What is Knowledge?

Big Data together with Cloud Computing are two of the popular buzz words. 'Big Data', to keep it simple, generally describes voluminous amount of diverse structured, semi-structured and unstructured data that has the potential to be mined for information and knowledge enabling enhanced insight and decision making (business intelligence). Sensor technology, wearable computing, and the Internet of Things (IoT) create a substantial amount of new data that will enable near-real-time decision systems. With plenty of data available, it is a matter of making sense of it or, in other words, to elicit actionable knowledge for improving organizations' performance.

According to Demarest [2], effective management of any organization's marketplace performance is primarily based on:

1. A shared notion of how value is created, maintained and distributed within the organization and shared with upstream and downstream elements of the value chain within which the organization participates;
2. A set of processes and systems that support and help channel the organization´s value creating activities along the lines suggested by the shared models;
3. A set of key metrics that link the value-creation process within the organization to more-or-less objective measures of the organization´s success;
4. A set of command and control systems that monitor the efficiency and effectiveness of that value creation process.

The challenge, for an organization whose strategy requires effective participation in knowledge-intensive sectors of the global economy, consists in building an organization able to manage efficiently and effectively these four aspects where value is represented by knowledge.

Data, Information and Knowledge

It is common to think at the current post-industrial global economy as an information-intensive environment. More remarkable is the high number of assertions that knowledge is the key of effective competition, marketplace distinction and profitability [1].

But which is the distinction between data, information and knowledge? The question can be answered in several different ways, here is one possible answer.

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According to Davenport and Prusak [4] it is possible to define:

Data

Is a set of discrete, objective facts about events. Data describes only a part of what happened; it provides no judgement or interpretation and no sustainable basis of action. But data is important to organizations because it is essential raw material for the creation of information. Data becomes information through the five "Cs":

Information

It has a meaning. It has a shape and it is organized to some purposes. "Information is data endowed with relevance and purpose. Converting data into information thus requires knowledge. And knowledge, by definition, is specialized" (Peter Drucker). Again information becomes knowledge through the four "Cs":

Knowledge

Is a fluid mix of framed experience, values, contextual information, and expert insight that provides a framework for evaluating and incorporating new experiences and information. It originates and is applied in the mind of knowers. In organizations, it often becomes embedded not only in documents or repositories but also in organizational routines, processes, practices and norms.

Davenport and Prusak asserted that data become information when its creator adds meaning through different possible methods: contextualized (defined with a given purpose), categorized, calculated (mathematically or statistically), corrected and condensed (summarized). Computers and technology may foster the process but they hardly solve the problem of contexts, categories and summaries without human help. Humans carry out also the knowledge-creating activities directed to transform information into knowledge through different methods: comparison, consequences, connections and conversation.

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The given knowledge definition is strictly connected to humans who are able to collect experiences, have belief, feelings, values and motivations. Knowledge is intuitive and exists within people, part and parcel of human complexity and unpredictability. Therefore knowledge is hard to capture in words or understand completely in logical terms [2]. The definition specifies also the function or purpose of knowledge: knowledge is a framework for "evaluating and incorporating new experiences and information". This is the basis for a process which continues along time.

This knowledge definition is in contrast with philosophical knowledge which is directed to discover the absolute truth (with the limit of the language that is not considered capable to express it) and with scientific knowledge which tends to discover general laws (through the experimental method). Demarest asserted that the goal of what he called "commercial knowledge" is not the truth in general, but effective performance: "what works" or even "what works better", where better is defined in competitive and financial contexts [1].

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In the same way, knowledge as been defined by Nonaka and Takeuchi [5] as "justified true belief". Knowledge is provisional and partial, strictly dependent from the context that can rapidly change, it is not truth but appropriateness, capability of adaptation in a mutable and not completely defined environment.

Knowledge Management (KM)

In other words it consists of processes to capture, distribute, and effectively use knowledge.

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Amrit [1] clearly defined the three fundamental processes of knowledge management as:

Knowledge acquisition: The process of development and creation of insights, skills, and relationships. Knowledge formalization tools are examples of direct knowledge acquisition. Data capture tools with filtering abilities, intelligent databases, note-capture tools, and electronic whiteboards are examples of information technology components that can support indirectly knowledge data acquisition.

Knowledge sharing: Disseminating and making available what is already known. A decision support system that provides a novice physician best clinical practice is an example of knowledge that is being shared with that clinical agent. Possibly, the sharing process has to be optimized on the specific context.

Knowledge utilization: Learning is integrated into the organization. Whaever is broadly available throughout the company can be generalized and applied, at least in part, to new situations.

Amrit [1] listed typical technology tools able to support each stage (see figure 3). Ideally, knowledge management tools, should support a continuous process enabling users, who are utilizing the system for accomplishing their processes, to add, in the same time, new knowledge and share it.

Keep in mind, KM is about process, not about digital networks or smart intranets, the management of knowledge has to support and improve the business process. Technology is only an enabler which is strongly dependent from the organization context.

Tacit and Explicit Knowledge

In the area of knowledge management, a large part of knowledge is not explicit but tacit. Following Polanyi’s [6] epistemological investigation, tacit knowledge is characterized by the fact that it is personal, context specific, and therefore hard to formalize and communicate. Explicit, on the other hand, is the knowledge that is transmittable through any systematic language. Polanyi contends that human beings acquire knowledge by actively creating and organizing their own experiences. Thus, explicit knowledge represents only the tip of the iceberg of the entire body of knowledge.

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In addition, Nonaka and Takeuchi [5] defined their dynamic model, called knowledge conversion process, on the assumption that human knowledge is created and expanded through social interaction between tacit and explicit knowledge.

Knowledge Conversion Process

Effective KM requires a continuous knowledge conversion process. According to Nonaka and Takeuchi [5], and to the contextualization in the medical area provided by Stefanelli [7], it represents a social process between individuals and not confined within an individual.

Four different modes of knowledge conversion have been postulated (Figure 5):

Externalization (tacit to explicit) is the process of conversion of tacit into explicit knowledge, for example the translation of a clinical trial result into a recommendation for clinical practice.

Combination: (explicit to explicit) is the process of enriching the available explicit knowledge to produce new bodies of knowledge, for example combining medical and organizational knowledge into a decision support system.

Internalization: (explicit to tacit) is the process of individual learning by repeatedly executing an activity applying some type of explicit knowledge, e.g. a CPG, and absorbing the relationship between actions and results as new personal tacit knowledge.

Socialization: (tacit to tacit) is the process of learning by sharing experiences that creates tacit knowledge as shared mental models and professional skills (e.g. apprentices learning process, or expert consensus achievement during medical meetings).

Implicit Knowledge

De Long defined four different states that better qualify the knowledge called "tacit":

Implicit rule-based knowledge it identifies knowledge that could be made explicit and codified if agents just have the time and inclination to do so. An example is when technicians are aware that the process explained in a manual is not the best way to complete a task.

Implicit know-how it is knowledge that can be communicated but usually is not codified because of the contextual complexity involved... sometimes it is just a matter of the right questions in the right moment.

It is strictly connected to expertise and t is really hard to articulate because it is so complex and situationally defined.

Deep tacit knowledge it is the colletively shared beliefs, mental models and values that determine what individuals view as important and even what they define as relevant knowledge. It is the most difficult to acquire because it is usually transferred unconciously through a set of practices that are unique to every organization.

Knowledge As Social Issue

Given the tacit/explicit conversion process, it is possible to assert that knowledge is social as produced and shared among a network of human and non-human actors within the organization. The mere existence of knowledge somewhere in the organization is of little benefit; it becomes useful only if it is accessible/sharable, and its value increases with the level of accessibility. This leads to the importance of communication/interaction (between humans and between humans and non-humans) as key factor for the success for a knowledge-intensive environment.

Social Networks

In the organizations it is important to map and measure relationships between people:

• how workers learn and share knowledge across the organizations
• who has critical knowledge and where it is [8]

Such relationships work on a mix of formal and informal channels not always easy to detect. The goal of Social Network Analysis (SNA) is to create a representation of the organization people relationships that can be used to improve organization performance. SNA is focused on discovering people's interaction patterns.

Computer Networks as Social Networks

Computer networks are inherently social networks, linking people, organizations, and knowledge [9]. Communities, like computers, has become networked. Although community was once synonymous with densely knit, bounded neighborhood groups, it is now seen as a less bounded social network of relationships that provide sociability support, information, and a sense of belonging. Many organizations are similar to networked communities in having multiple sets of work team members, physically dispersed relationships, and teams of co-workers shifting by the day and week as employees get involved in multiple projects.

Communication and Common Ground

It is well known that human agents communicate more easily with others of similar occupation and educational background, since they have similar experiences, beliefs, and knowledge [10]. Furthermore, the more individuals communicate, the more similar they become [11].

Whilst examining issues concerning direct communication between two human agents, this is quite obvious and the relevance of the communication process also in health care setups has been widely demonstrated [12]. For instance, clinical experts often meet and discuss actual and past clinical cases. The cognitive notion of common ground is the key point in this context. It refers to the knowledge shared by two communicating agents [13] (e.g. the discussion of a medical problem with a medical colleague is really different from that with a patient).

According to Coiera [14], the notion of common ground holds whether we refer to conversational interaction between human beings or human-computer interaction. Cost for reaching the desired level of communication depends on the balance between the classes of grounding:

• Pre-emptive grounding: agents share knowledge prior to a specific conversational task assuming that it will be needed in the future.
• Just in time grounding: agents share only specific task knowledge at the time they have a conversation.

Thus, any given interaction between two agents involves costs born at the time of conversation, as well as costs born previously in pre-emptive groundings. When there is a mixture of human and computer agents, an information system designer has a spectrum of options (based on the above mentioned balancing) for designing the interaction among them.

Knowledge Management Systems (KMS)

The previously mentioned interaction options could be considered for the development of an Information System. For a KM tool, interaction has to go a step further. In fact, in a traditional information system there is no separation between information level and knowledge level, and it is common that users adapt themselves to the system and, besides small customizations, never vice-versa. The model for interpretation of data is hardwired or, in the most advanced cases, based on simple categorizations. In contrast, KM tools treat humans (singularly or grouped in communities) as key components engaged in an active process of sense making to continuously assess the effectiveness of "best practices". Thus, in a KM tool, the human-computer interaction is more complex and will transfer not only information, but also the related meaning.

Again, such meaning can be transferred from the user to the knowledge system through the chosen interaction modality and the resulting balancing between the two different types of grounding. For instance, if the user is obliged to define all the entities that will be used in interaction before starting the interaction process itself, we are talking about preemptive interaction process. The possibility of defining entities when used effectively or "after" their first usage can be considered a just in time grounding (knowledge reorganization). KM tools or better Knowledge Management Systems (KMS) are systems designed to manage the different types of knowledge belonging to an organization.

Knowledge and Health Care Organizations

Knowledge has been recognized as the key to success for Health Care Organizations (HCOs) [16]. Knowledge includes both the experience and the understanding of the people in the HCO, and the information artefacts, such as documents, guidelines, protocols, reports and emails archives, available within the organization [1]. In particular, the increasing pressure on HCOs to ensure efficiency and cost-effectiveness, balancing quality of care and cost containment, drives them towards a more effective management of medical knowledge derived from research findings. Fostering HCOs knowledge management in general, and evidence-based best practice in particular, requires effective exploitation of new:

• Medica domain: it manages all medical aspects related to the health care activities;
• Organizational domain: it manages organizational aspects related to the health care activities;

In this context, the ICT aspect represents the third dimension, that Van der Aalst defined as technical domain [17].

The above mentioned three domains must be integrated with each other to obtain the best possible results in patients’ care delivery. ICT systems should be developed in such a way to facilitate people communication and collaboration within an organization. Berg writes that “the work of medical personnel is rewritten in the light of the tool and vice versa” [18]. This arises the already mentioned socio-technological issues related to common grounding and shared understanding.

Structures Versus Data

In someway, both medical and organizational domains refer to activities based on (at least partially) codified models and structures. But an effective KMS should not forget data. In front of structures, data are directly connected to the learning processes mentioned above. Rules, models, abstractions and categories arise in our minds after collecting information which come out form data, not before. This is why it's easier to learn a computer language from examples than from its theory, or a natural language by just being exposed to it instead of knowing all rules and exceptions.

Data emergence enables the learning process fostering a set of steps that starting from data look for some sort of patterns that are not known a priori. In a HCO typical data are the patient data which include for instance genetic data which are not affected by ongoing processes.

Big Data