Tony Rhem

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.

Jul 282015
 

KMandBigDataBig Knowledge! Knowledge Management and Big Data – Excerpt from Chapter 14: Knowledge Management in Practice:

A goal of knowledge management is to capture and share knowledge wherever it resides in the organization. Leveraging the corporate collective know-how will improve decision making and innovation where it is needed. The proliferation of data, information and knowledge has created a phenomenon called “Big Data”. Knowledge Management when applied to Big Data will enable the type analysis that will uncover the complete picture of the organization and be a catalyst for driving decisions. In order to leverage an organizations Big Data it must be broken down into smaller more manageable parts. This will facilitate a succinct analysis, which then can be regrouped with other smaller subsets to produce “big picture” results.
Volume, Velocity, and Variety are all aspects that define Big Data.
Volume: The proliferation of all types of data expanding many terabytes of information.
Velocity: The ability to process data quickly.
Variety: Refers to the different types of data (structured and unstructured data such as data in databases, content in Content Management and Knowledge Management systems/repositories, collaborative environments, blogs, wikis, sensor data, audio, video, click streams, log files, etc.).
Variety is the component of Big Data in which KM will play a major role in driving decisions. Enterprises need to be able to combine their analyses to include information from both structured databases and unstructured content.

Data, Information and Knowledge

Since the focus here is about leveraging knowledge management techniques to extract knowledge from Big Data, it is important to understand the difference between data, information and knowledge: Data, I often refer to as being represented by numbers and words representing a discrete set of facts. Information is an organized set of data (puts context around data). This can result in an artifact such as a stock report, news article, etc. Knowledge on the other hand emerges from the receiver of information applying his/her analysis (aided by their experience and training) to form judgments in order to make decisions. Erickson and Rothberg indicates that information and data only revel their full value when insights are drawn from them (knowledge). Big Data becomes useful when it enhances decision making, which in turn is only enhanced when analytical techniques and an element of human interaction is applied (Erickson and Rothberg, 2014).

In a February 26 2014 KM World article titled “Big Data Delivering Big Knowledge” Stefan Andreasen is Chief Technology Officer at Kapow Software indicates that “To gain a 360 degree view of their ecosystem, organizations should also monitor user-generated data, public data, competitor data and partner data to discover critical information about their business, customers and competitive landscape” (Andreasen, 2014). The user-generated data, public data, competitor data and partner data provides the variety of data needed to be analyzed by KM and it’s this type of data that will be examined more closely.

User-generated data
Customers are sharing information about their experience with products and services, what they like and don’t like, how it compares to the competition and many other insights that can be used for identifying new sales opportunities, planning campaigns, designing targeted promotions or guiding product and service development. This information is available in social media, blogs, customer reviews or discussions on user forums. Combining all this data contained in call center records and information from other back-office systems can help identify trends, have better predictions and improve the way organizations engage with customers (Andreasen, 2014).

Public data
Public information made available by federal, state and local agencies can be used to support business operations in human resources, compliance, financial planning, etc. Information from courthouse websites and other state portals can be used for background checks and professional license verifications. Other use cases include monitoring compliance regulation requirements, bill and legislation tracking, or in healthcare obtaining data on Medicare laws and which drugs are allowed per state (Andreasen, 2014).

Competitor data
Information about competitors is now widely available by monitoring their websites, online prices, and press releases, events they participate in, open positions or new hires. This data allows better evaluation of the competition, monitor their strategic moves, identify unique market opportunities and take action accordingly. As a retailer for example, correlate this data with order transaction history and inventory levels to design and implement a more dynamic pricing strategy to win over your competition and grow the business (Andreasen, 2014).

Partner data
Across your ecosystem, there are daily interactions with partners, suppliers, vendors and distributors. As part of these interactions organizations exchange data about products, prices, payments, commissions, shipments and other data sets that are critical for to business. Beyond the data exchange, intelligence can be gleaned by identifying inefficiencies, delays, gaps and other insights that can help improve and streamline partner interactions (Andreasen, 2014).
To comb through the various sources of user-generated data, public data, competitor data and partner data leveraging KM analytics (data analysis, statistics, and trend analysis) and content synthesis technology (technology that categorizes, analyze, combines, extracts details, and re-assess content aimed at developing new meanings and solutions) will be necessary.

Applying KM to Big Data
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. In the “KM World March 2012” issue it was pointed out that “organizations do not make decisions just based on one factor, such as revenue, employee salaries or interest rates for commercial loans. The total picture is what should drive decisions”. KM enables organizations to take the total picture Big Data provides, and along with leveraging tools that provide processing speed to break up the data into subsets for analysis will empower organizations to make decisions on the vast amount and variety of data and information being provided.

The emerging challenge for organizations is to derive meaningful insights from available data and re-apply it intelligently. Knowledge management plays a crucial role in efficiently managing this data and delivering it to the end users to aid in the decision making process. This involves the collection of data from direct and indirect, structured and unstructured sources, analyzing and synthesizing it to derive meaningful information and intelligence. Once this achieved it must be converted it into a useful knowledge base, storing it and finally delivering it to end users.

May 292015
 

TTFOver the next few months I will be posting excerpts from my latest book Knowledge Management in Practice. Today’s post is from Chapter 15 Drinking the KM Kool-Aid: Knowledge Management Adoption:

“Drinking the KM Kool-Aid” is a metaphor to indicate the adoption of Knowledge Management (program, policies, procedures and the methods and systems that enable it) throughout your organization…

Once the need for a KM Program has been determined and immediately after its official launch efforts must be on the way to initiate its adoption. In order to initiate the adoption of your knowledge Management program an effort to market the program and its various components has to be an intentional endeavor…

Utilizing the Task-Technology Fit (TTF) Model for Adoption of Knowledge Management Systems:

The TTF theory can be applied to examine the motivation of users to leverage a KM System to perform their organizational tasks and that applying TTF Theory to the KM Program can have a positive effect on the success and adoption of the KM Program.

In applying the TTF Theory to the KM Program a determination of where the TTF Theory fits within the KM Program structure must be identified. The TTF Theory holds that information technology is more likely to have a positive impact on individual and group performance if it is aligned with the tasks the users perform. When incorporating the KM System into the KM Program the system is aligned with the KM processes that have been identified that the KM Program will support. The KM processes of the KM Program reflect how workers within the organization use knowledge to perform their tasks. The TTF Theory is suited to measure this usage and can be leveraged to understand not only how the KM Systems are being used but will guide the KM Program administrators on the best way to increase adoption and contribute to increasing the performance of the workers who use the KM System.

TTF Theory is predicted to be a significant precursor to KMS usage. Furthermore the Task-Technology Fit (TTF) Theory has shown to be suitable for understanding the specific KM system needs of the program contributing and contribute to the KM Program Roadmap as it pertains to the alignment of technology to the specific milestones and objectives identified within the KM Program. This directly leads to understanding what the key aspects to adoption of the KM system are by the organization’s users and provides mechanisms to measure the rate of adoption contributing to measuring and improve the KM Program as it matures.

The examination of the TTF Theory and more about KM Adoption is detailed in chapter 15.

 

Apr 182015
 

IoTA lot has been said about the next big movement … the Internet of Things (IoT). Simply, IoT is a massive network of connected devices and/or objects (which also includes people). The relationship will be people-to-people, people-to-devices, and devices-to-devices. These devices will have network connectivity, allowing them to send and receive data.

The IoT will lead to Smart Grids and Smart Cities and Information Architecture (IA) will enable a “smart architecture” for delivering content in the right context to the right device (or object)!

So where does IA come into this scenario?

IA is all about connecting people to content (information and knowledge) and it is this ability that is at the core of enabling a myriad of devices and/or objects to connect and to send and receive content. It delivers that “smart architecture”.

The larger amounts of data brought in through the internet need a viable and clear information architecture to deliver consistency to a varied amount of devices. IA offers a viable option in which content (information and knowledge) can be represented in a flexible object-oriented fashion. However, with any option used for representing content, it will have to be able to design the “base” structure for all human content, everywhere. This, of course, is impossible.

It’s impossible because we simply cannot comprehend the extent of all content that is or will be available. This fully flexible object-oriented structure will need to be built similarly to how the human genome project scientists map DNA. This will allow the structure to continue to evolve and grow, which will continue to enable the delivery of content to devices and objects as they become connected to the internet.