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!

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.

Aug 312016
 

Knowledge Management in Practice by Anthony J RhemKnowledge 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. This book is a culmination of my years of experience within the knowledge management (KM) discipline. Since 1998 I have been involved in knowledge management, from researching, developing processes for capturing and codifying knowledge, developing knowledge management systems, developing and operationalizing knowledge management strategies across several industries, writing articles, books, developing and teaching KM curriculum and speaking at numerous KM conferences.

My latest book Knowledge Management in Practice covers how knowledge management is leveraged in several industries. An examination of the various uses of KM practices, policies, procedures and methods including tips and techniques to create a competitive advantage are presented. The industries that are covered include first responders, military, healthcare, Insurance, financial services, legal, human resources, merger and acquisition firms, and research institutions.

Essential Knowledge Management concepts are also explored not only from a foundational perspective, but from a practical application. These knowledge management concepts include capturing and codifying tacit and explicit knowledge, KM methods, information architecture, search, KM and social media, KM and Big Data, adoption of KM, and why KM Initiatives fail.

The following are the subjects that are covered and what you can expect from the various chapters:

  • The Case for Knowledge Management (KM): Chapter 2 – The Case for Knowledge Management details the factors you must consider to in order to make the case for your organization to start instituting KM and its various practices and policies. This chapter details what will be necessary for your organization to either launch a KM initiative/project, and/or establish a KM program.
  • Being Social – KM and Social Media: Chapter 3 – In this chapter KM and Social Media examines how social media tools and techniques are becoming facilitators of knowledge for the organization. In this chapter specific guidance and insight is given to develop your organizations social media strategy and to determine the social media tools, techniques and platforms that can be utilized to begin taking advantage of what social media can bring to KM.
  • Dude, where’s my car: Utilizing Search in KM: Chapter 4 – Utilizing search in KM details the importance of search in knowledge management and in particular a knowledge management system. Several aspects of implementing search are examined including the importance of having user centric information architecture.
  • The Age of Discovery: KM in Research Institutions: Chapter 5 – Research institutions play a key role in product innovation. Knowledge Management is a catalyst to stimulating and sustaining a high level of innovation. This chapter examines how KM is utilized; focusing on various KM methods that can and in some cases are being incorporated at research institutions.
  • Where has all my experts gone? – KM in Human Resources and Talent Management: Chapter 6 – When it comes to talent management KM can play a critical role in ensuring the knowledge assets are captured and made available to the enterprise. KM in talent management when applied holistically involves capturing and sharing employee knowledge from onboarding to exit interview.
  • Sound the Alarm! – KM in Emergency and Disaster Preparedness: Chapter 7  – Emergency and disaster preparedness is enhanced through the incorporation of knowledge management. Putting the right knowledge in the right context at the right time in the hands of First Responders could be the difference in saving lives and preventing casualties. It is important to begin with a comprehensive KM strategy in order to establishing a plan to deliver the knowledge in a timely manner.
  • Happily Ever After – KM in Mergers and Acquisitions: Chapter 8 – When organizations merge or are acquired there is a level of uncertainty both from a macro (organization) level and from a micro (employee) level. Applying KM to mergers and acquisitions will enable the organization to know what knowledge is important to retain, who those knowledge holders are, what are the knowledge gaps and how to quantify the knowledge of the organization. From an employee standpoint having the organization share knowledge about the pending transaction as well as incentify employees to share what they know and to assist employees in transitioning (within the new organization or to a new organization) will go a long way to ensure a smooth M&A transaction.
  • Is there a Doctor in the house? – KM in Healthcare: Chapter 9 – Healthcare has become focused on the individual. As the healthcare community moves to electronic record keeping and capturing patient information at the point of initial interaction; having accurate knowledge about that patient as well as having the patient knowledgeable about his/her own health is essential to the success of caring for that patient. KM is an essential ingredient for healthcare success, especially in the areas of drug interaction analysis, sharing of patient diagnosis between hospitals and doctors, and furthering the development of healthcare informatics.
  • Show me the Money! – KM in Financial Services: Chapter 10 – Knowledge Management in the financial services sector centers on being able to attract, serve and retain customers. By delivering the tools to customers that provide knowledge in order to make sound financial decisions is at the heart of what KM will provide. In order to bring innovative financial services and products to the marketplace and have an understanding of how it will best serve and benefit the customer; putting specific knowledge at the fingertips of employees serving the customer will also be critical component of what KM will bring.
  • Are you in Good Hands? – KM in Insurance Industry: Chapter 11 – In this chapter you will learn how KM in the insurance industry is used to communicate knowledge to customers, agents and customer contact centers while providing mechanisms for employees to share, capture and catalog knowledge. KM in the insurance industry will provide the knowledge to (among other things), complete applications, bind insurance, and service a claim.
  • Sign Right Here! – KM in the Legal Profession: Chapter 12 – In this chapter an examination of how KM can/should be used to enhance the management of a law firm and execute on client engagements will be presented. KM in law firms is primarily executed through the building and fostering communities of practice around practice specialties. This enables legal representatives to respond to a situation with the right expertise, equipped with the right knowledge to resolve a legal matter.
  • Get That Knowledge! – Knowledge Management Education: Chapter 13 – This chapter examines the state of knowledge management education. This examination includes KM certification programs, KM curriculum at institutions of higher learning, as well as KM education policies, procedures and future direction of KM education. In addition this chapter will present specific criteria to consider when selecting a KM education option.
  • Big Knowledge! KM in Big Data: Chapter 14 – In this chapter an examination of how KM can and should be used to gain knowledge from your Big Data resources will be presented. How KM will be used on Big Data to provide a rich structure to enable decisions to be made on a multitude and variety of data is the essence of this examination. Along with specific analysis of the various types of data and KM methods for examining this data, a detailed understanding of KM’s impact on Big Data can be realized.
  • What have you done for the War Fighter Today? – KM in the Military: Chapter 15  – There is a rich history when it comes to KM in the Military. An examination of how KM in the military is being used with special attention to such events as Base Realignment and Closures (BRAC) will be examined. In addition a look at the various branches of the military (Army, Air Force, and Navy) and their KM Strategies, KM systems and KM methods are presented.
  • Drinking the KM-Kool-Aid: Knowledge Management Adoption: Chapter 16 -Adoption of KM programs, policies, methods, and systems, is a challenge for all organizations. This chapter is all about adoption! If your organization does not adopt its KM principles, practices, processes, procedures or systems that deliver KM they may be recognized as a failure. This chapter will present specific guidance on how to improve KM adoption and how to position your KM initiatives for success.
  • Failure is not an option! – Why KM Projects Fail: Chapter 17 – With lofty promises come unrealized results. Knowledge Management gained widespread popularity in the 90’s, however many KM initiatives failed and this popularity has tapered quite a bit. Since the mid 2000’s a renaissance of KM began to occur, some disparate KM success started to be achieve (call centers, research, human resources, military ) and KM is now considered as a discipline to use as a competitive advantage. Although KM is being used with some success in this new knowledge economy, many KM initiatives still fail. This chapter details the factors that contribute to KM initiatives failing as well as measures to adhere to in order to achieve KM success.

Included in this publication is an in-depth synopsis of each chapter and an overall introduction to the book in chapter 1. The book concludes with chapter 18, which offers a summary of the book and insight on what’s next for knowledge management.

For those individuals and organizations who have purchased my book I would like to thank you and invite you to ask me questions about the material covered in the book and/or any KM questions that are on your mind.

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.