Knowledge Management Disciplines and Technologies

Knowledge management also requires specific attention to the human, organizational, and cultural aspects. It is important to understand that knowledge initiates, propagates, and permeates with people within organization. That is why, in contrast to technological knowledge management, socially based knowledge management emphasizes knowledge that can be acquired and shared via a socially interactive process. As we noted in Chapter 7, the evolution of social media has created a revolution in social interaction, and that renews the focus of knowledge management with new perspectives.

Knowledge management draws from a wide range of disciplines and technologies:

• Cognitive science
• Expert systems, artificial intelligence, and knowledge-based management systems (KBMSs)
• Computer-supported collaborative work (groupware)
• Library and information science
• Technical writing
• Document management
• Decision support systems
• Semantic networks
• Relational and object databases
• Simulation
• Organizational science
• Object-oriented information modeling
• Electronic publishing technology, hypertext, and the World Wide Web
• Help-desk technology
• Full-text search and retrieval
• Performance support systems

The sociotechnical paradigm combines the social and technical aspects, and could be described as the study of the relationships between the social and technical parts of any system. The sociotechnical view of knowledge management focuses on a firm's strategy for harmonizing knowledge management activities with technological drivers and social enablers to achieve its business objectives.

The new knowledge management systems must include databases, intranet, groupware, search engines, social media, big data, and predictive analytics. They may be divided into several major categories such as groupware, including e-mail and wikis; decision support systems; expert systems; document management systems; semantic networks; relational and object-oriented databases; simulation tools; big data; and artificial and predictive intelligence.

In general, a sociotechnical knowledge management system could be defined as a set of technological and social elements that ensures the development of a knowledge management process and the creation of appropriate organizational conditions.

According to the previous analysis, we could stress that a knowledge management system includes three main subsystems:

1. The subsystem of the knowledge management process
2. The subsystem of the technological context
3. The subsystem of the social context

The most obvious components of the sociotechnical knowledge management system are people and their knowledge. Knowledge includes explicit knowledge that is expressed in words and numbers and codified in manuals, databases, and information systems, as well as tacit knowledge that is shared collectively in the firm in the form of routines, culture, and know-how. Individual knowledge is transformed into the organizational knowledge through the process of knowledge management. (See Figure 9.1.) The process of knowledge management includes a set of practices or activities that are initiated in organization in order to identify, acquire, create, store, disseminate, and apply knowledge. These key processes are considered during designing the knowledge management system in the organization:

• Knowledge identification means the determination of all critical knowledge that is possessed by employees and their groups in the organization.
• Knowledge acquisition involves the renewal of employees' knowledge by attaining new information, knowledge, and experience.
• Knowledge creation is the creation of new knowledge that is materialized in new products, services, processes, and concepts.
• Knowledge dissemination means the diffusion of knowledge, experience, and valuable information among individuals and their groups in the organization.
• Knowledge application is the productive use of organizational knowledge in business processes through solving problems, making decisions, and designing new products and services for the benefit of the organization.

Knowledge management should be integrated into other organizational processes that create value. The process of knowledge management should also be harmonized with general corporate strategy and maintained by an appropriate culture. This requires the formation of a suitable organizational context (i.e., particular sociotechnical environment), which is created in order to ensure the working of the process of knowledge management. In accordance with the analysis of the main components of the sociotechnical knowledge management system, five major elements of the sociotechnical environment could be identified:

1. Strategic leadership means the active interest in knowledge management and its promotion by the leaders and chief officers of the organization.
2. Organizational infrastructure includes formal and informal structures that ensure the creation of formal and informal social networks through which knowledge and information flow in the organization.
3. Technological infrastructure is designed by technological products (tools) and their systems, which are based on information and communication technologies and used to facilitate the process of knowledge management.
4. Organizational learning is an area of knowledge within organizational theory that studies models and theories about the way an organization learns and adapts.
5. Knowledge culture deals with the systems of values, beliefs, and norms accepted and supported by all employees in the organization, and based on the acknowledgment of the importance of knowledge and its management.

A collection of data is not information. This implies that a collection of data for which there is no relationship between the pieces of data is not information. Wisdom arises when one understands the foundational principles responsible for the patterns representing knowledge being what they are. And wisdom, even more so than knowledge, tends to create its own context. So, in summary the following associations can reasonably be made:

• Information relates to description, definition, or perspective (what, who, when, where). Figure 9.1 illustrates the maturity of information to wisdom with reference to context and understanding.
• Knowledge comprises strategy, practice, method, or approach (how).
• Wisdom embodies principle, insight, moral, or archetype (why).

The following example with reference to a bank savings account illustrates how data, information, knowledge, understanding, and wisdom relate to interest, principal, and interest rate.

Data: The numbers 100 or 5 percent, completely out of context, are just pieces of data. Interest, principal, and interest rate, out of context, are not much more than data, as each has multiple meanings that are context dependent.

Information: If we establish a bank savings account as the basis for context, then interest, principal, and interest rate become meaningful in that context with specific interpretations.

• Principal is the amount of money, $100, in the savings account.
• Interest rate, 5 percent, is the factor used by the bank to compute interest on the principal.

Knowledge: If I put $100 in my savings account, and the bank pays 5 percent interest yearly, then at the end of one year the bank will compute the interest of $5 and add it to my principal and I will have $105 in the bank. This pattern represents knowledge that allows understanding of how the pattern will evolve over time and the results it will produce.

Understanding: Understanding is a cognitive and analytical process that takes knowledge and synthesizes new knowledge from the previously held knowledge. The difference between understanding and knowledge is the difference between learning and memorizing. Understanding can build upon currently held information, knowledge, and understanding itself. In computer parlance, artificial intelligence (AI) systems possess understanding in the sense that they are able to synthesize new knowledge from previously stored information and knowledge.

Wisdom: Wisdom is an extrapolative and nondeterministic, nonprobabilistic process. It calls upon all the previous levels of consciousness, and specifically upon special types of human programming (morality, ethical codes, etc.). Wisdom is the process by which we also discern, or judge, between right and wrong, good and bad. Wisdom is a uniquely human state that requires one to have the capability to make an intellectual judgment.

Some benefits of knowledge management correlate directly to bottom-line savings, while others are more difficult to quantify. In today's information-driven economy, knowledge resources are proven as an important asset to provide business agility. To get the most value from a company's intellectual assets, it is highly important that knowledge be shared and served as the foundation for collaboration. An effective knowledge ecosystem should help businesses do one or more of the following:

• Foster innovation by encouraging the free flow of ideas.
• Improve decision making.
• Improve customer service by streamlining response time.
• Boost revenues by getting products and services to market faster.
• Enhance employee retention rates by recognizing the value of employees' knowledge and rewarding them for it.
• Streamline operations and reduce costs by eliminating redundant or unnecessary processes.

Figure 9.2 reflects the main technologies that currently support knowledge management systems.

These technologies are well represented by four main stages of the KM life cycle:

1. Knowledge is acquired or captured using intranets, extranets, groupware, web conferencing, and document management systems.
2. An organizational memory is formed by refining, organizing, and storing knowledge using structured repositories such as data warehouses.
3. Knowledge is distributed through education, training programs, automated knowledge-based systems, and expert networks.
4. Knowledge is applied or leveraged for further learning and innovation via mining of the organizational memory and the application of expert systems such as decision support systems.

All of these stages are enhanced by effective work flow and project management.

By harnessing the capabilities of knowledge-infused customer relationship management (CRM) and intelligent integration of CRM and KM, businesses become intelligent and informed to address customers' needs more effectively. With the addition of KM, organizations gain the centralized intelligence they need for delivering fast, accurate, relevant answers. Providing this centralized intelligence is the best way to foster the knowledge and confidence that agents desire. Here are a few ways knowledge-infused CRM can benefit today's support organizations:

• Enhance agent efficiency and effectiveness.

  After downsizing, customer service organizations often lose some of their more experienced support agents. Therefore it is vital to move from a support model in which knowledge and expertise walk out the door when seasoned employees leave to a more collaborative support model in which solutions are developed, managed, and shared as part of the daily resolution process. This not only helps with overall morale and confidence but also empowers new agents to become more proficient faster. Knowledge-infused CRM systems can provide answers in real time with minimal agent involvement. For example, as a service agent is working on the case resolution screen, an integrated KM solution can automatically leverage the context of the interaction and provide immediate advice to assist with the resolution process.

• Minimize training time.

  In the wake of reorganization, a reduction in staff, and turnover, new as well as existing employees need to be trained. Knowledge solutions coupled with CRM can have a dramatic impact on reducing employee training schedules. Instead of investing in training programs that take agents away from servicing customers, management can focus on developing the essential content required for basic problem resolution and knowledge retrieval. Ultimately, organizations can shorten training schedules. Plus, with the KM system accessible through the CRM interface on each agent's desktop, agents can work to get the answers they need in a single application.

• Minimize call resolution times.

  To reduce costs, many organizations have been forced to consolidate call centers or business units, which can mean longer call times, increased escalations, and decreased first-call resolution rates. By arming agents with one-click access to better information, knowledge-infused CRM can help organizations avoid these negative trends. With access to knowledge through their existing CRM interface, problem resolution becomes faster and more seamless for agents.

• Improve consistency.

  When KM and CRM are fully integrated with all support channels, the playing field for agent competencies is leveled and every agent can more quickly and intuitively deliver consistent answers.

Knowledge-infused CRM can be fully leveraged in agent-assisted channels as well as in web self-service. Powering knowledge-infused CRM with a centralized knowledge platform means that customers get the same correct answers, regardless of the channel they use.

Knowledge culminates in wisdom, and collective knowledge captured and delivered within an enterprise helps in managing the business effectively for sustainable competitive advantage.

Force 5 Tornado Convergence Creates Business Agility

Cultivating a knowledge ecosystem is all about culture. A great example is the San Francisco Giants story of how the team parlayed an integrated big data analytics strategy into a World Series Championship in 2010 and again in 2012. The secret to the Giants chemistry is trust, and the master chemist is CIO Bill Schlough, who keeps his team ahead of the game with a world championship culture. After reading the December 17, 2012, article in Information Week titled “Chief of the Year” by Fritz Nelson describing his CIO of the Year award, it is clear that if you want to see the convergence of the Force 5 Tornado technologies in action, then you could go see a San Francisco Giants ballgame at AT&T Park.

The story that Bill Schlough tells is about the holistic business value of ubiquitous IT that has transformed the Giants organization in terms of customer experience in the park and online, as well as enabled world championship player performance on the field and reengineered the business of baseball in the front office. Such results are no coincidence; rather they reflect the San Francisco Giants official mission statement as their dedication “to enriching our community through innovation and excellence on and off the field.” They embrace the spirit and letter of the strategies and tactics that we have described in Creating Business Agility. The following examples are only some of the innovations:

• Dynamic ticket pricing… The Giants were a pioneer in this area. In 2000, when AT&T Park opened, the Giants' ticketing team, working with Schlough and the IT team, rolled out dynamic ticket pricing, where competitive forces drive the cost of attending a ballgame.
• One giant Wi-Fi hotspot… The Giants have also become the bellwether for enabling a digital fan experience at the ballpark…fans update their social networks and check scores and video highlights from around the league.
• Big data… teams evaluate factors such as player performance and optimal positioning on the field by analyzing thousands of slivers of data, beginning to let a handful of teams—the Giants among them—take the concept further with Sportvision's FIELDf/x, a video system that helps teams analyze player reaction times—biomechanics.
• Scouting… beyond picking players, the IT organization's data and video analyses extend to advanced scouting, like figuring out how to pitch and who to trade.
• High-definition video… The Giants were the third Major League Baseball (MLB) team to introduce an HD video scoreboard…replacing all of the stadium's TVs with HD sets transformed the fan experience.

Enterprise systems integration examples include: “replacing the homegrown CRM system the team has used for years, containing information on 700,000 customers, with Salesforce.com, and a new ticketing platform, these two systems (ticketing and CRM) will come together at a few points. The team is testing mobile point-of-sale systems in stores.…The goal is to integrate all customer data, from ticket purchasers to callers, into the Giants' ShoreTel VoIP system, and to ‘track the value of every customer and accurately assess the likelihood of losing that customer, or how to retain that customer,’ Schlough says. The organization wants to cater to each customer based on past behavior and interaction.”

As an example of his customer engagement thinking, Schlough wants to install mobile device charging stations around the ballpark in order to deliver apps with a “whole product” digital experience to Giants fans throughout the complete MLB ecosystem.

Another use case describing the role that knowledge ecosystems play in creating business agility is demonstrated by Oracle in winning the greatest comeback in sports history.

How Team Oracle Wins AC34 with Big Data Analytics for Adaptive Decision Making

The story, titled “Faster” by Carol Hildebrand, of how ORACLE TEAM USA has used IT as a strategic tool for defending the 34th America's Cup in San Francisco Bay was published in the September/October 2013 edition of Oracle Magazine. It provides a fascinating look at the ORACLE TEAM USA culture that leveraged a multipronged technology strategy for big data:

1. Real-time analytics
2. Onshore Analytics
3. Race Cutter App
4. Database Management

The AC72s are twenty-first-century sailboats. They are data-driven boats that can fly across the water at a speed faster than the wind that powers them. There are over 300 sensors throughout the boat that collect real-time performance data that is transmitted to a server in the hull. “Sensors measure the strain on the mast, hull, and wing; monitor the load generated on components ranging from the jib to the winches; and monitor the effectiveness of each change made by the trimmers, who constantly adjust the sail wing to fully exploit wind conditions.” The raw data consists of over 3,000 variables generated 10 times a second during sailing runs. ORACLE TEAM USA also produces several video feeds and still images of the sails every second.

A typical training run creates about a gigabyte of raw performance data as well as 150 to 200 gigabytes of video, adding it to about 80 gigabytes of weather and boat data, plus performance metadata from the current 2013 America's Cup campaign as well as a cache of historical race data. ORACLE TEAM USA uses this raw performance data, coupled with the videos and still images, tailored for diverse analytics scenarios.