file formats, synchronize production and non-production environments, and maintain data integrity. Services to be discussed include Amazon S3 and S3 Select, Amazon Athena, Amazon EMR, Amazon EC2, and Amazon Redshift Spectrum.
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.
DMBOK 2.0 and other frameworks including TOGAF & COBIT - keynote from DAMA Au...Christopher Bradley
This document provides biographical information about Christopher Bradley, an expert in information management. It outlines his 36 years of experience in the field working with major organizations. He is the president of DAMA UK and author of sections of the DAMA DMBoK 2. It also lists his recent presentations and publications, which cover topics such as data governance, master data management, and information strategy. The document promotes training courses he provides on information management fundamentals and data modeling.
This webinar from Gartner provided seven building blocks for a successful master data management (MDM) plan: vision, strategy, metrics, information governance, organization and roles, information lifecycle, and enabling infrastructure. The presentation emphasized the importance of establishing an MDM vision aligned with business goals, assessing the organization's current MDM maturity, defining metrics to measure success, establishing governance, and considering organizational roles and responsibilities. It also stressed understanding the information lifecycle and having the right technology infrastructure.
Accenture Cloud Platform: Control, Manage and Govern the Enterprise Cloudaccenture
The Accenture Cloud Platform is a multi-cloud management platform that enables organizations to manage all of their enterprise cloud
resources—public and private—and automate and accelerate solution delivery.
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.
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
The document discusses product information management (PIM) for HP Printing and Personal Systems. It outlines the challenges of managing vast amounts of product data across departments and systems. It then describes how a PIM solution could address these challenges by providing a single source of truth for product information through capabilities like data integration, governance and a centralized repository. The paper also provides details on how HP could implement a PIM architecture using a transactional hub model to manage master product data.
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.
TekMindz Master Data Management CapabilitiesAkshay Pandita
This document provides an overview of Master Data Management (MDM) offerings and benefits from TekMindz. MDM is an approach that centralizes master information such as customers, products, and suppliers to ensure consistent, up-to-date data across business systems. MDM addresses issues like data governance, quality and consistency. TekMindz' MDM capabilities include collaborative authoring, data quality management, event management, and integration with data quality tools. MDM implementations require data governance to construct trusted views of master data needed by business processes. TekMindz offers MDM solutions across four editions to meet different customer needs.
This document discusses epics and user stories in agile software development. It defines epics as large features or requirements too big to complete in a single sprint that need to be broken down into smaller user stories. User stories are simple descriptions of features written from the perspective of the end user that follow a who, what, why template. The document provides examples of epics and user stories and guidelines for when and how to split large stories or epics into smaller independent stories that can be estimated and implemented within a sprint.
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.
The document discusses Siperian's master data management solutions that help companies improve data quality, consistency, and access to key business data. Siperian's MDM Hub platform provides a centralized, shared repository that resolves data conflicts to create unified views of critical business entities. This allows companies to improve business functions like sales, marketing, customer service, and risk management. Siperian MDM Hub offers a flexible, model-driven architecture that can be deployed quickly for fast ROI.
- Customer data management is important to understand customers better and improve service, but traditional data management tools create siloed and inconsistent data.
- NexJ Customer Data Management provides an "Enterprise Customer View" that integrates customer data from multiple sources to create a holistic understanding of each customer.
- This unified view of customer data can be used across an organization to drive digital transformation initiatives, enhance customer insights, and meet compliance requirements.
This document discusses best practices for implementing a successful data quality initiative. It highlights common data quality challenges such as disparate data across systems and organizational silos. Successful initiatives establish clear metrics, involve a cross-functional team with executive support, develop data integration strategies, and select a comprehensive referential data source. The implementation process involves assembling a data quality team, defining key performance indicators, preparing the organization through communication and training, understanding current data processes, and integrating a referential data source to populate enterprise systems and ensure ongoing data integrity. Case studies from Lexmark, McGladrey, and Dow Corning are provided.
This document summarizes the challenges of data quality and opportunities for improvement. It discusses how most companies struggle with disparate and inaccurate data across systems, but that data quality initiatives can drive revenue growth by providing strategic customer intelligence. Case studies are then presented of three companies - Lexmark, McGladrey, and Dow Corning - that overcame data quality challenges through best practices like clear KPIs, cross-functional teams, data integration strategies, and change management.
Data quality is important for business success. This document outlines a 6-step approach to measuring data quality ROI: 1) Inventory systems relying on data, 2) Determine data quality rules, 3) Profile data to measure rule compliance, 4) Score each rule and system, 5) Measure impact of improved data quality, 6) Implement improvements. The approach is demonstrated by analyzing a targeted marketing system and identifying areas of non-compliance to improve data quality and ROI.
EMC Perspective: Big Data Transforms the Life Science Commercial ModelEMC
The pharmaceutical industry faces many challenges that are forcing changes to business models. Customers now demand data on health outcomes in addition to drug efficacy, requiring companies to transform promotional strategies. Sales and marketing departments are under pressure to evolve traditional commercial models that define customers, messaging, and channels. To succeed in this new environment, life sciences firms will need to use analytics to quickly gain insights from both structured and unstructured internal and external data sources to inform optimized commercial models and adjust strategies in real time.
Big Data in Financial Services: How to Improve Performance with Data-Driven D...Perficient, Inc.
Most banking and financial services organizations have only scratched the surface of leveraging customer data to transform their business, realize new revenue opportunities, manage risk and address customer loyalty. Yet a business’s digital footprint continues to evolve as automated payments, location-based purchases, and unstructured customer communications continue to influence the technology landscape for financial services.
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.
This document discusses concepts related to global sourcing, outsourcing, ASPs, and ERP systems. It defines key terms, explores different types of outsourcing relationships and the determinants of competitive advantage. Porter's five forces model and Moore's business ecosystem concept are examined. The functions and potential benefits of ERP systems are outlined, along with implementation requirements and challenges. Quotes from industry experts provide real-world perspectives on ERP objectives, problems experienced, and advice for others implementing ERP.
The document discusses how investing in data quality can provide a significant return on investment for companies. It outlines five tenets that leading companies embrace to realize this ROI from quality data: 1) view data quality as a business issue, not just an IT issue, 2) establish an explicit data governance strategy, especially at the point of data entry, 3) use a third-party data provider to consolidate and cleanse data, 4) address the challenge of maintaining accurate data given the rapid rate of data changes, and 5) strive for a 360-degree view of customers and suppliers across the organization.
With the right CRM solution, information workers can increase productivity, improve customer service, and boost collaboration, while the business simplifies complex processes, increases sales, and reduces costs.
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.
What is Big Data?
Big Data Laws
Why Big Data?
Industries using Big Data
Current process/SW in SCM
Challenges in SCM industry
How Big data can solve the problems?
Migration to Big data for an SCM industry
1) QMS Partners is an international market research and strategy consulting firm that provides services to help clients develop business growth strategies.
2) The firm conducted research for an industrial manufacturing client to understand where value is created across the client's value chain and to define an appropriate customer value model.
3) As a result of QMS's work, the client identified opportunities to improve how different functions contribute value at various points in the value chain, from product development to marketing to distribution. This allowed the client to make better decisions about processes and technology to support a new customer relationship management strategy.
The document discusses how maintaining accurate and complete data is important for business growth. It notes that poor data quality costs companies an average of $8.2 million annually and that only 12% of companies use customer intelligence to drive key business functions. Regular data cleansing and maintenance is needed to maximize revenue from existing customers, uncover new prospects, and strengthen customer relationships. Committing to continuous data quality improvement requires executive sponsorship and cross-departmental collaboration.
Microsoft Dynamics xRM4Legal 2013 Marketing OverviewDavid Blumentals
The document provides an overview of the Microsoft xRM4Legal 2013 software for legal firms. It discusses how the software can help law firms become more effective at business development by [1] giving staff the right tools to attract more clients, improve service, close deals faster, and enhance client relationships, [2] achieving new levels of business development productivity through better targeting, managing activities and communication, and opportunities, and [3] providing real-time business intelligence through customizable dashboards and reports.
Unica Detect provides a solution for event-based marketing that allows financial institutions to detect patterns in customer transaction data and other behaviors. This enables opportunities like reducing churn, cross-selling, fraud detection, and personalizing the customer experience. While banks have more customer data than ever, integrating diverse systems and making sense of large datasets presents challenges. Unica Detect uses an open and adaptable system to overcome these challenges and power behavior-based marketing actions.
It used to be that having an innovative product itself provided enough impetus for a company to reach its ultimate customer. But today, your go-to-market strategy is just as vital
For Life Sciences, the Future of Master Data Management is in the CloudCognizant
Cloud-based MDM is cost-effective and easily implemented; it is scalable, robust and mature; and it has the flexibility required to enable and support the evolving transformation of life sciences commercial operations.
This document provides guidance for creating an effective patch management program for industrial control systems. It recommends establishing several key plans: a configuration management plan to track systems and software; a patch management plan to evaluate vulnerabilities and deploy patches; a backup/archive plan to preserve system states; a patch testing plan to validate patches; an incident response plan to address vulnerabilities; and a disaster recovery plan in case patches fail. When these plans are integrated and their resources leveraged together, they provide a robust approach to patch management that balances security, reliability and operational needs.
The Medicaid Information Technology Architecture (MITA) is both an initiative and framework developed by CMS to promote improvements in Medicaid systems and processes. As an initiative, MITA provides a plan for collaboration between CMS and states. As a framework, MITA offers models, guidelines and principles for states to implement enterprise solutions. The goal of MITA is to change how states design, build and modify Medicaid systems and perform IT planning according to national guidelines. The MITA framework consists of a business architecture, information architecture and technical architecture. The business architecture describes business needs, goals and processes. The information architecture identifies Medicaid data and provides models and strategies for data management. The technical architecture provides guidance on technologies, tools and standards for implementing MITA.
States are working to apply healthcare information technology (HIT) within their Medicaid programs to improve efficiency and reduce costs. Through initiatives like the Medicaid Transformation Grants and the Medicaid Information Technology Architecture (MITA) framework, states are using HIT to enhance clinical decision making, expand e-prescribing, and provide electronic health records for Medicaid beneficiaries. Going forward, organizations like the National Association of State Medicaid Directors and the State Alliance for e-Health continue to support greater adoption of HIT within Medicaid.
Facets Overview and Navigation User Guide.pdfwardell henley
This document provides an overview and introduction to the Facets managed healthcare system. It begins with the lesson objectives, which include being introduced to Facets navigation, applications, and how to use the system. It then provides a high-level overview of Facets, explaining what it is, why Sierra Health Services implemented it, and an overview of the implementation process. The majority of the document introduces and describes the main Facets application groups and their related applications. These include accounting, billing, claims processing, customer service, and other groups. It concludes by explaining how to open and close Facets, explore the product navigation window, and change passwords.
This document provides guidance for contractors on conducting self-inspections of their security programs as required by the National Industrial Security Program Operating Manual (NISPOM). It outlines a three-step self-inspection process of pre-inspection, inspection, and post-inspection. The pre-inspection involves planning, while the inspection involves reviewing security practices against a checklist. Areas to check include classified storage, training, visits, and more. The post-inspection involves reporting findings, taking corrective actions, and briefing management. Interview techniques are also provided to verify security compliance with employees. The goal is for contractors to regularly inspect and improve their security programs.
The document provides guidance on Change Advisory Board (CAB) meetings in ITIL. It outlines that the Change Manager chairs CAB meetings to review pending changes and authorize or reject them. The CAB considers each change's risks, impacts, and resources required. It documents any issues, recommendations, and authorizations. Changes that the CAB cannot decide on may be escalated to higher levels for a final determination.
Boston Financial's Information Security Program is committed to ensuring customer data is protected from unauthorized access through a layered security approach. The program employs risk assessment, security policies and standards, awareness training, and a dedicated security team led by a Chief Information Security Officer to prevent breaches and adhere to industry best practices and compliance standards. The scope of the program encompasses security administration, technology infrastructure, and policy management to consistently monitor threats and protect customer information.
This document discusses Good Manufacturing Practices (GMP) for pharmaceuticals. It introduces GMP, explaining that GMP ensures pharmaceutical products are consistently manufactured and controlled to quality standards for their intended use. It also discusses the relationships between quality assurance (QA), GMP, and quality control (QC), noting that QA oversees the whole system, GMP ensures consistent manufacturing quality, and QC tests samples to ensure quality. Finally, it provides an overview of key aspects of GMP, including facilities, equipment, packaging, and documentation.
This document is a checklist and certification for information security program requirements for an RFP or contract. It requires identification of the type of information involved, applicable security categories and position sensitivity designations. It also requires certification by a Project Officer and Information Systems Security Officer that appropriate security requirements are specified to protect government interests in compliance with federal standards.
The document outlines the basic components of an information security program for mortgage industry professionals. It discusses 13 first priority cybersecurity practices like managing risk, protecting systems from malware, patching systems, and training employees. It also discusses 10 second priority practices such as encrypting sensitive data, third party risk management, and disaster recovery planning. The document is intended to provide a succinct overview of security risks and basic practices to help small and medium businesses manage those risks.
Best practices for_implementing_security_awareness_trainingwardell henley
- Security professionals are most concerned about data breaches, phishing, spearphishing, and ransomware attacks. These threats can be addressed through effective security awareness training.
- The vast majority of surveyed organizations had experienced security incidents like phishing attacks delivering malware, targeted email attacks, or data breaches in the past year.
- Over 90% of organizations report that phishing and spearphishing attempts reaching end users have increased or stayed the same over the past 12 months, indicating ongoing threats.
DMARC requires alignment between the authentication domain from SPF or DKIM and the From header domain. SPF authenticates the MAIL FROM or EHLO domain, while DKIM authenticates the domain in the d= tag. Alignment can be strict, requiring an exact match, or relaxed, allowing subdomains. DMARC policies use tags to specify whether strict or relaxed alignment is needed for SPF (aspf) or DKIM (adkim) to pass DMARC verification. Alignment helps validate the authenticity of the From header by linking it to the authenticated domains.
This document discusses security considerations for service-oriented architectures (SOA) and on-demand environments. It describes several subsystems that are important for a comprehensive security management architecture (MASS), including access control, identity and credential management, information flow control, security auditing, and solution integrity. Technologies that can be used to implement each subsystem are also outlined, such as directories, firewalls, encryption, and systems management solutions. The document stresses that security requires an integrated approach across all of these areas.
This chapter discusses security architecture and models, including computer organization, distributed systems, protection mechanisms, formal security models like Bell-LaPadula and Biba integrity models. It covers topics like certification and accreditation processes to establish security requirements are met. Evaluation criteria for security classifications from class D to class A are also mentioned to assess security assurance levels.
This document outlines an enterprise security architecture seminar. It introduces security architecture and the SABSA framework. SABSA is a step-by-step approach that involves identifying business drivers, attributes, threats, control objectives, and security services. A worked example is provided that protects customer information. The seminar covers developing a contextual and conceptual security understanding, logical security architecture components, and deliverables including control frameworks.
This document discusses security architecture and models, including differences between commercial and government security requirements. It covers security evaluation criteria, security practices for the Internet, technical platforms in terms of hardware/software, and system security techniques like preventative, detective and corrective controls. The document also describes the layered approach to security architecture.
This document provides a checklist for securing a Splunk software installation, including setting up authenticated users and managing access, using certificates to encrypt communications, hardening Splunk instances, setting consistent passwords across servers, securing service accounts, auditing the system regularly to monitor access and activities, and monitoring files and directories.
The document provides best practices for using the Splunk App for Windows Infrastructure, including synchronizing clocks, configuring Active Directory monitoring, limiting baseline data collection, and disabling SID translation to reduce domain controller resource usage. Specific configuration changes are recommended in the Splunk_TA_microsoft_ad and Splunk_TA_Windows add-ons to implement these practices.
This document provides an overview of Enterprise Content Management (ECM) and discusses how ECM solutions can be supported by various storage technologies and solutions. It begins with introductions to ECM and storage concepts for specialists in the opposite fields. It then discusses business drivers for ECM and provides a reference architecture for matching ECM requirements to appropriate storage strategies. The reference architecture addresses requirements for security, integrity, retention, availability and cost, among others. It also covers storage considerations for availability, backup/recovery, business continuity and capacity planning.
Oracle Services Procurement allows organizations to better manage services spending. It enables defining complex payment terms for services contracts, tracking project progress and payments, and ensuring compliance. The software also enforces the use of preferred suppliers through requisition processes and rate cards. This improves visibility and oversight of an organization's services spending.
AppSec PNW: Android and iOS Application Security with MobSFAjin Abraham
Mobile Security Framework - MobSF is a free and open source automated mobile application security testing environment designed to help security engineers, researchers, developers, and penetration testers to identify security vulnerabilities, malicious behaviours and privacy concerns in mobile applications using static and dynamic analysis. It supports all the popular mobile application binaries and source code formats built for Android and iOS devices. In addition to automated security assessment, it also offers an interactive testing environment to build and execute scenario based test/fuzz cases against the application.
This talk covers:
Using MobSF for static analysis of mobile applications.
Interactive dynamic security assessment of Android and iOS applications.
Solving Mobile app CTF challenges.
Reverse engineering and runtime analysis of Mobile malware.
How to shift left and integrate MobSF/mobsfscan SAST and DAST in your build pipeline.
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectorsDianaGray10
Join us to learn how UiPath Apps can directly and easily interact with prebuilt connectors via Integration Service--including Salesforce, ServiceNow, Open GenAI, and more.
The best part is you can achieve this without building a custom workflow! Say goodbye to the hassle of using separate automations to call APIs. By seamlessly integrating within App Studio, you can now easily streamline your workflow, while gaining direct access to our Connector Catalog of popular applications.
We’ll discuss and demo the benefits of UiPath Apps and connectors including:
Creating a compelling user experience for any software, without the limitations of APIs.
Accelerating the app creation process, saving time and effort
Enjoying high-performance CRUD (create, read, update, delete) operations, for
seamless data management.
Speakers:
Russell Alfeche, Technology Leader, RPA at qBotic and UiPath MVP
Charlie Greenberg, host
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.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/how-axelera-ai-uses-digital-compute-in-memory-to-deliver-fast-and-energy-efficient-computer-vision-a-presentation-from-axelera-ai/
Bram Verhoef, Head of Machine Learning at Axelera AI, presents the “How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-efficient Computer Vision” tutorial at the May 2024 Embedded Vision Summit.
As artificial intelligence inference transitions from cloud environments to edge locations, computer vision applications achieve heightened responsiveness, reliability and privacy. This migration, however, introduces the challenge of operating within the stringent confines of resource constraints typical at the edge, including small form factors, low energy budgets and diminished memory and computational capacities. Axelera AI addresses these challenges through an innovative approach of performing digital computations within memory itself. This technique facilitates the realization of high-performance, energy-efficient and cost-effective computer vision capabilities at the thin and thick edge, extending the frontier of what is achievable with current technologies.
In this presentation, Verhoef unveils his company’s pioneering chip technology and demonstrates its capacity to deliver exceptional frames-per-second performance across a range of standard computer vision networks typical of applications in security, surveillance and the industrial sector. This shows that advanced computer vision can be accessible and efficient, even at the very edge of our technological ecosystem.
Digital Banking in the Cloud: How Citizens Bank Unlocked Their MainframePrecisely
Inconsistent user experience and siloed data, high costs, and changing customer expectations – Citizens Bank was experiencing these challenges while it was attempting to deliver a superior digital banking experience for its clients. Its core banking applications run on the mainframe and Citizens was using legacy utilities to get the critical mainframe data to feed customer-facing channels, like call centers, web, and mobile. Ultimately, this led to higher operating costs (MIPS), delayed response times, and longer time to market.
Ever-changing customer expectations demand more modern digital experiences, and the bank needed to find a solution that could provide real-time data to its customer channels with low latency and operating costs. Join this session to learn how Citizens is leveraging Precisely to replicate mainframe data to its customer channels and deliver on their “modern digital bank” experiences.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
How information systems are built or acquired puts information, which is what they should be about, in a secondary place. Our language adapted accordingly, and we no longer talk about information systems but applications. Applications evolved in a way to break data into diverse fragments, tightly coupled with applications and expensive to integrate. The result is technical debt, which is re-paid by taking even bigger "loans", resulting in an ever-increasing technical debt. Software engineering and procurement practices work in sync with market forces to maintain this trend. This talk demonstrates how natural this situation is. The question is: can something be done to reverse the trend?
Discover top-tier mobile app development services, offering innovative solutions for iOS and Android. Enhance your business with custom, user-friendly mobile applications.
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
"Choosing proper type of scaling", Olena SyrotaFwdays
Imagine an IoT processing system that is already quite mature and production-ready and for which client coverage is growing and scaling and performance aspects are life and death questions. The system has Redis, MongoDB, and stream processing based on ksqldb. In this talk, firstly, we will analyze scaling approaches and then select the proper ones for our system.
What is an RPA CoE? Session 1 – CoE VisionDianaGray10
In the first session, we will review the organization's vision and how this has an impact on the COE Structure.
Topics covered:
• The role of a steering committee
• How do the organization’s priorities determine CoE Structure?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems
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 .
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
Introduction of Cybersecurity with OSS at Code Europe 2024
Infosys best practices_mdm_wp
1. Win in the flat world
Best Practices for a
Successful MDM Implementation
White Paper
Abstract
With the ever-increasing demand for cost optimization, faster product
launches, more efficient compliance with regulations and differentiated
business competitiveness, one of the biggest pain areas for enterprises is
achieving consistent quality data. Leading to sub-optimal decision making,
data misalignment within various systems is putting the brakes on
organizations looking to accelerate growth.
This paper outlines the experiential best practices that can help
organizations improve the odds and realize business value quickly and
predictably while planning and implementing an MDM solution to solve
their data issues.
For more information, contact askus@infosys.com
Sep 2010
2. The Data Problem revenue growth from the current customer base, cost
optimization, and regulatory compliance have all heightened
Quality data is a strategic asset for any organization. It
awareness of the risks of using poor quality data.
provides a strong and secure foundation to drive business
execution and differentiated services. Clean and These important business initiatives offer a compelling
consistent data leads to better decision making and business case for improving data quality by delivering
provides agility in a competitive marketplace. Quality data access to a single reference that represents the 'golden
also leads to improved stakeholder relationships - be it record' or 'Best Version of Truth' - which can be achieved
customers, suppliers or channel partners. with an efficient Master Data Management (MDM) system.
MDM offers a central repository to manage business-critical
data on an ongoing basis. It ensures synchronization with
business intelligence and operational systems by integrating
data in real time and empowering data stewards with the
capabilities to properly govern data across the enterprise. ,
this MDM enables the organization to gain critical
enterprise-wide insight about customers, products, partners
and so on, and facilitates more confident decision making,
accurate reporting and nimble action.
How Can MDM Help?
MDM is a deliberate initiative comprising of a set of
methodologies, strategies, disciplines and technologies that
Customer is 'owned' by everyone and no one! enable organizations to acquire, cleanse, enrich, consolidate,
In a traditional organization, critical data about customers, federate, and govern data across many disparate systems.
products and partners is fragmented across myriad systems From an analytics perspective, organizations can employ
- each independently trying to own and manage the data. quality data for reporting and compliance purposes, and
As business-critical data passes through the complex to optimize and enhance partner and channel engagement.
enterprise, it may get locked up, duplicated, or worse,
From an operations standpoint, organizations can build a
misrepresented and misinterpreted, obscuring the facts and
centralized hub representing the best version of the truth,
compromising performance. The increasing clamor for faster
providing an accurate, consistent and secure copy of
time-to-market for product introductions, incremental
• I don't know if or when my loyalty programs and campaigns have been effective
• I'm not sure if I am meeting my regulatory compliance requirements
Sounds • I look across my systems and find duplicate customers and dead products
familiar? • My reports are never consistent for the same question
• My sales channels have outdated product information
• My new product introductions take forever
My business competitiveness is jeopardized!
3. Customer and Compliance Putting the Best Foot Forward
Partner Management Requirements
Improved effectiveness
• Channel Optimization • Global Regulatory With organizations across industry verticals making
• Route-To-Market Compliance significant investments in MDM solutions, it is necessary
Analytics • Internal Regulations
• 360-degree View of • Effective Risk to recognize and act upon the best practices that help
Customer Management
• Reduced Duplicate organizations manage their MDM engagements
Customer effectively.
Communications
ACQ These best practices are:
CH
I
UIR
ENR
E Rear-View Check
Reduced Reduced Active Data Governance Keep checking the
TCO MDM manual effort Policies and procedures
must be formalized
benefits from one phase
to the next
CON
E
NS
LID Architectural Consistency Business Case Think Big, Start Small
SO
A
ATE CLE Technology and tools that Quantifiable and Phased Approach but
fit the IT ecosystem Measurable ROI make MDM a Program
Increase in Mergers and
Account Revenue Acquisitions Active Vendor Support
Get insight into upcoming
Eye-on-the-ball
Avoid scope creep of the
• Increased Cross-Sell • Ability to view features, avoiding
Enhanced analytics
MDM program
customization
Opportunities consolidated assets/
• Increased Up-Sell resources
Opportunities • Ability to have Continuous Collaboration
Business operations
• Increased uniform rules stakeholders to be involved
Predictability of Profit • Reduced costs of throughout
Margins failure
master data to all systems and business users across lines Give Wings to Your Vision
of business (LOBs). Not only must an organization's master data vision align
Some of the key benefits that organizations can gain by with its business vision, but it must also acknowledge
using MDM include: master data as a critical asset. The most crucial questions
• Identifying new opportunities to interact with to answer at kick-off are the 'Whys' of an MDM initiative -
customers and channel partners functionally, technically and financially. Identification of
• Realizing improved effeciency across business critical success factors along with clear achievable
processes objectives goes a long way in establishing early success.
• Enhancing business intelligence, reporting and The business case needs to outline the 'Whys', 'Hows' and
analytic capabilities 'Whos' of the MDM exercise clearly. A quantifiable and
• Optimizing the manual effort required to manage measurable return on investment (ROI) is the cornerstone
and use data across the enterprise of a successful initiative. Business pain points and data
Stage
Program Program Overall Requirement Product
Definition Socialization Strategy Assessment Evaluation
• Defining Engagement • Calculating Program • MDM and Data • To-be Conceptual • Requirement
Parameters ROI over 3 -5 years – Governance Structure Architecture Documentation and
• Identifying Critical This is the most Formulation • MDM Style – RFP Creation
Activities
Success Factors critical for MDM • Drivers/ Levers Transactional, • Vendor Scenario
• Identifying Program Governance Identification and Registry or Formulation and CRP
Candidates and programs Timelines Operational Demo
Sponsors • Sponsor Approvals of • Establishing Baseline • Roadmap Strategy • Vendor Negotiation
• Organizational MDM Business Case Processes and Guiding Principles and Selection
Objective Metrics High-level Release
Planning
Transition Architecture
4. issues must be identified and prioritized in the business establish confidence in the initiative. For example, after
case. It is also important to gain the buy-in and approval of implementing the customer domain MDM, ROI needs to be
all key stakeholders to endorse the business case. checked in terms of increase in cross-sell, up-sell and the
benefits that have accrued due to the quality of reporting
The strategy must be solidified and plans drawn keeping in
available. If the MDM initiative involved the phasing out of
mind the 'To-be' conceptual architecture and the MDM style
legacy systems, the cost of change management must also
that best fits the organization's need. Data Governance
be captured while calculating the ROI.
policy discussions must be set in motion within the
organization to get an enterprise-wide consensus. While
Don't Forget, It's a Collaborative Exercise
doing all this, it is essential to keep sufficient lead time for
product evaluations and vendor negotiations. MDM is a data governance, quality-oriented and business-
driven initiative for master data assets, which usually
Think Big. Start Small. Keep Your Eye on the Ball involves the adoption of new technology. Hence, MDM
initiatives usually span various units across the
Master Data Assets
organization. The success of the initiative depends on the
(such as Customer,
level of collaboration among the units coming together to
Product/ Item, Partner,
provide input and designing the end-solution. This also
Organization, Supplier,
provides the stakeholders a sense of continuous
etc.) do not exist in
involvement with the MDM program. It is also a good idea
isolation within an
to have a change management anchor identified for the
organization. Hence,
MDM program who can socialize the developments and
the potential to use
happenings of the program to the stakeholders and
quality data for all master data assets is tempting for many
champion the cause of data awareness within the
organizations. However, it is imperative to focus only on
organization.
one sub-set of master data asset at a time. Thus, an MDM
initiative works best if it adopts a multi-phase approach
tackling 1-2 entities per phase with the design and model
being scalable for the next phases. If you ignore the
Logistics & Sales &
scalability and future design considerations while building Supply Chain Marketing
MDM solutions for different entities, it can lead to isolated
master data silos - recreating the problem that the MDM Change
envisioned to solve. When it comes to MDM, it's important Management
Champions
to think ahead and think big, but take baby steps to achieve
quick wins and gain the buy in for next steps.
HR & Admin Finance
Check Your Rear-View Mirror
The business case must articulate broadly the parameters
and metrics needed to measure progress in quantifiable
terms. After each phase of the MDM program, the Look at Architectural Consistency and
organization needs to measure the ROI. Since MDM Product Fit
stakeholders belong to multiple departments within the
Organizations need to do thorough due diligence of the
organization having diverse objectives, it is essential to have
target architecture and the MDM-enabling technology or
a pre-defined and objective MDM success criterion to
5. package. The architecture style needs to be discussed and A
Define Scope for
firmed up while working on the business case. Data Quality
The various styles of architecture are Registry, Transaction F B
Hub and Co-Existence. Each style has its own set of pros Execute Data Measure Data
Quality Quality
and cons along with cost impact (both in terms of Improvement Plan
investment and performance). Most organizations tend Data Quality
to opt for Co-existence as it gives them the edge to meet Program
E C
all their business objectives at an optimal cost. The MDM Develop Plan to Assess Business
Address Data Impact
technology solution supports both analytical and Quality
operational processes in real-time/ batch in both the Co- D
existence and Transaction architecture style. Hence, it is Perform Root Cause
Analysis
important that the technology blends with the
organization's overall IT architecture and ecosystem.
and parcel of the daily job responsibilities of the users,
Small wins at definite phases help increase rather than a one-off initiative. For any MDM initiative to
MDM adoption succeed, it is imperative that effective data governance
is supported by senior management.
One of the key architectural points to note is the Service
Oriented Architecture (SOA) support by the MDM package. Ensure Active MDM Vendor Support
While evaluating MDM products and technologies, it is MDM is a rapidly growing area in terms of technology.
essential to pick one that closely supports the universe of There are numerous product vendors who offer best-in
use cases from all master data assets to avoid custom class MDM products with broad features such as Match,
development in implementation. Merge, Trust, Survivorship, Front-End Data Governance
Graphical User Interface (GUI) and Integration with third-
Embrace Data Governance
party information agencies such as Experian. Once your
MDM is not a one-time technology implementation or a organization selects an MDM product that fits your IT
one-time data cleansing exercise. Its primary purpose is
to enable a 'Be-Clean-and-Stay-Clean' data asset across Continuous Improvement is the key to an
the organization. The business owners within various effective MDM implementation
departments and units must own the data along with the
business processes. The data governance process needs architecture and ecosystem, it is important to involve the
to identify, measure, capture, and rectify data quality product vendor throughout the MDM program in an
issues in the source system itself. To keep the wheels of oversight role and have regular requirements discussion
the MDM initiative well-oiled and turning, a formal model sessions. Each business has its own nuances and a way
to manage data as a strategic resource - comprising of to optimally meet its requirements. A regular session with
well-defined business rules, data stewardship, and data the product vendor can lead to mutually beneficial action
control and compliance mechanisms - needs to be in place. points or product enhancements, minimizing custom
The technology provides the tools to manage master data development. Organizations benefit immensely from
assets. However, the data governance model must be built partnering with vendors who have demonstrated prior
to support analytical and operational processes. The MDM expertise.
governance aspects of data need to be treated as part
6. C CONCLUSION
Organizations need to take a holistic data
management perspective. The right data
management model helps the business
immensely across a range of activities - from
reporting, cross-selling and up-selling to
decision making and compliance. The chances
of an MDM initiative being successful increase
significantly if organizations:
• Consider MDM as a program within
their foundational structure rather than
a one-time project
• Focus on ROI right from business
case formulation through to post
implementation
• Ensure the continuous involvement of
senior management
• Provide an organizational structure to
address governance issues at regular
intervals
7. The Infosys Informatica MDM Advantage
Are you looking for:
• Effective partner relationship management? • Increasing account revenue?
• Reducing operational cost? • Managing consolidated resources post mergers and acquisitions?
• Reducing duplicate customer communications? • Complying with regulations?
The Informatica MDM Competency Centre at Infosys has been incubated with a vision to deliver technology-enabled business
solutions around the Informatica MDM suite of products. The scope includes business process streamlining, master data
governance, MDM package service, data quality, and business process outsourcing amongst others.
Our end-to-end service offerings for successful delivery of MDM initiatives
MDM VISION AND STRATEGY
• Support strategic business initiatives
• Business case, roadmap and data governance strategy definition
• Program effectiveness assessment and change management
BUSINESS PROCESS DEFINITION AND ALIGNMENT
Business
• As-is and To-be business processes mapping and Implementation capabilities
rodamap assessment
definition
• Customer value analysis and customer lifecycle
Informatica Vendor/product
process maps implementation evaluation of
master data hub core MDM hub
• Business process harmonization and process Service Offerings
automation
TECHNOLOGY SOLUTION AND IMPLEMENTATION Governance Technology
policy architecture
• Product configuration and custom development definition definition
• User experience design and development Master data
flows and
• Integration with back-end and legacy systems data models
• Solution testing (system, performance, user acceptance)
• Continual support and business process outsourcing