Sep 042017
 

CogTechIn part one I examined the connection of KM and AI and how this connection has lead the way for cognitive computing; while in part two I examined those industries that will or are soon to be disrupted by Cognitive Computing; and in this post I will examine those technologies that will lead in the disruption brought to many industries by the way of cognitive computing.

Cognitive computing is the simulation of human thought processes in a computerized model. Cognitive computing involves self-learning systems (Artificial Neural Network machine learning algorithms) that use data mining, pattern recognition and natural language processing to imitate how humans think. The goal of cognitive computing systems is to accelerate our ability to create, learn, make decisions and think.

According to Forbes, “cognitive computing comes from a mashup of cognitive science and computer science.” However, to understand the various aspects of this mashup we must peel back the various components of cognitive computing. These components are centered within AI and KM. The components of cognitive computing enable these applications to be trained in order to recognize images and understand speech, to recognize patterns, and acquire knowledge and learn from it as it evolves producing more accurate results over time.

Cognitive Technologies

Cognitive technologies have been evolving since I started developing AI applications (Expert Systems and Artificial Neural Networks) in the late 1980’s and early 1990’s. Cognitive technologies are now a prominent part of the products being developed within the field of artificial intelligence.

Cognitive computing is not a single technology: It makes use of multiple technologies and algorithms that allow it to infer, predict, understand and make sense of information. These technologies include Artificial Intelligence and Machine Learning algorithms that help train the system to recognize images and understand speech, to recognize patterns, and through repetition and training, produce ever more accurate results over time. Through Natural Language Processing systems based on semantic technology, cognitive systems can understand meaning and context in a language, allowing deeper, more intuitive level of discovery and even interaction with information.

The major list of cognitive technologies solutions include:

Expert Systems, Neural Networks, Robotics, Virtual Reality, Big Data Analytics, Deep Learning, Machine Learning Algorithms, Natural Language Processing, and Data Mining

Various cognitive technologies or applications are being developed by many organizations (large, small, including many startups). When it comes to cognitive technologies, IBM Watson has become the most recognized. IBM Watson includes a myriad of components that comprise the Watson eco system of products.

Companies Delivering Cognitive Solutions

Here are a few companies delivering cognitive solutions that take advantage of the cognitive technologies mentioned above as well as the industry they focus on.

Industry: Healthcare

Welltok: Welltok offers a cognitive powered tool called CaféWell Concierge that can process vast volumes of data instantly to answer individuals’ questions and make intelligent, personalized recommendations. Welltok offers CaféWell Concierge to health insurers, providers, and similar organizations as a way to help their subscribers and patients improve their overall health.

Industry: Finance

Vantage Software : provides reporting and analytics capabilities to private equity firms and small hedge funds. The company’s latest product, Coalesce, is powered by IBM Watson’s cognitive computing technology. This is an example of a company developing a software platform and using IBM Watson’s API’s to provide cognitive capabilities. This product addresses the need to absorb and understand huge volumes of information and use that information to make split-second, reliable decisions about where and when to invest client funds in a highly volatile market.

Industry: Legal

One of the major impediments to quality, affordable legal representation is the high cost of legal research. The body of law is a growing mountain of complex data, and requires increasingly more hours and manpower to parse. Lawyers are constantly analyzing data to find answers that will benefit their clients. For law firms to stay competitive they must find ways to cut cost and streamlining legal research is one way to do just that.

ROSS Intelligence: software is built on the Watson cognitive computing platform, ROSS has developed a legal research tool that will enable law firms to slash the time spent on research, while improving results.

AI & Blockchain

Detailing AI, KM and Cognitive computing would not be complete without adding blockchain to the technologies that will disrupt several industries. Functionally, a blockchain can serve as “an open, distributed ledger that can record transactions between two parties efficiently and in a verifiable and permanent way. The ledger itself can also be programmed to trigger transactions automatically. AI & Blockchain come together when analyzing digital rights. For example, AI will learn the rules by identifying actors who break copyright law. The use of AI applications will be extended by incorporating blockchain technology. When blockchains scale to encompass big-data, AI will provide the query and analysis engine to extract insights from the blockchain of data.

Cognitive technology solutions can be found in a number of applications across many industries. These industries include but are not limited to legal, customer service, oil & gas, healthcare, financial and automotive just to name a few. Cognitive technologies have the potential to disrupt Every industry and Every discipline — Stay Tuned!!

 

Sep 032017
 

hurricane-harvey-pol-ml-170830_4x3_992With the recent devastation left on Houston Texas by Hurricane Harvey , I want to focus this blog post on how Knowledge Management (KM) can be used to help our first responders in managing the response to emergencies and disaster preparedness.

During a time of crisis relevant information is usually not received in a timely manner by the individuals or groups of individuals that need it the most. The lack of timely and correct information increases the level of confusion, resulting in ineffectiveness that may cause a loss of life. The lack of timely and correct information also prevents First Responders, key leadership and the public from preparing for imminent danger, compromises the ability to make informed decisions and enact the proper emergency preparedness operations.

When I speak of information I also include the data that comprises the information and the decisions (knowledge) that is gained from and acted upon with the information.

Creating a Knowledge Management (KM) Strategy presents a holistic approach to leveraging knowledge and implementing technology to improve the access to correct and timely data, information and knowledge. The KM Strategy reflects several key aspects in delivering knowledge throughout an organization. The KM Strategy suitable for execution by first responders should align with the National Incident Management System (NIMS), which “provides a consistent nationwide template to enable federal, state, tribal, local governments, nongovernmental organizations (NGOs), and the private sector to work together to prevent, protect against, respond to, recover from, and mitigate the effects of incidents, regardless of cause, size, location, or complexity”. The KM Strategy for first responders will specifically address disaster preparedness, response and recovery including the technology that must be leveraged to support this strategy.

In order for any technology initiative to be successful it must address a need for the organization. The KM Strategy will identify the knowledge needs of first responders and determine the communication needs between national, state, and local entities and their corresponding first responder organizations (such as, Fire, Police, EMS, National Guard, and Cost Guard). Our preliminary research has determined that establishing a national alert system is a high priority initiative for the Department of Homeland Security. This initial system will incorporate the knowledge needs identified in the KM Strategy as well as technologies that will enable federal, state, and local government to support first responders more effectively and efficiently.

In determining a suitable knowledge management strategy for First Responders a determination must be made on the way they serve their clients, the types of knowledge that must be shared, captured and available for reuse, and should align with the strategic direction of the organization.

First Responders must have a KM strategy that supports the following:

  • Quick and Decisive Decision Making
  • Knowledge Recognition, Needs Assessment and Allocation, Feedback and Evaluation
  • Expertise Coordination Practices
  • Command and Control Structure
  • Learning and Knowledge Transfer

Quick and Decisive Decision Making

To support quick and decisive decision making, collaborative communication, and situational analysis there has to be an incident command structure that disseminates integrated information and knowledge utilizing real-time communications (see the National Incident Management System – NIMS). During an emergency first responders are operating in an atmosphere of panic fear and confusion as well as being under pressure to absorb information rapidly, judge its meaning, relevance and reliability. This information and knowledge of the crisis event is being passed along from individual to individual, team to team and agency to agency. As this communication escalates there is a need to incorporate technology to facilitate the rapid flow of information and knowledge that will enable quick and decisive decision making and situational analysis.

Knowledge Recognition, Needs Assessment and Allocation Feedback and Evaluation

During an emergency event first responders have to know details about the event as it is happening, what is needed to address the event, who needs specific information and knowledge, and what action(s) have to be taken. The NIMS protocols, procedures, and policies as indicated by the Communications and Information Management, and the Command and Management components support the knowledge recognition, needs assessment and allocation feedback and evaluation mechanisms needed in a KM strategy for First Responders.

Expertise Coordination Practices (ECP)

During an emergency event, knowledge is being exchanged in a rapid nature. Expertise coordination will establish the process to enable the management of this knowledge and skill interdependencies. ECP as part of the First Responder KM strategy will support knowledge sharing and expertise vetting during emergency events.

The ECP protocols supported by the KM strategy as identified by Faraj and Xiao will be:

  • Protocols to streamline work and reduce process uncertainty
  • Plug-n-play teaming arrangements, which allow for flexibility of personnel
  • Communities of Practice (CoP) for operational responsibility and training
  • Knowledge externalization to increase knowledge sharing

Expertise coordination activities are supported by the NIMS Resource Management component, which includes protocols, procedures and policies to support the facilitation and coordination of resources throughout every phase of the emergency event. It also addresses the coordination of knowledge between individuals, teams and agencies.

Command and Control Structure

Command and control address the management of information and knowledge at the tactical level. At the tactical level the KM strategy will address functional (tacit) knowledge at the operation level which includes, task planning (what tasks to do, when and how to execute the task), event monitoring (monitoring the actions taken and executed during an emergency event), understanding the time and place of emergency events, location and nature of the emergency event, reasoning about the cause and effect of the incident and lessons learned. Command and control has been identified as an integral part of any knowledge management system and the First Responders KM strategy must establish the protocols, processes, and procedures to address command and control. The KM strategy should specifically establish protocols, processes, and procedures for, planning, monitoring and learning, distributed knowledge framework to support teams, and support critical decision making. The establishment command and control activities are supported by the NIMS Command and Management component, which includes protocols to support Incident Command, Multiagency Coordination, and Public Information.

Learning and Knowledge Transfer

Since data, information and knowledge of the crisis event is being passed along from individual to individual, team to team and agency to agency there is a need to incorporate policies, procedures and protocols to facilitate an atmosphere of learning and knowledge transfer. The learning and knowledge transfer must not only take place between the various factions during an emergency but also within the various organizations. Learning and knowledge transfer will be a key ingredient in the KM strategy for the First Responders as they react to emergency events.

I urge everyone to contribute what you can in support of the Hurricane Harvey relief efforts to assist the people of Houston Texas. Access the following websites for information on how you can contribute; American Red Cross, YouCaring Fund, World Relief.

Jun 282017
 

RobotThis is the second of a three (3) part post on the connection between Artificial Intelligence (AI) and Knowledge Management (KM). In this post I examine those industries that will or are soon to be disrupted by AI and KM, specifically in the form of Cognitive Computing. Before we look ahead, let’s take a look back. During the time I first became involved in AI (late 80’s), it’s hype and promise at that time became too much to live up to (a typical phenomenon in software see Hype Cycle) and its promise faded into the background. Fast forward to 2010 and AI is beginning to become the “next big thing”. AI had already made its presence felt in the automobile industry (robotics), as well as with decision making systems in medicine, logistics, and manufacturing (expert systems and neural networks). Now AI in the form of Cognitive Computing is making its mark on several industries. In a recent CB Insights Newsletter, it was stated that the US Bureau of Labor Statistics indicates that 10.5 million jobs are at risk of automation. Due to the rapid adoption and application of better hardware processing capabilities which facilitate artificial intelligence algorithms use on big data this is leading the change in blue and white collar jobs.

At a recent Harvard University commencement address , Facebook Chief Executive Mark Zuckerberg stated “Our generation will have to deal with tens of millions of jobs replaced by automation like self-driving cars and trucks,”. Bill Gates, the founder of Microsoft and Chairman of the Bill and Melinda Gates Foundation in a recent MarketWatch story had this to say “In that movie, old Benjamin Braddock (Dustin Hoffman) was given this very famous piece of advice: “I just want to say one word to you. Just one word …Plastics,” And today? That word would likely be “robots“, and “artificial intelligence” would have a huge impact”.

Although there are many industries where Cognitive Computing will disrupt the way business is conducted including the economics around job loss and future job creation, I have chosen to look at three (3) industries; Legal Services, Automotive Industry, and Healthcare.

Legal Services

LegalKnowledge Management (KM) is becoming more prevalent within law firms as well as legal departments as the practice of KM has become more mature. AI technologies are also making its way into the practice of law. Ability to reuse internally developed knowledge assets such as precedents, letters, research findings, and case history information is vital to a law firm’s success. Paralegals currently play a critical role in assisting attorneys with discovery. With the use of AI systems attorneys will be able to “mine” more accurately and efficiently the large volumes of documents (i.e., precedents, research findings, and case history information) located in various repositories to aid in decision making and successful client outcomes. This ability will limit the use of paralegals and attorneys currently needed to perform these tasks.

Cognitive computing, will enable computers to learn how to complete tasks traditionally done by humans. The focus of cognitive computing is to look for patterns in data, carrying out tests to evaluate the data and finding results. This will provide lawyers with similar capabilities as it provides doctors; an in-depth look into the data that will provide insights that cannot be provided otherwise. According to a 2015 Altman Weil Law Firms in Transition survey  35% of law firm leaders indicate cognitive computing will replace 1st year associates in the next ten (10) years. While 20% of law firm leaders indicate cognitive computing will replace 2nd and 3rd year attorneys as well. In addition, 50% of law firm leaders indicate cognitive computing will replace paralegals altogether. Cognitive computing capability to mine big data is the essential reason lower level research jobs will be replaced by computers. This situation is not just limited to the legal profession.

Automotive Industry

Autonomous VehicleAutonomous Vehicles and Vehicle Insurance

Autonomous vehicles, also known as a driverless car, robot car (here we go with robots again!), and self-driving car can guide themselves without human intervention. This kind of vehicle is paving the way for future cognitive systems where computers take over the art of driving. Autonomous Vehicles are positioned to disrupt the insurance industry. Let’s take a look at what coverages are a part of the typical vehicle insurance policy.

Vehicle insurance typically addresses six (6) coverages. These coverages include (1) Bodily Injury Liability, which typically applies to injuries that you, the designated driver or policyholder, cause to someone else; (2) Medical Payments or Personal Injury Protection (PIP), which covers the treatment of injuries to the driver and passengers of the policyholder’s vehicle; (3) Property Damage Liability, which covers damage you (or someone driving the car with your permission) may cause to someone else’s property; (4) Collision, which covers damage to your car resulting from a collision with another car, object or and even potholes; (5) Comprehensive, which covers you for loss due to theft or damage caused by something other than a collision with another car or object, such as fire, falling objects, etc.; (6) Uninsured and Underinsured Motorist Coverage, which reimburses you, a member of your family, or a designated driver if one of you is hit by an uninsured or hit-and-run driver. The way these coverage’s are applied (or not) to a vehicle policy will be disrupted by the use of autonomous vehicles.

According to an 2016 Forbes article by Jeff McMahon  about 90 percent of car accidents are caused by human error. However, it is estimated that autonomous vehicles will significantly reduce the number of accidents. This will significantly disrupt the insurance revenue model, effecting all six (6) types of coverage identified above. When the risk of accidents drops, the demand for insurance will potentially drop as well (this will not happen unless the states no longer require insurance that covers accidents). So, there will be no doubt that auto insurance companies will the type of coverage and the language effecting the policies.

Some Unintended? Side Effects

The autonomous vehicle with its multiple sensors have the potential to eliminate accidents due to distractions and drunk driving. This will disrupt the vehicle repair industry by largely eliminating crashes so collision repair shops will lose a huge portion of their business. Indirectly, the decreased demand for new auto parts will hurt vehicle part manufacturers. According to the U.S. Department of Transportation in 2010 approximately 24 million vehicles were damaged in accidents, which had an economic cost of $76 billion in property damages. The loss of this revenue will put a strain on these manufacturers.

Healthcare

CogMedThe healthcare delivery process presents a consistent flow of data, information and knowledge for the delivery of healthcare. These areas include Patient Intake, Data Collection, Decision Support, Diagnosis and Treatment, and Patient Closeout. The areas of the healthcare delivery process that will be disrupted by cognitive computing include Patient Intake, Data Collection and Diagnosis and Treatment.

Patient Intake and Data Collection: 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. Cognitive computing executed through natural language processing (NLP) tools will capture medical insurance information, 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, information and knowledge about the patient to be automatically shared. NLP tools will limited or eliminate the need for a receptionist/admin to initially capture patient information.

Diagnosis and Treatment: Making a diagnosis is a very complex process, which includes cognitive tasks that involves both logical reasoning and pattern recognition. The development of Artificial Neural Networks that incorporates deep-learning capabilities are being developed to mine health related big data repositories. This innovation is providing clinicians and researchers with effective tools for improving and personalizing patient treatment options. It has been established that big-data analysis could help to identify which patients are most likely to respond to specific therapeutic approaches versus others. Analysis of such data may also improve drug development by allowing researchers to better target novel treatments to patient populations.

The clinical trials that pharmaceutical companies rely on for FDA approval and drug labeling capture too little of the information patients and physicians need. The trials only enroll a small percentage patients and can take years and tens of millions of dollars to finish. Many trials never enroll enough patients to get off the ground. Using cognitive computing will assist physicians to understand which patients are most likely to respond with standard approaches, and which need more aggressive treatment and monitoring. Enabling cognitive computing to harness the genetic and clinical data routinely generated by hospitals and physicians would also accelerate drug development, by rapidly matching targeted treatments sitting in companies’ research pipelines with the patients who are most likely to respond. In addition, the sheer number of clinical research and medical trials being published on an ongoing basis makes it difficult to analyze the resulting big data without the use of cognitive computing tools (for more information see Forbes article: IBM and Microsoft are Disrupting The Healthcare Industry with Cognitive Computing).

Where do we go from here!

AI, KM and Cognitive Computing will continue to evolve and more areas of disruption will be coming. So, the question is… how can we address the loss of jobs; how can we prepare for the new jobs; and how must business and government evolve to meet the challenges that cognitive computing present? It is clear that we must retrain/retool the current workforce and at the same time infuse our vocational schools, trade schools, colleges and universities with the right tools and experienced instructors/professors to teach the concepts and applications of AI, KM and Cognitive Computing. Businesses must continue to innovate. Innovating in the same old way will cause a business to become extinct. However, I’m talking about innovating by bringing a diversification of thought and experiences, including cultural into the innovation community. Creating your innovation intersection (for more on finding your innovation intersection – read The Medici Effect by Frans Johansson). Only by innovating differently will your business not only survive but thrive in this new world where interacting with computers (yes robots too!) will be an everyday occurrence in life!

May 312017
 

AI and KMThis is the first of a three (3) part post on the connection between Artificial Intelligence and Knowledge Management.

Artificial Intelligence (AI) has become the latest “buzzword” in the industry today. However, AI has been around for decades. The intent of AI is to enable computers to perform tasks that normally require human intelligence, as such AI will evolve to take many jobs once performed by humans. I studied and developed applications in AI from the mid to late 1980’s through the early 2000’s. AI in the late 1980’s and early 1990’s evolved into a multidisciplinary science which included expert systems, neural networks, robotics, Natural Language Processing (NPL), Speech Recognition and Virtual Reality.

Knowledge Management (KM) is also a multidisciplinary field. KM encompasses psychology, epistemology, and cognitive science. The goals of KM are to enable people and organizations to collaborate, share, create, use and reuse knowledge. Understanding this KM is leveraged to improve performance, increase innovation and expand what we know both from an individual and organizational perspective.

KM and AI at its core is about knowledge. AI provides the mechanisms to enable machines to learn. AI allows machines to acquire, process and use knowledge to perform tasks and to unlock knowledge that can be delivered to humans to improve the decision-making process. I believe that AI and KM are two sides of the same coin. KM allows an understanding of knowledge to occur, while AI provides the capabilities to expand, use, and create knowledge in ways we have not yet imagined.

The connection of KM and AI has lead the way for cognitive computing. Cognitive computing uses computerized models to simulate human thought processes. Cognitive computing involves self/deep learning artificial neural network software that use text/data mining, pattern recognition and natural language processing to mimic the way the human brain works. Cognitive computing is leading the way for future applications involving AI and KM.

In recent years, the ability to mine larger amounts of data, information and knowledge to gain competitive advantage and the importance of data and text analytics to this effort is gaining momentum. As the proliferation of structured and unstructured data continues to grow we will continue to have a need to uncover the knowledge contained within these big data resources. Cognitive computing will be key in extracting knowledge from big data. Strategy, process centric approaches and interorganizational aspects of decision support to research on new technology and academic endeavors in this space will continue to provide insights on how we process big data to enhance decision making.

Cognitive computing is the next evolution of the connection between AI and KM. In future post, I will examine and discuss the industries where cognitive computing is being a disruptive force. This disruption will lead to dramatic changes on how people will work in these industries.

Mar 312017
 

CognitiveThere are approximately 22,000 new cases of lung cancer each year with an overall 5-year survival rate of only ~18 percent (American Cancer Society). The economic burden of lung cancer just based on per patient cost is estimated $46,000/patient (lung cancer journal). Treatment efforts using drugs and chemotherapy are effective for some, however more effective treatment has been hampered by the inability of clinicians to better target treatments to patients. It has been determined that Big Data holds the key for providing clinicians with the ability to develop more effective patient centered cancer treatments.

Analysis of Big Data may also improve drug development by allowing researchers to better target novel treatments to patient populations. Providing the ability for clinicians to harness Big Data repositories to develop better targeted lung cancer treatments and to enhance the decision-making process to improve patient care can only be accomplished through the use of cognitive computing. However, having a source or sources of data available to “mine” for answers to improve lung cancer treatments is a challenge!

There is also a lack of available applications that can take advantage of Big Data repositories to recognize patterns of knowledge and extract that knowledge in any meaningful way. The extraction of knowledge must be presented in a way that researchers can use to improve patient centric diagnosis and the development of patient centric treatments. Having the ability to use cognitive computing and KM methods to uncover knowledge from large cancer repositories will provide researchers in hospitals, universities, and pharmaceutical companies with the ability to use Big Data to identify anomalies, discover new treatment combinations and enhance diagnostic decision making.

Content Curation

An important aspect to cognitive computing and Big Data is the ability to perform a measure of content curation. The lung cancer Big Data environment that will be analyzed should include both structured and unstructured data (unstructured being documents, spreadsheets, images, video, etc.). In order to ingest the data from the Big Data resource the data will need to be prepared. This data preparation includes applying Information Architecture (IA) to the unstructured data within the repository. Understanding the organization and classification schemes relating to the data both structured and unstructured is essential to unifying the data into one consistent ontology.

Are We Up for the Challenge!

Even if a Big Data source was available and content curation was successful, the vast amounts of patient data is governed by HIPAA laws which makes it difficult for researchers to gain access to clinical and genomic data shared across multiple institutions or firms including research institutions and hospitals. According to Dr. Tom Coburn in his January 14th article in the Wall Street Journal ‘A Cancer ‘Moonshot’ Needs Big Data; gaining access to a big data repository all inclusive of patient specific data is essential to offering patient centered cancer treatments. Besides the technology challenges, there are data and regulation challenges. I’m sure that many of these challenges are being addressed. Thus, far there have been no solutions. Are we up for the challenge? Big Data analysis could help tell us which cancer patients are most likely to be cured with standard approaches, and which need more aggressive treatment and monitoring. It is time we solve these challenges to make a moonshot a certain reality!

Jan 312017
 

AJ Rhem Logo with Tag LineKnowledge is recognized as a valuable asset in organizations across many industries. How knowledge is shared, leveraged, obtained and managed will be the difference in how successful and sustainable an organization will become. The use of knowledge management principles, practices and procedures has expanded enormously in recent years. This expansion has also brought about the proliferation of knowledge management systems in its many forms, Contact Center Knowledge Repositories, Expertise Locators, Content Management, Document Management, Knowledge Repositories/Libraries, Social Media Applications, Decision Support Systems, to name a few. The inclusion of KM from a strategic point of view to streamline revenue, increase revenue, improve performance, attract/retain customers and manage human capital have enabled organizations to maintain and/or improve their competitive edge. Knowledge Management in Practice is a resource which presents how KM is being implemented along with specific KM Methods, tips, techniques and best practices to get the most out of your KM investment.

This blog post features two videos from the presentation of my latest book: Knowledge Management in Practice. This presentation was conducted at the Knowledge Management Institute (KMI) Certified Knowledge Manager (CKM) training class held in Washington DC.

The second video features the question and answer session that followed. Feel free to ask questions regarding the book here on this blog and/or make comments on YouTube. I look forward to hearing from everyone!

 

 

 

 

Jan 112017
 

KM Wizard2As we enter into 2017, I am dedicating this first blog post to answering some of the pressing KM questions presented by my clients.

Do you recommend any standard model of KM to follow/implement? If so, please describe the model in summary.

There are several models of KM that I have worked with at various organizations to implement KM strategies, methods, processes and solutions, these include; The SECI Model (knowledge capture); The Continuous Knowledge Model (Connect, Collect, Catalog, Reuse, & Learn); KM Maturity Model (Maturity Level 1 – Initial: – Knowledge management is a one-time process with no formal KM practices within the organization, Maturity Level 2 – Repeatable: – KM processes are implemented and tested, Maturity Level 3 – Defined: KM roles are created, defined, and filled, Maturity Level 4 -Managed: – KM is more standardized and Organization-wide KM practices are defined and measured regularly, Maturity Level 5 – Optimized: – KM is mastered and flexible to external and internal changes ) for assessment, understanding knowledge gaps, implementation of KM and the evolution of KM within an organization.

How has the evaluation of effectiveness of KM actions been accomplished, such as readiness assessment, maturity assessment and ROI?

When my firm is engaged with an organization to assess KM we tailor the KM Maturity Model activities to assess where the organization is with KM as well as develop activities in which to resolve gaps that have been identified. This leads to the development and operationalization of a strategy/roadmap to achieve a desired level of maturity.

What are the main reasons of KM success in an organization?

There are many reasons (or factors) for KM success. These reasons include:

Implementing a KM Strategy: The KM Strategy must be positioned to drive specific initiatives that align with the mission and objectives of the organization. The KM Strategy includes formal procedures, methods and processes to collect knowledge throughout the organization, a well-established infrastructure, networks for transferring knowledge between employees, and tools to facilitate the process. The KM Strategy will lay the foundation to align specific tools/technology to enhance individual and organizational performance.

Besides implementing a KM Strategy, other reasons for KM success are:

  • Having Executive Leadership/Sponsorship
  • Having adequate Budgeting and Cost Expectations
  • Having participation from all levels of the organization
  • Having adequate (or developing) processes and technology that support KM
  • Having adequate (or access to) Resources
  • Having adequate education and understanding of KM
  • Implementing sufficient metrics to measure the impact of KM on the corporation
  • Having adequate monitoring and controls in place to ensure the knowledge is relevant and is current and accurate
  • These reasons can be applied to all companies implementing KM and the absence of one or more of these factors may cause the KM effort (i.e., KM Program or KM Project) to fail.

Which solutions do you recommend be utilized in order to motivate and to increase the participation of people in KM?

The organizational culture plays an important role in motivating people to participate in KM. Creating an environment where informal networks are encouraged within the organization play a key role in the level of participation in KM activities. I have found the following solutions help increase participation of people in KM:

  • Incentivize people by awarding those who ideas create value for the organization
  • Highlighting those that participate in KM related activities (communities of practice, contributing to Wikis, active in blogs, learning a new skill important to the enterprise, attending conferences and presenting key learnings to the organization (knowledge transfer)
  • Tie participation in KM related activities to opportunities in which employee bonuses are increased
  • Communicating successful KM initiatives and the people who worked on them throughout the organization.

Creating knowledge-driven culture is one of the most important challenges of KM implementation, what is your suggestion for tackling this problem?

Here are some suggestions for creating a knowledge-driven culture:

The structure of the organization plays an important role in determining how knowledge is distributed, how decisions are made, the degree to which people feel comfortable sharing. It is important to remove barriers that exist between different groups and individuals. Organizational structure strongly influences the ability and willingness of people and communities to share and create knowledge, create an environment both physical and mental (open and trusting of individuals to share ideas) will help in developing your knowledge-driven culture.

Centralization: The degree to which decision making is centralized. In highly centralized

Organizations, decisions are made by few managers at the top of the organization. This puts a heavy demand on the cognitive capacity of these managers. Research and experience have identified that decentralized structures as being more suited for KM.

Formalization: The extent to which behaviors in an organization are governed by rules, policies, will have an effect on developing your knowledge-driven culture. In general, rigid, formal structures are regarded as being detrimental to KM.

A simpler organizational structure, which leads to less silos tend to make it easier for KM to be implemented. The complexity of the organizational structure also affects how it must be managed and what managerial roles are necessary to effectively implement KM.

What is your idea about trends and orientation of KM in 2017 and the near future?

One of the major areas in which KM will make an impact in 2017 and in the near future 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. Customer service is where organizations are investing a major portion of their revenue and attention to improving their customer service. Knowledge structures to support cognitive engagement in customer service is a future trend. Cognitive engagement solutions, interactive computing systems that use artificial intelligence to collect data, information and knowledge along with having the ability to understand and communicate in natural ways are all aspects of future customer service.

BIG Data continues to make an impact and present a challenge in the industry, which specifically points to how KM will be positioned to gleam knowledge from the various repositories of structured and unstructured data contained within the organization. Infusing Big Data with KM will provide organizations with a competitive edge to not only bring about significant innovations, but deliver knowledge across the enterprise to the right people at the right time and in the right context.

Social media is another future impact where KM will make a difference. Younger employees and customers having grown up in the social media era, and are more open to sharing information than previous generations. With adoption of enterprise collaboration tools on the rise, new streams, formats and sources of enterprise knowledge are being created. This consists largely of unstructured content (social chats, team forums, etc.) and this must be incorporated into a broader KM strategy, and be made easily accessible/findable by customers and employees. This will see a future impact of the use of Information Architecture in the delivery of knowledge.

Personal KM and Wearable Technology, with all of the advances in technology becoming accessories for us to wear is producing a multitude of data, information and knowledge accessible to the user. This includes Fitbits, Apple Watch, Google Glasses and more… all deliver and collect information that allow us to make personal decisions during the course of the day. What to eat, drink, wear, how much we’re exercising (or not) are all decisions in some part influenced by our wearable tech!

Wearable technology is gathering information not only about us but also the environment around us. Where is this all taking us? Will our physicians have the capability to tap into all of this personal information? How about potential advertisers? KM will be at the center of how can we capture the decisions we make from this information to improve our lives. Personal Knowledge Management the key to taking control of our personal information created by these devises.

What are your more pressing KM questions? Feel free to join the conversation here… and/or receive KM Mentoring and Guidance through the KM Mentor.

Dec 152016
 

km-for-law-firmsLegal knowledge management is the driving force within law firms across the globe. The recent International Bar Association (IBA) conference in Washington DC attracted over 6000 legal professionals from around the world and Knowledge Management (KM) was prominently featured at the conference. In an article by Ron Friedmann of Fireman & Company in Bloomberg Law he indicates that legal knowledge management is on the rise as law firms realize that KM increases a lawyer’s productivity (Friedmann, 2016). This increase in productivity leads to delivering better value to clients. Ron Friedmann also indicates that The 2016 Citi-Hildebrandt client advisory expects “to see more focus on knowledge management” and The 2015 Altman Weil law firm report finds that 68% of firms with 250+ lawyers have incorporated KM initiatives to improve the firm’s efficiency (Friedmann, 2016).

In a Forbes 2014 article Micah Solomon indicates that creating true client loyalty is one of the most powerful and reliable ways to build a strategic, sustainable advantage for the law practice and that truly loyal clients are less price sensitive, and are less likely to be enticed by competitive entreaties from the firm across the street or across the continent (Solomon, 2014). Knowledge Management plays a key role in ensuring a high level of client support. KM staff operate smoothly between lawyers and a range of operational functions; ideally situated to increase intra-firm collaboration, communication, and understanding. Some KM programs have worked on operations for some time, but business conditions are now ripe for more extensive applications of KM to firm operations; arguably critical to keeping operational teams relevant and law firms profitable (Solomon, 2014).

Client support specifically focuses on dramatically improving the client experience. It is the expectation of all clients that legal professionals and law firms will provide high quality legal services and it’s that promise and demonstration of high quality legal services that are the intangibles that will set the firm apart. To read this full blog post access Globe Law and Business (The key ingredient for creating and sustaining law firm profitability). For more about Legal Knowledge Management see Knowledge Management in Law Firms – Expertise in Action and for a look at how other industries are leveraging KM pick up a copy of Knowledge Management in Practice. As always, I look forward to your comments and questions!

 

Nov 302016
 

chapter-13-figure-1-km-competency-model-rhemOver the last several weeks I have been in conversations with colleagues and clients about what are the specific Knowledge Management (KM) roles that are essential for a KM project and/or program to succeed. Since there could be a myriad of the types of KM projects that can be initiated, the roles needed to successfully execute the KM project will vary. In determining the role(s) that are suitable for your KM Program/Project you will need to know what responsibilities are associated with each role and the core competencies needed to be successful in executing effectively. Armed with this information you must match the KM role, responsibility and core competency to your needs. Once that is completed, assigning the right people is the next step and determining what gaps in personnel exist (if any). If there are gaps in the personnel needed for your KM initiative leverage the information detailing the KM roles, responsibilities and core competencies to fill the necessary roles.

The roles of KM professionals consist 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. Knowledge management has both soft and hard competencies. The soft competencies include ensuring that knowledge processing is aligned with the organization’s 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.

Note: The KM Roles, Responsibilities and Core Competencies originally appear in Knowledge Management in Practice, by Dr. Anthony J. Rhem, published by CRC Press.

The following is a snapshot of the KM Roles, Responsibilities and Core Competencies for Knowledge Manager and KM Specialist:

KM Role Role Description Responsibilities Core Competencies
Knowledge Manager Knowledge Manager works with the KM Program and/or Project Manager to implement KM initiatives. The Knowledge Manager has the following responsibilities: -Manages KM efforts (often serves as a KM Project Manager or Product Owner)

-Looks across KM processes to capture tacit and explicit knowledge

– Balances technology, information, processes and individual and organizational learning within a culture of shared values.·        Creates ways to maintain a sustainable competitive advantage

Knowledge managers should also have a general understanding of knowledge architecture, but do not need an in-depth knowledge.Extensive experience and senior technical expertise in the field of Knowledge Management or Capacity Development preferably in an international development organization with a proven track record of successfully delivering KM strategies. Has worked in a developing country and has a good knowledge of international development issues, trends and approaches. Proven experience in the design and delivery of capacity development, coaching and mentoring activities, particularly adult learning techniques, replication of best practices. Strong knowledge and practice of Results Based Management (RBM), experience in performance measurement and program evaluation. Strong communication skills both written and verbal, excellent report writing and organizational skills. Leadership· Excellent communication· Time management/ability to prioritize· Development or management of information systems to support complex business processes· Project management of IT projects· Significant knowledge and use of relational database systems· Survey design· Finding assembling and analyzing verbal and numerical data from internet, databases and paper-based sources· Dissemination of information in a way that is accessible, manageable and which supports the work of individualism an organization· Experience of working effectively in a diverse team, maintaining good working relationships· Excellent information technology skills including relationship database programming and/or reporting skills
KM Specialist The Knowledge Management specialist is engaged in the support of the KM Policy, Planning Research and Metrics for knowledge management. KM Specialist Responsibilities Include:

Lead/contribute the development of a knowledge management strategy and associated implementation plan.

·Lead/contribute to the development and execution of the KM Governance Plan·        Develop a comprehensive mapping of KM information sources and knowledge, including processes

-Contribute to the develop and ongoing maintenance of the knowledge management system(s)

·Create a approach for guiding on going analyses needed to address observed KM gaps and for identifying opportunities for innovation, process, procedure and policymaking/adjustments

– Oversee capacity building and support for internal knowledge acquisition, management and sharing; ensure relevant communities of practice are developed and strengthened. Support development of staff, consultants and key partners and on all aspects of knowledge management

Knowledge engineers need in-depth competency as it pertains to Knowledge Architectures as well as Knowledge sharing, collaboration and transfer techniques and methods.
Oct 272016
 

ajrhem-italy-corp-meetingThe Central Knowledge Management Office (CKMO) is comprised of Senior Management and Core Team members and is the vehicle for implementing and keeping under review the KM initiatives that will be championed by the organization. The CKMO will support KM innovation, and enhance individual and organizational performance, by delivering improved learning, collaboration, and knowledge sharing into the culture of your organization’s environment.

The challenge of knowledge management is to determine what knowledge within an organization qualifies as “valuable.” All information is not knowledge, and all knowledge is not valuable. The key is to find the worthwhile knowledge within a vast sea of information. Oftentimes, knowledge management is misunderstood as simply maintaining websites or other technology. Besides what is mentioned above, the following list outlines what a CKMO delivers:

  • CKMO is orderly and goal-directed. It is inextricably tied to the strategic objectives of the organization. It uses only the knowledge that is the most meaningful, practical, and purposeful.
  • CKMO is ever-changing. There is no such thing as an immutable law in CKMO. Knowledge is constantly tested, updated, revised, and sometimes even declared obsolete when it is no longer practicable. It is a fluid, ongoing process.
  • CKMO is value-added. It draws upon a vast amount of knowledge located throughout many repositories across your organization.
  • CKMO is visionary. This vision is expressed in strategic business terms rather than technical terms, and in a manner that generates enthusiasm, buy-in, and motivates managers to work together toward reaching common goals.
  • CKMO is complementary. It can be integrated with other organizational initiatives such as KCS and ITIL.

Once the process captures the organization’s knowledge, the real power occurs when an organization’s members act on that shared knowledge.

The following are essential components to establish and maintain the CKMO (Vision, Mission and KM Governance)

CKMO Vision

The CKMO enables the retrieval, creation, sharing, collaboration and management of knowledge and through the implementation of workflow, search and collaboration capabilities, the CKMO vision is to quickly provide reliable solutions to questions and support the organization’s knowledge management needs.

CKMO Mission

The CKMO mission is to support the vision of CKMO and implement the initiatives that support the best practices identified by the CKMO strategy. The CKMO Team will execute a Knowledge Management program that embodies situational understanding, organizational learning and decision making by providing knowledge products and services that are relevant, accurate, and timely.

CKMO Governance

Knowledge Management Governance ensures policy adherence and provides controls to guarantee that the knowledge stored and accessed provides the best value for the organization. CKMO Governance describes the policies, procedures, roles, and responsibilities to successfully maintain the organization’s knowledge assets. Effective governance planning and the application of the governance plan are critical for the ongoing success of knowledge management within the organization.

The governance plan will establish the processes and policies to:

  • Avoid proliferation of unnecessary knowledge by defining consistent review process (workflow).
  • Ensure that knowledge quality is maintained for the life of the knowledge asset by implementing quality management policies.
  • Provide a consistently high quality user experience by defining guidelines for knowledge creators.
  • Establish clear decision-making authority and escalation procedures so policy violations are managed and conflicts are resolved on a timely basis.
  • Ensure that the solution strategy is aligned with business objectives so that it continuously delivers business value.
  • Ensure that knowledge is retained in compliance with organizational retention guidelines.

As always, I look forward to comments and conversation on this topic.