Data and information are often identified, but there is a significant difference between the two terms:
● Information- knowledge concerning concepts and objects (facts, events, things, processes, ideas) in the human brain;
● Data- presentation of processed information suitable for transmission, interpretation, or processing (computer files, paper documents, records in the information system).
The difference between information and data is that:
1) data is fixed information about events and phenomena that are stored on certain media, and information appears as a result of data processing when solving specific problems.
For example, databases store various data, and upon a specific request, the database management system provides the required information.
2) data are media, not the information itself.
3) Data turns into information only when a person is interested in it. A person extracts information from data, evaluates, analyzes it and, based on the results of the analysis, makes one or another decision.
Data turns into information in several ways:
Contextualization: we know what this data is for;
Counting: we process data mathematically;
Correction: we correct mistakes and eliminate gaps;
Compression: we compress, concentrate, aggregate data.
Thus, if it is possible to use data to reduce the uncertainty of knowledge about a subject, then the data turns into information. Therefore, it can be argued that the information is the data used.
4) Information can be measured. The measure of measuring the content of information is associated with a change in the degree of ignorance of the recipient and is based on the methods of information theory.
2. Subject area- this is a part of the real world, the data about which we want to reflect in the database. The subject area is infinite and contains both essential concepts and data, and little or no data at all. Thus, the importance of the data depends on the choice of the subject area.
Domain Model... The domain model is our knowledge of the domain. Knowledge can be both in the form of informal knowledge in the brain of an expert, and it can be expressed formally using any means. Experience shows that the textual way of representing the domain model is extremely ineffective. Descriptions of the subject area, made using specialized graphical notations, are much more informative and useful in the development of databases. There are a large number of methods for describing the subject area. The most famous are the SADT structural analysis methodology and IDEF0 based on it, Heine-Sarson data flow diagrams, the UML object-oriented analysis method, etc. The domain model rather describes the processes occurring in the domain and the data used by these processes. The success of further application development depends on how correctly the subject area is modeled.
3. Data base- presented in an objective form, a set of independent materials (articles, calculations, regulations, court decisions and other similar materials), systematized in such a way that these materials can be found and processed using an electronic computer (computer).
Many experts point out a common mistake in using the term “database” instead of the term “database management system”, and point out the need to distinguish between these concepts.
At the very beginning of this chapter, it is necessary to emphasize the difference between the concepts of data and information. There is a difference between them, and a rather significant one. According to information theory, data should be understood as all the information that is collected and, which is especially important, is subjected to special processing in order to extract from them (including by means of calculations) only those information that will be necessary and useful for solving this specific task. This processed data will be information. And raw information is usually called data. Thus - a similar analogy is quite appropriate here - data can be likened to some kind of ore, and information - to useful substances extracted from it. Data is always associated with an excess of information, information - with the necessary sufficiency. Information, in other words, is what contributes to the growth of knowledge, it always bears the stamp of novelty, represents new information. But if you focus on market research, information is not all new information. Ultimately, this is such new information that is perceived and assessed by the relevant services (specialists) for the performance of specific professional actions.
Data transmission and appearance of information
Numerous data transformations on the way of their transformation into information can be traced according to the scheme proposed by Professor E.G. Yasin (Fig.5.1).
According to this scheme, some part of the data on the way to the recipient is initially lost in the physical channels of their transmission in the form of the so-called physical noise (for example, when conducting a survey in marketing research, some questionnaires were incorrectly filled out and they are removed from the forthcoming processing). The data (received) that has reached the recipient may not be all understood by him and perceived due to, say, an insufficient level of knowledge. Misunderstood and unperceived data pass by the receiver's consciousness in the form of semantic noise. And finally, of the data perceived by the recipient, some part may simply be ignored by him due to the fact that it turns out to be superfluous or simply unsuitable for the tasks being solved. In the form of pragmatic noise, this part of the messages also passes by the consciousness of the recipient. The rest of the data is actually information that can be used in solving practical problems. It is clear that at the stage of assessment, according to Yasin's scheme, the data are processed, the necessary computational procedures, comparisons, etc. are performed.
In practice, the concepts of data and information are often identified with each other, i.e. substitute one for another, which does not contribute to improving mutual understanding between, say, market researchers and customers of such research when concluding contracts between them for conducting marketing research. But sometimes such identifications are perfectly acceptable. In this book, the concept of information will be used much more often than the concept of data, although sometimes the author will use the concept of data. The explanation is simple: it's all about established traditions. In marketing, when they talk about justifying decisions, they often use the term information, even when it comes to choosing the necessary information for this justification (that is, information itself) from their large arrays (that is, from arrays of data). And the term data is used mainly in the initial collection of any information. There is no big contradiction of information theory here, and therefore the established tradition is not violated.
The Xerox company in recent years has positioned itself not as a manufacturer of copiers, but as a document processing company. The ZM company calls itself innovative problem solving companies. IBM identifies itself as a company that creates long-term economic benefits for customers by combining its business knowledge with broad technological capabilities. Steelcase, which manufactures office equipment, claims to sell its own knowledge and services to create a better workplace for people. What adds value to the activities of all these companies? These are mainly solutions based on knowledge: technical and technological know-how, product design, market research, identifying the true needs of customers. It is knowledge that gives these companies a sustainable competitive advantage.
Let's consider what is the difference between knowledge and data and information. The fact that these are different things, managers begin to realize especially clearly after the organization has spent significant funds to create a particular database, or information system, or simply these funds were spent on computerization, and without the corresponding effect.
Data is a collection of various objective facts. In corporations, these are, for example, structured transaction records (in particular, data on all sales: how much, when and who bought, how much and when paid, etc.). These data do not say why the buyer came here and whether he will come again.
Information is a hierarchical collection of data about certain aspects of the real world. Information is a stream of messages, and knowledge is created from this stream, it depends on the opinions and beliefs of the bearer of knowledge.
Information is a kind of message, usually in the form of a document or video or audio. It has a recipient and a sender. It informs, i.e. "gives shape" to the recipient by changing his ratings or behavior. How much the message is information is determined by the recipient. It is he who estimates how much the received message informs him, and how much it is just information noise.
Data turns into information in several ways:
o contextualization: we know what this data is for;
o count: we process data mathematically;
o correction: we fix bugs and eliminate gaps;
o compression: we compress, concentrate, aggregate data.
Knowledge- the concept is deeper and broader than just data or information. Each company in the course of its activities collects data, their structuring and generation of new knowledge. Most often, this knowledge concerns technology, when it comes to material production, as well as the technology of working with customers and technology of interaction with each other, when it comes to a customer service company. It can also be knowledge about the environment of the enterprise - about demographic, macroeconomic, social, macroeconomic, technological and market trends.
Distinguishing Knowledge from Information and Data: An Example
Chrysler has a collection of computer files called the "Engineering Knowledge Book" and provides comprehensive data and information about the creation of Chrysler cars, which can be used by any developer of new cars. When the manager received data on the crash tests carried out, he refused to put them in files without proper processing. He offered to answer the following questions:
o why these tests were carried out;
o what are the results in comparison with other similar tests of this company of other years and competitors;
o what are the conclusions of giving tests for the design of the car and its main components?
Similar questions transform information into knowledge; moreover, the answers to these questions add value to the information, or, in other words, add value. In practice, there are opposite examples, when, by adding unnecessary, empty information, the original information loses its value. There is a loss of value due to the blurring of the necessary information in the flow of information noise.
Knowledge is a combination of experience, values, contextual information, expert judgment, which provides a general framework for evaluating and incorporating new experiences and information. Knowledge exists in the minds of those who know. In organizations, it is recorded not only in documents, but also in processes, procedures, norms, in general, in the practice of activities.
Just as information arises from data, so knowledge arises from information by:
o comparison, definition of the scope (how and when we can apply information about this phenomenon to another, similar);
o linkages (how this information relates to other information);
o evaluations (how this information can be evaluated and how others evaluate it);
o determining the scope (what application does this information have to certain decisions or actions).
The process of transforming data into information, and information into knowledge is shown in Fig. 14.1.
Rice. 14.1. Data, information and knowledge
Distinguish between individual and group knowledge. Traditional views proceed from the fact that knowledge is the prerogative of individuals, while the group is just a simple sum of the members of this group, and group knowledge is the sum of their knowledge.
There is another, modern point of view, according to which a group of people forms a new entity with its own unique specifics. Within the framework of this concept, one can speak of group behavior and group knowledge, respectively. This new view is widely used in the science of knowledge management. Thus, knowledge can be not only for an individual person, but also for a group of people. Then they say that the organization as a whole knows something, the group, team, etc. knows something.
Bill Gates in his book "Business at the Speed of Thought" writes about the need to improve corporate IQ. At the same time, he means not only the number of smart employees, but also the accumulation of knowledge in the company as a whole and the free flow of information, which allows employees to use each other's ideas.
Knowledge can be explicit or implicit. Explicit knowledge can be expressed in the form of words and numbers and can be transmitted in a formalized form on media. This applies to those types of knowledge that are transmitted in the form of prescriptions, instructions, books, in various media, in the form of memoranda, etc.
Implicit knowledge in principle, it is not formalized and can only exist together with its owner - a person or a group of persons.
There are two types of tacit knowledge. The first is the technical skills that are shown by the masters of their craft and are, as a rule, the result of many years of practice. The second is the beliefs, ideals, values and mental models that we use without thinking about them.
Implicit knowledge is formed and developed in the process of creating and strengthening a positive corporate culture and using the means of group interaction (retreats, creative groups, etc.).
The attitude to explicit and implicit knowledge on the part of commercial firms is very controversial. On the one hand, many firms seek to translate tacit knowledge into explicit knowledge. This is done in order, on the one hand, not to depend on individuals, and on the other, to duplicate significant achievements. At the same time, these firms are not interested in the main competitive advantages being transformed into a form ready for duplication. That is why many companies try to maintain some of their competitive advantages in forms that cannot be duplicated (specific trainings, corporate culture, special service systems, etc.).
The bearer of both explicit and implicit knowledge can be not only a specific person, but also an organization... Consequently, we can talk about implicit group knowledge, which underlies stable models of collective reactions and internal interactions.
In Western literature, the term "routines" is sometimes used to denote tacit group knowledge, which are repetitive actions, regular behavioral patterns of an organization or firm. Routines are things that happen automatically, without instructions and in the absence of a selection procedure; however, routines cannot be codified.
In Russian, a routine is understood as a routine, established practice, a certain regime, a template, established rules regarding the occupation of people. At the same time, the concept of "routine" has one more definition: it is an inert order, i.e. such an order that gravitates towards the old, familiar, due to its backwardness, impervious to the new, progressive. In those cases where the term "routine" is used to denote group tacit knowledge, then the shades related to inertia are absent.
Thus, personal implicit knowledge is, first of all, skills. At the same time, group implicit knowledge is, first of all, routines. Routines do not exist in isolation, but in interdependence. Some routines may be implicit for some members of the group (organization) and explicit for others. Thus, the boundaries between explicit and implicit knowledge are relative, and we can also talk about the degree of implicitness of this knowledge. The ratio of explicit and implicit, individual and group knowledge is presented in table. 14.1.
Table 14.1
Knowledge ratio
The presence of tacit knowledge in an organization forces us to approach knowledge management in an unconventional way. Traditionally, knowledge management is understood as the creation, development and use of various databases and knowledge. The presence of tacit knowledge shifts attention to the means of direct communication between people. It is important not only and not so much to create a corporate encyclopedia in which everything that any of the employees knew and came across is recorded. In the case of tacit knowledge, it is more important to have at hand the coordinates of people who know the recipe and have the relevant experience, create a culture of communication using brainstorming sessions, meetings, "debriefing" and appropriate means of communication, such as email, personal sites, teleconferences etc.
Data is a collection of information that is recorded on any medium - paper, disk, film. This information must be in a form suitable for storage, transmission and processing. Further transformation of the data provides information. Thus, information can be called the result of data analysis and transformation. The database stores various data, and the control system can issue the required information at a specific request. For example, you can find out from the school database which of the students lives on a certain street or who has not received a bad grade during the year, etc. Data is turned into information when they are interested. It can be argued that information is data used.
The word "information" comes from the Latin informatio, "information, presentation, explanation." Information is also called information about objects, environmental phenomena, their properties, which reduce the degree of uncertainty, incompleteness of knowledge. As a result of the exchange of information, a more complete understanding of the subject is formed, the level of awareness increases.
Information does not exist in isolation, by itself. There is always a source that produces it and that perceives it. Any object - man, computer, animal, plant - acts as a source or receiver. Information is always intended for a specific object.
A person receives information from a variety of sources - when reading, listening to the radio, watching TV, when he touches an object, tastes food. Different people can perceive the same information in different ways.
Depending on the scope of use, there is scientific, technical, economic and other types of information. This is the most powerful means of influencing and on society as a whole. According to the well-known expression, whoever possesses the most information on any issue, he owns the world, that is, is in an advantageous position in comparison with others. In everyday life, the development of society, health and life of people depend on information.
Over the millennia, mankind has accumulated a tremendous amount of knowledge, which continues to grow. The amount of information these days doubles every two years. In any situation, even the most ordinary one, only relevant, complete, reliable and understandable information is effective. Only relevant, that is, timely information received can benefit people. It is important to know the weather forecast or hurricane warning the day before, and not on the same day.
5.1. Differences between knowledge and data
A characteristic feature of intelligent systems is the availability of knowledge necessary to solve problems in a specific subject area. This raises the natural question of what knowledge is and how it differs from ordinary data processed by computers.
Data is called information of an actual nature that describes objects, processes and phenomena of the subject area, as well as their properties. In the processes of computer processing, data goes through the following stages of transformation:
The original form of data existence (results of observations and measurements, tables, reference books, diagrams, graphs, etc.);
Presentation in special languages of data descriptions intended for input and processing of initial data in a computer;
Databases on computer media.
Knowledge is a more complex category of information than data. Knowledge describes not only individual facts, but also the relationships between them, therefore knowledge is sometimes called structured data. Knowledge can be obtained through the processing of empirical data. They represent the result of a person's mental activity aimed at generalizing his experience gained as a result of practical activity.
In order to endow IIS with knowledge, they must be presented in a certain form. There are two main ways of imparting knowledge to software systems. The first is to put knowledge into a program written in a conventional programming language. Such a system will represent a single program code in which knowledge is not placed in a separate category. Despite the fact that the main task will be solved, in this case it is difficult to assess the role of knowledge and understand how it is used in the process of solving problems. Modification and maintenance of such programs is not an easy task, and the problem of knowledge replenishment can become insoluble.
The second method is based on the concept of databases and consists in placing knowledge in a separate category, i.e. knowledge is presented in a specific format and placed in the knowledge base. The knowledge base is easily updated and modified. It is an autonomous part of an intelligent system, although the inference mechanism implemented in the logical block, as well as the means of dialogue, impose certain restrictions on the structure of the knowledge base and operations with it. This method is adopted in modern IIS.
It should be noted that in order to put knowledge into a computer, it must be represented by certain data structures corresponding to the chosen development environment for an intelligent system. Consequently, in the development of IMS, knowledge is first accumulated and presented, and at this stage, the participation of a person is mandatory, and then knowledge is represented by certain data structures that are convenient for storage and processing in a computer. Knowledge in IIS exists in the following forms:
Initial knowledge (rules derived from practical experience, mathematical and empirical relationships reflecting the mutual relationships between facts; patterns and trends that describe the change in facts over time; functions, diagrams, graphs, etc.);
Description of the initial knowledge by means of the chosen knowledge representation model (a set of logical formulas or production rules, a semantic network, frames, etc.);
Representation of knowledge by data structures that are intended for storage and processing in a computer;
Knowledge bases on machine data carriers.
What is knowledge? Here are some definitions.
From the explanatory dictionary of SI Ozhegov: 1) "Knowledge is the comprehension of reality by consciousness, science"; 2) "Knowledge is a set of information, knowledge in any area."
The definition of the term "knowledge" includes mostly philosophical elements. For example, knowledge is a practice-tested result of cognizing reality, its correct reflection in the mind of a person.
Knowledge is the result obtained by knowing the surrounding world and its objects. In the simplest situations, knowledge is considered as a statement of facts and their description.
AI researchers provide more specific definitions of knowledge.
"Knowledge is the regularities of the subject area (principles, connections, laws), obtained as a result of practical activities and professional experience, allowing specialists to set and solve problems in this area."
"Knowledge is well-structured data, or data about data, or metadata."
"Knowledge is formalized information that is referenced or used in the process of inference."
In the field of AI systems and knowledge engineering, the definition of knowledge is linked to a logical inference: knowledge is information on the basis of which the inference process is implemented, i.e. based on this information, you can make various conclusions about the data available in the system using logical inference. The inference engine allows you to link together separate fragments, and then draw conclusions on this sequence of related fragments.
Knowledge is formalized information that is referenced or used in the process of inference (Fig. 5.1.).
Rice. 5.1. Inference process in IC
By knowledge we mean a set of facts and rules. The concept of a rule representing a piece of knowledge is:
If<условие>then<действие>.
This definition is a special case of the previous definition.
However, it is recognized that the distinctive qualitative features of knowledge are due to their great opportunities in the direction of structuring and interconnectedness of constituent units, their interpretability, the presence of a metric, functional integrity, activity.
There are many classifications of knowledge. As a rule, classifications are used to systematize knowledge of specific subject areas. At the abstract level of consideration, we can talk about the signs according to which knowledge is subdivided, and not about classifications. By its nature, knowledge can be divided into declarative and procedural.
Declarative knowledge is a description of facts and phenomena, fixes the presence or absence of such facts, and also includes descriptions of the main connections and patterns in which these facts and phenomena are included.
Procedural knowledge is a description of actions that are possible when manipulating facts and phenomena to achieve the intended goals.
To describe knowledge at an abstract level, special languages have been developed - languages for describing knowledge. These languages are also divided into procedural and declarative languages. All knowledge description languages oriented to the use of traditional computers of von Neumann architecture are procedural languages. The development of declarative languages, convenient for the representation of knowledge, is an urgent problem of today.
According to the method of acquiring knowledge, it can be divided into facts and heuristics (rules that allow you to make a choice in the absence of precise theoretical justifications). The first category of knowledge usually indicates circumstances well known in the given subject area. The second category of knowledge is based on the expert's own experience working in a specific subject area, accumulated as a result of many years of practice.
By the type of representation, knowledge is divided into facts and rules. Facts are knowledge of the type "A is A", such knowledge is typical for databases and network models. Rules, or products, are knowledge of the type "IF A, THEN B".
In addition to facts and rules, there is also meta-knowledge - knowledge about knowledge. They are necessary for knowledge base management and for the effective organization of inference procedures.
The form of knowledge representation has a significant impact on the characteristics of IIS. Knowledge bases are models of human knowledge. However, all the knowledge that a person attracts in the process of solving complex problems cannot be modeled. Therefore, in intelligent systems, it is required to clearly divide knowledge into those that are designed to be processed by a computer and knowledge used by humans. Obviously, in order to solve complex problems, the knowledge base must have a sufficiently large volume, in connection with which problems of managing such a base inevitably arise. Therefore, when choosing a knowledge representation model, factors such as homogeneity of representation and ease of understanding should be taken into account. The uniformity of presentation leads to a simplification of the knowledge management mechanism. Ease of understanding is important for users of intelligent systems and experts, whose knowledge is embedded in the IMS. If the form of knowledge representation is difficult to understand, then the processes of acquiring and interpreting knowledge become more complicated. It should be noted that it is rather difficult to simultaneously fulfill these requirements, especially in large systems, where structuring and modular representation of knowledge becomes inevitable.
Solving knowledge engineering problems raises the problem of converting information received from experts in the form of facts and rules for their use into a form that can be effectively implemented by machine processing of this information. For this purpose, various models of knowledge representation have been created and are used in existing systems.
The classical models of knowledge representation include logical, production, frame and semantic network models.
Each model has its own knowledge representation language. However, in practice, it is rarely possible to get by with the framework of one model when developing IMS, with the exception of the simplest cases, so the representation of knowledge turns out to be complex. In addition to the combined representation using various models, special tools are usually used to reflect the features of specific knowledge about the subject area, as well as various ways to eliminate and take into account the fuzziness and incompleteness of knowledge.
