A template for capturing the overall high-level business requirements and expectations for business solutions with a significant impact on or requirement for data. (cf. the “Project Mandate” document in PRINCE2).
A template to define an outline structure for the clear and unambiguous definition of the discreet component data elements (atomic items of Entity/Attribute/Relationship/Rule) within the Logical layer of an Enterprise Information Model (a.k.a. Canonical Model).
A template defining an outline structure for the clear and unambiguous definition of the discreet data elements (tables, columns, fields) within the physical data management layers of the required data solution.
Example data specifications and info requirements framework OVERVIEWAlan D. Duncan
This example framework offers a set of outline principles, standards and guidelines to describe and clarify the semantic meaning of data terms in support of an Information Requirements Management process.
It provides template guidance to Information Management, Data Governance and Business Intelligence practitioners for such circumstances that need clear, unambiguous and reliable understanding of the context, semantic meaning and intended usages for data.
A Comparative Study of Data Management Maturity ModelsData Crossroads
In this presentation we compare existing Data Management / Data Governance Maturity Models and discuss different approaches to viewing Data Management / Data Governance.
We also present a new model for Data Management which unifies various existing models and provides a fresh perspective on Data Management, its assessment and implementation.
07. Analytics & Reporting Requirements TemplateAlan D. Duncan
This document template defines an outline structure for the clear and unambiguous definition of analytics & reporting outputs (including standard reports, ad hoc queries, Business Intelligence, analytical models etc).
03. Business Information Requirements TemplateAlan D. Duncan
A template for the clear and unambiguous definition of business data and information requirements. (cf. “Business Requirements Document”, “Functional Specification” or similar from standard SDLC processes). As such, the contents will typically form the basis for population and publication of a business glossary of information terms.
A template to define an outline structure for the clear and unambiguous definition of the discreet component data elements (atomic items of Entity/Attribute/Relationship/Rule) within the Logical layer of an Enterprise Information Model (a.k.a. Canonical Model).
A template defining an outline structure for the clear and unambiguous definition of the discreet data elements (tables, columns, fields) within the physical data management layers of the required data solution.
Example data specifications and info requirements framework OVERVIEWAlan D. Duncan
This example framework offers a set of outline principles, standards and guidelines to describe and clarify the semantic meaning of data terms in support of an Information Requirements Management process.
It provides template guidance to Information Management, Data Governance and Business Intelligence practitioners for such circumstances that need clear, unambiguous and reliable understanding of the context, semantic meaning and intended usages for data.
A Comparative Study of Data Management Maturity ModelsData Crossroads
In this presentation we compare existing Data Management / Data Governance Maturity Models and discuss different approaches to viewing Data Management / Data Governance.
We also present a new model for Data Management which unifies various existing models and provides a fresh perspective on Data Management, its assessment and implementation.
07. Analytics & Reporting Requirements TemplateAlan D. Duncan
This document template defines an outline structure for the clear and unambiguous definition of analytics & reporting outputs (including standard reports, ad hoc queries, Business Intelligence, analytical models etc).
03. Business Information Requirements TemplateAlan D. Duncan
A template for the clear and unambiguous definition of business data and information requirements. (cf. “Business Requirements Document”, “Functional Specification” or similar from standard SDLC processes). As such, the contents will typically form the basis for population and publication of a business glossary of information terms.
The Business Value of Metadata for Data GovernanceRoland Bullivant
In today’s digital economy, data drives the core processes that deliver profitability and growth - from marketing, to finance, to sales, supply chain, and more. It is also likely that for many large organizations much of their key data is retained in application packages from SAP, Oracle, Microsoft, Salesforce and others. In order to ensure that their foundational data infrastructure runs smoothly, most organizations have adopted a data governance initiative. These typically focus on the people and processes around managing data and information. Without an actionable link to the physical systems that run key business processes, however, governance programs can often lack the ‘teeth’ to effectively implement business change.
Metadata management is a process that can link business processes and drivers with the technical applications that support them. This makes data governance actionable and relevant in today’s fast-paced and results-driven business environment. One of the challenges facing data governance teams however, is the variety in format, accessibility and complexity of metadata across the organization’s systems.
RWDG Slides: A Complete Set of Data Governance Roles & ResponsibilitiesDATAVERSITY
Roles and responsibilities are the backbone to a successful Data Governance program. The way you define and utilize the roles will be the biggest factor of program success. From Data Stewards to the steering committee and everyone in between, people will need to understand the role they play, why they are in the role and how the role fits in with their existing job.
Join Bob Seiner for this RWDG webinar where he will provide a complete and detailed set of Data Governance roles and responsibilities. Bob will share an Operating Model of Roles and Responsibilities that can be customized to address the specific needs of your organization.
In this webinar, Bob will discuss:
- Executive, Strategic, Tactical, Operational, and Support Level Roles
- How to customize an Operating Model to fit your organization
- Detailed Responsibilities for each level
- Defining who participates at each level
- Using working teams to implement tactical solutions
Real-World Data Governance: Governance Risk and ComplianceDATAVERSITY
The target of many a Data Governance Program is to nail their regulatory and compliance requirements first to appease the government and industry regulators before doing anything else. Risk Management, as a practice, is already in place in most organizations under a variety of names. Even though most organizations do not consider Risk Management the same thing as Data Governance, the similarities abound. Compliance is not optional. Nothing about Regulatory and Compliance mentions optional. Governance is not optional either.
The session will cover:
Risk Management Vs. Data Governance – A Close Comparison
Risk Management as the Face of Data Governance
Measuring Success of Governance in terms of Risk Management
Using Risk and Compliance to Explain Governance
Using “Not Optional” as Your Crutch
Introduction to DCAM, the Data Management Capability Assessment ModelElement22
DCAM is a model to assess data management capability within the financial industry. It was created by the EDM Council. This presentation provides an overview of DCAM and how financial institutions leverage DCAM to improve or establish their data management programs and meet regulatory requirements such as BCBS 239.
Metadata management is critical for organizations looking to understand the context, definition and lineage of key data assets. Data models play a key role in metadata management, as many of the key structural and business definitions are stored within the models themselves. Can data models replace traditional metadata solutions? Or should they integrate with larger metadata management tools & initiatives?
Join this webinar to discuss opportunities and challenges around:
How data modeling fits within a larger metadata management landscape
When can data modeling provide “just enough” metadata management
Key data modeling artifacts for metadata
Organization, Roles & Implementation Considerations
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.
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
Estrategia de datos en las organizaciones SAS Colombia
Si se ha preguntado ¿cómo los datos de su organización pueden contribuir al desarrollo de los lineamientos corporativos? La respuesta es sí: implementando una estrategia de Data Management.
Master Data Management - Practical Strategies for Integrating into Your Data ...DATAVERSITY
Master Data Management (MDM) provides organizations with an accurate and comprehensive view of their business-critical data such as Customers, Products, Vendors, and more. While mastering these key data areas can be a complex task, the value of doing so can be tremendous – from real-time operational integration to data warehousing & analytic reporting. This webinar provides practical strategies for gaining value from your MDM initiative, while at the same time assuring a solid architectural and governance foundation that will ensure long-term, enterprise-wide success.
Introduction to DCAM, the Data Management Capability Assessment Model - Editi...Element22
DCAM stands for Data management Capability Assessment Model. DCAM is a model to assess data management capabilities within the financial industry. It was created by the EDM Council in collaboration with over 100 financial institutions. This presentation provides an overview of DCAM and how financial institutions leverage DCAM to improve or establish their data management programs and meet regulatory requirements such as BCBS 239. Also the benefits of DCAM are described as part of this presentation.
Metadata is hotter than ever, according a number of recent DATAVERSITY surveys. More and more organizations are realizing that in order to drive business value from data, robust metadata is needed to gain the necessary context and lineage around key data assets. At the same time, industry regulations are driving the need for better transparency and understanding of information.
While metadata has been managed for decades, new strategies & approaches have been developed to support the ever-evolving data landscape, and provide more innovative ways to drive business value from metadata. This webinar will provide an overview of metadata strategies & technologies available to today’s organization, and provide insights into building successful business strategies for metadata adoption & use.
Driving Data Intelligence in the Supply Chain Through the Data Catalog at TJXDATAVERSITY
Roles and responsibilities are a critical component of every Data Governance program. Building a set of roles that are practical and that will not interfere with people’s “day jobs” is an important consideration that will influence how well your program is adopted. This tutorial focuses on sharing a proven model guaranteed to represent your organization.
Join Bob Seiner for this lively webinar where he will dissect a complete Operating Model of Roles and Responsibilities that encompasses all levels of the organization. Seiner will detail the roles and describe the most effective way to associate people with the roles. You will walk out of this webinar with a model to apply to your organization.
In this session Bob will share:
- The five levels of Data Governance roles
- A proven Operating Model of Roles and Responsibilities
- How to customize the model to meet your requirements
- Setting appropriate role expectations
- How to operationalize the roles and demonstrate value
DAS Slides: Building a Data Strategy - Practical Steps for Aligning with Busi...DATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task. The opportunity in getting it right can be significant, however, as data drives many of the key initiatives in today’s marketplace: digital transformation, marketing, customer centricity, and more. This webinar will help de-mystify Data Strategy and Data Architecture and will provide concrete, practical ways to get started.
How to Make a Data Governance Program that LastsDATAVERSITY
Traditional data governance initiatives fail by focusing too heavily on policies, compliance, and enforcement, which quickly lose business interest and support. This leaves data management and governance leaders having to continually make the case for data governance to secure business adoption. Join Cameron, VP, Product Management, Precisely, as he shares a lean, business-first data governance approach that connects key initiatives to governance capabilities and quickly delivers business value for the long-term. He will give examples of organizations worldwide who have successfully implemented a data governance program by engaging with key stakeholders using innovative techniques such as gamification and data catalog scavenger hunts.
Introduction to Data Management Maturity ModelsKingland
Jeff Gorball, the only individual accredited in the EDM Council Data Management Capability Model and the CMMI Institute Data Management Maturity Model, introduces audiences to both models and shares how you can choose which one is best for your needs.
Data Governance and Metadata ManagementDATAVERSITY
Metadata is a tool that improves data understanding, builds end-user confidence, and improves the return on investment in every asset associated with becoming a data-centric organization. Metadata’s use has expanded beyond “data about data” to cover every phase of data analytics, protection, and quality improvement. Data Governance and metadata are connected at the hip in every way possible. As the song goes, “You can’t have one without the other.”
In this RWDG webinar, Bob Seiner will provide a way to renew your energy by focusing on the valuable asset that can make or break your Data Governance program’s success. The truth is metadata is already inherent in your data environment, and it can be leveraged by making it available to all levels of the organization. At issue is finding the most appropriate ways to leverage and share metadata to improve data value and protection.
Throughout this webinar, Bob will share information about:
- Delivering an improved definition of metadata
- Communicating the relationship between successful governance and metadata
- Getting your business community to embrace the need for metadata
- Determining the metadata that will provide the most bang for your bucks
- The importance of Metadata Management to becoming data-centric
Review existing data management maturity models to identify core set of characteristics of an effective data maturity model:
DMBOK (Data Management Book of Knowledge) from DAMA (Data Management Association)
MIKE2.0 (Method for an Integrated Knowledge Environment) Information Maturity Model (IMM)
IBM Data Governance Council Maturity Model
Enterprise Data Management Council Data Management Maturity Model
Gathering And Documenting Your Bi Business RequirementsWynyard Group
Business requirements are critical to any project. Recent studies show that 70% of organisations fail to gather business requirements well. What is worse is that poor requirements can lead a project to over spend its original budget by 95%.
Business Intelligence and Performance Management projects are no different. This session will provide a series of tips, techniques and ideas on how you can discover, analyse, understand and document your business requirements for your BI and PM projects. This session will also touch on specific issues, hurdles and obstacle that occur for a typical BI or PM project
• The importance of business requirements and a well defined business requirements process
• Understanding the difference between a “wish-list” or vision and business requirements
• The need and benefits of having a business traceability matrix
Start your BI projects on the right foot – understand your requirements
a whistlestop tour through some of the ethical dilemmas and challenges that arise in this "Big Data Age" and the various approaches to considering them, if not solving them.
In this 10 minute "lightning talk" delegates will get insights into some of the research agenda and issues being considered in this area, touching on Business Analytics, Data Quality, analytic risks, ethics and evidence-based decision-making culture
The Business Value of Metadata for Data GovernanceRoland Bullivant
In today’s digital economy, data drives the core processes that deliver profitability and growth - from marketing, to finance, to sales, supply chain, and more. It is also likely that for many large organizations much of their key data is retained in application packages from SAP, Oracle, Microsoft, Salesforce and others. In order to ensure that their foundational data infrastructure runs smoothly, most organizations have adopted a data governance initiative. These typically focus on the people and processes around managing data and information. Without an actionable link to the physical systems that run key business processes, however, governance programs can often lack the ‘teeth’ to effectively implement business change.
Metadata management is a process that can link business processes and drivers with the technical applications that support them. This makes data governance actionable and relevant in today’s fast-paced and results-driven business environment. One of the challenges facing data governance teams however, is the variety in format, accessibility and complexity of metadata across the organization’s systems.
RWDG Slides: A Complete Set of Data Governance Roles & ResponsibilitiesDATAVERSITY
Roles and responsibilities are the backbone to a successful Data Governance program. The way you define and utilize the roles will be the biggest factor of program success. From Data Stewards to the steering committee and everyone in between, people will need to understand the role they play, why they are in the role and how the role fits in with their existing job.
Join Bob Seiner for this RWDG webinar where he will provide a complete and detailed set of Data Governance roles and responsibilities. Bob will share an Operating Model of Roles and Responsibilities that can be customized to address the specific needs of your organization.
In this webinar, Bob will discuss:
- Executive, Strategic, Tactical, Operational, and Support Level Roles
- How to customize an Operating Model to fit your organization
- Detailed Responsibilities for each level
- Defining who participates at each level
- Using working teams to implement tactical solutions
Real-World Data Governance: Governance Risk and ComplianceDATAVERSITY
The target of many a Data Governance Program is to nail their regulatory and compliance requirements first to appease the government and industry regulators before doing anything else. Risk Management, as a practice, is already in place in most organizations under a variety of names. Even though most organizations do not consider Risk Management the same thing as Data Governance, the similarities abound. Compliance is not optional. Nothing about Regulatory and Compliance mentions optional. Governance is not optional either.
The session will cover:
Risk Management Vs. Data Governance – A Close Comparison
Risk Management as the Face of Data Governance
Measuring Success of Governance in terms of Risk Management
Using Risk and Compliance to Explain Governance
Using “Not Optional” as Your Crutch
Introduction to DCAM, the Data Management Capability Assessment ModelElement22
DCAM is a model to assess data management capability within the financial industry. It was created by the EDM Council. This presentation provides an overview of DCAM and how financial institutions leverage DCAM to improve or establish their data management programs and meet regulatory requirements such as BCBS 239.
Metadata management is critical for organizations looking to understand the context, definition and lineage of key data assets. Data models play a key role in metadata management, as many of the key structural and business definitions are stored within the models themselves. Can data models replace traditional metadata solutions? Or should they integrate with larger metadata management tools & initiatives?
Join this webinar to discuss opportunities and challenges around:
How data modeling fits within a larger metadata management landscape
When can data modeling provide “just enough” metadata management
Key data modeling artifacts for metadata
Organization, Roles & Implementation Considerations
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.
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
Estrategia de datos en las organizaciones SAS Colombia
Si se ha preguntado ¿cómo los datos de su organización pueden contribuir al desarrollo de los lineamientos corporativos? La respuesta es sí: implementando una estrategia de Data Management.
Master Data Management - Practical Strategies for Integrating into Your Data ...DATAVERSITY
Master Data Management (MDM) provides organizations with an accurate and comprehensive view of their business-critical data such as Customers, Products, Vendors, and more. While mastering these key data areas can be a complex task, the value of doing so can be tremendous – from real-time operational integration to data warehousing & analytic reporting. This webinar provides practical strategies for gaining value from your MDM initiative, while at the same time assuring a solid architectural and governance foundation that will ensure long-term, enterprise-wide success.
Introduction to DCAM, the Data Management Capability Assessment Model - Editi...Element22
DCAM stands for Data management Capability Assessment Model. DCAM is a model to assess data management capabilities within the financial industry. It was created by the EDM Council in collaboration with over 100 financial institutions. This presentation provides an overview of DCAM and how financial institutions leverage DCAM to improve or establish their data management programs and meet regulatory requirements such as BCBS 239. Also the benefits of DCAM are described as part of this presentation.
Metadata is hotter than ever, according a number of recent DATAVERSITY surveys. More and more organizations are realizing that in order to drive business value from data, robust metadata is needed to gain the necessary context and lineage around key data assets. At the same time, industry regulations are driving the need for better transparency and understanding of information.
While metadata has been managed for decades, new strategies & approaches have been developed to support the ever-evolving data landscape, and provide more innovative ways to drive business value from metadata. This webinar will provide an overview of metadata strategies & technologies available to today’s organization, and provide insights into building successful business strategies for metadata adoption & use.
Driving Data Intelligence in the Supply Chain Through the Data Catalog at TJXDATAVERSITY
Roles and responsibilities are a critical component of every Data Governance program. Building a set of roles that are practical and that will not interfere with people’s “day jobs” is an important consideration that will influence how well your program is adopted. This tutorial focuses on sharing a proven model guaranteed to represent your organization.
Join Bob Seiner for this lively webinar where he will dissect a complete Operating Model of Roles and Responsibilities that encompasses all levels of the organization. Seiner will detail the roles and describe the most effective way to associate people with the roles. You will walk out of this webinar with a model to apply to your organization.
In this session Bob will share:
- The five levels of Data Governance roles
- A proven Operating Model of Roles and Responsibilities
- How to customize the model to meet your requirements
- Setting appropriate role expectations
- How to operationalize the roles and demonstrate value
DAS Slides: Building a Data Strategy - Practical Steps for Aligning with Busi...DATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task. The opportunity in getting it right can be significant, however, as data drives many of the key initiatives in today’s marketplace: digital transformation, marketing, customer centricity, and more. This webinar will help de-mystify Data Strategy and Data Architecture and will provide concrete, practical ways to get started.
How to Make a Data Governance Program that LastsDATAVERSITY
Traditional data governance initiatives fail by focusing too heavily on policies, compliance, and enforcement, which quickly lose business interest and support. This leaves data management and governance leaders having to continually make the case for data governance to secure business adoption. Join Cameron, VP, Product Management, Precisely, as he shares a lean, business-first data governance approach that connects key initiatives to governance capabilities and quickly delivers business value for the long-term. He will give examples of organizations worldwide who have successfully implemented a data governance program by engaging with key stakeholders using innovative techniques such as gamification and data catalog scavenger hunts.
Introduction to Data Management Maturity ModelsKingland
Jeff Gorball, the only individual accredited in the EDM Council Data Management Capability Model and the CMMI Institute Data Management Maturity Model, introduces audiences to both models and shares how you can choose which one is best for your needs.
Data Governance and Metadata ManagementDATAVERSITY
Metadata is a tool that improves data understanding, builds end-user confidence, and improves the return on investment in every asset associated with becoming a data-centric organization. Metadata’s use has expanded beyond “data about data” to cover every phase of data analytics, protection, and quality improvement. Data Governance and metadata are connected at the hip in every way possible. As the song goes, “You can’t have one without the other.”
In this RWDG webinar, Bob Seiner will provide a way to renew your energy by focusing on the valuable asset that can make or break your Data Governance program’s success. The truth is metadata is already inherent in your data environment, and it can be leveraged by making it available to all levels of the organization. At issue is finding the most appropriate ways to leverage and share metadata to improve data value and protection.
Throughout this webinar, Bob will share information about:
- Delivering an improved definition of metadata
- Communicating the relationship between successful governance and metadata
- Getting your business community to embrace the need for metadata
- Determining the metadata that will provide the most bang for your bucks
- The importance of Metadata Management to becoming data-centric
Review existing data management maturity models to identify core set of characteristics of an effective data maturity model:
DMBOK (Data Management Book of Knowledge) from DAMA (Data Management Association)
MIKE2.0 (Method for an Integrated Knowledge Environment) Information Maturity Model (IMM)
IBM Data Governance Council Maturity Model
Enterprise Data Management Council Data Management Maturity Model
Gathering And Documenting Your Bi Business RequirementsWynyard Group
Business requirements are critical to any project. Recent studies show that 70% of organisations fail to gather business requirements well. What is worse is that poor requirements can lead a project to over spend its original budget by 95%.
Business Intelligence and Performance Management projects are no different. This session will provide a series of tips, techniques and ideas on how you can discover, analyse, understand and document your business requirements for your BI and PM projects. This session will also touch on specific issues, hurdles and obstacle that occur for a typical BI or PM project
• The importance of business requirements and a well defined business requirements process
• Understanding the difference between a “wish-list” or vision and business requirements
• The need and benefits of having a business traceability matrix
Start your BI projects on the right foot – understand your requirements
a whistlestop tour through some of the ethical dilemmas and challenges that arise in this "Big Data Age" and the various approaches to considering them, if not solving them.
In this 10 minute "lightning talk" delegates will get insights into some of the research agenda and issues being considered in this area, touching on Business Analytics, Data Quality, analytic risks, ethics and evidence-based decision-making culture
The one question you must never ask!" (Information Requirements Gathering for...Alan D. Duncan
Presentation from 2014 International Data Quality Summit (www.idqsummit.org, Twitter hashtag #IDQS14). Techniques for business analysts and data scientists to facilitate better requirements gathering in data and analytic projects.
In this new paper, I explore the organisational and cultural challenges of implementing information governance and data quality. I identify potential problems with the traditional centralised methods of data quality management, and offer alternative organistional models which can enable a more distributed and democratised approach to improving your organisations data. I also propose a simple four-step approach to delivering immediate business value from your data.
Managing for Effective Data Governance: workshop for DQ Asia Pacific Congress...Alan D. Duncan
This session reflects on the human aspects of Data Governance and examines what it takes to be successful in implementing effective information-enabled business transformation:
* Do we need to rethink our Data Governance strategies?
* Is enterprise-wide Data Management & Governance really achievable?
* What techniques and capabilities do we need to focus on?
* What skills and personal attributes does a Data Governance Manager need?
06. Transformation Logic Template (Source to Target)Alan D. Duncan
This document template defines an outline structure for the clear and unambiguous definition of transmission of data between one data storage location to another. (a.k.a. Source to Target mapping)
Data Quality in Data Warehouse and Business Intelligence Environments - Disc...Alan D. Duncan
Time and again, we hear about the failure of data warehouses – while things may be improving, they’re moving only slowly. One explanation data quality being overlooked is that the I.T. department is often responsible for delivering and operating the DWH/BI
environment. What ensues ends up being an agenda based on “how do we build it”, not a “why are we doing this”. This needs to change. In this discussion paper, I explore the issues of data quality in data warehouse, business intelligence and analytic environments, and propose an approach based on "Data Quality by Design"
The ABC of Data Governance: driving Information ExcellenceAlan D. Duncan
Overview of Data Governance requirements, techniques and outcomes. Presented at 5th Annual Records & Information Officers' Forum, Melbourne 19-20 Feb 2014.
A presentation to the ETIS Business Intelligence & Data Warehousing Working Group in Brussels 22-Mar-13 discussing what Saas & Cloud means and how they will affect BI in Telcos
Business intelligence requirements are changing and business users are moving more and more from historical reporting into predictive analytics in an attempt to get both a better and deeper understanding of their data. Traditionally, building an analytical platform has required an expensive infrastructure and a considerable amount of time for setup and deployment. Here we look at a quick and simple alternative.
Data Driven Insurance Underwriting (Dutch Language Version)David Walker
A discussion on how insurance companies could use telematics data, social media and open data sources to analyse and better price policies for their customers
Basics of Microsoft Business Intelligence and Data Integration TechniquesValmik Potbhare
The presentation used to get the conceptual understanding of Business Intelligence and Data warehousing applications. This also gives a basic knowledge about Microsoft's offerings on Business Intelligence space. Lastly but not least, it also contains some useful and uncommon SQL server programming best practices.
A discussion on how insurance companies could use telematics data, social media and open data sources to analyse and better price policies for their customers
O futuro de TI passa por compreender a transformação digital dos negócios e qual papel ela deve se desempenhar neste novo cenário cada vez mais próximo. A resposta para esta transformação passa pela Arquitetura Corporativa (EA).
I built this presentation for Informatica World in 2006. It is all about Data Administration, Data Quality and Data Management. It is NOT about the Informatica product. This presentation was a hit, with standing room only full of about 150 people. The content is still useful and applicable today. If you want to use my material, please put (C) Dan Linstedt, all rights reserved, http://LearnDataVault.com
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 Donna Burbank and Nigel Turner as they provide practical ways to control data quality issues in your organization.
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.
IRM Data Governance Conference February 2009, London. Presentation given on the Data Governance challenges being faced by BP and the approaches to address them.
AI&BigData Lab 2016. Сергей Шельпук: Методология Data Science проектовGeeksLab Odessa
4.6.16 AI&BigData Lab
Upcoming events: goo.gl/I2gJ4H
Проекты в области анализа данных - вызов не только для инженеров, но и для менеджеров. Доклад будет посвящён особенностям таких проектов по сравнению с обычной разработкой, ролям в команде и построению взаимодействия с заказчиком в условиях неопределённости R&D.
Big data is a term that describes the huge amount of data (structured and unstructured) that floods the enterprise every day.
Big Data includes the quantity of data , the speed or speed at which it's created and picked up , and therefore the variety or scope of the info points being covered. It very often comes from several sources and arrives in multiple formats.
From the perspective of a project manager or project manager, big data does not necessarily revolve around the amount of data that individuals and companies deal with. Data can be obtained from any source and analyzed to find the answer for the following purposes:
Reduce the time cut costs
Wise decision
Optimized product
New products development
Your present project management and soft skills are likely ultimate for establishing the framework for a replacement or existing Big Data project team and their projects. you only got to enhance the talents and knowledge you have already got .
This is where Tonex training can help.
Tonex Offers Big Data for Project and Program Managers Training
participants will find out how to profit from big data in their projects and programs
Why does one Need This Training?
Need project managers with big data expertise and business awareness
Must have expert judgment ability to use technology
The plan manager should assist in expanding and coordinating tasks throughout the project
Audience
Project managers
Program managers
Big data analytics
Decision makers of organizations
Strategic leaders
Executives
Training Objectives
Describe the big data analytics
Explain the business values of massive data
Talk about the opportunities and challenges of using big data
Choose if big data analytics serve their client’s interest, situation and knowledge
Manage data analytic projects
Assess risks related to the large data
Distinguish between a knowledge analytic project and a fishing expedition
Decide the best approach
Conclude the time to stop the analysis
Talk about how project management can be used to sustain your data analytics capability
Elaborate how big data can be used to secure the progress of the project
Identify what analytics should be implemented
Course Outline:
Overview to Big Data and Project/Program Management
Project Management Process
Where Does Big Data Analytics expertise is Required?
Introduction to Big Data Management
Big Data Challenges
The Status of Big Data Management
Data Science Methods
Technical Practices for Big Data Management
Analytic Exercises and Big Data Management
Applicable Programming Languages
Corporation Practices for Big Data Management
Top Priorities of Big Data Management
Choosing the Best Strategy
Organizational Leadership
Tonex Hands-On Sample Workshop
Learn More:
https://www.tonex.com/training-courses/big-data-project-program-managers-training/
Optimizing Solution Value – Dynamic Data Quality, Governance, and MDMPrecisely
Data quality used to be a one-dimensional term: Is your data right? As the data landscape has become more complex and sophisticated, data quality has evolved to require a more holistic approach to encompass much more to ensure trust in data. Integrated data quality, governance, and multi-domain data management provides a more robust single view that data-driven companies require to have more complete confidence in critical business decisions.
Join Chuck Kane, VP of Product Management, Precisely, as he shares use case trends that illustrate how innovation in data management continues to evolve. Topics that you will hear addressed:
Evolving Data Monetization: improve your bottom line with the ability to confidently leverage data as an asset Evolving to a Single View of Data: break down silos, and gain a powerful, comprehensive single view of your organization’s data Evolving Data on the Move: ensure consistent levels of data quality by monitoring data as it moves throughout the business Evolving Operational Value: enrich, fix, and validate data to open the door to new possibilities
Data Integrity: From speed dating to lifelong partnershipPrecisely
Governance has little to do with governance…it’s about delivering and demonstrating value. It’s one thing for your colleagues to intellectually believe in the value of data, good data, and governed data, but it’s another thing entirely to have them emotionally engaged and excited to be involved. In this presentation from the CDO Sit-Down series, Shaun Connolly, Vice President of International Strategic Services, shares his thoughts and experience on approaches to win over reluctant leaders and business teams and describe the key components of successful programs.
Adjusting OpenMP PageRank : SHORT REPORT / NOTESSubhajit Sahu
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Analysis insight about a Flyball dog competition team's performance
02. Information solution outline template
1. http://informationaction.blogspot.com
Tw: @Alan_D_Duncan
Information Strategy | Data Governance | Analytics | Better Business Outcomes
Example Data Specifications &
Information Requirements Framework
INFORMATION SOLUTION OUTLINE &
HIGH-LEVEL REQUIREMENTS
TEMPLATE (Project Mandate)
Alan D. Duncan
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
2. Information Solution Outline & High-Level Requirements
http://informationaction.blogspot.com
Tw: @Alan_D_Duncan
Information Strategy | Data Governance | Analytics | Better Business Outcomes
1 Purpose
This is a document template for capturing the overall high-level business requirements and
expectations for business information solutions with a significant impact on or requirement for data.
(cf. the “Project Mandate” document in PRINCE2).
At this stage, expectations are captured in general terms and while additional detail is of value, the
main aims are to ensure that the overall expectations and goals of the solution are captured in terms
that are meaningful to the business community. For guidance, the information solution will be outlined
at a level of understanding sufficient to support the outline Business Case.
This template forms part of example data specification & information requirements framework. The
framework offers a set of outline principles, standards and guidelines to describe and clarify the
semantic meaning of data terms in support of an Information Requirements Management process.
(See the Framework Overview for further details.)
3. Information Solution Outline & High-Level Requirements
http://informationaction.blogspot.com
Tw: @Alan_D_Duncan
Information Strategy | Data Governance | Analytics | Better Business Outcomes
2 Information Solution Outline & High-Level Requirements
(Project Mandate)
SOLUTION
REQUIREMENT:
The area of information & data management under review and the overall
expectations for the solution.
SOLUTION
OBJECTIVES:
Purpose or outcome required.
Why is the change to information (or associated systems) needed? What
business outcomes are intended?
SOLUTION
DESCRIPTION:
What data or information is required? How is the solution intended to
function?
What is the scope of changes to the information within the solution?
Where does this apply?
What will happen to the data? What will change?
SOLUTION DATA
OWNER:
Who is requesting the information solution?
Who is accountable for the solutions completion and outcomes of the
requirements for any resulting changes to the business information?
DATA STEWARD(s): Who is responsible for the execution of the changes to the data?
SYSTEMS
IMPACTED
Which information systems or architectural components will be involved
when a change is being made to the definition or consumption of this data
e.g.:
SOA
DWH
MDM
Application
Project
SOLUTION
DELIVERY
METHODS & DATA
UPDATE
PROCESSES
How is the solution to be implemented?
What methodologies are to be applied? (e.g. MIKE2.0, DMBOK, SCRUM)
What changes to the data will be applied?
What steps are involved?
EXCEPTIONS,
CONSTRAINTS &
EXCLUSIONS
Are there any exceptions or constraints that limit the scope of data delivery?
Is there are any data that explicitly won’t be included?
DATA VERIFICATION
MODE
How will the solution changes and impacts on the data be verified as
effective and complete?
Audit: after-the-fact review for compliance.
Self-Certification: team compiles the evidence to demonstrate that
the checkpoint has been applied
Intervention: Requirement for Data Governance Unit participation
will be proactively flagged
SOLUTION
DOCUMENTATION
INPUTS
What information or deliverables will be used as an input to the solution
delivery process?
E,g. requirements documents, draft design documents, Business Case,
options papers etc. etc.
SOLUTION
DOCUMENTATION
OUTPUTS
What information or deliverables will be created as an output of the
process?
E,g. minutes, design documents, updates to metadata repository etc.
4. Information Solution Outline & High-Level Requirements
http://informationaction.blogspot.com
Tw: @Alan_D_Duncan
Information Strategy | Data Governance | Analytics | Better Business Outcomes
RELEVANT
CONTROL
ARTEFACTS
Which Data Governance and Information Management standards,
guidelines, methods and constraints need to be applied? E.g.
Update the Enterprise Conceptual Model
Update the Enterprise Logical Model
Update the solution Physical Design
What other principles, guidelines and reference materials could be useful?
(e.g. legislation, regulation, policies and standards)
RESOURCES &
ROLES:
Who is to participate in making the required changes to the data and
systems?
In what capacity/role will they contribute?
SOLUTION DATA DEFINITIONS:
Capture any known business & technical metadata
Are any data lineage impacts identified?
Define any updates than need to be applied to Business Data Network & Business Glossary
IDENTIFIED DEPENDENCIES
Pre-requisites etc. that need to be satisfied before the solution can be implemented.
RELATED BUSINESS PROCESSES
Identify existing business processes impacted by the delivery of the new data solution.
OTHER INFORMATION & NOTES
5. Information Solution Outline & High-Level Requirements
http://informationaction.blogspot.com
Tw: @Alan_D_Duncan
Information Strategy | Data Governance | Analytics | Better Business Outcomes
About the author
Alan D. Duncan is an evangelist for information and analytics as
enablers of better business outcomes, and a member of the
Advisory Board for QFire Software.
An executive-level leader in the field of Information and Data
Management Strategy, Governance and Business Analytics, he
has over 20 years of international business experience, working
with blue-chip companies in a range of industry sectors. Alan
was named by Information-Management.com in their 2012 list of
“Top 12 Data Governance gurus you should be following on
Twitter”.
Twitter: @Alan_D_Duncan
Blog: http://informationaction.blogspot.com.au/
6. Information Solution Outline & High-Level Requirements
http://informationaction.blogspot.com
Tw: @Alan_D_Duncan
Information Strategy | Data Governance | Analytics | Better Business Outcomes
Intellectual curiosity
Skeptical scrutiny
Critical thinking
http://www.informationaction.blogspot.com.au/
@Alan_D_Duncan
http://www.linkedin.com/in/alandduncan