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!!

 

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!

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.

Nov 292012
 

 

TimeDoin’ Time* somewhere south of Normal…

Time = KM Time

Similarities between Knowledge Management (KM) and “other kind of time”
      Confined to small space with other detainees…..

 

         Most others don’t know what you do (or why)…

 

         Time is not an enemy but a constant challenge…

 

         Unable to leave until requirements are fulfilled…

 

         Having done time, KMer will never be the same…
Are you doin’ time? We would like to hear from you….
Bruce Fransen
Knowledge Management Consultant
b_fransen@comcast.net

 

Aug 312012
 
Hurricane Isaac

As the US begins to recover from the aftermath of Hurricane Isaac, I am reminded of how knowledge management (KM) can be used to respond to disasters such as these.

The lack of response, or the inadequate nature of the response, has led to a need to increase the effectiveness and efficiencies of first responders.

Due to the nature of their work Disaster Response Teams (DRT), are usually first to arrive in a crisis situation.  KM applied to DRTs – in particular, first responders – will enable the DRTs to arrive at the scene in a more timely manner, be equipped with the right knowledge of the situation, and have the right tools and technology to execute their job, putting them in a position to save lives.

When a disaster occurs, first responders often do not arrive in a timely manner, are not fully aware of the situation and are not fully equipped to handle the situation.

Applying KM to DRT first responders will not only save the lives of the people in the community, but in many cases the response teams themselves. When fully knowledgeable of the situation they are responding to, the team will increase the confidence of the community by delivering a faster, more efficient response, assuring the community that they will receive the help they need. Applying KM must begin with a comprehensive KM strategy that promotes a proactive stance and preparation before disaster strikes!

Knowledge management is not a “silver bullet”, however I believe it will make a difference.

As always I’m interested in receiving and responding to all comments on this post…  be safe!

Feb 172012
 

KM in Research InstituitonsIn a previous post I wrote about KM for Collaboration and Innovation, and in this post I pointed out that research areas are critical to new product creation and the speed to market for new products are essential to stay ahead of your competitors. KM plays a central role not only from the perspective of innovation by knowing what has been done and/or what is being done in other areas of research that can be utilized, but also from the collaboration and knowledge sharing among researchers contributing to the speed of new products to market.

At its core the nature of research is to nurture open access to extensive amounts of tacit knowledge (knowledge within the minds of people) and explicit knowledge (knowledge that is written down) by applying a model that reflects the natural of flow of knowledge. The model of Connect – Collect —Reuse and Learn depicts a knowledge flow model that supports KM within research institutions and R&D functions within organizations. For KM to work within a research environment (as with other environments) a culture and structure that supports, rewards and proves the value KM can bring will encourage the continued use and adoption of the KM practice.

In addition, the choice of IT tools (which is of secondary importance) should be brought in to the organization to automate the knowledge flow and its associated process. The KM tool(s) must support KM goals/strategies, provide a means to connect, collect, catalog, access, and reuse tacit and explicit knowledge. In addition the KM tool(s) must capture new learning to share across the organization, and provide search and retrieval mechanisms to bring pertinent knowledge to the user.

For those who are working in or interacting with research institutions and/or R&D departments I want to hear from you. I look forward to hearing your perspective on what KM is bringing to your world of research!