This document provides an overview of network analysis and its applications. It discusses the origins and history of network study in fields like graph theory and sociology. Various network patterns and metrics are described, including density, distance, centrality, and structural measures. Case studies are presented on using network analysis to understand expertise management, trust, and performance issues in organizations. The document emphasizes that network analysis can provide insights through metrics and visualization to inform important business and organizational questions.
This workshop will introduce some of the main principles and techniques of Social Network Analysis (SNA). We will use examples from organizational and social media-based networks to understand concepts such as network density, diameter, centrality measures, community detection algorithms, etc. The session will also introduce Gephi, a popular program for SNA. Gephi is a free and open-source tool that is available for both Mac and PC computers.
By the end of the session, you will develop a general understanding of what SNA is, what research questions it can help you answer, and how it can be applied to your own research. You will also learn how to use Gephi to visualize and examine networks using various layout and community detection algorithms.
Instructor’s Bio: Dr. Anatoliy Gruzd is a Canada Research Chair in Social Media Data Stewardship, Associate Professor at the Ted Rogers School of Management at Ryerson University, and Director of Research at the Social Media Lab. Anatoliy is also a Member of the Royal Society of Canada’s College of New Scholars, Artists and Scientists; a co-editor of a multidisciplinary journal on Big Data and Society; and a founding co-chair of the International Conference on Social Media and Society. His research initiatives explore how social media platforms are changing the ways in which people and organizations communicate, collaborate and disseminate information and how these changes impact the norms and structures of modern society.
A high-level overview of social network analysis using gephi with your exported Facebook friends network. See more network analysis at http://allthingsgraphed.com.
An introduction in the world of Social Network Analysis and a view on how this may help learning networks. History, data collection and several analysis techniques are shown.
Social Network Analysis power point presentation Ratnesh Shah
Basics of social network analysis,Application and also explain interesting study done by facebook , twitter, youtube and many more social media network ,slide contains some of interesting study to get knowledge about online social network analysis.
Social Network Analysis: What It Is, Why We Should Care, and What We Can Lear...Xiaohan Zeng
The advent of the social networks has completely changed our daily life. The deluge of data collected on Social Network Services (SNS) and recent developments in complex network theory have enabled many marvelous predictive analysis, which tells us many amazing stories.
Why do we often feel that "the world is so small?" Is the six-degree separation purely imagination or based on mathematical insights? Why are there just a few rockstars who enjoy extreme popularity while most of us stay unknown to the world? When science meets coffee shop knowledge, things are bound to be intriguing.
I will first briefly describe what social networks are, in the mathematical sense. Then I will introduce some ways to extract characteristics of networks, and how these analyses can explain many anecdotes in our life. Finally, I'll show an example of what we can learn from social network analysis, based on data from Groupon.
This workshop will introduce some of the main principles and techniques of Social Network Analysis (SNA). We will use examples from organizational and social media-based networks to understand concepts such as network density, diameter, centrality measures, community detection algorithms, etc. The session will also introduce Gephi, a popular program for SNA. Gephi is a free and open-source tool that is available for both Mac and PC computers.
By the end of the session, you will develop a general understanding of what SNA is, what research questions it can help you answer, and how it can be applied to your own research. You will also learn how to use Gephi to visualize and examine networks using various layout and community detection algorithms.
Instructor’s Bio: Dr. Anatoliy Gruzd is a Canada Research Chair in Social Media Data Stewardship, Associate Professor at the Ted Rogers School of Management at Ryerson University, and Director of Research at the Social Media Lab. Anatoliy is also a Member of the Royal Society of Canada’s College of New Scholars, Artists and Scientists; a co-editor of a multidisciplinary journal on Big Data and Society; and a founding co-chair of the International Conference on Social Media and Society. His research initiatives explore how social media platforms are changing the ways in which people and organizations communicate, collaborate and disseminate information and how these changes impact the norms and structures of modern society.
A high-level overview of social network analysis using gephi with your exported Facebook friends network. See more network analysis at http://allthingsgraphed.com.
An introduction in the world of Social Network Analysis and a view on how this may help learning networks. History, data collection and several analysis techniques are shown.
Social Network Analysis power point presentation Ratnesh Shah
Basics of social network analysis,Application and also explain interesting study done by facebook , twitter, youtube and many more social media network ,slide contains some of interesting study to get knowledge about online social network analysis.
Social Network Analysis: What It Is, Why We Should Care, and What We Can Lear...Xiaohan Zeng
The advent of the social networks has completely changed our daily life. The deluge of data collected on Social Network Services (SNS) and recent developments in complex network theory have enabled many marvelous predictive analysis, which tells us many amazing stories.
Why do we often feel that "the world is so small?" Is the six-degree separation purely imagination or based on mathematical insights? Why are there just a few rockstars who enjoy extreme popularity while most of us stay unknown to the world? When science meets coffee shop knowledge, things are bound to be intriguing.
I will first briefly describe what social networks are, in the mathematical sense. Then I will introduce some ways to extract characteristics of networks, and how these analyses can explain many anecdotes in our life. Finally, I'll show an example of what we can learn from social network analysis, based on data from Groupon.
Social network analysis [SNA] is the mapping and measuring of relationships and flows between people, groups, organizations, computers, URLs, and other connected information/knowledge entities. SNA provides both a visual and a mathematical analysis of human relationships.
Social Network Analysis Workshop
This talk will be a workshop featuring an overview of basic theory and methods for social network analysis and an introduction to igraph. The first half of the talk will be a discussion of the concepts and the second half will feature code examples and demonstrations.
Igraph is a package in R, Python, and C++ that supports social network analysis and network data visualization.
Ian McCulloh holds joint appointments as a Parson’s Fellow in the Bloomberg School of Public health, a Senior Lecturer in the Whiting School of Engineering and a senior scientist at the Applied Physics Lab, at Johns Hopkins University. His current research is focused on strategic influence in online networks. His most recent papers have been focused on the neuroscience of persuasion and measuring influence in online social media firestorms. He is the author of “Social Network Analysis with Applications” (Wiley: 2013), “Networks Over Time” (Oxford: forthcoming) and has published 48 peer-reviewed papers, primarily in the area of social network analysis. His current applied work is focused on educating soldiers and marines in advanced methods for open source research and data science leadership.
More information about Dr. Ian McCulloh's work can be found at https://ep.jhu.edu/about-us/faculty-directory/1511-ian-mcculloh
Network measures used in social network analysis Dragan Gasevic
Definition of measures (diameter, density, degree centrality, in-degree centrality, out-degree centrality, betweenness centrality, closeness centrality) used in social network analysis. The presentation is prepared by Dragan Gasevic for DALMOOC.
Gephi is an open source software for graph and network analysis. It uses a 3D render engine to display large networks in real-time and to speed up the exploration. A flexible and multi-task architecture brings new pos- sibilities to work with complex data sets and produce valuable visual results. We present several key features of Gephi in the context of interactive exploration and interpretation of networks. It provides easy and broad access to network data and allows for spatializing, fil- tering, navigating, manipulating and clustering
The world has moved on from 1973 when this paper appeared in the Journal of Sociology. At the core of human interaction is the desire to impart knowledge, that hasn't changed. If you want insight into how to strategically manage your networks to maximise knowledge exchange I have summarised the Granovetter's landmark paper here. The paper itself has been cited over 50,000 times and is seminal to the debate. You can access it here https://www.sciencedirect.com/science/article/pii/B9780124424500500250
The reason why we keep coming back to a paper like this is because no matter how far and fast we move forward, understanding why, is always an expression of interest and a depth of understanding
Introduction to Knowledge Graphs for Information Architects.pdfHeather Hedden
There is a growing interest in knowledge graphs to organize information and make it findable in organizations with large amounts of data and content. Unlike other data technologies, a knowledge graph has a structure that is typically based on a taxonomy and ontology, and thus should involve information architects. Knowledge graphs also have more benefits than information findability, including discovery, analysis, and recommendation. Knowledge graphs bring together content and data.
An enterprise knowledge graph involves a change in thinking about information and its access. Instead of designing information architecture in individual applications, an intranet, or website, a knowledge graph extracts data and links to content that exists in multiple different applications and repositories, linking them in a web or graph-like structure by means of customized, semantic relationships.
1. Basics of Social Networks
2. Real-world problem
3. How to construct graph from real-world problem?
4. What graph theory problem getting from real-world problem?
5. Graph type of Social Networks
6. Special properties in social graph
7. How to find communities and groups in social networks? (Algorithms)
8. How to interpret graph solution back to real-world problem?
Switch: How to Change Things When Change is Hardslls01
Based on the book "Switch" by Dan and Chip Heath, this session was presented at the 2010 National Leadership SpecialQuest. This version of the presentation is designed for individuals or groups to reflect on change, engage in learning more about the content of the Switch framework, and analyzing a change for inclusion. Periodically, viewers will want to pause the slidecast in order to do these things.
Social network analysis [SNA] is the mapping and measuring of relationships and flows between people, groups, organizations, computers, URLs, and other connected information/knowledge entities. SNA provides both a visual and a mathematical analysis of human relationships.
Social Network Analysis Workshop
This talk will be a workshop featuring an overview of basic theory and methods for social network analysis and an introduction to igraph. The first half of the talk will be a discussion of the concepts and the second half will feature code examples and demonstrations.
Igraph is a package in R, Python, and C++ that supports social network analysis and network data visualization.
Ian McCulloh holds joint appointments as a Parson’s Fellow in the Bloomberg School of Public health, a Senior Lecturer in the Whiting School of Engineering and a senior scientist at the Applied Physics Lab, at Johns Hopkins University. His current research is focused on strategic influence in online networks. His most recent papers have been focused on the neuroscience of persuasion and measuring influence in online social media firestorms. He is the author of “Social Network Analysis with Applications” (Wiley: 2013), “Networks Over Time” (Oxford: forthcoming) and has published 48 peer-reviewed papers, primarily in the area of social network analysis. His current applied work is focused on educating soldiers and marines in advanced methods for open source research and data science leadership.
More information about Dr. Ian McCulloh's work can be found at https://ep.jhu.edu/about-us/faculty-directory/1511-ian-mcculloh
Network measures used in social network analysis Dragan Gasevic
Definition of measures (diameter, density, degree centrality, in-degree centrality, out-degree centrality, betweenness centrality, closeness centrality) used in social network analysis. The presentation is prepared by Dragan Gasevic for DALMOOC.
Gephi is an open source software for graph and network analysis. It uses a 3D render engine to display large networks in real-time and to speed up the exploration. A flexible and multi-task architecture brings new pos- sibilities to work with complex data sets and produce valuable visual results. We present several key features of Gephi in the context of interactive exploration and interpretation of networks. It provides easy and broad access to network data and allows for spatializing, fil- tering, navigating, manipulating and clustering
The world has moved on from 1973 when this paper appeared in the Journal of Sociology. At the core of human interaction is the desire to impart knowledge, that hasn't changed. If you want insight into how to strategically manage your networks to maximise knowledge exchange I have summarised the Granovetter's landmark paper here. The paper itself has been cited over 50,000 times and is seminal to the debate. You can access it here https://www.sciencedirect.com/science/article/pii/B9780124424500500250
The reason why we keep coming back to a paper like this is because no matter how far and fast we move forward, understanding why, is always an expression of interest and a depth of understanding
Introduction to Knowledge Graphs for Information Architects.pdfHeather Hedden
There is a growing interest in knowledge graphs to organize information and make it findable in organizations with large amounts of data and content. Unlike other data technologies, a knowledge graph has a structure that is typically based on a taxonomy and ontology, and thus should involve information architects. Knowledge graphs also have more benefits than information findability, including discovery, analysis, and recommendation. Knowledge graphs bring together content and data.
An enterprise knowledge graph involves a change in thinking about information and its access. Instead of designing information architecture in individual applications, an intranet, or website, a knowledge graph extracts data and links to content that exists in multiple different applications and repositories, linking them in a web or graph-like structure by means of customized, semantic relationships.
1. Basics of Social Networks
2. Real-world problem
3. How to construct graph from real-world problem?
4. What graph theory problem getting from real-world problem?
5. Graph type of Social Networks
6. Special properties in social graph
7. How to find communities and groups in social networks? (Algorithms)
8. How to interpret graph solution back to real-world problem?
Switch: How to Change Things When Change is Hardslls01
Based on the book "Switch" by Dan and Chip Heath, this session was presented at the 2010 National Leadership SpecialQuest. This version of the presentation is designed for individuals or groups to reflect on change, engage in learning more about the content of the Switch framework, and analyzing a change for inclusion. Periodically, viewers will want to pause the slidecast in order to do these things.
Takeaways from the international bestseller: "Getting to Yes"BuyerZone
BuyerZone's sales team highlights important takeaways and tips from the international bestseller "Getting to Yes" by Roger Fisher and William Ury.
For more sales tips, visit our blog: www.buyerzone.com/blog
Give and Take - Why helping others drives your success Yee Pam
For generations, we have focused on the individual drivers of success: passion, hard work, talent, and luck. But in today’s dramatically reconfigured world, success is increasingly dependent on how we interact with others. In Give and Take, Adam Grant, an award-winning researcher and Wharton’s highest-rated professor, examines the surprising forces that shape why some people rise to the top of the success ladder while others sink to the bottom. Praised by social
What is the Nudge Theory?
A mixure of beavourial economics, psychology, political theory, marketing and sales. Its the theory that considers how people make decisions – and how others impact them.
We spend more time working than doing anything else in life. Yet for too many people, the experience of work is demotivating and dehumanizing.
I don’t think it has to be this way, and I’m willing to bet you don’t either.
At Google, we’ve learned a ton about what makes for an enjoyable and productive workplace. We’re not alone – lots of other companies, ranging from grocers (e.g., Wegmans) to textile companies (e.g., the Brandix Group) to Brooklyn delis (e.g., Russ & Daughters), as well as academics and scientists, have learned the same simple truth: there are straightforward things we can do to make work better.
My new book, "Work Rules!", is an attempt to bring this together and offer you practical tools to improve work, no matter what you do. Check out this visual preview of the book and visit www.workrules.net if you’d like to pick up a copy or learn more!
An Introduction to The Challenger Customer [Pat Spenner, CEB]Quarry
Pat Spenner, co-author of The Challenger Customer presented to animal health marketing and sales professionals at NAVC 2016 about the changing landscape of B2B buying.
Influence: the Psychology of Persuasion (Cialdini)Hugo Guyader
Lecturing on Cialdini's Influence book to Master students for a course in Advanced Consumer Marketing at Linköping University, Sweden.
Cialdini (2016) - "Pre-Suasion": http://www.slideshare.net/guyaderhugo/presuasion-a-revolutionary-way-to-influence-and-persuade
Inside Sales Virtual Summit - Access all of the live sessions here: http://www.insidesales.com/summit/register-2
Matt Dixon - Author of the Challenger Sales & Executive Director, Sales & Service Practice at CEB
LinkedIn Profile: www.linkedin.com/pub/matt-dixon/1/17a/8b2
The Challenger Sale Twitter: https://twitter.com/CEB_Challenger
Matt Dixon's Twitter: https://twitter.com/matthewxdixon
Purchase The Challenger Sale here: http://www.amazon.com/dp/1591844355/?tag=googhydr-20&hvadid=12861934284&hvpos=1t2&hvexid=&hvnetw=g&hvrand=13918257381288675219&hvpone=19.26&hvptwo=&hvqmt=b&hvdev=c&ref=pd_sl_4ltsmqgotn_b
Try InsideSales.com free for 10 days: PowerDialer™ — #1 Power Dialer Software for B2B Sales - http://www.insidesales.com/outbound_power_dialer.php
Part 1: Concepts and Cases (the language of networks, networks in organizations, case studies and key concepts)
Part 2: (Starts on #44) Mapping Organizational, Personal, and Enterprise Networks: Tools
An update to last year's Social Network Analysis Introduction and Tools...
Social Network Analysis & an Introduction to ToolsPatti Anklam
This presentation was delivered as part of an intense knowledge management curriculum. It covers the basics of network analysis and then goes into the different types of tool that support analyzing networks.
Revision of Previous Show on SNA and Introduction to Tools
The Language of Networks
Introduction to Social Network Analysis/ Cases
Tools for Analyzing social networks, including graphing Facebook, LinkedIn, and Twitter networks
A high-level overview of social network analysis, providing background on how it came into the knowledge management field. Includes an example and core concepts pertinent to the audience, online community managers.
A critical review of the literature pertaining to the networking behaviours of young jobseekers, in both offline and online environments. A model from information behaviour theory is proposed as a suitable theoretical framework for future research in this area.
Research IT @ Illinois: Establishing Service Responsive to Investigator NeedsJohn Towns
Over the past two years an ongoing effort has been underway to further develop the research support IT resources and services necessary to make our faculty more productive and more competitive in the granting process. During this discussion we will first review a year long effort in gathering the needs of researchers and distilling a set of recommendations to address those identified need. This will be followed by a review of elements of a proposal prepared for campus administration articulating a vision and plan to create a dynamic research support environment in which a broad portfolio of resources, services and support are easily discoverable and accessible to the campus research community.
An interactive presentation on social network theory and analysis. Content includes information on tie formation and social capital. Network relations are explained by using the example of The A Team. Granovetter's Strength of Weak Ties Theory (1973) is also covered and weak ties and strong ties are explained. Appropriate application of social network theory to individuals understanding how to best take advantage of social networking platforms to find jobs as well as companies taking advantage of social media platforms to find followers are introduced.
Yes, I still do KM and KM is not dead. I thought I would share the basic deck that I use in workshops that are part of my KM Assessment and Strategy consulting practice. In addition to interviews, surveys, and inventories, it is important during a KM assessment to educate and engage the organization.
Brief description of ONA (Organizational network analysis) followed by a summary and comparison of the emerging SAAS vendors who provide support for network surveys and analysis.
The Key Success Factor in Knowledge Management... What Else? Change ManagementPatti Anklam
Presented at SLA 2013, on a panel with Ethel Salonen of MITRE Corporation. Provides perspective on change management and how it is used in understanding and creating interventions in knowledge networks.
Network analysis methods for assessment & measurementPatti Anklam
Presentation slides for a webinar produced by the Leadership Learning Community. Full audio is available on their site, at http://www.leadershiplearning.org/blog/eleanor-cooney/2012-12-17/2013-webinar-network-analysis-snaona-methods-assessment-measurement
NetWorkShop: Boston Facilitators RoundtablePatti Anklam
The NetWorkShop offers a new perspective – a network lens – that sheds light on how human networks are structured and how technologies can enhance our ability to collaborate and co-create. For facilitators, it offers possibilities of new ways of thinking about client work as well as leadership coaching.
This workshop provides a clear presentation of basic network concepts, including:
· Reflective exercises in creating and interpreting network maps of relationships (organizational and personal) using network concepts
· An introduction to value networking analysis, with a focus on mapping roles and deliverables (gives and gets) in an organizational ecosystem
· A short overview of how social media (Twitter, Facebook, LinkedIn) is altering the landscape of how people create and work in networks.
This short set of slides summarizes the characteristics of people who play specific roles in networks. In a social network analysis, people in these roles can be discovered by running mathematical algorithms through the social graphs. But you don't need to be an algorithm to spot some of these people in your networks!
Personal Network Management Km Forum Oct 2009Patti Anklam
Presentation to the Boston KM Forum. Describes how an understanding of networks dynamic and structure can help with the development of one's personal network.
"𝑩𝑬𝑮𝑼𝑵 𝑾𝑰𝑻𝑯 𝑻𝑱 𝑰𝑺 𝑯𝑨𝑳𝑭 𝑫𝑶𝑵𝑬"
𝐓𝐉 𝐂𝐨𝐦𝐬 (𝐓𝐉 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬) is a professional event agency that includes experts in the event-organizing market in Vietnam, Korea, and ASEAN countries. We provide unlimited types of events from Music concerts, Fan meetings, and Culture festivals to Corporate events, Internal company events, Golf tournaments, MICE events, and Exhibitions.
𝐓𝐉 𝐂𝐨𝐦𝐬 provides unlimited package services including such as Event organizing, Event planning, Event production, Manpower, PR marketing, Design 2D/3D, VIP protocols, Interpreter agency, etc.
Sports events - Golf competitions/billiards competitions/company sports events: dynamic and challenging
⭐ 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐝 𝐩𝐫𝐨𝐣𝐞𝐜𝐭𝐬:
➢ 2024 BAEKHYUN [Lonsdaleite] IN HO CHI MINH
➢ SUPER JUNIOR-L.S.S. THE SHOW : Th3ee Guys in HO CHI MINH
➢FreenBecky 1st Fan Meeting in Vietnam
➢CHILDREN ART EXHIBITION 2024: BEYOND BARRIERS
➢ WOW K-Music Festival 2023
➢ Winner [CROSS] Tour in HCM
➢ Super Show 9 in HCM with Super Junior
➢ HCMC - Gyeongsangbuk-do Culture and Tourism Festival
➢ Korean Vietnam Partnership - Fair with LG
➢ Korean President visits Samsung Electronics R&D Center
➢ Vietnam Food Expo with Lotte Wellfood
"𝐄𝐯𝐞𝐫𝐲 𝐞𝐯𝐞𝐧𝐭 𝐢𝐬 𝐚 𝐬𝐭𝐨𝐫𝐲, 𝐚 𝐬𝐩𝐞𝐜𝐢𝐚𝐥 𝐣𝐨𝐮𝐫𝐧𝐞𝐲. 𝐖𝐞 𝐚𝐥𝐰𝐚𝐲𝐬 𝐛𝐞𝐥𝐢𝐞𝐯𝐞 𝐭𝐡𝐚𝐭 𝐬𝐡𝐨𝐫𝐭𝐥𝐲 𝐲𝐨𝐮 𝐰𝐢𝐥𝐥 𝐛𝐞 𝐚 𝐩𝐚𝐫𝐭 𝐨𝐟 𝐨𝐮𝐫 𝐬𝐭𝐨𝐫𝐢𝐞𝐬."
Enterprise Excellence is Inclusive Excellence.pdfKaiNexus
Enterprise excellence and inclusive excellence are closely linked, and real-world challenges have shown that both are essential to the success of any organization. To achieve enterprise excellence, organizations must focus on improving their operations and processes while creating an inclusive environment that engages everyone. In this interactive session, the facilitator will highlight commonly established business practices and how they limit our ability to engage everyone every day. More importantly, though, participants will likely gain increased awareness of what we can do differently to maximize enterprise excellence through deliberate inclusion.
What is Enterprise Excellence?
Enterprise Excellence is a holistic approach that's aimed at achieving world-class performance across all aspects of the organization.
What might I learn?
A way to engage all in creating Inclusive Excellence. Lessons from the US military and their parallels to the story of Harry Potter. How belt systems and CI teams can destroy inclusive practices. How leadership language invites people to the party. There are three things leaders can do to engage everyone every day: maximizing psychological safety to create environments where folks learn, contribute, and challenge the status quo.
Who might benefit? Anyone and everyone leading folks from the shop floor to top floor.
Dr. William Harvey is a seasoned Operations Leader with extensive experience in chemical processing, manufacturing, and operations management. At Michelman, he currently oversees multiple sites, leading teams in strategic planning and coaching/practicing continuous improvement. William is set to start his eighth year of teaching at the University of Cincinnati where he teaches marketing, finance, and management. William holds various certifications in change management, quality, leadership, operational excellence, team building, and DiSC, among others.
Memorandum Of Association Constitution of Company.pptseri bangash
www.seribangash.com
A Memorandum of Association (MOA) is a legal document that outlines the fundamental principles and objectives upon which a company operates. It serves as the company's charter or constitution and defines the scope of its activities. Here's a detailed note on the MOA:
Contents of Memorandum of Association:
Name Clause: This clause states the name of the company, which should end with words like "Limited" or "Ltd." for a public limited company and "Private Limited" or "Pvt. Ltd." for a private limited company.
https://seribangash.com/article-of-association-is-legal-doc-of-company/
Registered Office Clause: It specifies the location where the company's registered office is situated. This office is where all official communications and notices are sent.
Objective Clause: This clause delineates the main objectives for which the company is formed. It's important to define these objectives clearly, as the company cannot undertake activities beyond those mentioned in this clause.
www.seribangash.com
Liability Clause: It outlines the extent of liability of the company's members. In the case of companies limited by shares, the liability of members is limited to the amount unpaid on their shares. For companies limited by guarantee, members' liability is limited to the amount they undertake to contribute if the company is wound up.
https://seribangash.com/promotors-is-person-conceived-formation-company/
Capital Clause: This clause specifies the authorized capital of the company, i.e., the maximum amount of share capital the company is authorized to issue. It also mentions the division of this capital into shares and their respective nominal value.
Association Clause: It simply states that the subscribers wish to form a company and agree to become members of it, in accordance with the terms of the MOA.
Importance of Memorandum of Association:
Legal Requirement: The MOA is a legal requirement for the formation of a company. It must be filed with the Registrar of Companies during the incorporation process.
Constitutional Document: It serves as the company's constitutional document, defining its scope, powers, and limitations.
Protection of Members: It protects the interests of the company's members by clearly defining the objectives and limiting their liability.
External Communication: It provides clarity to external parties, such as investors, creditors, and regulatory authorities, regarding the company's objectives and powers.
https://seribangash.com/difference-public-and-private-company-law/
Binding Authority: The company and its members are bound by the provisions of the MOA. Any action taken beyond its scope may be considered ultra vires (beyond the powers) of the company and therefore void.
Amendment of MOA:
While the MOA lays down the company's fundamental principles, it is not entirely immutable. It can be amended, but only under specific circumstances and in compliance with legal procedures. Amendments typically require shareholder
What are the main advantages of using HR recruiter services.pdfHumanResourceDimensi1
HR recruiter services offer top talents to companies according to their specific needs. They handle all recruitment tasks from job posting to onboarding and help companies concentrate on their business growth. With their expertise and years of experience, they streamline the hiring process and save time and resources for the company.
Business Valuation Principles for EntrepreneursBen Wann
This insightful presentation is designed to equip entrepreneurs with the essential knowledge and tools needed to accurately value their businesses. Understanding business valuation is crucial for making informed decisions, whether you're seeking investment, planning to sell, or simply want to gauge your company's worth.
Putting the SPARK into Virtual Training.pptxCynthia Clay
This 60-minute webinar, sponsored by Adobe, was delivered for the Training Mag Network. It explored the five elements of SPARK: Storytelling, Purpose, Action, Relationships, and Kudos. Knowing how to tell a well-structured story is key to building long-term memory. Stating a clear purpose that doesn't take away from the discovery learning process is critical. Ensuring that people move from theory to practical application is imperative. Creating strong social learning is the key to commitment and engagement. Validating and affirming participants' comments is the way to create a positive learning environment.
India Orthopedic Devices Market: Unlocking Growth Secrets, Trends and Develop...Kumar Satyam
According to TechSci Research report, “India Orthopedic Devices Market -Industry Size, Share, Trends, Competition Forecast & Opportunities, 2030”, the India Orthopedic Devices Market stood at USD 1,280.54 Million in 2024 and is anticipated to grow with a CAGR of 7.84% in the forecast period, 2026-2030F. The India Orthopedic Devices Market is being driven by several factors. The most prominent ones include an increase in the elderly population, who are more prone to orthopedic conditions such as osteoporosis and arthritis. Moreover, the rise in sports injuries and road accidents are also contributing to the demand for orthopedic devices. Advances in technology and the introduction of innovative implants and prosthetics have further propelled the market growth. Additionally, government initiatives aimed at improving healthcare infrastructure and the increasing prevalence of lifestyle diseases have led to an upward trend in orthopedic surgeries, thereby fueling the market demand for these devices.
3.0 Project 2_ Developing My Brand Identity Kit.pptxtanyjahb
A personal brand exploration presentation summarizes an individual's unique qualities and goals, covering strengths, values, passions, and target audience. It helps individuals understand what makes them stand out, their desired image, and how they aim to achieve it.
RMD24 | Debunking the non-endemic revenue myth Marvin Vacquier Droop | First ...BBPMedia1
Marvin neemt je in deze presentatie mee in de voordelen van non-endemic advertising op retail media netwerken. Hij brengt ook de uitdagingen in beeld die de markt op dit moment heeft op het gebied van retail media voor niet-leveranciers.
Retail media wordt gezien als het nieuwe advertising-medium en ook mediabureaus richten massaal retail media-afdelingen op. Merken die niet in de betreffende winkel liggen staan ook nog niet in de rij om op de retail media netwerken te adverteren. Marvin belicht de uitdagingen die er zijn om echt aansluiting te vinden op die markt van non-endemic advertising.
Affordable Stationery Printing Services in Jaipur | Navpack n PrintNavpack & Print
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3. Columbia IKNS Residency April 2016
Origins of Network Study
• Graph theory
– Euler, the seven bridges
of Königsberg (1736)
• Sociometry
– Jacob Moreno, Hudson
Training School for Girls
(1932)
3
4. Columbia IKNS Residency April 2016
Symposium on Social Networks: Dartmouth, 1975
http://eclectic.ss.uci.edu/~drwhite/Networks/MSSB1975.html
5. Columbia IKNS Residency April 2016
2007
Network Theory Reaches the Business World
2002
2002
2002
2003
2004
2004
5
2005
2009
2009
2002
2002
8. Columbia IKNS Residency April 2016
Networks of Companies
8
Source: Laurie Lock Lee, http://www.optimice.com.au
Equipment Manufacturers
Systems integrators
9. Columbia IKNS Residency April 2016
https://kumu.io/UnLtdUSA/austin-social-entrepreneurship
People and Companies
9
Austin Social Entrepreneurship
10. Columbia IKNS Residency April 2016
Mapping Ideas and Topics
10
http://www.smrfoundation.org/2009/09/12/networks-in-the-news-news-dots-on-slate/
12. Columbia IKNS Residency April 2016
The Premise: Networks Matter
• Social Capital
– People with stronger personal networks are
more productive, happier, and better
performers
– Companies who know how to manage
alliances are more flexible, adaptive and
resilient
– Our personal health and well-being is often
tied to our social networks
• Making Sense
– Once we have the distinction “network”
then we can use our knowledge of the
networks we live in to make sense
12
13. Columbia IKNS Residency April 2016
The Opportunity: Leverage the Science
13
• Graph theory provided the
underlying math and science to
help us make sense of the
network structure
• The structure of a network
provides insights into network
patterns:
• About the structure of the
network
• About people in the network
• Once you understand the
structure, you can make
decisions about how to manage
the network’s context – this is
Net Work
14. I’ve become convinced that understanding
how networks work is an essential 21st
century literacy.
Howard Rheingold
15. Columbia IKNS Residency April 2016
The Importance of Understanding Networks
15
Burt, Ronald S. and Don Ronchi, Teaching executives to see social capital: Results from a field
experiment http://faculty.chicagobooth.edu/ronald.burt/research/files/TESSC.pdf
16. Columbia IKNS Residency April 2016
The Two Parts
―The language of networks
―Networks in organizations
16
Social Network Analysis:
Cases and Concepts
Mapping Networks: Tools
18. Columbia IKNS Residency April 2016
The Business Case
18
Management Practice Business Need
Talent Management Finding the natural leaders in the organization
Innovation Identify boundary crossers
Ensure organization has access to new ideas
Collaboration Finding gaps in knowledge flow within groups,
or across organizations or geographies
Monitor or measure changes
Knowledge
management
Identify and retain vital expertise
Monitor or measure changes in k. exchange
Organizational Change
and Development
Identifying opinion leaders for change
management initiatives or during integration
following mergers and acquisitions
Organizational
Performance
Diagnosing cohesion among team members
and targeting critical connections for
improvement
19. Columbia IKNS Residency April 2016
Rob Cross’s Classic Case: A Performance Issue
19
From: The Organizational Network Fieldbook, Rob Cross et al, Jossey-Bass 2010
Where are the most frequent information flows?
Formal Structure Informal Structure
20. Columbia IKNS Residency April 2016
A Classic Case
20
From: The Organizational Network Fieldbook, Rob Cross et al, Jossey-Bass 2010
Formal Structure Informal Structure
21. Columbia IKNS Residency April 2016
A Classic Case
From: The Hidden Power of Social Networks, Rob Cross and Andrew Parker, Harvard Business School Press, 2004
21
From: The Organizational Network Fieldbook, Rob Cross et al, Jossey-Bass 2010
Formal Structure Informal Structure
22. Columbia IKNS Residency April 2016
A Classic Case
22
From: The Organizational Network Fieldbook, Rob Cross et al, Jossey-Bass 2010
Formal Structure Informal Structure
23. Columbia IKNS Residency April 2016
A Classic Case
23
From: The Organizational Network Fieldbook, Rob Cross et al, Jossey-Bass 2010
Formal Structure Informal Structure
24. Columbia IKNS Residency April 2016
What Factors Influence Connections?
• Homophily: Birds of a
feather, flock
together
• Propinquity: Those
close by, form a tie
24
25. Columbia IKNS Residency April 2016
Elements in a Network Diagram
25
• A network diagram shows a
collection of entities (nodes) linked
by a type of relationship
(represented by an edge) Nodes
Edges
Node: Vertex, Alter
Edge: Tie, connection, link
Network diagram: graph, sociogram
Synonyms
26. Columbia IKNS Residency April 2016
Nodes Have Attributes
• Information from survey and/or
HR data*:
– Organizational unit
– Job title/role
– Location
– Expertise
– Job level
– Age
– Gender
• Additional attributes may come
from the survey data itself
26
*within the bounds of what is legal and appropriate
27. Columbia IKNS Residency April 2016
About Edges
27
• Edges (and the graph as a whole)
are either:
• Undirected (merely connected)
• Directed (edges go “from-to”)
• Reciprocity sometimes matters
Undirected
Node: Vertex, Alter
Edge: Tie, connection, link
Network diagram: graph, sociogram
Synonyms
Directed
Reciprocal
28. Columbia IKNS Residency April 2016
Edges Define the Shape of the Network
28
• In a survey we might ask:
• “I get information from this
person”
• “I socialize with this person”
• “I think this person is an expert”
• “I go to this person when I have an
idea I want to explore”
• In looking at data, we might want to
find out:
• People who responded to each
others’ emails
• People who attended the same
meetings or who appeared at the
same event – or in the same scene!
In creating a social network diagram, we define what we mean by an edge
29. Columbia IKNS Residency April 2016
Weights and Tie Strength
29
• Edges may have values, or
weights, associated with them. For
example the difference between:
• Exchanging a few emails
• Being best friends
• The strength between two nodes
may also reflected having multiple
relationships:
• Exchange information
frequently AND
• Socialize AND
• Share trusted information
Node: Vertex, Alter
Edge: Tie, connection, link
Network diagram: graph, sociogram
Synonyms
30. Columbia IKNS Residency April 2016
Edge Data from Surveys
30
• Surveys:
– Edge data may or may not
be weighted
– People may answer
questions about everyone
in the network or
nominate people they
communicate with, seek
advice from, etc.
• Weighted questions may
denote frequency or
some kind of
strength
32. Columbia IKNS Residency April 2016
How Are We Managing Expertise?
Acknowledged Expert
Colleague
Questions visualized on the map:
1. Whom do you turn to for professional
advice regarding your daily work?
2. Who is the most acknowledged professional in your field?
Source: Maven7/Orgmapper
33. Columbia IKNS Residency April 2016
How Are We Managing Expertise?
Accessible knowledgeAcknowledged Expert
Colleague
Group with no
direct access to a
knowledge center
Questions visualized on the map:
1. Whom do you turn to for professional
advice regarding your daily work?
2. Who is the most acknowledged professional in your field?
Non-accessible
knowledge
Source: Maven7/Orgmapper
34. Columbia IKNS Residency April 2016
How Are We Managing Expertise?
Acknowledged Expert
Colleague
Cluster with no
direct access to a
knowledge center
Questions visualized on the map:
1. Whom do you turn to for professional
advice regarding your daily work?
2. Who is the most acknowledged professional in your field?
Source: Maven7/Orgmapper
35. Columbia IKNS Residency April 2016
California Computer
35
From “Informal Networks: The Company”
David Krackhardt and Jeffrey R. Hanson
HBR, 1993
CEO Leers must choose someone to lead a strategic task force.
Bair
Stewart
Ruiz
O'Hara
S/W Applications
Harris
Benson
Fleming
Church
Martin
Lee
Wilson
Swinney
Huberman
Fiola
Calder
Field Design
Muller
Jules
Baker
Daven
Thomas
Zanados
Lang
ICT
Huttle
Atkins
Kibler
Stern
Data Control
Leers
CEO
36. Columbia IKNS Residency April 2016
California Computer
36
From “Informal Networks: The Company”
David Krackhardt and Jeffrey R. Hanson
HBR, 1993
CEO Leers must choose someone to lead a strategic task force.
Bair
Stewart
Ruiz
O'Hara
S/W Applications
Harris
Benson
Fleming
Church
Martin
Lee
Wilson
Swinney
Huberman
Fiola
Calder
Field Design
Muller
Jules
Baker
Daven
Thomas
Zanados
Lang
ICT
Huttle
Atkins
Kibler
Stern
Data Control
Leers
CEO
37. Columbia IKNS Residency April 2016
Was Harris a Good Choice?
37
Whom do you
go to for help
or advice?
Field Design
Data Control Systems
Software Applications
CEO
ICT
38. Columbia IKNS Residency April 2016
Was Harris a Good Choice?
38
Whom do you
go to for help
or advice?
Field Design
Data Control Systems
Software Applications
CEO
ICT
39. Columbia IKNS Residency April 2016
The Question of Trust
39
Whom would
you trust to
keep in
confidence
your concerns
about a work-
related issue?
40. Columbia IKNS Residency April 2016
The Question of Trust
40
Whom would
you trust to
keep in
confidence
your concerns
about a work-
related issue?
41. Columbia IKNS Residency April 2016
The Question of Trust
41
Whom would
you trust to
keep in
confidence
your concerns
about a work-
related issue?
42. Columbia IKNS Residency April 2016
Network Patterns
Multi-Hub
Clustered Core/Periphery
42
Hub and Spoke
43. Columbia IKNS Residency April 2016
Core/Periphery
43
Core
Periphery
Structural
Hole
Isolates
44. Columbia IKNS Residency April 2013
It’s all about Questions
44
Patterns provide
insights that provoke
good questions.
Full stop.
45. Columbia IKNS Residency April 2016
• Look at the whole network
and its components
Network Analysis Also Provides Metrics
• Look at positions of
individuals in the network
Centrality Metrics
Structural (Network) Metrics
45
46. Columbia IKNS Residency April 2016
Structural Metrics
46
• Common measures:
–Density of interactions
–Distance (average degree of separation)
–Diversity
–Communities, or groups
–Centralization
• Good for comparing questions, groups within
networks or for comparing changes in a
network over time
Look at the whole network and its components
47. Columbia IKNS Residency April 2016
The Metrics: Density
47
Density. Data provides the percentage of information-getting
relationships that exist out of the possible number that could exist. It
is not a goal to have 100%, but to target the junctures where
improved collaboration could have a business benefit.
Percent of connections that exist out of the total possible
Low Density
High Density
48. Columbia IKNS Residency April 2016
Impact on Business of Connectivity
• Bank management was
trying to understand the
differences across branches
in sales at credit and
deposit figures
• Using network analysis, the
bank was able to
understand where to direct
mentoring and “best
practice” exchanges across
banks
48
Figures show the performance differences in bank
branches based on the density of their relationships
Total credit /
person
Total deposit /
person
Low density
branches
High density
branches
Low density
branches
High density
branches
Source: Maven7/Orgmapper
49. Columbia IKNS Residency April 2016
Metrics help reveal diversity within networks
SmA Ops PL A PL B PL C LgA
10 5 8 8 9 10
Small Accounts 72% 2% 11% 0% 2% 5%
Operations 4% 85% 10% 5% 7% 12%
Product Line A 8% 3% 77% 0% 1% 4%
Product Line B 0% 13% 2% 73% 0% 17%
Product Line C 2% 16% 1% 3% 54% 17%
Large Accounts 2% 18% 5% 16% 12% 73%
Density. Data provides the percentage of information-getting
relationships that exist out of the possible number that could exist. It
is not a goal to have 100%, but to target the junctures where
improved collaboration could have a business benefit.
The diagonal shows the interconnectivity among groups in the
organization
Off-diagonal, the metrics illustrate the extent to which people are
reaching across organizational boundaries
49
50. Columbia IKNS Residency April 2016
Tracking Metrics Over Time
50
2010
2011
Year # Density Degree
2009 55 2.2% 1.2
2010 90 2.7% 2.4
2011 85 5.3% 4.5
2012 82 8% 6.88
2009
2012
51. Columbia IKNS Residency April 2016
Structural Metrics: Distance
51
Maximum number of steps to get from one node to another: 12
Average number of steps: 5
52. Columbia IKNS Residency April 2016
Centrality Metrics: Degree
52Based on: https://plus.google.com/+DaveGray/posts/CQRVeKEsUvF
Raw number of connections (undirected network)
6
7
10
Average Degree: 3.28
53. Columbia IKNS Residency April 2016
Centrality Metrics: In-Degree and Out-Degree
53Based on: https://plus.google.com/+DaveGray/posts/CQRVeKEsUvF
Number of in-coming and out-going connections
Outdegree = 7
Indegree = 5
54. Columbia IKNS Residency April 2016
Centrality Metrics: Betweenness
54Based on: https://plus.google.com/+DaveGray/posts/CQRVeKEsUvF
How many paths does a single node lie on?
855
1080
785
793
55. Columbia IKNS Residency April 2016
Centrality Metrics: Betweenness
Highest Bee-tweenness?
https://www.timeshighereducation.com/sites/default/files/styles/the_breaking_news_image_style/public/bees_teamwork.jpg
h/t: Valdis Krebs
56. Columbia IKNS Residency April 2016
Centrality Metrics: Closeness
56Based on: https://plus.google.com/+DaveGray/posts/CQRVeKEsUvF
Able to reach all the other nodes in the fewest steps
57. Columbia IKNS Residency April 2016
Using Metrics: Finding Key Opinion Leaders
57
Source: Maven7
58. Columbia IKNS Residency April 2016
Using Metrics: Finding Key Opinion Leaders
58
Source: Maven7
59. Columbia IKNS Residency April 2016
Using Metrics: Finding Key Opinion Leaders
59
Dunbar’s number: 150
• Strong ties:
– Close, frequent
– Reciprocal
– May be embedded in a
strong “local network”
• Weak ties
– Infrequent interaction
– Likely embedded in other
(diverse) networks
– Accessible as needed
Source: Maven7
60. Columbia IKNS Residency April 2016
Centrality Metrics: Brokerage, Closure
60Based on: https://plus.google.com/+DaveGray/posts/CQRVeKEsUvF
Working cross-cluster or within clusters?
61. Columbia IKNS Residency April 2016
Centrality Metric: Eigenvector
61
Connected to well-connected nodes
62. Columbia IKNS Residency April 2016
Putting Some Metrics Together
62
http://qz.com/650796/mathematicians-mapped-out-every-game-of-thrones-relationship-to-find-the-main-character/
63. Columbia IKNS Residency April 2016
Which Technology Scout is Most Successful?
63
It's Whom You Know Not What You Know: A Social Network Analysis Approach to
Talent Management, Eoin Whelan, SSRN: http://ssrn.com/abstract=1694453
Technology Scout
Connector
Gatekeeper
Group member
64. Columbia IKNS Residency April 2016
Using Metrics: Ego Networks and Diversity
• Organization
• Expertise
• Age, Tenure
65
External/Internal Ratio: Proportion of an
individual’s ties that are in the same
demographic cohort as the individual
node (“ego”). Ranges from +1 (all
external) to -1 (all internal)
AB’s E/I index: .308
DC’s E/I index: -.714
Can be derived from any demographic:
• Social Ties
• Geographic location
• Hierarchical position
65. Columbia IKNS Residency April 2016
The Importance of Diversity
People who live in the intersection of social worlds are at
higher risk of having good ideas. – Ron Burt
66
66. Columbia IKNS Residency April 2016
Organizational Networks Summary
67
• The science of networks has brought insights into the structure
of organizational networks
• Organizational network analysis lets us map relationships to:
• Identify patterns of connection, disconnection, and flows
of knowledge and ideas
• Understand the roles that individuals play and their
potential for enhancing organizational effectiveness
• Developing and sharing maps and metrics helps organizations
to ask good questions and design targeted interventions
• A map represents a moment in time; when maps are shared
the relationships start to shift
67. Columbia IKNS Residency April 2016
Interventions: Net Work
Ways to change patterns in networks Practices from the KM/OD Repertoire
Create more connections Make introductions through meetings and webinars, face-to-face events
(like knowledge fairs); implement social software or social network
referral software; social network stimulation
Increase the flow of knowledge Establish collaborative workspaces, install instant messaging systems,
make existing knowledge bases more accessible and usable
Discover connections Implement expertise location and/or; discovery systems; social
software; social networking applications
Decentralize Social software; blogs, wikis; shift knowledge to the edge
Connect disconnected clusters Establish knowledge brokering roles; expand communication channels
Create more trusted relationships Assign people to work on projects together
Alter the behavior of individual nodes Create awareness of the impact of an individual’s place in a network;
educate employees on personal knowledge networking
Increase diversity Add nodes; connect and create networks; encourage people to bring
knowledge in from their networks in the world
68
69. Columbia IKNS Residency April 2016
What Sorts of Tools Are There?
Category of Tool What you need to know
Expert/Researcher Mapping
and Analysis Tools
Range in complexity of
function and cost
Emerging Platforms Network diagrams can be
shared on the web
Consulting Vendors Specialized solutions with
project life cycle
management
Mapping social metadata Email and log file analysis
Personal network
assessment
DIY or $$$
71. Columbia IKNS Residency April 2016
Data Flow
Analysis
& Mapping
Tools
Maps
Metrics
Edge Data
UCINET
NetDraw
InFlow
NodeXL
Collection
Tools
Spreadsheets
Online Surveys
Paper
Node Data
Social Media
72. Columbia IKNS Residency April 2016
ONASurveys
• Specifically designed for doing network analysis
• Demographic questions as well as network relationship
questions
• Users respond to network questions only about people they
indicate they know
• Outputs datasets for:
– NetDraw/UCINET
– NodeXL
– Gephi
74
73. Columbia IKNS Residency April 2016
Tool Basics – the Dataset (0s and 1s)
75
Information about the nodes (vertices) and the ties (edges)
82. Columbia IKNS Residency April 2016
https://kumu.io/UnLtdUSA/austin-social-entrepreneurship
Kumu is Based on Community
84
83. Columbia IKNS Residency April 2016
Emerging Platforms: Polinode
• Create and
manage
surveys
• Upload and
manage
networks
85
https://polinode.com/
84. Columbia IKNS Residency April 2016
Quick Comparison
Feature/Capability Kumu Polinode
Create and manage surveys No Yes; cost is based on # of survey
respondents and # of names listed
Metrics Yes Yes
Control of colors, shapes, sizes
& overall diagram
GUI and CSS
Stylesheets
Via GUI and specializing attributes
Publish maps on the web Yes Yes
Share data and mapping Yes Yes
Public network pricing Free • Free with basic metrics, up to
250 nodes and 1,000 edges
• $20/month for advanced metrics
and up to 50,000 nodes
Private network pricing (per
month)
$23 (3 projects)
$34 (5 projects)
$49 (10 projects)
$29
User community Yes Yes
86
85. Columbia IKNS Residency April 2016
Network Insights Don’t Require Fancy Software
• If it’s a network, you can draw it.
87
86. Columbia IKNS Residency April 2016
Mapping from Social Media
• Social network platforms:
– A Facebook Friend
– A LinkedIn Connection
– A Twitter Following
• Social media content platforms:
– Likes, posts, replies, shares,
and uploads
– Mentions or “retweet”
#hashtags
• In-house:
– Email
88
87. Columbia IKNS Residency April 2016
Twitter Networks in NodeXL: Patterns
89
Polarized Crowd Tight Crowd Brand Clusters
Community Clusters Broadcast Networks Support Network
http://www.pewinternet.org/2014/02/20/mapping-twitter-topic-networks-from-polarized-crowds-to-community-clusters/
88. Columbia IKNS Residency April 2016
Networks in Social Media
1. Krugman tweets a
link to an article
2. There are a
number of
Tweeters who
publish links to
the article but
these are not
connected to
other Tweeters
3. There are two
densely
interconnected
groups of people
who share the
link and discuss it
90
Analyzing Twitter networks with NodeXL: Broadcast Networks
http://www.pewinternet.org/2014/02/20/mapping-twitter-topic-networks-from-polarized-crowds-to-community-clusters/
90. Columbia IKNS Residency April 2016
Swoop Analytics
• Use interaction data to
create and analyze
edges in the network
• External/internal ratios
• Edges & reciprocal
edges
92
Personal and Enterprise-level dashboards
91. Columbia IKNS Residency April 2016
SWOOP User Characterization
• Using the metrics showing
give/receive balance,
SWOOP can provide
feedback on typical user
communication personas
• Using overall metadata,
SWOOP can provide
benchmark information on
an organization’s online
collaboration engagement/
adoption
93
http://www.swoopanalytics.com/index.php/benchmarking/
93. Columbia IKNS Residency April 2016
Consulting Vendor Options
Vendor If you are looking for… Working with Them
Maven7
OrgMapper
Complete project management of large scale (10,000’s
employees) analysis for Change Management or
Organizational Performance initiatives
Licensing is per survey, based on #
of participants and whether or not
you are certified and doing the
project with them in consultation.
Syndio Social Change Management
Talent Management
Communications Impact
Be their “customers for life” – bring
in the tool, develop expertise and
use it throughout the enterprise to
manage large-scale change.
DNA-7 Organizational Design
Talent Management
Leadership and Collaboration
Projects are one-off at this point.
Keynetiq A tool that provides 12 different survey templates,
analytics, and interactive network maps with
members’ profiles that employees can navigate and
use to search for expertise.
Monthly fee based on number of
people in the company. Custom
pricing for networks with more
than 1000 employees. Also
available ONA consulting, study
design and coordination, and full
ONA project management.
94. Columbia IKNS Residency April 2016
Maven7 OrgMapper
• Methodology embedded in
the analysis and mapping
tools
– Change management (Influence)
– Organizational performance
(Excellence)
• Customizations managed
through the consulting
services
96
Customized surveys and reports
95. Columbia IKNS Residency April 2016
Syndio Social
97
Syndio Social Uses SNA to Build Management Dashboards
97
Highest social capital
Most favorable to change
97. Columbia IKNS Residency April 2016
What to Consider in Selecting Tools
• How often will you do this in-house?
– If you want this to be an organizational competency, then you will want to
designate one or more people to learn to use the tools
– If you designate someone, will it be a data junkie (who will want the DIY
tools) or an organizational expert with solid computer expertise?
– If you want to do this on an occasional basis, then a consultant may be
the right choice
• How much flexibility do you need?
– Do you want to run a range of metrics and dig into the data yourself or
are you comfortable with using a standard set of metrics provided by a
vendor?
99
98. Columbia IKNS Residency April 2016
Summary
100
• Social network analysis tools and methods are available to map
organizational as well as your individual, personal network
• The tools matter less than the network mindset – and the understanding
that the structure of a network matters
99. Columbia IKNS Residency April 2016
http://about.me/pattianklam
• 30 years in software engineering
• 10 years in professional services knowledge management &
methodology (Digital, Compaq, Nortel)
• Independent consultant 14 years; thought leader in knowledge
management and social network analysis
• Charter member of Change Agents Worldwide
101