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!
Jun 142011
 

UML for Developing Knowledge Management SystemsWithin knowledge management (KM), the ability to harvest or capture the knowledge of workers has been a challenge for many years (see blog post: Capturing Tacit Knowledge).

The capturing, cataloging, and reuse of explicit knowledge of workers has been accomplished effectively through the use of content management systems. However, capturing, cataloging, and reuse of tacit knowledge remains an elusive, and often controversial subject within KM.

To address the issue of capturing, cataloging, and reuse of tacit knowledge I have developed a methodology which I believe effectively addresses this issue. This methodology is the Knowledge Acquisition Unified Framework (KAUF). The original basis for this framework is detailed in my publication from CRC Press UML for Developing Knowledge Management Systems. This framework has been utilized for the military at the Surface Deployment Distribution Command (SDDC) and also leveraged at a major retail company with some measurable success.

The framework’s flexibility allows for many project management and software tools to drive and implement applications based on the guidance of the framework. The framework consists of seven (7) major steps:

  1. Define domain knowledge
  2. Decompose the domain knowledge
  3. Determine interdependency
  4. Recognize knowledge patterns
  5. Determine judgments in knowledge
  6. Perform conflict resolution
  7. Capture an catalogue the knowledge

These steps provide a repeatable process for identifying, understanding, and cataloguing the tacit knowledge of the organization during the knowledge elicitation process.

In the post to follow over the next couple of weeks I will detail more about the KAUF and welcome your questions and comments. In the meantime I can be reached via twitter at Tony Rhem.

Feb 012011
 

knowledge management at your organizationHave you ever wondered what all the fuss is about concerning knowledge management (KM)? What is knowledge management anyway?

At its core KM is about sharing and collaborating about what you know, capturing what you know, and reusing that knowledge so as to not reinvent the wheel and/or to combine with other ideas to foster innovation.

Recently I had the privilege to attend a KM meeting conducted by the APQC (APQC’s January 2011 KM Community Call), which had representatives from Conoco Phillips, Fluor, IBM, GE, and Schlumberger. What I came away from this meeting with is the need to have KM become part of an organization’s culture. I believe that this is important because we do not want KM to be “another task to complete on the checklist”, but the way we conduct business. This includes the business between the various individuals and entities within our corporations as well as with our customers. Talking, listening, capturing, and applying what we learn from each other is a constant never-ending and always evolving process.

I challenge all of us to take this attitude into our workplace and remember that when you share what you know you don’t lose that knowledge, but rather you enhance that knowledge with the other individuals you share it with. Take a minute to review the slides from the APQC Jan 2011 KM Community Call as well as this video from YouTube Discover What You Know.

Feel free to comment and share your knowledge!