How to use the EU e-Competence Framework together with the Body of Knowledge Edison when implementing a new workforce model. In this use case Workforce Transformation, Curricula, Career paths and assessments. Human Resource Development tooling.
Incorporating SAP Metadata within your Information ArchitectureChristopher Bradley
Incorporating SAP Metadata into your overall Information Management architecture. Case study from BP and IPL presented at Enterprise Data World, Tampa, FL April 2009
This presentation was held by Professor Christine Legner (HEC Lausanne) at the Swiss Day on November 8, 2017, in Lausanne, Switzerland. It addresses the need for organisations to think about data and its management in new ways, as many corporations engage in the digital and data-driven transformation of their business. It concludes with three recommendations: 1) assess data's business value and impact, 2) measure and improve data quality, and 3) democratize data and support data citizenship.
Improving the customer experience using big data customer-centric measurement...Business Over Broadway
This presentation provides an overview of some of the content of my new book, TCE: Total Customer Experience. In the presentation, I discuss customer experience management, customer loyalty, the optimal customer survey, the value of analytics and using a Big Data customer-centric approach to improve the value of all your business data
Incorporating SAP Metadata within your Information ArchitectureChristopher Bradley
Incorporating SAP Metadata into your overall Information Management architecture. Case study from BP and IPL presented at Enterprise Data World, Tampa, FL April 2009
This presentation was held by Professor Christine Legner (HEC Lausanne) at the Swiss Day on November 8, 2017, in Lausanne, Switzerland. It addresses the need for organisations to think about data and its management in new ways, as many corporations engage in the digital and data-driven transformation of their business. It concludes with three recommendations: 1) assess data's business value and impact, 2) measure and improve data quality, and 3) democratize data and support data citizenship.
Improving the customer experience using big data customer-centric measurement...Business Over Broadway
This presentation provides an overview of some of the content of my new book, TCE: Total Customer Experience. In the presentation, I discuss customer experience management, customer loyalty, the optimal customer survey, the value of analytics and using a Big Data customer-centric approach to improve the value of all your business data
In this lecture we discuss data quality and data quality in Linked Data. This 50 minute lecture was given to masters student at Trinity College Dublin (Ireland), and had the following contents:
1) Defining Quality
2) Defining Data Quality - What, Why, Costs
3) Identifying problems early - using a simple semantic publishing process as an example
4) Assessing Linked (big) Data quality
5) Quality of LOD cloud datasets
References can be found at the end of the slides
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 (CC-BY-SA-40) International License.
Building an Effective Data & Analytics Operating Model A Data Modernization G...Mark Hewitt
This is the age of analytics—information resulting from the systematic analysis of data.
Insights gained from applying data and analytics to business allows large and small organizations across diverse industries—be it healthcare, retail, manufacturing, financial, or others—to identify new opportunities, improve core processes, enable continuous learning and differentiation, remain competitive, and thrive in an increasingly challenging business environment.
The key to building a data-driven practice is a Data and Analytics Operating Model (D&AOM) which enables the organization to establish standards for data governance, controls for data flows (both within and outside the organization), and adoption of appropriate technological innovations.
Success measures of a data initiative may include:
• Creating a competitive advantage by fulfilling unmet needs,
• Driving adoption and engagement of the digital experience platform (DXP),
• Delivering industry standard data and metrics, and
• Reducing the lift on service teams.
This green paper lays out the framework for building and customizing an effective data and analytics operating model.
Machine learning techniques to improve data management and data quality - this presentation by Prof. Christine Legner and Martin Fadler summarizes research conducted in the Competence Center Corporate Data Quality (CC CDQ). It was held on February 13, 2019 at the DSAG Technologietage in Bonn.
Data-Ed Online Presents: Data Warehouse StrategiesDATAVERSITY
Integrating data across systems has been a perpetual challenge. Unfortunately, the current technology-focused solutions have not helped IT to improve its dismal project success statistics. Data warehouses, BI implementations, and general analytical efforts achieve the same levels of success as other IT projects – approximately 1/3rd are considered successes when measured against price, schedule, or functionality objectives. The first step is determining the appropriate analysis approach to the data system integration challenge. The second step is understanding the strengths and weaknesses of various approaches. Turns out that proper analysis at this stage makes actual technology selection far more accurate. Only when these are accomplished can proper matching between problem and capabilities be achieved as the third step and true business value be delivered. This webinar will illustrate that good systems development more often depends on at least three data management disciplines in order to provide a solid foundation.
Takeaways:
Data system integration challenge analysis
Understanding of a range of data system-integration technologies including
Problem space (BI, Analytics, Big Data), Data (Warehousing, Vault, Cube) and alternative approaches (Virtualization, Linked Data, Portals, Meta-models)
Understanding foundational data warehousing & BI concepts based on the Data Management Body of Knowledge (DMBOK)
How to utilize data warehousing & BI in support of business strategy
Agile BI: How to Deliver More Value in Less TimePerficient, Inc.
Learn how to:
Construct a BI and analytical environment that provides the critical functionality that enables your customers to provide timely answers, supporting modern agile business
Leverage agile delivery concepts to deliver value in days rather than in months
Build a support organization that enables your users to create increased value from your company’s information assets
ADV Slides: How to Improve Your Analytic Data Architecture MaturityDATAVERSITY
Many organizations are immature when it comes to data use. The answer lies in delivering a greater level of insight from data, straight to the point of need. Enter: machine learning.
In this webinar, William will look at categories of organizational response to the challenge across strategy, architecture, modeling, processes, and ethics. Machine learning maturity levels tend to move in harmony across these categories. As a general principle of maturity models, you can’t skip levels in any category, nor can you advance in one category well beyond the others.
Vis-à-vis ML, attaining and retaining momentum up the model is paramount for success. You will ascend the model through concerted efforts delivering business wins utilizing progressive elements of the model, and thereby increasing your machine learning maturity. The model will evolve. No plateaus are comfortable for long.
With ML maturity markers, sequencing, and tactics, this webinar provides a plan for how to build analytic Data Architecture maturity in your organization.
Why not start with data sharing? Asset sharing reduces costs, improves utilization and sustainability. Only data assets are still managed in silos.
One of the few successful examples for data sharing is the CDQ Data Sharing Community for business partner data. This talk analyzes the approach and distinguishes two levels of data sharing:
(1) data knowledge sharing (semantics, rules and reference data), and
(2) data asset sharing (peer-based sharing of validated data)
Data sharing leads to higher data quality, lower data maintenance efforts, reduced risks and higher trust in data.
Data Management Meets Human Management - Why Words MatterDATAVERSITY
At Fifth Third Bank, about 450 people use data every day. They all start with Alation. But this wasn't always the case. In fact, getting hundreds of folks working in sync has been a monumental task.
Just ask Greg Swygart, VP of enterprise data at Fifth Third Bank. Greg has led data consumption and interaction efforts since adopting Alation. Currently he’s scaling out data literacy for Fifth Third, replicating data capabilities to all roles across the company.
Join Greg to learn how Fifth Third Bank moved from a command-and-control governance approach to non-invasive — and reaped the benefits. Greg will be followed by Bob Seiner, creator of Non-Invasive Data Governance, who will speak to data governance’s evolution, with an eye to what’s next.
In this webinar, you'll learn:
• About Fifth Third’s transition away from command-and-control governance
• How Fifth Third leverages Alation as its data marketplace for curation & consumption
• Why words matter when driving adoption
• About the data catalog — and its role in human management
Slides zum Impuls-Vortrag "Data Strategy & Governance" - BI or DIE LEVEL UP 2022
Aufzeichnung des Vortrags: https://www.youtube.com/watch?v=705DfyfF5-M
Machine learning techniques to improve data management and data quality - presentation by Tobias Pentek and Martin Fadler from the Competence Center Corporate Data Quality. This presentation was presented during the Marcus Evans Event in Amsterdam 08.02.2019
Applying reference models with archi mateBas van Gils
This is the slidedeck for a webinar that I presented for the opengroup. It presents a high-level overview of the use of reference model in the field of EA. Even more I present with some tips on how to use BiZZdesign architect to effectivdely implement reference models in organizations
Data-Ed Online: Data Architecture RequirementsDATAVERSITY
Data architecture is foundational to an information-based operational environment. It is your data architecture that organizes your data assets so they can be leveraged in your business strategy to create real business value. Even though this is important, not all data architectures are used effectively. This webinar describes the use of data architecture as a basic analysis method. Various uses of data architecture to inform, clarify, understand, and resolve aspects of a variety of business problems will be demonstrated. As opposed to showing how to architect data, your presenter Dr. Peter Aiken will show how to use data architecting to solve business problems. The goal is for you to be able to envision a number of uses for data architectures that will raise the perceived utility of this analysis method in the eyes of the business.
Takeaways:
Understanding how to contribute to organizational challenges beyond traditional data architecting
How to utilize data architectures in support of business strategy
Understanding foundational data architecture concepts based on the DAMA DMBOK
Data architecture guiding principles & best practices
Data Quality: A Raising Data Warehousing ConcernAmin Chowdhury
Characteristics of Data Warehouse
Benefits of a data warehouse
Designing of Data Warehouse
Extract, Transform, Load (ETL)
Data Quality
Classification Of Data Quality Issues
Causes Of Data Quality
Impact of Data Quality Issues
Cost of Poor Data Quality
Confidence and Satisfaction-based impacts
Impact on Productivity
Risk and Compliance impacts
Why Data Quality Influences?
Causes of Data Quality Problems
How to deal: Missing Data
Data Corruption
Data: Out of Range error
Techniques of Data Quality Control
Data warehousing security
Karya develops mobile application services that fits the unique needs of your business. Our Mobile Application Services helps the users to better utilize the power of Mobile Technology.
Businesses cannot compete without data. Every organization produces and consumes it. Data trends are hitting the mainstream and businesses are adopting buzzwords such as Big Data, data vault, data scientist, etc., to seek solutions for their fundamental data issues. Few realize that the importance of any solution, regardless of platform or technology, relies on the data model supporting it. Data modeling is not an optional task for an organization’s data remediation effort. Instead, it is a vital activity that supports the solution driving your business.
This webinar will address emerging trends around data model application methodology, as well as trends around the practice of data modeling itself. We will discuss abstract models and entity frameworks, as well as the general shift from data modeling being segmented to becoming more integrated with business practices.
Takeaways:
How are anchor modeling, data vault, etc. different and when should I apply them?
Integrating data models to business models and the value this creates
Application development (Data first, code first, object first)
In this lecture we discuss data quality and data quality in Linked Data. This 50 minute lecture was given to masters student at Trinity College Dublin (Ireland), and had the following contents:
1) Defining Quality
2) Defining Data Quality - What, Why, Costs
3) Identifying problems early - using a simple semantic publishing process as an example
4) Assessing Linked (big) Data quality
5) Quality of LOD cloud datasets
References can be found at the end of the slides
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 (CC-BY-SA-40) International License.
Building an Effective Data & Analytics Operating Model A Data Modernization G...Mark Hewitt
This is the age of analytics—information resulting from the systematic analysis of data.
Insights gained from applying data and analytics to business allows large and small organizations across diverse industries—be it healthcare, retail, manufacturing, financial, or others—to identify new opportunities, improve core processes, enable continuous learning and differentiation, remain competitive, and thrive in an increasingly challenging business environment.
The key to building a data-driven practice is a Data and Analytics Operating Model (D&AOM) which enables the organization to establish standards for data governance, controls for data flows (both within and outside the organization), and adoption of appropriate technological innovations.
Success measures of a data initiative may include:
• Creating a competitive advantage by fulfilling unmet needs,
• Driving adoption and engagement of the digital experience platform (DXP),
• Delivering industry standard data and metrics, and
• Reducing the lift on service teams.
This green paper lays out the framework for building and customizing an effective data and analytics operating model.
Machine learning techniques to improve data management and data quality - this presentation by Prof. Christine Legner and Martin Fadler summarizes research conducted in the Competence Center Corporate Data Quality (CC CDQ). It was held on February 13, 2019 at the DSAG Technologietage in Bonn.
Data-Ed Online Presents: Data Warehouse StrategiesDATAVERSITY
Integrating data across systems has been a perpetual challenge. Unfortunately, the current technology-focused solutions have not helped IT to improve its dismal project success statistics. Data warehouses, BI implementations, and general analytical efforts achieve the same levels of success as other IT projects – approximately 1/3rd are considered successes when measured against price, schedule, or functionality objectives. The first step is determining the appropriate analysis approach to the data system integration challenge. The second step is understanding the strengths and weaknesses of various approaches. Turns out that proper analysis at this stage makes actual technology selection far more accurate. Only when these are accomplished can proper matching between problem and capabilities be achieved as the third step and true business value be delivered. This webinar will illustrate that good systems development more often depends on at least three data management disciplines in order to provide a solid foundation.
Takeaways:
Data system integration challenge analysis
Understanding of a range of data system-integration technologies including
Problem space (BI, Analytics, Big Data), Data (Warehousing, Vault, Cube) and alternative approaches (Virtualization, Linked Data, Portals, Meta-models)
Understanding foundational data warehousing & BI concepts based on the Data Management Body of Knowledge (DMBOK)
How to utilize data warehousing & BI in support of business strategy
Agile BI: How to Deliver More Value in Less TimePerficient, Inc.
Learn how to:
Construct a BI and analytical environment that provides the critical functionality that enables your customers to provide timely answers, supporting modern agile business
Leverage agile delivery concepts to deliver value in days rather than in months
Build a support organization that enables your users to create increased value from your company’s information assets
ADV Slides: How to Improve Your Analytic Data Architecture MaturityDATAVERSITY
Many organizations are immature when it comes to data use. The answer lies in delivering a greater level of insight from data, straight to the point of need. Enter: machine learning.
In this webinar, William will look at categories of organizational response to the challenge across strategy, architecture, modeling, processes, and ethics. Machine learning maturity levels tend to move in harmony across these categories. As a general principle of maturity models, you can’t skip levels in any category, nor can you advance in one category well beyond the others.
Vis-à-vis ML, attaining and retaining momentum up the model is paramount for success. You will ascend the model through concerted efforts delivering business wins utilizing progressive elements of the model, and thereby increasing your machine learning maturity. The model will evolve. No plateaus are comfortable for long.
With ML maturity markers, sequencing, and tactics, this webinar provides a plan for how to build analytic Data Architecture maturity in your organization.
Why not start with data sharing? Asset sharing reduces costs, improves utilization and sustainability. Only data assets are still managed in silos.
One of the few successful examples for data sharing is the CDQ Data Sharing Community for business partner data. This talk analyzes the approach and distinguishes two levels of data sharing:
(1) data knowledge sharing (semantics, rules and reference data), and
(2) data asset sharing (peer-based sharing of validated data)
Data sharing leads to higher data quality, lower data maintenance efforts, reduced risks and higher trust in data.
Data Management Meets Human Management - Why Words MatterDATAVERSITY
At Fifth Third Bank, about 450 people use data every day. They all start with Alation. But this wasn't always the case. In fact, getting hundreds of folks working in sync has been a monumental task.
Just ask Greg Swygart, VP of enterprise data at Fifth Third Bank. Greg has led data consumption and interaction efforts since adopting Alation. Currently he’s scaling out data literacy for Fifth Third, replicating data capabilities to all roles across the company.
Join Greg to learn how Fifth Third Bank moved from a command-and-control governance approach to non-invasive — and reaped the benefits. Greg will be followed by Bob Seiner, creator of Non-Invasive Data Governance, who will speak to data governance’s evolution, with an eye to what’s next.
In this webinar, you'll learn:
• About Fifth Third’s transition away from command-and-control governance
• How Fifth Third leverages Alation as its data marketplace for curation & consumption
• Why words matter when driving adoption
• About the data catalog — and its role in human management
Slides zum Impuls-Vortrag "Data Strategy & Governance" - BI or DIE LEVEL UP 2022
Aufzeichnung des Vortrags: https://www.youtube.com/watch?v=705DfyfF5-M
Machine learning techniques to improve data management and data quality - presentation by Tobias Pentek and Martin Fadler from the Competence Center Corporate Data Quality. This presentation was presented during the Marcus Evans Event in Amsterdam 08.02.2019
Applying reference models with archi mateBas van Gils
This is the slidedeck for a webinar that I presented for the opengroup. It presents a high-level overview of the use of reference model in the field of EA. Even more I present with some tips on how to use BiZZdesign architect to effectivdely implement reference models in organizations
Data-Ed Online: Data Architecture RequirementsDATAVERSITY
Data architecture is foundational to an information-based operational environment. It is your data architecture that organizes your data assets so they can be leveraged in your business strategy to create real business value. Even though this is important, not all data architectures are used effectively. This webinar describes the use of data architecture as a basic analysis method. Various uses of data architecture to inform, clarify, understand, and resolve aspects of a variety of business problems will be demonstrated. As opposed to showing how to architect data, your presenter Dr. Peter Aiken will show how to use data architecting to solve business problems. The goal is for you to be able to envision a number of uses for data architectures that will raise the perceived utility of this analysis method in the eyes of the business.
Takeaways:
Understanding how to contribute to organizational challenges beyond traditional data architecting
How to utilize data architectures in support of business strategy
Understanding foundational data architecture concepts based on the DAMA DMBOK
Data architecture guiding principles & best practices
Data Quality: A Raising Data Warehousing ConcernAmin Chowdhury
Characteristics of Data Warehouse
Benefits of a data warehouse
Designing of Data Warehouse
Extract, Transform, Load (ETL)
Data Quality
Classification Of Data Quality Issues
Causes Of Data Quality
Impact of Data Quality Issues
Cost of Poor Data Quality
Confidence and Satisfaction-based impacts
Impact on Productivity
Risk and Compliance impacts
Why Data Quality Influences?
Causes of Data Quality Problems
How to deal: Missing Data
Data Corruption
Data: Out of Range error
Techniques of Data Quality Control
Data warehousing security
Karya develops mobile application services that fits the unique needs of your business. Our Mobile Application Services helps the users to better utilize the power of Mobile Technology.
Businesses cannot compete without data. Every organization produces and consumes it. Data trends are hitting the mainstream and businesses are adopting buzzwords such as Big Data, data vault, data scientist, etc., to seek solutions for their fundamental data issues. Few realize that the importance of any solution, regardless of platform or technology, relies on the data model supporting it. Data modeling is not an optional task for an organization’s data remediation effort. Instead, it is a vital activity that supports the solution driving your business.
This webinar will address emerging trends around data model application methodology, as well as trends around the practice of data modeling itself. We will discuss abstract models and entity frameworks, as well as the general shift from data modeling being segmented to becoming more integrated with business practices.
Takeaways:
How are anchor modeling, data vault, etc. different and when should I apply them?
Integrating data models to business models and the value this creates
Application development (Data first, code first, object first)
Data-Ed Online: Trends in Data ModelingDATAVERSITY
Businesses cannot compete without data. Every organization produces and consumes it. Data trends are hitting the mainstream and businesses are adopting buzzwords such as Big Data, data vault, data scientist, etc., to seek solutions for their fundamental data issues. Few realize that the importance of any solution, regardless of platform or technology, relies on the data model supporting it. Data modeling is not an optional task for an organization’s data remediation effort. Instead, it is a vital activity that supports the solution driving your business.
This webinar will address emerging trends around data model application methodology, as well as trends around the practice of data modeling itself. We will discuss abstract models and entity frameworks, as well as the general shift from data modeling being segmented to becoming more integrated with business practices.
Takeaways:
How are anchor modeling, data vault, etc. different and when should I apply them?
Integrating data models to business models and the value this creates
Application development (Data first, code first, object first)
Translating AI from Concept to Reality: Five Keys to Implementing AI for Know...Enterprise Knowledge
Lulit Tesfaye explains how foundational knowledge management and knowledge engineering approaches can play a key role in ensuring enterprise Artificial Intelligence (AI) initiatives start right, quickly demonstrate business value, and “stick” within the organization. The presentation includes real world case studies and examples of how organizations are approaching their data and AI transformations through knowledge maturity models to translate organizational information and data into actionable and clickable solutions. Originally delivered at data.world Summit, Spring 2022.
Advanced Analytics and Machine Learning with Data VirtualizationDenodo
Watch full webinar here: https://bit.ly/3aXysas
Advanced data science techniques, like machine learning, have proven to be extremely useful to derive valuable insights from your data. Data Science platforms have become more approachable and user friendly. With all the advancements in the technology space, the Data Scientist is still spending most of the time massaging and manipulating the data into a usable data asset. How can we empower the data scientist? How can we make data more accessible, and foster a data sharing culture?
Join us, and we will show you how Data Virtualization can do just that, with an agile and AI/ML laced data management platform. It can empower your organization, foster a data sharing culture, and simplify the life of the data scientist.
Watch this webinar to learn:
- How data virtualization simplifies the life of the data scientist, by overcoming data access and manipulation hurdles.
- How integrated Denodo Data Science notebook provides for a unified environment
- How Denodo uses AI/ML internally to drive the value of the data and expose insights
- How customers have used Data Virtualization in their Data Science initiatives.
[DSC Europe 22] The Making of a Data Organization - Denys HolovatyiDataScienceConferenc1
Data teams often struggle to deliver value. KPIs, data pipelines, or ML driven predictions aren't inherently useful - unless the data team enables the business to use them. Having worked on 37 data projects over the past 5 years, with total client revenue clocking at about $350B, I started noticing simple success factors - and summarized those in the Operating Model Canvas & the Value Delivery Process. With those, I branched out into what I call data organization consulting and help clients build their data teams for success, the one you see not only on paper but also in your P&L. In this talk, I'll share some insight with you.
Data Science Operationalization: The Journey of Enterprise AIDenodo
Watch full webinar here: https://bit.ly/3kVmYJl
As we move into a world driven by AI initiatives, we find ourselves facing new and diverse challenges when it comes to operationalization. Creating a solution and putting it into practice, is certainly not the same. The challenges span various organizational and data facades. In many instances, the data scientists may be working in silos and connecting to the live data may not always be possible. But how does one guarantee their developed model in a silo is still relevant to live data? How can we manage the data flow and data access across the entire AI operationalization cycle?
Watch on-demand to explore:
- The journey and challenges of the Data Scientist
- How Denodo data virtualization with data movement streamlines operationalization
- The best practices and techniques when dealing with siloed data
- How customers have used data virtualization in their data science initiatives
Organizations must realize what it means to utilize data quality management in support of business strategy. This webinar will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor data quality. Showing how data quality should be engineered provides a useful framework in which to develop an effective approach. This in turn allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.
Data-Ed Webinar: Data Quality EngineeringDATAVERSITY
Organizations must realize what it means to utilize data quality management in support of business strategy. This webinar will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor data quality. Showing how data quality should be engineered provides a useful framework in which to develop an effective approach. This in turn allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.
Takeaways:
Understanding foundational data quality concepts based on the DAMA DMBOK
Utilizing data quality engineering in support of business strategy
Data Quality guiding principles & best practices
Steps for improving data quality at your organization
DAS Slides: Metadata Management From Technical Architecture & Business Techni...DATAVERSITY
Metadata provides context for the “who, what, when, where, and why” of data, and is of critical interest in today’s data-driven business environment. Since metadata is created and used by both business and IT, architectural and organizational techniques need to encompass a holistic approach across the organization to address all audiences. This webinar provides practical ways to manage metadata in your organization using both technical architecture and business techniques.
Employment PracticesRegulation and Multinational CorporationsRoopaTemkar
Employment PracticesRegulation and Multinational Corporations
Strategic decision making within MNCs constrained or determined by the implementation of laws and codes of practice and by pressure from political actors. Managers in MNCs have to make choices that are shaped by gvmt. intervention and the local economy.
The case study discusses the potential of drone delivery and the challenges that need to be addressed before it becomes widespread.
Key takeaways:
Drone delivery is in its early stages: Amazon's trial in the UK demonstrates the potential for faster deliveries, but it's still limited by regulations and technology.
Regulations are a major hurdle: Safety concerns around drone collisions with airplanes and people have led to restrictions on flight height and location.
Other challenges exist: Who will use drone delivery the most? Is it cost-effective compared to traditional delivery trucks?
Discussion questions:
Managerial challenges: Integrating drones requires planning for new infrastructure, training staff, and navigating regulations. There are also marketing and recruitment considerations specific to this technology.
External forces vary by country: Regulations, consumer acceptance, and infrastructure all differ between countries.
Demographics matter: Younger generations might be more receptive to drone delivery, while older populations might have concerns.
Stakeholders for Amazon: Customers, regulators, aviation authorities, and competitors are all stakeholders. Regulators likely hold the greatest influence as they determine the feasibility of drone delivery.
Specific ServPoints should be tailored for restaurants in all food service segments. Your ServPoints should be the centerpiece of brand delivery training (guest service) and align with your brand position and marketing initiatives, especially in high-labor-cost conditions.
408-784-7371
Foodservice Consulting + Design
A presentation on mastering key management concepts across projects, products, programs, and portfolios. Whether you're an aspiring manager or looking to enhance your skills, this session will provide you with the knowledge and tools to succeed in various management roles. Learn about the distinct lifecycles, methodologies, and essential skillsets needed to thrive in today's dynamic business environment.
Public Speaking Tips to Help You Be A Strong Leader.pdfPinta Partners
In the realm of effective leadership, a multitude of skills come into play, but one stands out as both crucial and challenging: public speaking.
Public speaking transcends mere eloquence; it serves as the medium through which leaders articulate their vision, inspire action, and foster engagement. For leaders, refining public speaking skills is essential, elevating their ability to influence, persuade, and lead with resolute conviction. Here are some key tips to consider: https://joellandau.com/the-public-speaking-tips-to-help-you-be-a-stronger-leader/
Integrity in leadership builds trust by ensuring consistency between words an...Ram V Chary
Integrity in leadership builds trust by ensuring consistency between words and actions, making leaders reliable and credible. It also ensures ethical decision-making, which fosters a positive organizational culture and promotes long-term success. #RamVChary
Org Design is a core skill to be mastered by management for any successful org change.
Org Topologies™ in its essence is a two-dimensional space with 16 distinctive boxes - atomic organizational archetypes. That space helps you to plot your current operating model by positioning individuals, departments, and teams on the map. This will give a profound understanding of the performance of your value-creating organizational ecosystem.
Senior Project and Engineering Leader Jim Smith.pdfJim Smith
I am a Project and Engineering Leader with extensive experience as a Business Operations Leader, Technical Project Manager, Engineering Manager and Operations Experience for Domestic and International companies such as Electrolux, Carrier, and Deutz. I have developed new products using Stage Gate development/MS Project/JIRA, for the pro-duction of Medical Equipment, Large Commercial Refrigeration Systems, Appliances, HVAC, and Diesel engines.
My experience includes:
Managed customized engineered refrigeration system projects with high voltage power panels from quote to ship, coordinating actions between electrical engineering, mechanical design and application engineering, purchasing, production, test, quality assurance and field installation. Managed projects $25k to $1M per project; 4-8 per month. (Hussmann refrigeration)
Successfully developed the $15-20M yearly corporate capital strategy for manufacturing, with the Executive Team and key stakeholders. Created project scope and specifications, business case, ROI, managed project plans with key personnel for nine consumer product manufacturing and distribution sites; to support the company’s strategic sales plan.
Over 15 years of experience managing and developing cost improvement projects with key Stakeholders, site Manufacturing Engineers, Mechanical Engineers, Maintenance, and facility support personnel to optimize pro-duction operations, safety, EHS, and new product development. (BioLab, Deutz, Caire)
Experience working as a Technical Manager developing new products with chemical engineers and packaging engineers to enhance and reduce the cost of retail products. I have led the activities of multiple engineering groups with diverse backgrounds.
Great experience managing the product development of products which utilize complex electrical controls, high voltage power panels, product testing, and commissioning.
Created project scope, business case, ROI for multiple capital projects to support electrotechnical assembly and CPG goods. Identified project cost, risk, success criteria, and performed equipment qualifications. (Carrier, Electrolux, Biolab, Price, Hussmann)
Created detailed projects plans using MS Project, Gant charts in excel, and updated new product development in Jira for stakeholders and project team members including critical path.
Great knowledge of ISO9001, NFPA, OSHA regulations.
User level knowledge of MRP/SAP, MS Project, Powerpoint, Visio, Mastercontrol, JIRA, Power BI and Tableau.
I appreciate your consideration, and look forward to discussing this role with you, and how I can lead your company’s growth and profitability. I can be contacted via LinkedIn via phone or E Mail.
Jim Smith
678-993-7195
jimsmith30024@gmail.com
Comparing Stability and Sustainability in Agile SystemsRob Healy
Copy of the presentation given at XP2024 based on a research paper.
In this paper we explain wat overwork is and the physical and mental health risks associated with it.
We then explore how overwork relates to system stability and inventory.
Finally there is a call to action for Team Leads / Scrum Masters / Managers to measure and monitor excess work for individual teams.
Enriching engagement with ethical review processesstrikingabalance
New ethics review processes at the University of Bath. Presented at the 8th World Conference on Research Integrity by Filipa Vance, Head of Research Governance and Compliance at the University of Bath. June 2024, Athens
Enriching engagement with ethical review processes
Case study uwv using eCF and edison
1. Case Study UWV
From Data Services to Data Science
using e-CF, Professional Profiles and Edison
Co Siebes, Workforce Transformation Consultant
April 2020
Working
together
2. About UWV:
UWV (Employee Insurance Agency) is an autonomous administrative authority (ZBO) and is
commissioned by the Ministry of Social Affairs and Employment (SZW) to implement employee
insurances and provide labour market and data services.
UWV has core tasks in four areas:
Employment – helping the client remain employed or find employment, in close cooperation with
the municipalities;
Social Medical Affairs – evaluating illness and labour incapacity according to clear criteria;
Benefits – ensuring that benefits are provided quickly and correctly if work is not possible, or not
immediately possible;
Data Management – ensuring that the client needs to provide the government with data on
employment and benefits only once.
UWV
3. UWV Organisation in 5 operational divisions
1. Client and Service (klant & Service) is responsible for all communication with our clients. K&S
makes it possible for all clients to easily find their way within UWV.
2. Public Employment service (Werkbedrijf) is engaged in job placement and re-integration. Our
aim is to help as many people as possible find work by bringing together supply and demand.
3. Social Medical Affairs (Sociaal Medische Zaken) is the expertise centre and service provider for
socio medical and work-related assessments and recommendations in the Netherlands. We use
our expertise to assess our clients' labour capacity and ability to take on workload and give
recommendations to promote recovery and reintegration.
4. Benefits (Uitkeren) this division is responsible for the prompt and correct handling of benefit
applications and the payment of benefits.
5. Data Services (Gegevensdiensten) compiles and manages data on wages, benefits and labour
relations of all insured persons in the Netherlands. UWV needs these data in order to
determine the height of benefits. But we also make these data available to third parties.
UWV Organisation
4.
5. Workforce Transformation for UWV Data Services
The introduction of new technologies, new business management concepts, a different view
of management and responsibilities.
A new workforce model of Data Services in which professional development is used to
enable employees to prepare them selves for this Data Science transformation.
1
6. Workforce Transformation Model
TO BE
SPRINTS
AS IS
SURVEY
Assessment
Traninig
COMMUNICATION, COORDINATION, IMPLEMENTATION
WORKFORCE PLAN
1
3
4
5
6
Measurement of Performance
Unlock Knowledge
•COMPETENCES
•JOB PROFILES
•CURRICULA
•CAREER PATHS
2
7. Workshops to define job profiles and competences
Competence Profiles: first run > 6 new job profiles
8. 8
Plan A.6. Application Design Working accurately
Build B.1. Application Development Creativity
Build B.2. Component Integration Flexibility
Build B.3. Testing Result orientation
Build B.4. Solution Deployment Stress resilience
Build B.5. Documentation Production
Soft SkillsCompetences (e-CF)
1 4
DATA ENGINEER
2
0
1
3
3
2
Starter level
1 2
1 3
Expert level
Example DATA BUSINESS ANALYST and DATA ENGINEER
Plan A.1. IS/Business Strategy Alignment Analysing
Plan A.3. Business Plan Development Creativity
Build B.6. Systems Engineering Active Listening
Enable D.10. Info/Knowledge Management Judgement
Enable D.11. Needs Identification Persuasiveness
Manage E.5. Process Improvement
DATA BUSINESS ANALYST
4
3
3
0
4
4
Soft Skills
Starter level Expert level
0 4
0 4
3 3
Competences (e-CF)
2
9. 9
EDISON Data Science Framework
EDISON Body of Knowledge
5 Knowledge area groups
e.g. Data Management
23 Knowledge areas
e.g. Data Governance
171 Knowledge Units
e.g. Data Curation
All Knowledge Units mapped to the
standard of Computer Classification
System (CCS2012) AND existing
BOKs (DMBOK, BABOK, PMI-BOK,
SWEBOK and ACM-BOK)
11. 11
Workshops to define new profiles and data knowledge: second run
Profile title
Summary statement
Mission
Core activities
Starter Expert
A.6. Application Design 1 3
B.1. Application Development 2 3
B.2. Component Integration 0 3
B.3. Testing 2 2
B.4. Solution Deployment 1 2
B.5. Documentation Production 1 3
4. Workng accurately* ✓ ✓
10. Creativity ✓ ✓
15. Flexibility ✓ ✓
32. Result orientation ✓ ✓
35. Stress resilience* ✓ ✓
Personal competences
(Government Competence
Guide)
* = core competences
DATA ENGINEER
Provision of data
Finds, manages and merges multiple data sources and ensures consistency of datasets.
Ensures asset protection through the provision of clean, consistent, quality assured data.
Maintains the integrity of data, stores and searches data and supports presentation of data
analysis.
Client/customer:
• Matching customer wishes
Production
• Design and develop
• Measure data quality
• Roll-out / transfer data solutiona
• Coordinating solutions / delivering functional requirements
• Monitoring and advising on market developments
• Loading data for developers
• Problem management
• Determining and accessing data sources
• Performing data analysis
• Conduct impact analysis
• Expert: Coaching BI and Data Engineers
Quality:
• Apply standards
• Review
• Propose process improvements
• Quality measurement of own work.
Seniority level
e-competences
(e-CF )
Data knowledge Data Management General Principles Passive Active
Data type registries, (PID) Persistent Identifier,
Metadata
✓
Data lifecycle management ✓
Data infrastructure and Data factories ✓
Ethical principle and Data privacy ✓
FAIR (Findable, Accessible, Interoperable) principles in
Data management
✓
Data Management Systems Passive Active
Data architectures; (OLAP) Online Analytical Processing,
(OLTP) Online Transaction Processing, Extraction
Transformation and Load (ETL)
✓
Data modelling, Databases and Database management
systems
✓
Data structures ✓
Data models and Query languages ✓
Database design and Models ✓
Data warehouses ✓
Data Management Architecture Passive Active
Data management, including Reference and Master data ✓
Data warehousing and Business intelligence ✓
Metadata, Linked data, Data provenance ✓
Data infrastructure, Data registries and Data factories ✓
Data backup ✓
Data anonymisation ✓
Data privacy ✓
Data Governance Passive Active
Data governance, Data quality, Data integration and
Interoperability
✓
Data management planning ✓
Data management policy ✓
Data interoperability ✓
Data curation ✓
Data provenance ✓
Business Analytics Passive Active
Business analytics and Business intelligence: Data,
Models (statistical) and Decisions
✓
Data driven Customer Relations Management (CRM),
User Experience (UX) requirements and design
✓
Data warehouses technologies, Data integration and
Analytics
✓
DATA ENGINEER
12. 12
Survey Data Knowledge (Knowledge Areas)
a = active knowledge needed, p = passive knowledge needed
green = knowledge present at right level, brown = no knowledge present at right level
3
13. 13
Data Management Principles
Low score, passive knowledge present, too little active knowledge.
Data Management Systems
Score ok, knowledge needed on “Data Base Design and Models”.
Data Management Architecture
Score on active knowledge for Data Business Analist and Data Engineer low.
Data Governance
Score on active knowledge for Data Engineer too low.
Business Analytics
Score ok except active knowledge Data Business Analyst.
Business Analytics Management
Score passive knowledge ok, no active knowledge.
Findings on Developing Data Knowledge (Knowledge Areas)
14. 14
Results survey and FTE Needed
score 2,0-2,5
and
preference 2
score 2,0-2,5
and
preference 1
score > -2,5
and
preference 2
score > -2,5
and
preference 1
FTE Needed
Product Owner
5 2 7 6 3-5
Surplus
Scrum Master
2 1 3 1 2-3
Balanced
Data Business
Analyst 1 6 4 3 10-16
Enough interest, development
necessary
Data Engineer
3 5 1 4 10-16
Balanced, development necessary
Data
Administrator 0 8 0 1 4-5
Surplus and develpment necessary
Tester
11 2 1 1 5-6
Second preference surplus but
overall development needed
15. 15
Curriculum 5
Personal Competences
Curriculum UWV Datawarehouse
∆ 21. Active Listening
Consultancy Skills - Communicating
Communicating in teams
Giving Feedback
∆ Data Management Systems
Data Awareness
Dimensional Modelling
ETL - Extract, Transform, Load
Operational Data Modelling
∆ Data Management Architecture
Master Data Management & Reference Data
Management
Agile Information Management
Business Intelligence Data Warehouse Concepts
Data Warehouse Concepts
∆ A.1. IS and Business Strategy Alignment
Enterprise Design Foundation
Management Development Program
∆ A.3. Business Plan Development
Business Case
Analysing techniques
∆ A.4. Product/Service Planning
Scrum Kick-start
∆ A.6. Application Design
Scrum Kickstart
∆ A.9. Innovating
Scrum Product Owner
Data Knowledge & Tooling Professional Competences
∆ B.2. Component Integration
DevOps Awareness
∆ Data Management General Principles
Introduction Data Modelling
∆ Data Governance
e.g. Hadoop Advanced Administration or
Hydra or HPCC or Google Big Query etc.
∆ Business Analytics Management
Agile Requirements
UML Fundamentals
Define & Refine Use Cases
Requirements Engineering - the life cycle
∆ B.1. Application Development
Scrum Kickstart
Software Engineering Track
MTA HTML5 Application Development
Fundamentals
∆ 23. Motivating others
Understanding Behaviour Patterns (REED 1)
Affecting Behaviour Patterns (REED 2)
Leadership and coaching
Strategic coaching
Train the trainer
∆ 25. Organisational awareness
Separate the people from the problem
Psychology in organisations
Essence in behaviour
∆ 4. Working accurately
Pyramid Principle
Working in teams
Working effectively in teams
Time management
Communicating in teams
Giving Feedback
∆ Business Analytics
UX Awareness
Customer Journey Design
∆ 20. Customer Focus
Client centricity
Consultancy Skills - Advising
∆ A.5. Architecture Design
Agile Architecture
Enterprise Design Foundation
16. 16
Actions
- Individual Assessment reports
- Individual and Management discussed and decide on career path
- Individual training plans
- Group training
- Extra capacity for maintaining current data warehouse
Extra
• Motivated employees by providing new services in Data Science to UWV stakeholders and
personal investment in people.
• Same approach will be used for other parts of Data Services and UWV
Current situation
17. ‘We offer people new
prospects of
participating in work and
society'