Reference matter data management:
Two categories of structured data :
Master data: is data associated with core business entities such as customer, product, asset, etc.
Transaction data: is the recording of business transactions such as orders in manufacturing, loan and credit card payments in banking, and product sales in retail.
Reference data: is any kind of data that is used solely to categorize other data found in a database, or solely for relating data in a database to information beyond the boundaries of the enterprise .
Master Data Management – Aligning Data, Process, and GovernanceDATAVERSITY
Master Data Management (MDM) provides organizations with an accurate and comprehensive view of their business-critical data such as customers, products, vendors, and more. While mastering these key data areas can be a complex task, the value of doing so can be tremendous – from real-time operational integration to data warehousing and analytic reporting. This webinar will provide practical strategies for gaining value from your MDM initiative, while at the same time assuring a solid architectural and governance foundation that will ensure long-term, enterprise-wide success.
Gartner: Master Data Management FunctionalityGartner
Gartner will further examine key trends shaping the future MDM market during the Gartner MDM Summit 2011, 2-3 February in London. More information at www.europe.gartner.com/mdm
Master Data Management – Aligning Data, Process, and GovernanceDATAVERSITY
Master Data Management (MDM) provides organizations with an accurate and comprehensive view of their business-critical data such as customers, products, vendors, and more. While mastering these key data areas can be a complex task, the value of doing so can be tremendous – from real-time operational integration to data warehousing and analytic reporting. This webinar will provide practical strategies for gaining value from your MDM initiative, while at the same time assuring a solid architectural and governance foundation that will ensure long-term, enterprise-wide success.
Gartner: Master Data Management FunctionalityGartner
Gartner will further examine key trends shaping the future MDM market during the Gartner MDM Summit 2011, 2-3 February in London. More information at www.europe.gartner.com/mdm
To take a “ready, aim, fire” tactic to implement Data Governance, many organizations assess themselves against industry best practices. The process is not difficult or time-consuming and can directly assure that your activities target your specific needs. Best practices are always a strong place to start.
Join Bob Seiner for this popular RWDG topic, where he will provide the information you need to set your program in the best possible direction. Bob will walk you through the steps of conducting an assessment and share with you a set of typical results from taking this action. You may be surprised at how easy it is to organize the assessment and may hear results that stimulate the actions that you need to take.
In this webinar, Bob will share:
- The value of performing a Data Governance best practice assessment
- A practical list of industry Data Governance best practices
- Criteria to determine if a practice is best practice
- Steps to follow to complete an assessment
- Typical recommendations and actions that result from an assessment
How to Build & Sustain a Data Governance Operating Model DATUM LLC
Learn how to execute a data governance strategy through creation of a successful business case and operating model.
Originally presented to an audience of 400+ at the Master Data Management & Data Governance Summit.
Visit www.datumstrategy.com for more!
Tackling Data Quality problems requires more than a series of tactical, one-off improvement projects. By their nature, many Data Quality problems extend across and often beyond an organization. Addressing these issues requires a holistic architectural approach combining people, process, and technology. Join Nigel Turner and Donna Burbank as they provide practical ways to control Data Quality issues in your organization.
How to identify the correct Master Data subject areas & tooling for your MDM...Christopher Bradley
1. What are the different Master Data Management (MDM) architectures?
2. How can you identify the correct Master Data subject areas & tooling for your MDM initiative?
3. A reference architecture for MDM.
4. Selection criteria for MDM tooling.
chris.bradley@dmadvisors.co.uk
Master Data Management's Place in the Data Governance Landscape CCG
For many organizations, Master Data Management is a necessity to ensure consistency and accuracy of essential business entities. It further plays alongside data architecture, metadata management, data quality, security & privacy, and program management in the Data Governance ecosystem.
Join CCG's data governance subject matter experts as they overview the fundamentals of Master Data Management at our Atlanta-based Data Analytics Meetup. This event will discuss how to enable components of data governance within your organization and review how to best leverage Microsoft's SQL Server Master Data Services.
The Importance of Master Data ManagementDATAVERSITY
Despite its immaterial nature, data has a tendency to pile up as time goes on, and can quickly be rendered unusable or obsolete without careful maintenance and streamlining of processes for its management. This presentation will provide you with an understanding of reference and Master Data Management (MDM), one such method for keeping mass amounts of business data organized and functional towards achieving business goals.
MDM’s guiding principles include the establishment and implementation of authoritative data sources and effective means of delivering data to various business processes, as well as increases to the quality of information used in organizational analytical functions (such as BI). To that end, attendees of this webinar will learn how to:
Structure their Data Management processes around these principles
Incorporate Data Quality engineering into the planning of reference and MDM
Understand why MDM is so critical to their organization’s overall data strategy
Discuss foundational MDM concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK)
This introduction to data governance presentation covers the inter-related DM foundational disciplines (Data Integration / DWH, Business Intelligence and Data Governance). Some of the pitfalls and success factors for data governance.
• IM Foundational Disciplines
• Cross-functional Workflow Exchange
• Key Objectives of the Data Governance Framework
• Components of a Data Governance Framework
• Key Roles in Data Governance
• Data Governance Committee (DGC)
• 4 Data Governance Policy Areas
• 3 Challenges to Implementing Data Governance
• Data Governance Success Factors
Master Data Management - Aligning Data, Process, and GovernanceDATAVERSITY
Master Data Management (MDM) can provide significant value to the organization in creating consistent key data assets such as Customer, Product, Supplier, Patient, and the list goes on. But getting MDM “right” requires a strategic mix of Data Architecture, business process, and Data Governance. Join this webinar to learn how to find the “sweet spot” between technology, design, process, and people for your MDM initiative.
This presentation was part of the IDS Webinar on Data Governance. It gives a brief overview of the history on Data Governance, describes how governing data has to be further developed in the era of business and data ecosystems, and outlines the contribution of the International Data Spaces Association on the topic.
In business, master data management is a method used to define and manage the critical data of an organization to provide, with data integration, a single point of reference.
Reference 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.
Introduction to Data Governance
Seminar hosted by Embarcadero technologies, where Christopher Bradley presented a session on Data Governance.
Drivers for Data Governance & Benefits
Data Governance Framework
Organization & Structures
Roles & responsibilities
Policies & Processes
Programme & Implementation
Reporting & Assurance
Activate Data Governance Using the Data CatalogDATAVERSITY
Data Governance programs depend on the activation of data stewards that are held formally accountable for how they manage data. The data catalog is a critical tool to enable your stewards to contribute and interact with an inventory of metadata about the data definition, production, and usage. This interaction is active Data Governance in the truest sense of the word.
In this RWDG webinar, Bob Seiner will share tips and techniques focused on activating your data stewards through a data catalog. Data Governance programs that involve stewards in daily activities are more likely to demonstrate value from their data-intensive investments.
Bob will address the following in this webinar:
- A comparison of active and passive Data Governance
- What it means to have an active Data Governance program
- How a data catalog tool can be used to activate data stewards
- The role a data catalog plays in Data Governance
- The metadata in the data catalog will not govern itself
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.
Overcoming the Challenges of your Master Data Management JourneyJean-Michel Franco
This Presentaion runs you through all the key steps of an MDM initiative. It considers and showcase the key milestones and building blocks that you will have to roll-out to make your MDM
journey
-> Please contact Talend for a dedicated interactive sessions with a storyboard by customer domain
leewayhertz.com-AI in Master Data Management MDM Pioneering next-generation d...KristiLBurns
Master data refers to the critical, core data within an enterprise that is essential for conducting business operations and making informed decisions. This data encompasses vital information about the primary entities around which business transactions revolve and generally changes infrequently. Master data is not transactional but rather plays a key role in defining and guiding transactions.
Making Information Management The Foundation Of The Future (Master Data Manag...William McKnight
More complex and demanding business environments lead to more heterogeneous systems environments. This, in turn, results in requirements to synchronize master data. Master Data Management (MDM) is an essential discipline to get a single, consistent view of an enterprise\’s core business entities – customers, products, suppliers, and employees. MDM solutions enable enterprise-wide master data synchronization. Given that effective master data for any subject area requires input from multiple applications and business units, enterprise master data needs a formal management system. Business approval, business process change, and capture of master data at optimal, early points in the data lifecycle are essential to achieving true enterprise master data.
To take a “ready, aim, fire” tactic to implement Data Governance, many organizations assess themselves against industry best practices. The process is not difficult or time-consuming and can directly assure that your activities target your specific needs. Best practices are always a strong place to start.
Join Bob Seiner for this popular RWDG topic, where he will provide the information you need to set your program in the best possible direction. Bob will walk you through the steps of conducting an assessment and share with you a set of typical results from taking this action. You may be surprised at how easy it is to organize the assessment and may hear results that stimulate the actions that you need to take.
In this webinar, Bob will share:
- The value of performing a Data Governance best practice assessment
- A practical list of industry Data Governance best practices
- Criteria to determine if a practice is best practice
- Steps to follow to complete an assessment
- Typical recommendations and actions that result from an assessment
How to Build & Sustain a Data Governance Operating Model DATUM LLC
Learn how to execute a data governance strategy through creation of a successful business case and operating model.
Originally presented to an audience of 400+ at the Master Data Management & Data Governance Summit.
Visit www.datumstrategy.com for more!
Tackling Data Quality problems requires more than a series of tactical, one-off improvement projects. By their nature, many Data Quality problems extend across and often beyond an organization. Addressing these issues requires a holistic architectural approach combining people, process, and technology. Join Nigel Turner and Donna Burbank as they provide practical ways to control Data Quality issues in your organization.
How to identify the correct Master Data subject areas & tooling for your MDM...Christopher Bradley
1. What are the different Master Data Management (MDM) architectures?
2. How can you identify the correct Master Data subject areas & tooling for your MDM initiative?
3. A reference architecture for MDM.
4. Selection criteria for MDM tooling.
chris.bradley@dmadvisors.co.uk
Master Data Management's Place in the Data Governance Landscape CCG
For many organizations, Master Data Management is a necessity to ensure consistency and accuracy of essential business entities. It further plays alongside data architecture, metadata management, data quality, security & privacy, and program management in the Data Governance ecosystem.
Join CCG's data governance subject matter experts as they overview the fundamentals of Master Data Management at our Atlanta-based Data Analytics Meetup. This event will discuss how to enable components of data governance within your organization and review how to best leverage Microsoft's SQL Server Master Data Services.
The Importance of Master Data ManagementDATAVERSITY
Despite its immaterial nature, data has a tendency to pile up as time goes on, and can quickly be rendered unusable or obsolete without careful maintenance and streamlining of processes for its management. This presentation will provide you with an understanding of reference and Master Data Management (MDM), one such method for keeping mass amounts of business data organized and functional towards achieving business goals.
MDM’s guiding principles include the establishment and implementation of authoritative data sources and effective means of delivering data to various business processes, as well as increases to the quality of information used in organizational analytical functions (such as BI). To that end, attendees of this webinar will learn how to:
Structure their Data Management processes around these principles
Incorporate Data Quality engineering into the planning of reference and MDM
Understand why MDM is so critical to their organization’s overall data strategy
Discuss foundational MDM concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK)
This introduction to data governance presentation covers the inter-related DM foundational disciplines (Data Integration / DWH, Business Intelligence and Data Governance). Some of the pitfalls and success factors for data governance.
• IM Foundational Disciplines
• Cross-functional Workflow Exchange
• Key Objectives of the Data Governance Framework
• Components of a Data Governance Framework
• Key Roles in Data Governance
• Data Governance Committee (DGC)
• 4 Data Governance Policy Areas
• 3 Challenges to Implementing Data Governance
• Data Governance Success Factors
Master Data Management - Aligning Data, Process, and GovernanceDATAVERSITY
Master Data Management (MDM) can provide significant value to the organization in creating consistent key data assets such as Customer, Product, Supplier, Patient, and the list goes on. But getting MDM “right” requires a strategic mix of Data Architecture, business process, and Data Governance. Join this webinar to learn how to find the “sweet spot” between technology, design, process, and people for your MDM initiative.
This presentation was part of the IDS Webinar on Data Governance. It gives a brief overview of the history on Data Governance, describes how governing data has to be further developed in the era of business and data ecosystems, and outlines the contribution of the International Data Spaces Association on the topic.
In business, master data management is a method used to define and manage the critical data of an organization to provide, with data integration, a single point of reference.
Reference 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.
Introduction to Data Governance
Seminar hosted by Embarcadero technologies, where Christopher Bradley presented a session on Data Governance.
Drivers for Data Governance & Benefits
Data Governance Framework
Organization & Structures
Roles & responsibilities
Policies & Processes
Programme & Implementation
Reporting & Assurance
Activate Data Governance Using the Data CatalogDATAVERSITY
Data Governance programs depend on the activation of data stewards that are held formally accountable for how they manage data. The data catalog is a critical tool to enable your stewards to contribute and interact with an inventory of metadata about the data definition, production, and usage. This interaction is active Data Governance in the truest sense of the word.
In this RWDG webinar, Bob Seiner will share tips and techniques focused on activating your data stewards through a data catalog. Data Governance programs that involve stewards in daily activities are more likely to demonstrate value from their data-intensive investments.
Bob will address the following in this webinar:
- A comparison of active and passive Data Governance
- What it means to have an active Data Governance program
- How a data catalog tool can be used to activate data stewards
- The role a data catalog plays in Data Governance
- The metadata in the data catalog will not govern itself
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.
Overcoming the Challenges of your Master Data Management JourneyJean-Michel Franco
This Presentaion runs you through all the key steps of an MDM initiative. It considers and showcase the key milestones and building blocks that you will have to roll-out to make your MDM
journey
-> Please contact Talend for a dedicated interactive sessions with a storyboard by customer domain
leewayhertz.com-AI in Master Data Management MDM Pioneering next-generation d...KristiLBurns
Master data refers to the critical, core data within an enterprise that is essential for conducting business operations and making informed decisions. This data encompasses vital information about the primary entities around which business transactions revolve and generally changes infrequently. Master data is not transactional but rather plays a key role in defining and guiding transactions.
Making Information Management The Foundation Of The Future (Master Data Manag...William McKnight
More complex and demanding business environments lead to more heterogeneous systems environments. This, in turn, results in requirements to synchronize master data. Master Data Management (MDM) is an essential discipline to get a single, consistent view of an enterprise\’s core business entities – customers, products, suppliers, and employees. MDM solutions enable enterprise-wide master data synchronization. Given that effective master data for any subject area requires input from multiple applications and business units, enterprise master data needs a formal management system. Business approval, business process change, and capture of master data at optimal, early points in the data lifecycle are essential to achieving true enterprise master data.
Enterprise-Level Preparation for Master Data Management.pdfAmeliaWong21
Master Data Management (MDM) continues to play a foundational role in the Data Management Architecture of every 21st century enterprise. In a forward-looking organization, MDM is significant in the Enterprise Integration Hub.
The article is intended as a quick overview of what effective master data management means in today’s business context in terms of risks, challenges and opportunities for companies and decision makers. The article is structured in two main areas, which cover in turn the importance of an effective master data
management implementation and the methodology to get there.
Data Ownership:
Most companies and organizations have this notion that data governance should be taken care of ,
by the Information Technology department, because IT owns the system which stores the data.
The owner of the data is responsible for providing attributes to the data and answerable to any questions regarding data.
The people answerable to these kinds of data are generally the ones involved in defining business rules,
data cleaning and consolidation.?
Data Stewardship:?
Data stewards should be favorably those people who are familiar with the data. It is often seen that
there is need to deploy several people, to handle and correct data,
whereas a single data steward could have done the same job. Since the data being handled involves
organizational level data, it is important that there are governance rules for this process.?
If there is some certain rule in the data which causes large data volumes to fail, this rule should be fixed while data cleansing.
So it is important to take care of the amount of clean data sent to the stewards,
since we are not aware of which rules might trigger what amount of data.?
Choice of data stewards is again a difficult selection.
Data Security:?
Although the master data is data on organization level, but there is some confidentiality level linked to it.?
Not every employee has the authorization to view its aspects.
Security rules can be applied to the data.
The various departments in the organization must set different rules to the data they own.
They need to grant permissions to these rules , so that the user can view the data.
A large company can have data sourced out of many regions.
It is to be ensured that they are responsible to correct only their own data.?
Data survivorship:
There are some guidelines which are set up by data governance.
These rules can often change over hthe time according to new data sources being added.
The changes made to the data , are communicated to the organization so that data stewards and users can understand the process.
So from a data steward's point of view, it is important to apply security rules to the people who are involved
in data handling and correction. This is a result of how data governance and data security can be applied while implementing MDM.?
?
Mike Ferguson, managing director of Intelligent Business Strategies, highlights his top ten worst practices in Master Data Management (MDM) in this Information Builders webinar slideshow.
Adopting a Process-Driven Approach to Master Data ManagementSoftware AG
What is a lasting solution to the sea of errors, headaches, and losses caused by inconsistent and inaccurate master data such as customer and product records? This is the data that your business counts on to operate business processes and make decisions. But this data is often incomplete or in conflict because it resides in multiple IT systems. Master Data Management (MDM)'s programs are the solution to this problem, but these programs can fail without the investment and involvement of business managers.
Listen to Rob Karel, Forrester analyst, and Jignesh Shah from Software AG to learn about a new, process-driven approach to MDM and why it is a win-win for both business and IT managers.
Visit us at http://www.softwareag.com Become part of our growing community: Facebook: http://www.facebook.com/softwareag Twitter: http://www.twitter.com/softwareag LinkedIn: http://www.linkedin.com/company/software-ag YouTube: http://www.youtube.com/softwareag
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.
data collection, data integration, data management, data modeling.pptxSourabhkumar729579
it contains presentation of data collection, data integration, data management, data modeling.
it is made by sourabh kumar student of MCA from central university of haryana
Webinar: Initiating a Customer MDM/Data Governance ProgramDATAVERSITY
Mastering your customer data is on the critical path for any business undertaking the transformation to a data centric approach. Whether it is to enable effective CRM to enhance day to day operations or leverage in depth customer analytics for strategic planning, understanding your customer data is the foundation of truly understanding and responding to your customer. The first step in mastering your customer data is to discover and document the existing data landscape.
In this session we will present a case study to uncover the drivers, challenges and benefits of mastering your customer data and detail how a customer data discovery pilot, using erwin modeling can underpin and accelerate this initiative, reduce the associated costs and provide a facility to enable ongoing analysis, stakeholder awareness and mitigate the risks involve in re-engineering your customer data management approach.
Enterprise resource planning:
What is ERP and its History ?
ERP Components.
Commercial Applications.
The steps to Successful of ERP Implementation.
Consulting Services.
ERP System Architecture.
Characteristics of ERP systems.
Model driven requirements engineering in the context of erp implementationDr. Hamdan Al-Sabri
Model-driven requirements engineering in the context of erp implementation:
Introduction
Definition of Concepts
Knowledge Gap
Research Questions &Objectives
Scope of the Thesis
The Proposed Solutions:
Analysis of the ERP Reference Models (RMs) (O1)
Developing a new framework (LORS) for building the Enterprise Model (EM) (O2)
Developing a Structure Approach (SEAC) for Model Matching (O3)
Conclusion &Future Work
Using a kmerp framework to enhance enterprise resource planning (erp) impleme...Dr. Hamdan Al-Sabri
Using a KMERP Framework to Enhance Enterprise Resource Planning (ERP) Implementation:
Abstract
Introduction
Background and Related Work
The Methodology for ERP Implementation with KM
Benefits of ERP systems
Most reasons for failure the ERP
The Methodology for KM Implementation
The relationship between KM and ERP
The knowledge challenges in ERP implementation
Motivation for develop KMERP Framework,
The proposed Framework
Implementation KMERP Framework on ERP
Type of knowledge required to manage ERP
Conclusions and further work
Development of E-government: a STOPE view:
Uses STOPE-based development (Strategy / Technology / Organization / People /Environment) to examine and contribute the transition.
Uses TQM and BRB and six sigma to maximize the benefits.
E government an analysis of the present and suggestions for the futureDr. Hamdan Al-Sabri
E-Government an Analysis of the present and suggestions for the future:
E-Government, has significantly contributed to the quality governance and, more importantly, has emerged as an efficient and effective quality tool for the people.
Requirements engineering as a structured process:
Requirements Engineering
Requirements Engineering Field
Why are Requirements so important?
Requirements Engineering Activities
Requirements Elements
Requirements Quality
Requirements quality indicators
Conclusion
Software requirements engineering problems and challenges erp implementation as a case study:
Requirements Engineering
Why are Requirements so important?
Purpose of Requirements Engineering
RE process inputs and outputs
Requirements Engineering Activities
Requirements Quality
Requirements quality indicators
Systems RE Standards
Requirements problems and challenges
Research Strategies in RE
RE Research directions
Conclusion
P2P collaboration systems: Peer-to-peer (P2P) systems inherently support redundancy, scalability, fault tolerance, and load balancing P2P systems support these features at a lower cost than client/server systems.
In a P2P collaboration system, users share the resources necessary to host and distribute articles that can be modified by any other user such as Wikipedia.
Developing a research proposal in the field of software engineering model dri...Dr. Hamdan Al-Sabri
Developing a research proposal in the field of software engineering model driven requirements matching as an example:
Introduction
Concept Definition
Pervious Studies
Knowledge Gap
Research Objectives
Research Significance
Research Methodology and Techniques
Delimitations and assumptions
Research Strategies
Timetable and initial division of Thesis
Conclusion
Data warehouse systems adopt a multidimensional data model tackling the challenges of the online analytical processing (OLAP).
Standard data warehouse systems do not provide methodological guidelines for managing heterogeneous dimensions.
In relational OLAP systems, multidimensional views of data, or data cubes, are structured using a star or a snowflake schema consisting of fact tables and dimension hierarchies.
Decision support systems: An interactive computer-based system that helps decision makers in the solution of semi-structured and unstructured problems.
Decision Support Systems
Decision Making
Type of Decision-makings
Phases of Decision Making
Decision Support Framework
Components of DSS
Types of DSS
Information systems:
Information System is a framework in which the coordination between human resources and material resources to transform inputs into outputs (information).
Exploratory data analysis data visualization:
Exploratory Data Analysis (EDA) is an approach/philosophy for data analysis that employs a variety of techniques (mostly graphical) to
Maximize insight into a data set.
Uncover underlying structure.
Extract important variables.
Detect outliers and anomalies.
Test underlying assumptions.
Develop parsimonious models.
Determine optimal factor settings
Multimedia networking:
Multimedia
Characteristics of multimedia
Components of Interactive Multimedia
Multimedia Classification
Multimedia Requirements
Multimedia Applications
Networked Multimedia
Challenges of multimedia networking
Major Components of Multimedia Networking
Technologies of Multimedia Networking
MM Networking Applications
Multimedia &Protocols :TCP vs. UDP
Multimedia Networking Systems
Multimedia on the Internet
Properties of current Internet
Multimedia & Security
Multimedia networking:
The term ‘multimedia’ refers to diverse classes of media employed to represent information.
The term ‘Networked Multimedia’ refers to the transmission and distribution of multimedia information on the network
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptxEduSkills OECD
Andreas Schleicher presents at the OECD webinar ‘Digital devices in schools: detrimental distraction or secret to success?’ on 27 May 2024. The presentation was based on findings from PISA 2022 results and the webinar helped launch the PISA in Focus ‘Managing screen time: How to protect and equip students against distraction’ https://www.oecd-ilibrary.org/education/managing-screen-time_7c225af4-en and the OECD Education Policy Perspective ‘Students, digital devices and success’ can be found here - https://oe.cd/il/5yV
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Ethnobotany and Ethnopharmacology:
Ethnobotany in herbal drug evaluation,
Impact of Ethnobotany in traditional medicine,
New development in herbals,
Bio-prospecting tools for drug discovery,
Role of Ethnopharmacology in drug evaluation,
Reverse Pharmacology.
2. Introduction MDM Architecture
Master Data Vs Other Data Types Of MDM Strategies
Master Data Categories Steps In The Process For Managing And
Maintaining Master Data
What Is Master Data Management? Scoping An MDM Project
Common Misconceptions About
Master Data
Three Scenarios For MDM
Traditional Approaches To MDM MDM Implementation Options
Why Do Enterprises Need MDM? Data Quality & MDM
MDM Challenges Requirements For MDM Solutions
Selected Case Studies MDM Sample Products
2Reference/Master Data Management, by Dr. Hamdan Al-Sabri
3. 3Reference/Master Data Management, by Dr. Hamdan Al-Sabri
Two categories of structured data [1]:
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 [2].
4. 4Reference/Master Data Management, by Dr. Hamdan Al-Sabri
Master data differ from other types of data in four ways [11]:
Unlike transaction data and inventory data ,master data describe always basic
characteristics (e.g. the age, height, or weight).
Pieces of master data usually remain largely unaltered.
Instances of master data classes (data on a certain customer, for example) are
quite constant with regard to volume .
Master data constitute a reference for transaction data.
5. 5Reference/Master Data Management, by Dr. Hamdan Al-Sabri
Business Category People Places Things
Abstract Objects
(Concepts)
Examples of
Domain Areas
» Customer
» Employee
» Salesperson
» Office locations
» Customer locations
» Product
» Store
» Asset
» Contract
» License
» Warranty
6. 6Reference/Master Data Management, by Dr. Hamdan Al-Sabri
Master data management (MDM) is a set of processes, policies, services and
technologies used to create, maintain and manage data associated with a
company’s core business entities as a system of record (SOR) for the enterprise [1].
Many organizations are implementing master data management solutions–under a
number of different labels, including MDM, Customer Data Integration (CDI),
Product Information Management (PIM), or Global Supplier Management (GSM)
[9].
7. 7Reference/Master Data Management, by Dr. Hamdan Al-Sabri
Master data management (MDM) is a set of processes, policies, services and
technologies used to create, maintain and manage data associated with a
company’s core business entities as a system of record (SOR) for the enterprise [1].
Many organizations are implementing master data management solutions–under a
number of different labels, including MDM, Customer Data Integration (CDI),
Product Information Management (PIM), or Global Supplier Management (GSM)
[9].
9. 9Reference/Master Data Management, by Dr. Hamdan Al-Sabri
Master data management is a new problem.
Master data is equivalent to metadata
A data warehouse solves master data management issues.
Managing master data is equivalent to ensuring data quality.
Enterprise application suites solve the problem by providing a single point of
master data management.
11. 11Reference/Master Data Management, by Dr. Hamdan Al-Sabri
Redundant Data [3].
Inconsistent Data [3].
Data confects [1].
Process delays and process defects caused by data errors [1].
Additional operational cost [1].
Mergers and acquisitions [4].
Multiple ERP instances [4].
Compliance [4].
Service oriented architecture [4].
Supporting Business change [5].
12. 12Reference/Master Data Management, by Dr. Hamdan Al-Sabri
Creating a Partnership between Business and IT [8].
Managing Change [8].
Globalization [1].
Risk [1].
Customer loyalty [1].
Supply chain complexity [1].
Cost‐cutting [1].
15. 15Reference/Master Data Management, by Dr. Hamdan Al-Sabri
MDM Applications for managing and publishing master data and metadata.
A Master Data Store (MDS) containing consolidated master data.
A Master Metadata Store (MMS) containing the master data business model, and
master data rules and definitions. The master data business model documents
master data entities, attributes, relationships and their business meaning.
A set of Master Data Integration (MDI) services for consolidating, federating and
propagating master data.
16. 16Reference/Master Data Management, by Dr. Hamdan Al-Sabri
MDM Applications for managing and publishing master data and metadata.
A Master Data Store (MDS) containing consolidated master data.
A Master Metadata Store (MMS) containing the master data business model, and
master data rules and definitions. The master data business model documents
master data entities, attributes, relationships and their business meaning.
A set of Master Data Integration (MDI) services for consolidating, federating and
propagating master data.
17. 17Reference/Master Data Management, by Dr. Hamdan Al-Sabri
Three Types of Master Data Management Strategies [3]:
Operational MDM aims to integrate the data for operational applications such
as ERP, CRM, financial reporting, etc.
Analytic MDM is focused on the creation and support of a data warehouse
and business intelligence (BI) platforms.
Enterprise MDM focuses on creating enterprise-wide strategy that
encompasses both operational and analytic MDM.
18. 18Reference/Master Data Management, by Dr. Hamdan Al-Sabri
Option MDM Functional Scope
1 Master data synchronization
2 Option 1 + Master data integration + Master data vocabulary
3 Option 2 + A consolidated master data system of record (SOR)
4 Option 3 + Master data hierarchy management
5 Option 4 + Master data CRUD services
6 Option 5 + Single Master data entry system (MDES)
7 Option 6 + New master data business processes
19. 19Reference/Master Data Management, by Dr. Hamdan Al-Sabri
Management reporting. Enabling reporting by rolling up of business data (such
as revenue or profit) according to business dimensions such as customer or
product.
Data synchronization. In this second scenario, the main objective shifts from
reporting to operations. The local systems continue to update master data records
or attributes, causing periods when master data may not be consistent across all
systems. Master data consistency is reestablished upon completion of the
synchronization process.
Single point of origination. This is the most demanding scenario, all changes to
master data must originate at the hub, rather than at local systems.
20. 20Reference/Master Data Management, by Dr. Hamdan Al-Sabri
Is the goal improved reporting or increased efficiency of operations? For
improved reporting use Management Reporting. In case of efficient operations,
Data Synchronization or Single Point of Origination is better.
How often must updates to master data be propagated from the hub to the
participating systems? The single point of origination scenario provides the
capability to get to near real-time synchronization if the business decision cycle
warrants that level of consistency.
Can business processes be altered? The single point of origination scenario
requires change of processes, such as new product introduction or customer
registration.
21. 21Reference/Master Data Management, by Dr. Hamdan Al-Sabri
BUILD BUY
Data store already in existence, with most of
the master data already integrated
Data is heavily fractured with a large number
of data entry systems
Maximum flexibility Sometimes not easy to modify
Ensures conformance with
corporate policies and strategy
May not supporting all corporate scenarios
Too expensive if the enterprise lacks
personnel qualified
Well-qualified
service providers
22. 22Reference/Master Data Management, by Dr. Hamdan Al-Sabri
There are four approaches for implementing MDM [5]:
Consolidate the master data from multiple operational systems of entry in a
single master data store (MDS) that becomes the system of record for master
data.
Propagate and synchronize master data changes between operational systems
of entry so that master data in all systems of entry is kept consistent.
Consolidate and propagate master data using combination of first and second
approaches.
Migrate and centralize operational master data systems of entry to a new
enterprise MDM system that acts as both the system of record and system of
entry for master data.
23. 23Reference/Master Data Management, by Dr. Hamdan Al-Sabri
Consolidate
Propagate
Propagate +
Consolidate
Centralize
MDM
System
Operational
System
Data
Warehouse
System
Of Entry
System
Of record
Master data can be
used in BI for both
historical and
predictive analysis
24. 24Reference/Master Data Management, by Dr. Hamdan Al-Sabri
Data quality is the First Step on the Path to MDM.
In a recent MDM study carried out by The Data Warehousing Institute (TDWI), 83%
of respondents reported that their organizations have suffered problems due to
poor quality master data, and 54% claimed to have derived benefits from good
master data.
25. 25Reference/Master Data Management, by Dr. Hamdan Al-Sabri
Some of the problems associated with poor quality master data outlined in the
TDWI report:
Poor customer service.
Inefficient marketing or purchasing.
New products delayed.
Inaccurate reporting.
Arguments over which data is appropriate.
Bad decisions based on incorrect definitions.
Data governance and stewardship limitations.
Limited visibility for data lineage.
No understanding of master data homonyms.
26. 26Reference/Master Data Management, by Dr. Hamdan Al-Sabri
Most of the benefits of high-quality data referred to by respondents to the TDWI:
Risk reduction.
Superior customer service.
Supply chain optimization.
Accurate reporting.
Better decision making.
Easier auditing of information’s origins.
Consistent definitions.
27. 27Reference/Master Data Management, by Dr. Hamdan Al-Sabri
Support for definition of roles with access rights enforced depending on the
responsibilities assigned for that role in the master data management process.
ETL (extract-transform-load) capabilities for extracting master data/reference data
files or tables from multiple sources and loading the data into the master data
repository.
Data cleansing capabilities for de-duplication and matching of master data records.
A collaborative platform for coordinating decisions on master data reconciliation
and rationalization.
Data synchronization and replication support for applying changes established in a
central server to each consuming application.
Version control at the central policy hub combined with change monitoring across
all of the participating systems.
28. 28Reference/Master Data Management, by Dr. Hamdan Al-Sabri
IBM InfoSphere
Oracle Master Data Management Suite
SAP NetWeaver Master Data Management
TIBCO Collaborative Information Manager
29. 29Reference/Master Data Management, by Dr. Hamdan Al-Sabri
Components of the product family are [10]:
IBM InfoSphere Warehouse.
IBM InfoSphere Information Server.
IBM InfoSphere MDM Server.
30. 30Reference/Master Data Management, by Dr. Hamdan Al-Sabri
Components of Oracle Master Data Management Suite are [10]:
Business Rules.
Customer Data Hub / Product Data Hub.
Data Integrator.
Data Quality Matching Server / Data Quality Cleansing Server.
Hyperion Data Relationship Management.
31. 31Reference/Master Data Management, by Dr. Hamdan Al-Sabri
Components of SAP NetWeaver MDM are [10]:
MDM Console.
MDM Import Manager.
MDM Data Manager.
MDM Syndicator.
32. 32Reference/Master Data Management, by Dr. Hamdan Al-Sabri
CIM comprises the following components [10]:
Information Repository.
Data Governance.
Process Automation.
Synchronization.
Reporting and Business Intelligence.
33. 33Reference/Master Data Management, by Dr. Hamdan Al-Sabri
SAP NetWeaver MDM at Oerlikon Textile
Employing more than 7,400 people
Is a manufacturer of textile machinery and a provider of textile industry solutions covering the
entire value chain.
Has branches for development, manufacture, and distribution in more than fifty locations
world‐wide.
Oerlikon’s business units are supported by seven independent systems based on the SAP ERP
application.
Over time, more and more data silos came into existence, leading to more and more isolated
data repositories partially containing conflicting data. Inconsistent and redundant master data
led to problems.
Oerlikon Textile searched for a solution capable of consolidating data from disparate source
systems and making harmonized data available to all users working in the seven business
units.
34. 34Reference/Master Data Management, by Dr. Hamdan Al-Sabri
SAP NetWeaver MDM at Oerlikon Textile
It was decided to use SAP NetWeaver MDM, as this product was considered to suit perfectly
the needs of Oerlikon Textile regarding master data consolidation and harmonization.
To do so, it was first necessary to consolidate, cleanse, and update all data repositories
accommodated by the SAP ERP systems. This effort took six months, with SAP Consulting
offering support during implementation. Estimates put up by SAP experts prior to the project’s
start turned out to be stable, so that deadlines were met and costs remained within an
acceptable frame.
The individual business units of Oerlikon Textile now benefit from high consistency and
relevance of master data facilitating cross‐unit business activities and helping exploit
cross‐selling and up‐selling potentials. By centralizing MDM, risks and shortcomings in data
processing could clearly be reduced, and the quality of the database could substantially be
improved.
35. 35Reference/Master Data Management, by Dr. Hamdan Al-Sabri
IBM Master Data Management at PostFinance
Employing over 3,500 people
A business unit of Swiss Post, is a major provider of financial services in Switzerland.
In 2008, about 120,000 new customers opted for the services of PostFinance, increasing the
number of accounts by 311,000.
Master data used to be processed in more than thirty application systems, both by online
access and by data replication. Because of this, customer account management became more
and more complex and difficult to oversee, particularly when customers had more than one
account.
After a short phase of evaluation PostFinance decided in April 2006 to implement IBM
WebSphere Customer Center (which is a component of IBM InfoSphere MDM Server since the
beginning of 2009).
Since November 2008, more than 1,000 employees of PostFinance have been working with
this new system.
36. 36Reference/Master Data Management, by Dr. Hamdan Al-Sabri
IBM Master Data Management at PostFinance
Although it has been only a couple of months since the new era of MDM started at
PostFinance, the benefits have become quite obvious already.
Apart from the benefits for the company itself, the new MDM concept at PostFinance has
positive consequences for the customers too.
37. 37Reference/Master Data Management, by Dr. Hamdan Al-Sabri
[1] Mike Ferguson, “Getting Started With Master Data Management,” Intelligent Business Strategies, March 2008.
[2] Malcolm Chisholm, “Master Data versus Reference Data,” Information Management Magazine, April 2006.
[3] Info-Tech Research Group, “Master Data Management for the Masses of Data,” February 24, 2009.
[4] IBM, “Master Data Management: One Step at a Time,” February 2006.
[5] Colin White, BI Research, “Using Master Data in Business Intelligence,” March 2007.
[6] Henry D. Morris, ”Managing Master Data for Business Performance Management: The Issues and Hyperion' s
Solution,” April 2005.
[7] IBM, ”IBM Multiform Master Data Management: The evolution of MDM applications,” June 2007.
[8] Deloitte, ”Getting Started with Master Data Management,” 2005.
[9] INFORMATICA, ” Data Quality: the First Step on the Path to Master Data Management,” June 2007.
[10]Boris Otto, Kai M. Hüner, ”Functional Reference Architecture for Corporate Master Data Management,” May 31, 2009.
[11] David Waddington, “Master Data Management –Research Agenda for 2005” 2005.