BI governance involves regulating and authorizing the information lifecycle for decision making and strategy execution. It ensures the responsible management of data models, quality, security, warehousing and metadata. The governance model includes processes, roles, tools and accountability to structure BI within the corporation. While it requires resources for skills and change management, BI governance provides benefits like reliable decision making information and protection of information assets. A case study demonstrates developing a governance model for requirements analysis and change management of an organization's BI system.
Stop the madness - Never doubt the quality of BI again using Data GovernanceMary Levins, PMP
Does this sound familiar? "Are you sure those numbers are right?" "Why are your numbers different than theirs?"
We've all heard it and had that gut wrenching feeling of doubt that comes with uncertainty around the quality of the numbers.
Stop the madness! Presented in Dunwoody on April 18 by industry leading expert Mary Levins who discusseses what it takes to successfully take control of your data using the Data Governance Framework. This framework is proven to improve the quality of your BI solutions.
Mary is the founder of Sierra Creek Consulting
Create a 'Customer 360' with Master Data Management for Financial ServicesPerficient, Inc.
This document summarizes Perficient's capabilities in providing master data management (MDM) solutions for financial services clients. Perficient has expertise in implementing MDM to create a unified customer view across systems and business units. Key benefits of MDM include improved customer experience, increased revenue opportunities, and reduced costs. The document also discusses current industry trends like social media, mobility, and big data that are driving greater need for MDM.
SharePoint Online has grown up. The new release, available with Office 365, Office 365, includes a wide variety of improvements and new features. SharePoint Online can help you share your work and work with others, organize your projects and teams and discover people and information.
This presentation takes a look at the new SharePoint Online, including:
• New features designed for the cloud
• How these new features equate to increased scale for larger companies
• How social business and Yammer fit into the equation
Capital Planning And Investment Management And Control In Information TechnologyAlan McSweeney
This document discusses capital planning and investment control for information technology (CPIC-IT). It provides an overview of CPIC-IT and how it is a structured process for managing risks and returns associated with IT investments. It ensures investments are implemented on time and within budget, and contribute to improved organizational performance. The document also covers topics like IT investment management, cost estimation, and analyzing IT investments. Overall it provides information on applying a systematic approach to managing IT investments through their entire lifecycle.
The document discusses best practices for data governance and stewardship. It recommends starting with cataloging all data assets, identifying current and future states, and planning governance roles and processes. It then provides details on assessing data quality, cleaning data, and establishing a data governance team with roles like stewards and custodians. It emphasizes the importance of data lifecycles and having the right data at the right time to drive business goals.
Information management and enterprise architecturenvvrajesh
The document discusses practical enterprise information management. It describes EIM as managing data in all forms as a strategic asset. A good EIM program results in integrated, accurate and timely enterprise data through policies, frameworks, technologies and processes. These include data models, data lineage, data quality, data profiling and stewardship. The document recommends aligning EIM with enterprise architecture and developing conceptual, logical and physical data models across three layers to support the information architecture.
Enterprise Data World Webinars: Master Data Management: Ensuring Value is Del...DATAVERSITY
Now that your organization has decided to move forward with Master Data Management (MDM), how do you make sure that you get the most value from your investment? In this webinar, we will cover the critical success factors of MDM that ensure your master data is used across the enterprise to drive business value. We cover:
· The key processes involved in mastering data
· Data Governance’s role in mastering data
· Leveraging data stewards to make your MDM program efficient
· How to extend MDM from one domain to multiple domains
· Ensuring MDM aligns to business goals and priorities
BI governance involves regulating and authorizing the information lifecycle for decision making and strategy execution. It ensures the responsible management of data models, quality, security, warehousing and metadata. The governance model includes processes, roles, tools and accountability to structure BI within the corporation. While it requires resources for skills and change management, BI governance provides benefits like reliable decision making information and protection of information assets. A case study demonstrates developing a governance model for requirements analysis and change management of an organization's BI system.
Stop the madness - Never doubt the quality of BI again using Data GovernanceMary Levins, PMP
Does this sound familiar? "Are you sure those numbers are right?" "Why are your numbers different than theirs?"
We've all heard it and had that gut wrenching feeling of doubt that comes with uncertainty around the quality of the numbers.
Stop the madness! Presented in Dunwoody on April 18 by industry leading expert Mary Levins who discusseses what it takes to successfully take control of your data using the Data Governance Framework. This framework is proven to improve the quality of your BI solutions.
Mary is the founder of Sierra Creek Consulting
Create a 'Customer 360' with Master Data Management for Financial ServicesPerficient, Inc.
This document summarizes Perficient's capabilities in providing master data management (MDM) solutions for financial services clients. Perficient has expertise in implementing MDM to create a unified customer view across systems and business units. Key benefits of MDM include improved customer experience, increased revenue opportunities, and reduced costs. The document also discusses current industry trends like social media, mobility, and big data that are driving greater need for MDM.
SharePoint Online has grown up. The new release, available with Office 365, Office 365, includes a wide variety of improvements and new features. SharePoint Online can help you share your work and work with others, organize your projects and teams and discover people and information.
This presentation takes a look at the new SharePoint Online, including:
• New features designed for the cloud
• How these new features equate to increased scale for larger companies
• How social business and Yammer fit into the equation
Capital Planning And Investment Management And Control In Information TechnologyAlan McSweeney
This document discusses capital planning and investment control for information technology (CPIC-IT). It provides an overview of CPIC-IT and how it is a structured process for managing risks and returns associated with IT investments. It ensures investments are implemented on time and within budget, and contribute to improved organizational performance. The document also covers topics like IT investment management, cost estimation, and analyzing IT investments. Overall it provides information on applying a systematic approach to managing IT investments through their entire lifecycle.
The document discusses best practices for data governance and stewardship. It recommends starting with cataloging all data assets, identifying current and future states, and planning governance roles and processes. It then provides details on assessing data quality, cleaning data, and establishing a data governance team with roles like stewards and custodians. It emphasizes the importance of data lifecycles and having the right data at the right time to drive business goals.
Information management and enterprise architecturenvvrajesh
The document discusses practical enterprise information management. It describes EIM as managing data in all forms as a strategic asset. A good EIM program results in integrated, accurate and timely enterprise data through policies, frameworks, technologies and processes. These include data models, data lineage, data quality, data profiling and stewardship. The document recommends aligning EIM with enterprise architecture and developing conceptual, logical and physical data models across three layers to support the information architecture.
Enterprise Data World Webinars: Master Data Management: Ensuring Value is Del...DATAVERSITY
Now that your organization has decided to move forward with Master Data Management (MDM), how do you make sure that you get the most value from your investment? In this webinar, we will cover the critical success factors of MDM that ensure your master data is used across the enterprise to drive business value. We cover:
· The key processes involved in mastering data
· Data Governance’s role in mastering data
· Leveraging data stewards to make your MDM program efficient
· How to extend MDM from one domain to multiple domains
· Ensuring MDM aligns to business goals and priorities
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
3 Keys To Successful Master Data Management - Final PresentationJames Chi
This document discusses keys to successful master data management including process, governance, and architecture. It summarizes a survey finding that while many companies see data as an asset, only around 20% have implemented master data management. Successful MDM requires alignment with business objectives, clear governance models, and comprehensive solution architectures. The document advocates establishing policies, procedures, standards, governance, and tools to create and maintain high-quality shared reference data.
This document discusses an agile solution for enterprise data modeling and data management provided by A.I. Consultancy Limited and Pacific Rim Telecomm Datacomm Ltd. It outlines the benefits of enterprise data modeling, problems with traditional top-down approaches, and their hybrid agile solution using off-the-shelf modeling tools. Their solution aims to deliver initial data models quickly and support ongoing data governance through modular implementation and tailored training.
Data-Ed Webinar: Implementing the Data Management Maturity Model (DMM) - With...DATAVERSITY
The Data Management Maturity (DMM) model is a framework for the evaluation and assessment of an organization’s data management capabilities. This model—based on the Capability Maturity Model pioneered by the U.S. Department of Defense for improving software development processes—allows an organization to evaluate its current state data management capabilities, discover gaps to remediate, and identify strengths to leverage. In doing so, this assessment method reveals organizational priorities, business needs, and a clear path for rapid process improvements.
In this webinar, we will:
- Describe the DMM model, its purpose and evolution, and how it can be used as a roadmap for assessing and improving organizational data management and data management maturity
- Discuss how to get the most out of a DMM assessment, including its dependencies and requirements for use
Data-Ed Online: Unlock Business Value through Reference & MDMDATAVERSITY
In order to succeed, organizations must realize what it means to utilize reference and MDM in support of business strategy. This presentation provides you with an understanding of the goals of reference and MDM, including the establishment and implementation of authoritative data sources, more effective means of delivering data to various business processes, as well as increasing the quality of information used in organizational analytical functions, e.g. BI. We also highlight the equal importance of incorporating data quality engineering into all efforts related to reference and master data management.
Learning objectives include:
What is Reference & MDM and why is it important?
Reference & MDM Frameworks and building blocks
Guiding principles & best practices
Understanding foundational reference & MDM concepts based on the Data Management Body of Knowledge (DMBOK)
Utilizing reference & MDM in support of business strategy
DataEd Slides: Data Management Maturity - Achieving Best Practices Using DMMDATAVERSITY
ince its release in 2014, the CMMI/Data Management Maturity (DMM)℠ model has become the de facto standard for planning and implementing programmatic improvements to organizational Data Management programs. It permits organizations to evaluate its current-state Data Management capabilities and discover gaps to remediate and strengths to leverage. The DMM reveals priorities, business needs, and a clear, rapid path for process improvements. This webinar will describe the DMM framework for assessing an organization's Data Management capabilities, its evolution, and illustrate its use as a roadmap guiding organizational Data Management improvements.
Key Takeaways:
- Our profession is advancing its knowledge and has a widespread basis for partnerships
- New industry assessment standard is based on successful CMM/CMMI foundation
- A clear need for Data Strategy
- A clear and unambiguous call for participation
Presentation I put together while at Business Objects where I spearheaded the development of the Business Intelligence Competency Center practice and service offering.
The Data Governance Annual Conference and International Data Quality Conference in San Diego was very good. I recommend this conference for business and IT persons responsible for data quality and data governenance. There will be a similar event in Orlando, December 2010. This is the presentation I delivered to a grateful audience.
The document discusses six key questions organizations should ask about data governance: 1) Do we have a government structure in place to oversee data governance? 2) How can we assess our current data governance situation? 3) What is our data governance strategy? 4) What is the value of our data? 5) What are our data vulnerabilities? 6) How can we measure progress in data governance? It provides details on each question, highlighting the importance of leadership, benchmarks, strategic planning, risk assessment, and metrics in developing an effective data governance program.
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
Join this month’s CDO Vision webinar to hear and understand how Data Governance is being implemented by real-world Chief Data Officer Jennifer Ippoliti. Hear how Data Governance strategies are being implemented and prioritized across financial institutions.
Listen in as Jennifer is interviewed about:
•How regulatory drives are impacting decisions
•How Data Governance is positioned within the organizations
•What Data Governance responsibilities the CDO has
•How Data Governance strategies are implemented
•How the office of the Chief Data Officer continues to evolve
New & Improved Office 365: Is it Right for Your Business?Perficient, Inc.
This document provides an overview of Perficient, a leading information technology consulting firm, and their expertise in implementing Microsoft Office 365 and other cloud-based solutions. Some key points:
- Perficient has over 2,000 employees located throughout North America and global delivery centers. They focus on business-driven technology solutions to improve productivity.
- Perficient has a large Microsoft practice and is a top Microsoft NSI partner. They have experience migrating hundreds of thousands of users to Office 365 and expertise implementing SharePoint, Exchange, Lync and other Microsoft solutions.
- The document outlines Perficient's capabilities including their certified consultants, innovative approaches, and quick start offerings to help clients deploy Office 365
A common misconception is that IT Governance is only for big enterprises. Cloud computing and the increasing pervasive use of technology in the workplace requires that smaller organizations take a more strategic and risk-aware approach to managing their technology and business information. Attend this session to learn how to apply IT governance principles and practices to smaller not-for-profit organizations to help develop your IT strategy, manage your IT risk, and enable better business decisions through information.
The Data Driven University - Automating Data Governance and Stewardship in Au...Pieter De Leenheer
The document discusses implementing data governance and stewardship programs at universities. It provides examples of programs at Stanford University, George Washington University, and in the Flanders region of Belgium. The key aspects covered are:
- Establishing a data governance framework with roles, processes, asset definitions. and oversight council.
- Implementing data stewardship activities like data quality management, metadata development, and reference data management.
- Stanford's program established foundations for institutional research through data quality and context definitions.
- George Washington runs a centralized program managed by the IT governance office.
- The Flanders program provides research information and services across universities through consistent definitions, roles and collaborative workflows.
White Paper - The Business Case For Business IntelligenceDavid Walker
This white paper looks at the business case that should lie behind the decision to build a data warehouse and provide a business intelligence solution.
There are three primary drivers for making the investment in a business intelligence solution
1. Measurement and management of the business process
2. Analysis of why things change in the business in order to react better in the future
3. Providing information for stakeholders
As a consequence of the investment there will also be a number of secondary benefits that will help to justify the investment and these are also discussed. Finally there are a number of ‘anti-drivers’ – reasons for not embarking on a business intelligence programme.
Real-World Data Governance: BI Governance and the Governance of BI DataDATAVERSITY
This document discusses a webinar on the differences between business intelligence (BI) governance and governance of BI data. BI governance refers to governance over all activities in a BI environment, while governance of BI data focuses on applying data governance principles to data used in BI. Both are necessary but distinct disciplines. The webinar aims to clearly define the two, recognize how they are complementary, and provide simple steps to improve their relationship through reusable tools and artifacts.
Requirements for a Master Data Management (MDM) Solution - PresentationVicki McCracken
Working on Requirements for a Master Data Management solution and looking for thoughts on how to approach the requirements? This is an overview presentation that complements my guide on how to approach requirements for a Master Data Management solution (Requirements for an MDM Solution). You may be able to leverage all or some of the approach described in this guide to formulate your approach.
This webinar from Gartner provided seven building blocks for a successful master data management (MDM) plan: vision, strategy, metrics, information governance, organization and roles, information lifecycle, and enabling infrastructure. The presentation emphasized the importance of establishing an MDM vision aligned with business goals, assessing the organization's current MDM maturity, defining metrics to measure success, establishing governance, and considering organizational roles and responsibilities. It also stressed understanding the information lifecycle and having the right technology infrastructure.
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
Getting Data Quality Right
High quality data is important for organizational success, but achieving good data quality requires a programmatic approach. Data quality challenges are often the root cause of IT and business failures. To improve, organizations need to take a systems thinking approach, understand data issues over time, and not underestimate the role of culture. Developing repeatable data quality capabilities and expertise can help organizations identify problems, determine causes, and prevent future issues. Effective data quality engineering provides a framework for utilizing data to support business strategy and goals.
Trends in Enterprise Advanced AnalyticsDATAVERSITY
This document summarizes trends in enterprise analytics presented by William McKnight. It discusses the increasing importance of data and analytics for businesses. Key trends include greater use of data lakes, multi-cloud strategies, master data management, data virtualization, graph databases, stream processing, self-service analytics, and the rise of roles like Chief Data Officer. Data science and analytics skills will become more operational. Selection of big data platforms will consider factors like SQL support, data size, and workload complexity. Overall, data maturity correlates strongly with business success and organizations must continually advance to remain competitive.
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
3 Keys To Successful Master Data Management - Final PresentationJames Chi
This document discusses keys to successful master data management including process, governance, and architecture. It summarizes a survey finding that while many companies see data as an asset, only around 20% have implemented master data management. Successful MDM requires alignment with business objectives, clear governance models, and comprehensive solution architectures. The document advocates establishing policies, procedures, standards, governance, and tools to create and maintain high-quality shared reference data.
This document discusses an agile solution for enterprise data modeling and data management provided by A.I. Consultancy Limited and Pacific Rim Telecomm Datacomm Ltd. It outlines the benefits of enterprise data modeling, problems with traditional top-down approaches, and their hybrid agile solution using off-the-shelf modeling tools. Their solution aims to deliver initial data models quickly and support ongoing data governance through modular implementation and tailored training.
Data-Ed Webinar: Implementing the Data Management Maturity Model (DMM) - With...DATAVERSITY
The Data Management Maturity (DMM) model is a framework for the evaluation and assessment of an organization’s data management capabilities. This model—based on the Capability Maturity Model pioneered by the U.S. Department of Defense for improving software development processes—allows an organization to evaluate its current state data management capabilities, discover gaps to remediate, and identify strengths to leverage. In doing so, this assessment method reveals organizational priorities, business needs, and a clear path for rapid process improvements.
In this webinar, we will:
- Describe the DMM model, its purpose and evolution, and how it can be used as a roadmap for assessing and improving organizational data management and data management maturity
- Discuss how to get the most out of a DMM assessment, including its dependencies and requirements for use
Data-Ed Online: Unlock Business Value through Reference & MDMDATAVERSITY
In order to succeed, organizations must realize what it means to utilize reference and MDM in support of business strategy. This presentation provides you with an understanding of the goals of reference and MDM, including the establishment and implementation of authoritative data sources, more effective means of delivering data to various business processes, as well as increasing the quality of information used in organizational analytical functions, e.g. BI. We also highlight the equal importance of incorporating data quality engineering into all efforts related to reference and master data management.
Learning objectives include:
What is Reference & MDM and why is it important?
Reference & MDM Frameworks and building blocks
Guiding principles & best practices
Understanding foundational reference & MDM concepts based on the Data Management Body of Knowledge (DMBOK)
Utilizing reference & MDM in support of business strategy
DataEd Slides: Data Management Maturity - Achieving Best Practices Using DMMDATAVERSITY
ince its release in 2014, the CMMI/Data Management Maturity (DMM)℠ model has become the de facto standard for planning and implementing programmatic improvements to organizational Data Management programs. It permits organizations to evaluate its current-state Data Management capabilities and discover gaps to remediate and strengths to leverage. The DMM reveals priorities, business needs, and a clear, rapid path for process improvements. This webinar will describe the DMM framework for assessing an organization's Data Management capabilities, its evolution, and illustrate its use as a roadmap guiding organizational Data Management improvements.
Key Takeaways:
- Our profession is advancing its knowledge and has a widespread basis for partnerships
- New industry assessment standard is based on successful CMM/CMMI foundation
- A clear need for Data Strategy
- A clear and unambiguous call for participation
Presentation I put together while at Business Objects where I spearheaded the development of the Business Intelligence Competency Center practice and service offering.
The Data Governance Annual Conference and International Data Quality Conference in San Diego was very good. I recommend this conference for business and IT persons responsible for data quality and data governenance. There will be a similar event in Orlando, December 2010. This is the presentation I delivered to a grateful audience.
The document discusses six key questions organizations should ask about data governance: 1) Do we have a government structure in place to oversee data governance? 2) How can we assess our current data governance situation? 3) What is our data governance strategy? 4) What is the value of our data? 5) What are our data vulnerabilities? 6) How can we measure progress in data governance? It provides details on each question, highlighting the importance of leadership, benchmarks, strategic planning, risk assessment, and metrics in developing an effective data governance program.
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
Join this month’s CDO Vision webinar to hear and understand how Data Governance is being implemented by real-world Chief Data Officer Jennifer Ippoliti. Hear how Data Governance strategies are being implemented and prioritized across financial institutions.
Listen in as Jennifer is interviewed about:
•How regulatory drives are impacting decisions
•How Data Governance is positioned within the organizations
•What Data Governance responsibilities the CDO has
•How Data Governance strategies are implemented
•How the office of the Chief Data Officer continues to evolve
New & Improved Office 365: Is it Right for Your Business?Perficient, Inc.
This document provides an overview of Perficient, a leading information technology consulting firm, and their expertise in implementing Microsoft Office 365 and other cloud-based solutions. Some key points:
- Perficient has over 2,000 employees located throughout North America and global delivery centers. They focus on business-driven technology solutions to improve productivity.
- Perficient has a large Microsoft practice and is a top Microsoft NSI partner. They have experience migrating hundreds of thousands of users to Office 365 and expertise implementing SharePoint, Exchange, Lync and other Microsoft solutions.
- The document outlines Perficient's capabilities including their certified consultants, innovative approaches, and quick start offerings to help clients deploy Office 365
A common misconception is that IT Governance is only for big enterprises. Cloud computing and the increasing pervasive use of technology in the workplace requires that smaller organizations take a more strategic and risk-aware approach to managing their technology and business information. Attend this session to learn how to apply IT governance principles and practices to smaller not-for-profit organizations to help develop your IT strategy, manage your IT risk, and enable better business decisions through information.
The Data Driven University - Automating Data Governance and Stewardship in Au...Pieter De Leenheer
The document discusses implementing data governance and stewardship programs at universities. It provides examples of programs at Stanford University, George Washington University, and in the Flanders region of Belgium. The key aspects covered are:
- Establishing a data governance framework with roles, processes, asset definitions. and oversight council.
- Implementing data stewardship activities like data quality management, metadata development, and reference data management.
- Stanford's program established foundations for institutional research through data quality and context definitions.
- George Washington runs a centralized program managed by the IT governance office.
- The Flanders program provides research information and services across universities through consistent definitions, roles and collaborative workflows.
White Paper - The Business Case For Business IntelligenceDavid Walker
This white paper looks at the business case that should lie behind the decision to build a data warehouse and provide a business intelligence solution.
There are three primary drivers for making the investment in a business intelligence solution
1. Measurement and management of the business process
2. Analysis of why things change in the business in order to react better in the future
3. Providing information for stakeholders
As a consequence of the investment there will also be a number of secondary benefits that will help to justify the investment and these are also discussed. Finally there are a number of ‘anti-drivers’ – reasons for not embarking on a business intelligence programme.
Real-World Data Governance: BI Governance and the Governance of BI DataDATAVERSITY
This document discusses a webinar on the differences between business intelligence (BI) governance and governance of BI data. BI governance refers to governance over all activities in a BI environment, while governance of BI data focuses on applying data governance principles to data used in BI. Both are necessary but distinct disciplines. The webinar aims to clearly define the two, recognize how they are complementary, and provide simple steps to improve their relationship through reusable tools and artifacts.
Requirements for a Master Data Management (MDM) Solution - PresentationVicki McCracken
Working on Requirements for a Master Data Management solution and looking for thoughts on how to approach the requirements? This is an overview presentation that complements my guide on how to approach requirements for a Master Data Management solution (Requirements for an MDM Solution). You may be able to leverage all or some of the approach described in this guide to formulate your approach.
This webinar from Gartner provided seven building blocks for a successful master data management (MDM) plan: vision, strategy, metrics, information governance, organization and roles, information lifecycle, and enabling infrastructure. The presentation emphasized the importance of establishing an MDM vision aligned with business goals, assessing the organization's current MDM maturity, defining metrics to measure success, establishing governance, and considering organizational roles and responsibilities. It also stressed understanding the information lifecycle and having the right technology infrastructure.
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
Getting Data Quality Right
High quality data is important for organizational success, but achieving good data quality requires a programmatic approach. Data quality challenges are often the root cause of IT and business failures. To improve, organizations need to take a systems thinking approach, understand data issues over time, and not underestimate the role of culture. Developing repeatable data quality capabilities and expertise can help organizations identify problems, determine causes, and prevent future issues. Effective data quality engineering provides a framework for utilizing data to support business strategy and goals.
Trends in Enterprise Advanced AnalyticsDATAVERSITY
This document summarizes trends in enterprise analytics presented by William McKnight. It discusses the increasing importance of data and analytics for businesses. Key trends include greater use of data lakes, multi-cloud strategies, master data management, data virtualization, graph databases, stream processing, self-service analytics, and the rise of roles like Chief Data Officer. Data science and analytics skills will become more operational. Selection of big data platforms will consider factors like SQL support, data size, and workload complexity. Overall, data maturity correlates strongly with business success and organizations must continually advance to remain competitive.
Webinar: Maximizing Your Potential with Data LeadershipDATAVERSITY
Data is everywhere in today’s businesses, and there are countless things for the data professional to do! It can be overwhelming to figure out what we should be doing now, tomorrow, and further down the road. Data Leadership helps us simplify, prioritize, and ultimately find the direction we need.
The value that comes from data can impact an organization in three fundamental ways: increasing revenues, decreasing costs, and managing risk. Data professionals are tasked to optimize data’s impact on these. But knowing our goals—versus how to best achieve them—are two very different things.
The Data Leadership Framework guides us in sorting out the dozens of choices to determine the best actions to take, no matter where we are in our data journey. Attend this DATAVERSITY webinar to start maximizing data value with Data Leadership!
Business Intelligence (BI) and Data Management Basics amorshed
This document provides an overview of business intelligence (BI) and data management basics. It discusses topics such as digital transformation requirements, data strategy, data governance, data literacy, and becoming a data-driven organization. The document emphasizes that in the digital age, data is a key asset and organizations need to focus on data management in order to make informed decisions. It also stresses the importance of data culture and competency for successful BI and data initiatives.
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.
DataEd Webinar: Reference & Master Data Management - Unlocking Business ValueDATAVERSITY
Data tends to pile up and can be rendered unusable or obsolete without careful maintenance processes. Reference and Master Data Management (MDM) has been a popular Data Management approach to effectively gain mastery over not just the data but the supporting architecture for processing it. This webinar presents MDM as a strategic approach to improving and formalizing practices around those data items that provide context for many organizational transactions—its master data. Too often, MDM has been implemented technology-first and achieved the same very poor track record (one-third succeeding on-time, within budget, and achieving planned functionality). MDM success depends on a coordinated approach typically involving Data Governance and Data Quality activities.
Learning Objectives:
- Understand foundational reference and MDM concepts based on the Data Management Body of Knowledge (DMBOK)
- Understand why these are an important component of your Data Architecture
- Gain awareness of Reference and MDM Frameworks and building blocks
- Know what MDM guiding principles consist of and best practices
- Know how to utilize reference and MDM in support of business strategy
Data-Ed Online Webinar: Business Value from MDMDATAVERSITY
This presentation provides you with an understanding of the goals of reference and master data management (MDM), including establishing and implementing authoritative data sources, establishing and implementing more effective means of delivery data to various business processes, as well as increasing the quality of information used in organizational analytical functions (such as BI). You will understand the parallel importance of incorporating data quality engineering into the planning of reference and MDM.
Takeaways:
What is reference and MDM?
Why are reference and MDM important?
Reference and MDM Frameworks
Guiding principles & best practices
This presentation provides you with an understanding of the goals of reference and master data management (MDM), including establishing and implementing authoritative data sources, establishing and implementing more effective means of delivery data to various business processes, as well as increasing the quality of information used in organizational analytical functions (such as BI). You will understand the parallel importance of incorporating data quality engineering into the planning of reference and MDM.
Check out more of our Data-Ed webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
Building innovative digital platform dashboards to improve business and opera...Steve Ng
Presentation around “Building innovative digital platform dashboards to improve business and operational visibility and readiness” in the CIO/CISO Leaders Summit 2018 held on 28 Nov 2018 at Marina Bay Sands Singapore.
https://www.linkedin.com/feed/update/urn:li:activity:6473438094501777408/
Increasing Your Business Data and Analytics MaturityDATAVERSITY
For a few years now, companies of all sizes have been looking at data as a lever to increase revenues, reduce costs or improve efficiency. However, we believe the power of using data as a strategic asset is still in its early stages. One of the main reasons for that is business leaders still do not understand that the data & analytics maturity should be seen as a long time journey and an evolving enterprise learning. This webinar will present some key points on how data management leaders can succeed in their mission by sharing some practical experiences.
Five Attributes to a Successful Big Data StrategyPerficient, Inc.
The veracity, variety and sheer volume of data is increasing exponentially. With Hadoop and NoSQL solutions becoming commonplace, there are many technical options for managing and extracting value from this data. Many companies create labs to experiment with Big Data solutions, only later become IT playgrounds or unstructured dumping grounds.
To help avoid these pitfalls,companies with successful Big Data projects approach challenges by formulating a strategy that assures real business value is derived from their Big Data investments. In a Perficient poll, 73% of companies stated they are in the early-evaluation stage to find solutions to their Big Data problems and are only beginning to create their strategy.
Join us for a webinar featuring thought-provoking best practices used by successful companies to quickly realize business value from their Big Data investments. You'll learn:
The top five steps to increased business value
What the top companies are doing in Big Data that you need to know
Next steps to lay the ground work for a successful Big Data strategy
Data Integration is a key part of many of today’s data management challenges: from data warehousing, to MDM, to mergers & acquisitions. Issues can arise not only in trying to align technical formats from various databases and legacy systems, but in trying to achieve common business definitions and rules.
Join this webinar to see how a data model can help with both of these challenges – from ‘bottom-up’ technical integration, to the ‘top-down’ business alignment.
Business Intelligence (BI) solutions can help manufacturing business users to analyse cost factors and make appropriate decisions for acquisition of raw material and sold goods.
Data-Ed Webinar: Monetizing Data Management - Show Me the MoneyDATAVERSITY
Practicality and profitability may share a page in the dictionary, but incorporating both into a data management plan can prove challenging. Many data professionals struggle to demonstrate tangible returns on data management investments, especially in industries such as healthcare where financial results aren’t necessarily an organization’s primary concern. The key to “monetizing” data management, therefore, is thinking about data in a different way: as an information solution rather than simply an IT one, using data to drive decision-making towards increased profits and potentially alternative returns on investment or value outcomes as well. Taking a broader view of data assets facilitates easier sharing of information across organizational silos, and allows for a wider understanding of the investment’s requirements and benefits.
In this webinar—designed to appeal to both business and IT attendees—your presenter will:
Describe multiple types of value produced through data-centric development and management practices
Expand on and beyond metrics meant for increasing revenues or decreasing costs—i.e. investments that directly impact an organization’s financial position
Detail how alternative statistics and valuations can be used to justify data management and quality initiatives
Mastering your data with ca e rwin dm 09082010ERwin Modeling
This document discusses using data modeling to build the foundations for strong data quality. It outlines a process with six steps: [1] defining metadata standards, [2] encouraging collaboration, [3] organizing models and data, [4] enforcing standards, [5] changing organizational culture, and [6] creating a "to be" target state. The key points are that data quality requires treating data as a valuable asset, establishing good metadata and modeling habits, and ongoing cultural changes rather than a single solution.
This document outlines a presentation on developing a data-centric strategy and roadmap. It discusses the importance of aligning data management goals to business needs through frameworks like Porter's competitive strategies and operating models. Metrics and success criteria must be defined by collaborating with business partners to measure improvements in specific opportunities. An example shows how a chemical company defined objects of measurement and metrics to quantify increased efficiency from a data integration solution. Developing a holistic solution requires understanding a business's competitive advantage, goals and needs.
This document outlines a presentation on developing a data-centric strategy and roadmap. It discusses the importance of aligning data management goals to business needs through frameworks like Porter's competitive strategies and operating models. Metrics and success criteria must be defined by collaborating with business partners to measure improvements in specific opportunities. An example shows how a chemical company measured reductions in testing time and increases in researcher productivity after implementing a solution to integrate data across disparate systems.
Tips & tricks to drive effective Master Data Management & ERP harmonizationVerdantis
This document summarizes a presentation given by Jeffrey Karson of Siemens Water Technologies and Arthur Raguette of Verdantis regarding their master data management and ERP harmonization initiative at Siemens Water Technologies. Siemens Water Technologies had legacy data quality issues due to multiple acquisitions. They implemented a master data initiative using Verdantis' Harmonize solution to cleanse and enrich historical data and Verdantis Integrity solution for ongoing data governance. The initiative improved data quality, reduced costs, and enabled greater visibility and efficiency. Key metrics like duplicates avoided and data enrichment rates were used to measure success.
Webinar: Initiating a Customer MDM/Data Governance ProgramDATAVERSITY
This document discusses using erwin Modeling to execute a data discovery and analysis pilot for an MDM and data governance initiative. It provides an overview of MDM and describes a case study of an initial failed MDM attempt. The benefits of a model-driven approach using erwin Modeling are outlined, including discovering and documenting the as-is data landscape, enabling stakeholder collaboration, and specifying the to-be MDM architecture and governance foundation. Key activities of the proposed pilot with erwin Modeling are reverse engineering data sources, analyzing and harmonizing differences, centralizing models, and deriving an MDM specification blueprint. The benefits of accelerating MDM analysis cycles and establishing reusable processes for governance are summarized.
Information Strategy: Updating the IT Strategy for Information, Insights and ...Jamal_Shah
The document discusses the need for organizations to update their IT strategies to address the growing amounts of data from various sources and how emerging technologies enable new approaches to managing data and insights. It recommends that an updated IT strategy focus on business capabilities and prioritize information, insights, and governance. The strategy should emphasize cross-functional use of data and analytics to enable fast, fact-driven decisions.
Similar to Staying relevant in todays changing dm environment 09282010 (20)
The document discusses metadata and the importance of metadata management. It describes the speaker's role as a data architect at the City and County of San Francisco Department of Public Works. It outlines an agenda covering data, metadata, business requirements, metadata types and philosophies, how the CIA compiles and uses metadata, and examples. It emphasizes that metadata is data about data and discusses the co-existence of data and metadata in business processes and requirements.
Using ca e rwin modeling to asure data 09162010ERwin Modeling
Data profiling analyzes data content to infer metadata and increase the accuracy of data assets and models. It can help with data quality assessments, master data management, and reducing risks in data warehousing projects. The presentation provided examples of how profiling was used to uncover issues, validate models and requirements, standardize values, and reduce development times for various organizations.
Sneak peak ca e rwin data modeler r8 preview09222010ERwin Modeling
This document provides an overview of new features and enhancements in the upcoming CA ERwin Data Modeler r8 release. Key highlights include a state-of-the-art visualization with dynamic and customizable user interface, productivity-enhancing workflows, support for additional databases, and improvements to modeling tools, editors, and licensing. The goal is to provide the leading data modeling solution while balancing usability, productivity, and database currency.
not the same. Wisdom is
1) Organizations have lost millions due to poor data management practices but remain unaware of the root causes. the ability to apply one's
2) Unless the costs of poor data management are quantified, gaining approval for basic investments will be difficult. knowledge and experience
3) The talk illustrates how to identify specific costs of poor practices in HR, finance, supply chain, and compliance with good judgment.
to show data management as the root cause of problems and gain support for required investments.
Integrating data process a roundtrip modeling using e rwin data modeler_erwin...ERwin Modeling
This document discusses integrating data and process modeling using ERwin Data Modeler and ERwin Process Modeler. It provides an overview of the modeling tools and a 4 step process for mapping process models to data models. The steps include: 1) Mapping entities to process arrows, 2) Mapping attributes to entities, 3) Identifying process actions on entities, and 4) Identifying process actions on attributes. Rules for allowable actions on entities and attributes based on their usage in the process model are also defined.
Effective capture of metadata using ca e rwin data modeler 09232010ERwin Modeling
CA ERwin Data Modeler provides flexible features to effectively capture metadata in data warehouse environments. This includes data sources, transformation rules, and data movement rules. It uses a customer dimension example to demonstrate capturing source tables from various sources, defining transformation templates, and attaching data movement rules to tables. Other options like importing metadata definitions from Excel allow business stakeholders to define and import column metadata. Effective metadata capture helps communicate requirements, identify issues, and understand the data model.
This document discusses a methodology for organizations to assess moving their data and applications to the cloud. It involves 5 steps: 1) conducting a thorough analysis of business processes, 2) utilizing data modeling to understand how data relates, 3) generating prototypes using simulated data, 4) reevaluating the cloud offering if needed, and 5) preparing a transition initiative including training for both IT and business users. The methodology helps organizations prioritize what data and applications make the most sense to move first by addressing concerns around data privacy, security and control.
This document discusses using high-level data modeling to facilitate communication between business and IT stakeholders. It provides examples of high-level data models and discusses best practices for building high-level models, including getting input from all relevant parties, choosing an intuitive notation, and using the model to achieve consensus on key business concepts and definitions. The document also describes how modeling tools from CA like ERwin can help manage technical data sources from multiple systems and databases, and share information with various audiences.
Cust experience a practical guide 09152010ERwin Modeling
This document outlines how WorkSafe BC adopted CA ERwin Data Modeler and CA ERwin Model Manager to build and manage their enterprise data models. They faced challenges like standards adoption, new tool learning curves, and change management. They built infrastructure like standards, templates, and procedures. They then built their enterprise data model by merging existing models and resolving conflicts. For model management, they implemented processes for checking models in and out, using the model manager. This centralized their metadata and allowed for faster development and data consistency across projects.
The document discusses how to create enterprise data standards using CA ERwin Data Modeling. It describes leveraging naming standards, domains, user defined properties, and data type standards to promote consistency and reuse. The presentation also demonstrates sharing standards using CA ERwin Model Manager and reporting standards to stakeholders using various reporting options like Crystal Reports. Live demo examples are provided of implementing standards in CA ERwin Data Modeling.
CA ERwin Modeling provides data modeling solutions to help reduce costs and increase ROI. Their next release, r8, will include improved visualization, customization, and productivity features. r8 is focused on balancing usability improvements and database support to increase user satisfaction and ROI. The data modeling market is seeing new players and a diversification in how solutions are used, with CA ERwin striving to scale their products to meet new requirements.
Ca e rwin modeling global user communities_09232010 - webcastERwin Modeling
Tom Bilcze, president of the CA Technologies Modeling Global User Communities, provided an overview of the global and regional user communities for CA ERwin® modeling tools. He discussed the resources and activities offered through the global community portal on communities.ca.com. Bilcze also provided updates on recent activities like feedback from regional presidents and the board's focus on the upcoming CA ERwin® 8 release and knowledge sharing initiatives. He encouraged attendees to get involved with their local and global user communities.
The document discusses 10 strategic and detailed pitfalls to avoid in data modeling. Strategic pitfalls include having a vague purpose, modeling literally instead of abstracting, creating large models, including speculative content, and lack of clarity. Detailed pitfalls include recklessly violating normal forms, including needless redundancy, using parallel attributes, symmetric relationships, and anonymous fields. The document emphasizes that data modeling is pivotal to a project's success and avoiding these pitfalls can improve data quality, extensibility, and performance.
5 physical data modeling blunders 09092010ERwin Modeling
The document discusses 5 common physical data modeling mistakes that can negatively impact performance, development timelines, and cause bugs. These include: 1) Assuming that numbers will always be numeric, 2) Choosing the wrong primary key, 3) Incorrectly applying surrogate keys, 4) Turning off referential integrity for development, and 5) Relying on default settings rather than customizing the physical model. The presentation provides tips on how to identify and avoid these mistakes such as establishing primary key standards, using the correct data types, and testing physical models in a separate database before implementing.
Optimizing the design of your data warehouse 09222010ERwin Modeling
This document discusses optimizing the design of a data warehouse using ERwin Data Modeler (ERwin DM) and ERwin Process Modeler (ERwin PM). It recommends using ERwin PM to model the data flow through context and level 1 diagrams, then using ERwin DM to further model external entities, data stores, and activities identified in the diagrams. Subject areas in ERwin DM are named based on the activity/entity they represent. Together, the two tools provide technical and business users a comprehensive understanding of how data will flow and be stored in the data warehouse.
Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
Digital Banking in the Cloud: How Citizens Bank Unlocked Their MainframePrecisely
Inconsistent user experience and siloed data, high costs, and changing customer expectations – Citizens Bank was experiencing these challenges while it was attempting to deliver a superior digital banking experience for its clients. Its core banking applications run on the mainframe and Citizens was using legacy utilities to get the critical mainframe data to feed customer-facing channels, like call centers, web, and mobile. Ultimately, this led to higher operating costs (MIPS), delayed response times, and longer time to market.
Ever-changing customer expectations demand more modern digital experiences, and the bank needed to find a solution that could provide real-time data to its customer channels with low latency and operating costs. Join this session to learn how Citizens is leveraging Precisely to replicate mainframe data to its customer channels and deliver on their “modern digital bank” experiences.
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
FREE A4 Cyber Security Awareness Posters-Social Engineering part 3Data Hops
Free A4 downloadable and printable Cyber Security, Social Engineering Safety and security Training Posters . Promote security awareness in the home or workplace. Lock them Out From training providers datahops.com
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...Tatiana Kojar
Skybuffer AI, built on the robust SAP Business Technology Platform (SAP BTP), is the latest and most advanced version of our AI development, reaffirming our commitment to delivering top-tier AI solutions. Skybuffer AI harnesses all the innovative capabilities of the SAP BTP in the AI domain, from Conversational AI to cutting-edge Generative AI and Retrieval-Augmented Generation (RAG). It also helps SAP customers safeguard their investments into SAP Conversational AI and ensure a seamless, one-click transition to SAP Business AI.
With Skybuffer AI, various AI models can be integrated into a single communication channel such as Microsoft Teams. This integration empowers business users with insights drawn from SAP backend systems, enterprise documents, and the expansive knowledge of Generative AI. And the best part of it is that it is all managed through our intuitive no-code Action Server interface, requiring no extensive coding knowledge and making the advanced AI accessible to more users.
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
Staying relevant in todays changing dm environment 09282010
1. Staying Relevant in Today's Changing
Data Management Environment
By Sanjay Shirude
President
Phone (206) 372-2505
E-mail: Sanjay@ebuzworld.com
www.eBuzWorld.com
simplifying Business Information Technology Integration
2. Agenda
• Business Environment
• Information Technology Trends
• Data Management and IT Job Functions
• Why Data Modeling?
• Staying Relevant
• Questions and Answers
3. Business Environment
• Acquisition and Mergers
• SMB Market
• Government Initiatives
4. Business Environment
• Acquisitions and Mergers
• Due to Economic Conditions and Cash Flow Issues, Large Numbers of Businesses are being
Sold, or Corporations are Merging to Solidify their Business. This Results in:
– Application System Mergers
– Data Collaboration Challenges
– Data and Process Redundancy
– Data Quality Issues
• SMB Market
• Focus is to Reduce Over-Head
• SMB Market is Growing
– New Data Requirements
– Faster Growth
– Dynamic Environment
PAGE 4
5. Business Environment
• Government Initiatives
• Green Solutions
– More Electronic Storage
– Faster Information Retrieval
– Compliance Management
– Disaster Recovery
• Global Competition
• Solutions Need to Address World-Wide Accessibility
• Quality, Delivery Time, Cost and Return On Investment are More Important than Ever Before
PAGE 5
6. Information Technology Trends
• 'Cost Centers' to 'Strategic Business Drivers'
• 'Mobile' Computing
• New Ways of Out-Sourcing
• 'Spend' Control
7. Information Technology Trends
• 'Cost Centers' to 'Strategic Business Drivers'
– Data is a Business Asset
– 'Business Intelligence' Focus
– Data Governance
– Data Analytics
– Data Warehousing
– Historical Data
• Mobile Computing
– Hardware Costs are Minimal
– Faster Data Exchange
– Higher Demands on Data Retrieval
– Very Large Data Bases (VLDB)
PAGE 7
8. Information Technology Trends
• New Ways of Out-Sourcing
– 'Service-Oriented' Architecture
– 'Cloud' Computing
– Virtualization
• De-Centralized structure
• 'Spend' Control
– Cost-Cutting Measures
– Focus more on 'Managed' Solutions than 'Custom' Solutions
– Specialty Tasks Out-Sourced
– Computer Hardware Becoming Less Expensive
PAGE 8
9. Data Management and IT Job Functions
• Profound Paradigm Shift
• Challenges As A Result
10. Data Management and IT Job Functions
• Profound 'Paradigm' Shift
– High-Level Management Commitment
• Shifting from 'Cost Centers' to 'Strategic Business Drivers', High-Level Management is More
Interested in Performing their Own Data Analysis than Looking for Large Number of Data
Sets
• Data Forecasting, Various Analysis Requires Higher Data Quality
• Provide Solutions than Reports
• Simple Unified Terminology
– Various Data Initiatives
• Data Governance
• Master Data Management
• Business Intelligence
• Data Quality
• Systems Integration
PAGE 10
11. Data Management and IT Job Functions
• Challenges
– Re-structuring
• Lack of Subject Matter Experts' Knowledge
• Fewer Staff Members
• Lack of Documentation
• System Mergers and Virtualization
– No Specialized Position
• Development - Out-Sourced
• Management Positions Over-Loaded with Additional Analysis Work
• Need to become Multi-Skilled
• IT Staff Role must become a Consultant Role to Business Unit(s).
– Agile versus Waterfall SDLC
• Faster Development, Less or No Documentation
PAGE 11
13. ‘Why Data Modeling?
• Excellent Communication/Collaboration Tool
• Improves Information Quality, not just Data Quality
• Supports Various Data Initiatives
• End-Users' Confidence in Received Information
• Effective Participation in Building Information Systems
• Reduce Duplicate Effort and Content to Increase Productivity,
Collaboration and Work Quality
• Use Off-the-Shelf 'ERwin Saphir' tool to Reduce your Development Costs
• Manage Multiple Platforms from a Single, Graphical Interface
PAGE 13
14. Staying Relevant
Changing the face will not change anything but facing the
change will do everything
• Eight Point Program
1. Meditation - Perform Data Profiling
2. Repetition of Mantra - 'Advocate’ Modeling
3. Slowing Down - User Education
4. One Point Attention - Redefine your Customer Definition
5. Training the Senses - Get Certified in the product base skills
6. Putting Others First - Wear Multiple Hats
7. Reading - Learn more on Source and Target Technology
8. Association - Participate in Local Data Associations
15. Staying Relevant
1. Meditation Perform Data Profiling
– Excellent tool to cash on your management attention
– Effective tool to improve your Data Quality
2. Repetition of Mantra – ‘Advocate’ Modeling
– Learn the Skills to Market the Modeling
– Create high level Business model
– Learn more on easily available ERP modeling tools – Direct Cost Saving
– Show Value of Data Modeling in System Mergers and Integration
3. Slowing Down - Focus on Data Driven Relationships
– Slowdown to improve users pace in modeling
– Keep it Simple
– Use Averages, Mean, Mode, Data Patterns, and % during communication with users
PAGE 15
16. Staying Relevant
4. One Point Attention - Redefine your Customer Definition
– Internal IT Staff
– External Consultants
– Business Users
– Subject Matter Experts
5. Training the Senses - Get Certified in the product base skills
– Generic Concept Training is not Sufficient
• It is Necessary to have Complete Conceptual Training;
• Product-Based Training Gives you the Ability to Convert your understanding of Theory into
Practice;
• Share your Modeling Expertise with Non-IT Users in their common Language.
PAGE 16
17. Staying Relevant
6. Putting Others First - Wear Multiple Hats
– Become Consultant
– Be Ready to play various roles within and out side of IT functions
– Share your modeling expertise to non-IT users in their language
7. Reading - Learn more on Source and Target Technology
– Choose the teacher
– Give your teacher your full loyalty
8. Association - Participate in Local Data Association
– Learning from associate is more effective than trial and errors
– Sharing your solution and problem help everyone grow
– Attend Local ERwin user group meetings
– Associate with Associations such DAMA, TDWI
PAGE 17
18. Question and Answers
Contact Information
Dr. Sanjay Shirude, PhD.
President
Phone (206)372-2505
E-mail: Sanjay@ebuzworld.com
www.eBuzWorld.com