Tony Rhem

Jun 302016

WCG Content ModelContent modeling is a powerful tool for fostering communication and alignment between User Experience (UX) design, editorial, and technical resources on a Information Architecture effort. By clearly defining the content domains, content types, content attributes (metadata) and relationships, we can make sure that the envisioned content strategy becomes a reality for the content creators.

The Content Model is a logical depiction of what an organization knows about things of interest to the business and graphically shows how they relate to each other in an entity relationship (ER) diagram or class diagram. An entity relationship diagram is an abstract conceptual representation of structured data.  It uses standard symbols to denote the things of interest to the business (entities), the relationships between entities and the cardinality and optionality of those relationships.  The Content Model, contains detailed characteristics of the content types or concepts, attributes or properties and their definitions.  It is a result of detailed analysis of the business requirements.

When starting a content modeling effort, it is important to begin with a high-level (conceptual content model). The conceptual content model is the first output from content modeling. After some initial work identifying, naming and agreeing on what content domains and content types are important within your problem domain you are now ready to structure them together into a conceptual content model.

It is essential that content strategists, information architects and business stakeholders engage with content modeling early on in the process. These are the people best positioned to find and classify content types that make sense for the business. They bring that understanding of why content needs to be structured, named and related in a certain way. In addition, the business subject matter experts bring knowledge of the rules about content that drives the naming and determining of relationships between content types.

Finding Content Types

Content types live in existing web sites, customer call centers (call logs), product documentation, communications, as input & output of processes and functions as well as in the mind of people performing various tasks. The mission is to find them, document and define them. here are other reasons to make something a separate type of content:

  1. Distinct, reusable elements. You might decide to create an Author content type that contains the name, bio and photo of each author. These can then be associated with any piece of content that person writes.
  2. Functional requirements. A Video might be a different type of content because the presentation layer needs to be prepared to invoke the video player.
  3. Organizational requirements. A Press Release may be very similar to a general Content Page, but only the Press Release is going to appear in an automatically aggregated Newsroom. It’s easier for these to be filtered out if they’re a unique type of content.

Content models progress along a continuum of constant refinement, there are three important stages in the content modeling lifecycle:

  • Conceptual: The initial content model which aims to capture the content domains, content types and high level relationships between content types.
  • Design: Adds the descriptive elements (metadata) to each content type and further refines the structural relationships between them.
  • Implementation: Models the content within the context of the target technology, e.g. CMS, Search Engines, Semantic Tools, etc.

Remember Content is KING!

Mar 312016

LeadershipEarlier this month I had an opportunity to examine my leadership style in order to learn more about my leadership characteristics. I took the leadership assessment test created by Lee Bolman and Terrence Deal in Reframing Organizations. The Leadership Orientations assessment is keyed to four different conceptions of organizations and of the task of organizational leadership. Lee Bolman and Terry Deal in Reframing Organizations present these orientations as four frames; a distinct way of thinking about leadership and organizations. My goal was to leverage this information in order to become a better leader. The following represents my results:

My leadership style according to the self-assessment indicates that I am adept in working in all four leadership frames. The majority of my scores range from the 50-59th to 60-69th percentile with the only outlier Human Resources at the 30-39th percentile, although it’s my highest score at 17. My scores overall range from 12 to 17, with the highest percentile leadership frame being Political (but it’s the lowest score of 12, go figure).

The leadership assessment suggest my primary leadership style is Political. However, I do not feel that I “emphasize the importance of building a power base: allies, networks, coalitions and that a good leader is an advocate and negotiator who understands politics and is comfortable with conflict” (Bolman & Deal, n.d.). I certainly believe (as indicated by the overall assessment results) that being Political is part of being a good leader, but not a predominate trait.

One strength of my leadership style is that I utilize all four frames. However, the weakest frame is in Human Resources and that indicates that I am NOT best at facilitating and being participative, supporting and empowering others. This is an area in which I thought would be my strongest leadership trait.

The Four Frames Are:

STRUCTURAL LEADERS: emphasize rationality, analysis, logic, facts, and data. They are likely to believe strongly in the importance of clear structure and well-developed management systems. A good leader in the structural leader’s view is someone who thinks clearly, makes the right decisions, has good analytic skills, and can design structures and systems that get the job done.

HUMAN RESOURCE LEADERS: emphasize the importance of people. They endorse the view that the central task of management is to develop a good fit between people and organizations. They believe in the importance of coaching, participation, motivation, teamwork, and good interpersonal relations. A good leader in the view of a human resource leader is a facilitator and participative manager who supports and empowers others.

POLITICAL LEADERS: believe that managers and leaders live in a world of conflict and scarce resources. The central task of management is to mobilize the resources needed to advocate and fight for the unit’s or the organization’s goals and objectives. Political leaders emphasize the importance of building a power base: allies, networks, coalitions. A good leader to a political leader means an advocate and negotiator who understands politics and is comfortable with conflict.

SYMBOLIC LEADERS: believe that the essential task of management is to provide vision and inspiration. They rely on personal charisma and a flair for drama to get people excited and committed to the organizational missions. A good leader in their view is a prophet and visionary, who uses symbols, tells stories, and frames experience in ways that give people hope and meaning.

The leadership assessment states “Your raw scores alone do not provide a full picture of your leadership orientations in relation to other leaders’. Most leaders rate themselves considerably higher on the human resources and structural frames than the political and symbolic frames. Paradoxically, Bolman and Deal have found that the political and symbolic frames, which may puzzle or even repel many, are actually more critical for effective leadership (Bolman & Deal, n.d). The leadership assessment states that all four frames are critical for leading organizations successfully, but few leaders are adept with working in all four frames. This is something I will continue to strengthen and I would encourage everyone to take this assessment to put you on the road to becoming a better leader!

Feb 292016

Cancer MoonshotOn January 12, 2016 in his State of the Union address, President Obama called for America to become “the country that cures cancer once and for all” As he introduced the “Moonshot” initiative that will be guided by Vice President Joe Biden.

Dr. Tom Coburn, former Republican Senator from the state of Oklahoma and three time cancer survivor, in his January 14th article in the Wall Street Journal ‘A Cancer ‘Moonshot’ Needs Big Data ; indicates that “harnessing that information (“big data”) would allow us to personalize prevention and treatment based on the genetic characteristics of a patient’s tumor, family history and personal preferences, while minimizing unwanted side effects.”

On February 5, 2016 on CNN’s Global Public Square show: Big data could be a health care game-changer author and doctor, David Agus tells Fareed Zakaria how using big data and examining thousands of cases might increase how long we live and our quality of life.

At this time in our history, with the continuing electronic capture of patient information from intake to discharge, the opportunity could not be brighter to cure cancer. The Obama administration’s 2010 initiative to capture electronic health records has enabled the opportunity to improve patient care, increase patient participation, improve diagnostics and patient outcomes, improve care coordination, as well as create practice efficiencies and cost savings.

The electronic capture of patient information has created medical big data repositories. One such repository is the American College of Surgeons/American Cancer Society’s National Cancer Database – NCDB. Resources such as these will benefit by utilizing knowledge management and information architecture techniques to identify and unlock knowledge patterns contained within these big data sources. In several of my blog post dating back to January 2013, I wrote about the advantages of applying KM to big data. From understanding Contextual Intelligence KM and Big Data ; to devoting a chapter on KM and Big Data in my upcoming book KM in Practice; I believe when executed the right way KM, powered by information architecture will provide the essential ingredient when applied to big data. This will enable researchers to discover better treatments and possible cures for many diseases including cancer and we will realize the dream presented by the Moonshot initiative!

Jan 132016

K15968-v2As we move into 2016 it is time to reflect on knowledge management and look at the future of this discipline. In my latest book: Knowledge Management in Practice I address what I believe is the future of KM in 2016 and beyond. An excerpt from chapter 18 follows.

Future of Knowledge Management:

One of the major areas in which KM will make an impact is within the customer service industry. Customer Service is the area in which most customers will have their only connection and interaction with your organization. It is this area where customers will form their opinions about the organization and determine if they remain a customer or move on to another competitor. Due to this scenario (and others) organizations invest a major portion of their revenue and attention to improving their customer service.

In an August 2014 Harvard Business Review article by Peter Kriss entitled “The Value of Customer Experience, Quantified” he states “Intuitively, most people recognize the value of a great customer experience. Brands that deliver them are ones that we want to interact with as customers that we become loyal to, and that we recommend to our friends and family”. Also, he states that the “value of delivering such an experience is often a lot less clear, because it can be hard to quantify” Delivering consistent and concise knowledge to provide answers to customer inquiries in an efficient way leads to providing value to the customer and improving the overall customer experience.

In support of this trend of KM in customer service, Forrester’s “Top Trends For Customer Service in 2015”, author Kate Leggett points out in trend #4, knowledge management’s impact when she states “Knowledge Will Evolve From Dialog To Cognitive Engagement. Organizations will look at ways to reduce the manual overhead of traditional knowledge management for customer service. They will start to explore cognitive engagement solutions, interactive computing systems that use artificial intelligence to collect information, automatically build models of understanding and inference, and communicate in natural ways. These solutions have the potential to automate knowledge creation, empower agents with deeply personalized answers and intelligence, scale a company’s knowledge capability, and uncover new revenue streams by learning about customer needs.”

IBM Watson is playing a significant role in the evolution of applications that automate knowledge creation by providing deeply personalized answers and intelligence. This technology will not only effect customer service, but a multitude of industries with its capability to extract knowledge from Big Data sources. The IBM Watson ecosystem will provide deep content analytics and intensive scientific discovery that will lead to improve cognition contributing to an organization’s knowledge capabilities. This supports Kate Leggett’s research and points out that KM will continue to play a significant role in delivering knowledge and decision making capabilities to the customer service industry for the foreseeable future (2016 and beyond).

Global View of KM

In reviewing the 2015 Global Knowledge Management Observatory Report, authors David Griffiths, Abi Jenkins and Zoe Kingston-Griffiths state “The Knowledge Management function in many organizations is in a state of general decline”. This as they indicate is due to the following factors:

  • “Satisfaction in Knowledge Management’s contribution to strategic and operational objectives within organizations is often poor.”
  • “Knowledge Management lacks maturity and integration within the vast majority of organizations.”
  • “Knowledge Management continues to be predominantly seen as a technology-led function.”
  • “Satisfaction with technology-led Knowledge Management solutions is not improving.”
  • “Many Knowledge Management professionals do not appear to have the necessary awareness and/or permissions required to respond to unmet demand for KM activities in organizations.”
  • “Knowledge Management, as a field or area of practice, is argued to be suffering from a lack of specialist practitioners.”
  • “The value and/or significance of Knowledge Management activities is still not being appropriately recognized or reported within most organizations.”

Solutions that address many of the findings of the 2015 Global Knowledge Management Observatory Report are essential for KM success in 2016 and as KM evolves as a discipline. This includes producing a comprehensive KM strategy, KM education options, adopting KM programs, project and systems, and addressing why KM programs/projects fail.

All of these aspects from the 2015 Global KM Observatory Report are addressed in Knowledge Management in Practice. This book should be leveraged as a reference/guide and presents a tremendous resource to support the growth of KM at your organization.

Dec 092015

EducationAs knowledge management challenges once again top the agenda of many CEOs, an emphasis on getting more value from corporate knowledge assets has heightened the interest in knowledge management as a professional area of practice. Providing education in KM, which can include specialized courses, seminars, certifications, and formal undergraduate, graduate, and doctoral programs are leading the way in preparing future KM practitioners to meet this challenge. On the other hand it has also raised questions about the educational foundation needed to support the profession.

Despite the wealth of published and informal literature, although derived from practice, and dialog on the foundational learning needs of KM practitioners, there is no consensus on what comprises a professional education and training in knowledge management. In 2011 the Knowledge Management Education Forum (KMEF) a collaboration between Kent State University and George Washington University was formed. “The mission of KMEF in part is to provide an on-going, annual dialog to identify and grow consensus on the knowledge management body of knowledge, competencies, roles and curriculum. The goal of the KMEF is to create an environment in which a consensus can evolve. It brings together the current and past thought leaders in the field of knowledge management to discuss their work and to open the dialog where others can contribute” (KMEF – 2011).

Besides the educational options mentioned above, KM education opportunities are occurring in KM-focused departments, which are delivering subject-specific education and strategic learning programs. All of these KM educational products must operate under one cohesive and holistic set of standards and policies in order to provide the KM practitioners with consistent industry recognized education. According to the KMEF, a special effort will be needed to connect the various educational entities to the business community and vice versa, while providing “the core and elective elements of a knowledge management curriculum for the 21st century (KMEF – 2011).

Participating in the KMEF it was generally recognized that while “there is general agreement that KM, knowledge services, and knowledge strategy require, an understanding of shared concepts, a basic lexicon, and some level of mutual understanding about the elements and framework of KM, there continues to be concern that too much “standardization” might work against the success of KM in the workplace” (KMEF – 2011).

As a KM practitioner who has (and continues to) work across various and different sectors and industries of the 21st-century “knowledge economy” I recognize that every organization is different and therefore the success of KM, knowledge services, and knowledge strategy in each is going to depend on how well the elements of knowledge management align with the corporate objectives, unique management methodologies and leadership structures of the various organizations implementing KM programs.

As knowledge management education evolves for the 21st century and beyond, especially as the delivery of education and the workforce becomes more mobile there is a need to establish a philosophy of teach and learn anywhere and anytime. This will facilitate the need to incorporate standards for KM course design, need to provide students (class participants) a practical way to apply KM, deliver technology that will facilitate the ability to teach and learn anywhere and anytime, provide learning outcomes and assess them, provide an understanding of the various KM roles and their responsibilities.

Roles and Responsibilities of Knowledge Professionals

The roles of knowledge professionals cover areas from strategic, tactical, program related to executing specific projects and system development. The KM roles and responsibilities vary according to the category in which the knowledge professional works. The roles and responsibilities depicted here (see Table 1: KM Roles, Responsibilities & Core Competencies) consists of but are not limited to Chief Knowledge Officer (CKO), KM Program Manager, KM Project Manager, KM Director, Operations KM Director, KM Author, KM Lead, KM Liaison, KM Specialist, KM System Administrator, Knowledge Engineer, Knowledge Architect, KM Writer, Knowledge Manager, and KM Analyst.

Core KM Competencies

In determining core KM competencies we must first understand what it takes to perform in the various KM roles and execute their responsibilities.  The KM core competencies include: connecting education and strategic learning competencies with skill and ability in knowledge strategy development and operationalization, collaboration, leadership and management skills, plus technical competencies.

Knowledge management has both soft and hard competencies. The soft competencies include ensuring that knowledge processing is aligned with the organizations business goals and objectives, and is integrated into the organization’s everyday business and work. It also includes software development, business and systems architecture and workflow management. The hard competencies include elicitation and representation of knowledge (both tacit and explicit) and it also includes structural knowledge in the form of business rules and business process.

The KM Competency Model

Knowledge Management (KM) focuses on people, process and technology that enable and support knowledge sharing, transfer, access, and identification. KM competencies represents what KM practitioners must understand to facilitate KM methods established by the organization. A KM competency model (see Figure 1: KM Competency Model) reflects the strategy, goals, and objectives of the organization. Competency alone is not sufficient; it must be accompanied by an organizational culture shift towards knowledge-sharing.

To determine the KM Competency Model, a rigorous process was initiated to provide consensus on core competency areas (see Table 2: KM Competency Model Details). This methodology will apply to any modern organization, regardless if a CKO role is established or not. It can be used by any department or individual who has the vision, leadership, and determination to infuse KM principles in the enterprise. A KM competency model serves as the foundation for functions such as training, education, development, and performance management because it specifies what essential knowledge, skills, and abilities.

KM Competency Model that will serve as the foundation for enterprise-wide KM adoption and use, and create a culture of collaboration and knowledge sharing where personalized and contextual information and knowledge is “pushed and pulled” from across the enterprise to meet corporate objectives, where good ideas are valued regardless of the source, where knowledge sharing is recognized and rewarded, and where the knowledge base is accessible without technological or structural barriers.

(This is an excerpt from my latest book Knowledge Management in Practice)

Nov 302015

Chapter 9 - Figure 2 - Model for KM in Healthcare

Healthcare is a knowledge intensive business. Making the best use of knowledge within any healthcare provider organization (hospital, clinic, pharmacy, physician private practice, etc.) is essential for optimal patient care as well as cutting and/or streamlining costs. Knowledge Management (KM) in healthcare is about sharing know-how through collaboration and integration of systems to enable access to knowledge. Applying knowledge sharing and collaboration to healthcare would include sharing medical research, giving visibility to patient decisions, and collaboration between physicians and healthcare provider organizations. Collaborative work environments will bring more effective communication and more physician responsiveness to patients.

Healthcare is also a massive industry, and every healthcare provider organization faces challenges where incorporating knowledge management would be beneficial. The processes and systems that enable the delivery and management of healthcare services to patients are faced with the prospect of failing to prevent (and can indirectly or directly cause) suffering and in some cases death to the various patients it serves. It is for this reason that knowledge management is attracting much attention from the industry as a whole. However, it is now time that the healthcare industry start to implement KM and begin realizing the benefits that it can provide.

KM is a particularly complex issue for health organizations. The potential benefits knowledge-management implementation could bring are enormous. Some of these benefits include: better outcomes for patients, cost reduction, enhanced job flexibility, and improved responsiveness to patients’ needs and changing lifestyles and expectations and ensure more effective communication, leading to focused and (hopefully) seamless care interventions and a better patient experience.

In realizing these benefits it is understood that healthcare delivery is a knowledge driven process and KM provides the opportunity to incorporate knowledge management practices to improve the various healthcare processes.

To leverage knowledge management in the appropriate way to address the complex and process nature of healthcare, healthcare organizations should adopt a broad strategy to capture, communicate and apply explicit and tacit knowledge throughout the healthcare delivery process.

Healthcare Delivery Process

Delivery of healthcare is a complex endeavor. It includes primary organizations for healthcare delivery such as healthcare providers having inter-organizational relationships with other players (i.e., Blue Cross/Blue Shield and its member organizations, physician and hospital affiliations) to provide a foundation. The increasing cost of healthcare is putting pressure on access and quality of healthcare delivery and this is calling for increased accountability, because of high rates of medical errors, and globalization which leads to demands of higher standards of quality, are also putting pressures on healthcare delivery organizations.

The healthcare delivery process includes the following areas Patient Intake, Data Collection, Decision Support, Diagnosis and Treatment, and Patient Closeout. Each of these areas (depending on medical organization) have more complex processes, procedures and systems that enable them to integrate and function together. Below provides more details of each of the areas that comprise the healthcare delivery process:

Patient Intake Process: Due to the increase in patient demand partially caused by health reform initiatives that focus on broadening patient access to insurance, many medical practices are experiencing an increase in new patient enrollment. Whenever new patients use the services of a hospital or physician practice, they must complete forms that list their contact information, medical history, insurance information, and acknowledgement of various HIPAA regulations.

The patient intake process is the first opportunity to capture knowledge about the patient and his/her condition at the time of arrival at the healthcare facility. At this point the patient information is captured, along with method of payment, medical history and current vital condition. All of this data is transitioned to the facilities database. This presents an opportunity for the data to be shared, an opportunity for information to be processed from the data, and knowledge to be acquired from the information.

Data Collection: At this point of the process all the data that was taken during the intake process is collected and sent to the healthcare facilities’ database. The collection of healthcare data involves a diverse set of public and private data collection systems, including health surveys, administrative enrollment and billing records, and medical records, used by various entities, including hospitals, clinics, physicians, and health plans. This suggest the potential of each entity to contribute data, information and knowledge on patients or enrollees. As it stands now a fragmentation of data flow occurs because of these silos of data collection. One way to increase the flow of data, information and knowledge is to integrate them with data from other sources. However, it should be noted that a substantial fraction of the U.S. population does not have a regular relationship with a provider who integrates their care.

Decision Support System: This area of the healthcare delivery process involves integrating the Clinical Decision Support Systems (CDSS). The CDSS will enable the standardization and sharing of clinical best practices and protocols with staff, patients, and partners on demand, anywhere, and on any device. Physicians, nurses and other healthcare professionals use a CDSS to prepare a diagnosis and to review the diagnosis as a means of improving the final result. Data Mining (which will be examined later in this chapter) is conducted to examine the patient’s medical history in conjunction with relevant clinical research. Such analysis will provide the necessary knowledge to help predict potential events, which can range from drug interactions to disease symptoms. Some physicians may use a combination of a CDSS and their professional experience to determine the best course of care for a patient.

There are two main types of clinical decision support systems. One type of CDSS, which uses a knowledgebase (expert system), which applies rules to patient data using an inference engine and displays the results to the end user. Systems without a knowledge base, on the other hand, rely on machine learning to analyze clinical data. The challenge here is that a CDSS to be most effective it must be integrated with the healthcare organizations clinical workflow, which is often very complex. If a CDSS is a standalone system it will lack the interoperability needed to provide the knowledge necessary for healthcare professions to make a good determine of the best course of care for a patient.

However, the sheer number of clinical research and medical trials being published on an ongoing basis makes it difficult to incorporate the resulting data (Big Data). Additionally, incorporating Big Data into existing systems could causes a significant increase in infrastructure and maintenance.

Diagnosis and Treatment: Making a diagnosis is a very complex process, which includes cognitive tasks that involves both logical reasoning and pattern recognition. Although the process happens largely at an unconscious level, there are two essential steps where knowledge can be captured and applied.

In the first step, the healthcare professional will enumerate the diagnostic possibilities and estimate their relative likelihood. Experienced clinicians often group the findings into meaningful clusters and summarizes in brief phrases about the symptom, body location, or organ system involved.

In the second step in the diagnostic process, healthcare professional would incorporate new data, information and/or knowledge to change the relative probabilities, rule out some of the possibilities, and ultimately, choose the most likely diagnosis. For each diagnostic possibility, the additional knowledge increases or decreases its likelihood. At this point the diagnosis and treatment is rendered by the healthcare professional and the patient records are updated.

Patient Closeout/Patient Discharge: In the case of a simple patient closeout from a routine/scheduled physician visit or simple visit to the local clinic, the patient receives medication (if applicable), sets follow up appointments if necessary and finalized payment arrangements and the patient records are updated. However if you have had a hospital stay the discharge process can be quite involved. In the case of a discharge a set series of tasks must occur prior to discharging a patient. These tasks include examination and sign-off by appropriate providers and patient education. For each patient, the time of discharge and the tasks that need to be performed will be provided one day ahead of time. This allows for everyone involved in the discharge to self-organize on the day of discharge to get the work done within the window necessary to meet the scheduled discharge time (Institute for Health Improvement, 2015). At the conclusion of the discharge, patients receive information and instructions for continued care and follow up, in addition all patient records should be updated.

The following was an excerpt from my latest book: Knowledge Management in Practice


Oct 312015

Fin ServFinancial service enterprises operate in a highly challenged market where consolidation, increasing regulation and economic realities are negatively impacting their ability to achieve key objectives. This has created a culture where there is a constant need to find more predictable revenue streams and cost efficiency gains.

Regulatory bodies such as the Financial Industry Regulatory Authority (FINRA), Securities and Exchange Commission (SEC), Commodity Futures Trading Commission (CFTC), and the various international bodies’ present challenges to financial service organizations to deliver fair and open products and services, while providing answers, and direction to the various customers interacting with their organizations. In order to address these challenges knowledge management is needed to streamline processes and deliver content at the right time, in the right way and in the right context to meet the demand of customers.

In meeting the demand for customers, it is increasingly important for financial services organizations to address customer needs. KM through the implementation of processes and technology (including Information Architecture – see Chapter 4) will ensure customer information is shared with the right people at the right time across the organization. By utilizing a customer-focused, integrated knowledge management system, all employees interacting with a customer will have up to date knowledge of that customer’s breadth of relationship and experience with the organization. This will assist the organization with cross selling, up selling and reporting on the effectiveness of any new customer initiatives.

In addition the staff must start (if they are not already doing so) working together using knowledge as a focal point to service the customer. With this emphasis, as more financial products and services become available through mobile devices the ability for those financial companies to respond rapidly to customer demands with the right answers, at the right time, and in the right context will be met. 

Empowering Employees to Satisfy Customers

The objective of knowledge management is to capture knowledge of different stakeholders of the organization and make it explicitly available to all employees. Sharing of knowledge will enable improved and quicker decision making. Employees empowered with improved decision making will increase the ability to address customer needs and create more satisfied customers. Empowering your employees through knowledge management will assist your organization in addressing competition driven by reduced barriers to switch companies, the proliferation of products and product commoditization, mergers and acquisitions and the ever changing product portfolios, and shifts in customer behaviors.

Financial services organizations (including banks) value of Knowledge Management as a business practice. From managing intellectual capital, to the vast array of customer data, one of the goals of KM is to enhance customer satisfaction and increase revenue.

Whether the organization is regional or global, a key aspect of your business and specifically your KM strategy must be to treat each client as an individual with individual needs. By implementing a comprehensive KM program and associated processes and systems a determination as to which customers are most likely to buy which products, who is at risk of leaving, which unprofitable clients are most likely to be profitable, and who is most likely to respond to which marketing campaigns based on their demographics, can start to be addressed and the organization will have a sustainable model for success!

Knowledge management practices, policies, procedures and applications all aimed at delivering financial services that enable people to build financial stability should be the focus of all financial services organizations. This chapter focuses on the use of Knowledge Management (KM) within the financial industry and will present how KM is being leveraged to increase sales through customer satisfaction, capturing and cataloging knowledge for a personal interaction, the advantage of creating and leveraging communities for improved employee performance and extending your knowledge to customers to provide self-service provides a competitive edge.


Sep 192015

contextual%20intelligence-technologyThis all started during a conversation I had with a colleague (Baron Murdock of GreenBox Ventures, LLC) and he mentioned the term Contextual Intelligence. Due to the fact that we were talking about knowledge management and big data I believe that I understood what he was talking about. However, I had never heard of the term. Not long after our meeting I began to do a little research on the concept of contextual intelligence.

What is Contextual Intelligence?

It is during my initial research (consisting of a series of internet search queries) where I began to understand that the term Contextual Intelligence is not new. As a matter of fact it’s a term that has been used in graduate business schools since the 80’s.

Contextual Intelligence is, according to Matthew Kutz “a leadership competency based on empirical research that integrates concepts of diagnosing context and exercising knowledge”; Tarun Khanna states that ”understanding the limits of our knowledge is at the heart of contextual intelligence” and Dr. Charles Brown states that “Contextual intelligence is the practical application of knowledge and information to real-world situations. This is an external, interactive process that involves both adapting to and modifying an environment to accomplish a desired goal; as well as recognizing when adaptation is not a viable option. This is the ability that is most closely associated with wisdom and practical knowledge”

While there are several positions on what contextual intelligence is. I align more to Dr. Brown’s assertion of Contextual Intelligence. When it comes to knowledge management (KM) and contextual intelligence, context matters! Understanding that contextual intelligence is link to our tacit knowledge, I immediately thought of what is the connection between KM and Contextual Intelligence. Knowledge management among other aspects is concerned with the ability to understand knowledge and adapt that knowledge across a variety of environments (cultures) different from the origin of that knowledge.

To enable the flow of knowledge to the right person in the right time and in the right context, it is essential to understand the context of that knowledge. Information Architecture (IA) is the backbone of delivering knowledge in the right context to users of Knowledge Management Systems (KMS). IA focuses on organizing, structuring, and labeling content (information and knowledge). IA enables users to find relevant content in the right context, understand how content fits together, connects questions to answers and people to experts. It is the incorporation of IA that contributes to giving knowledge its context.

Understanding the context of knowledge consists of:

  • Understanding the intent of the knowledge
  • Understanding the cultural and environmental influences on the knowledge
  • Understanding the role (or who) the knowledge is intended to be used by
  • Understanding the relevancy of the knowledge (The knowledge could only be valid for a specific period of time)
  • Understanding the origin (lineage) of the knowledge

Big Data

Without context data is meaningless, this includes structured and unstructured data. Big Data resources contain a proliferation of structured and unstructured data. Knowledge management techniques applied to big data resources to extract knowledge will need to understand the context of the data in order to deliver pertinent knowledge to its users. Knowledge Management has the ability to integrate and leverage information from multiple perspectives. Big Data is uniquely positioned to take advantage of KM processes and procedures. These processes and procedures enables KM to provide a rich structure to enable decisions to be made on a multitude and variety of data.

We know that context matters. Especially when it comes to what we know (our knowledge). Being able to adapt our knowledge with others is at the heart of successfully communicating, sharing what we know and to fuel innovation.

Obtaining contextual intelligence for your organization consists of leveraging or hiring people who are fluent in more than one culture, partnering with local companies, developing localized talent and enabling your employees to do more field work to immerse themselves in other cultures (tuning in to cultural and environmental differences).

A couple of great resources to read on Contextual Intelligence are “Contextual Intelligence” by Tarun Khanna from the September 2014 issue of Harvard Business Review and “Understanding Contextual Intelligence: a critical competency for today’s leaders” by Matthew R Kutz and Anita Bamford-Wade from the July 2013 Emergent Publications, Vol. 15 No. 3.

Sep 182015

CollaborationEarly this month on a Southwest Airlines flight from Chicago to Greenville South Carolina I read an article in the Southwest The Magazine, by Katie Rich entitled “Comedy of Errors, Five lessons on teamwork and failure from the halls of Saturday Night Live”. After reading this article I began to think about how the lessons discussed in this article applies directly to our ability to successfully collaborate and share knowledge. As a Knowledge Management practitioner I’m always looking for keys to improve how individuals and teams collaborate. Collaboration is at the cornerstone of sharing what we know. On that note… The Five (5) Ingredients of successful Collaboration and Knowledge Sharing are:

1 – Be present

Being present means participate in the conversation! I know that there are many personality types that we work with. However, if you are an introvert this is the time to come out of your shell and participate in the conversation and the free form exchange of ideas. Believe me everyone in the group will benefit from you sharing what you know and the questions that you may have. If you are an extrovert, by all means share but let others share and I guarantee that you will also learn something in the process.

2 – Know why you are there

When you are brought together to participate in a group discussion it’s more than likely you are there to share your expertise on a certain subject(s). Understanding your significance to the overall team dynamic will allow you to focus your participation in the way that the team will benefit the most from. Contributing your knowledge and experience will bring about positive outcomes that the entire team can benefit from and contribute to a successful collaboration session.

3 – Do not try to change the people you’re working with

Please understand that “you cannot change the people you work with. However, you can change the way you react to the people you work with” (a direct quote from the article). Listen to the ideas, views and comments of all of your team members regardless of how you may feel personally about them. You may feel that someone may annoy you, always produce less than quality work, always has to have the last word and/or constantly dresses inappropriately at work … don’t shoot the messenger … always respect everyone’s input.

4 – Know (or see) the Big Picture

There should be stated issue(s), subject(s), problem(s) or reasons the team has come to together. This information should have been communicated before the collaborative session is held. However, if that is not the case, stating (or restating) the reason(s) everyone has come together at the beginning of the session is always a great idea. Seeing the big picture, everyone is more likely to understand better how their expertise fits and are better able to focus on contributing to the overall discussion.

5 – Treat everyone with respect

At the end of the day no matter how we feel about each other (see #3) just be good to one another and treat everyone with respect. Sometimes people make it hard, and sometimes it may be you! If you have a difficult teammate just lay on additional kindness and respect. Treat everyone like they are the most important person and their contribution matters to the big picture. Everyone likes to feel that they are respected and that they matter.

One Final Note

An excellent KM method to use to conduct a collaborative session is a Knowledge Café. A knowledge café brings a group of people together to have an open, creative conversation on a topic of mutual interest to surface their collective knowledge, to share ideas and to gain a deeper understanding of the issues involved. Ultimately the conversation should lead to action in the form of better decision-making and innovation. This will be a great opportunity to practice the five (5) ingredients of successful collaboration and knowledge sharing. Click here for more information on knowledge café’s.