Feb 292016
 

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

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

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

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

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

Jan 132016
 

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

Future of Knowledge Management:

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

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

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

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

Global View of KM

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

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

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

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

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.

 

Jul 282015
 

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

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

Data, Information and Knowledge

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

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

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

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

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

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

Applying KM to Big Data
Knowledge Management has the ability to integrate and leverage information from multiple perspectives. Big Data is uniquely positioned to take advantage of KM processes and procedures.

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

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

Apr 302013
 
KM in FinanceThis blog post represents a sneak peak at my upcoming book KM in Practice

Show Me the Money!  –  KM in Finance

The financial services industry is a highly dynamic and competitive marketplace. As the fight for customers intensifies, it is increasingly important to attend to customer needs while ensuring customer information is shared with the right people at the right time across the institution. To this end the technology supporting the institution is vital to facilitating the movement of information and knowledge to the customer. KM systems will have an increased importance as trends in personal investing move towards broader services and integrated product offerings.

By utilizing a 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 institution. This helps the institution with cross selling, up selling and reporting on the effectiveness of any new customer initiatives.

It is well recognized that the financial services business environment is ever changing and is doing so at an ever increasing rate. This presents financial organizations with the challenge of acting and reacting to this volatility and communicating an appropriate value proposition to the market. In addition having an increasingly sophisticated consumer who is armed with the latest trading technology has added further stress to these companies to deliver the right knowledge at the right time in the right way to their customers.

If you are involved in the financial services sector as an employee and/or customer I would like to hear from you. Are you utilizing the tools and receiving the knowledge to make you successful? Look forward to all of your comments!

Apr 012013
 
Knowledge Management in Research InstitutionsOn Wednesday April 17th, 2013, there will be a 60 minute webinar detailing the use of Knowledge Management (KM) at and for research departments and/or institutions. The following is a brief description of the webinar:

Research Institutions are critical to innovation and new product creation. The speeds to market for new products are essential to stay ahead of your competitors. Knowledge Management (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.

This webinar will cover the KM strategy, techniques, best practices and application of KM necessary for research institutions to innovate more effectively and shorten the time to bring new products to market.

In a previous blog post I covered KM at Research Institutions and this topic will be presented in depth in my next book Knowledge Management in Practice. For more information click on KM for Research Institutions link. I look forward to your questions and comments.

Jan 112010
 

Army Enterprise Knowledge Management Competency ModelI recently read the discussion, and the associated comments, around KM education, which includes university courses (Masters programs), Certification programs, and Certificate programs.

This discussion is hosted by Art Schlussel in the CKO (Chief Knowledge Officer) forum in LinkedIn. It inspired me to elaborate on my thoughts concerning KM education. As I stated in my comments to Art, for any education to be effective it must be supported by practical application, including having experienced mentors work with participants who have recently completed any number of various KM training venues.

In the discussion, Art mentioned that a partnership between the US military and a well know accredited university would build a comprehensive KM training program is in its preliminary stages. However, the major issue is, what does or will this training consist of, taking into account the fact that the US military wants it to follow their KM Competency Model (see above).

I believe that the KM Training should have a holistic approach, which will cover the following:

  • The basics, and differences between data, information, and knowledge.
  • Establishing “your” definition of knowledge management.
  • Developing/executing knowledge management strategy (including knowledge audits, knowledge mapping, KM process.)
  • Identifying and addressing knowledge gaps (result from knowledge audit.)
  • Collaboration and knowledge sharing (Communities of Practice.)
  • Knowledge transfer planning (mentor protege, knowledge codification.)
  • Collecting and applying knowledge management metrics.
  • Identifying, planning, and executing KM projects/initiatives.
  • Knowledge management tools (wikis, blogs, search, KM systems.)

While this is not an exhaustive list, the approach must include the planning, strategy, and processes applied for knowledge management as well as the software that will enable and support the execution of the KM program initiatives.

The Army’s KM Competency Model serves as a foundation to how the Army will approach KM and forms the basis of what KM will address from the Army’s perspective. The Army’s Enterprise KM Competency Model represents one holistic approach to institutionalizing KM.

I believe that a holistic approach to KM is where we must begin in our training as well as our execution of KM at our organizations.