What is the difference between data information. How data differs from information
Data- this is also knowledge, but knowledge of a very special kind. To a first approximation, data is the result of linguistic recording of a single observation, experiment, fact or situation. Examples of data could be:
a) “on such and such a date, such and such a year, at moment t it was raining in a certain area” (meteorological data)”;
b) “the price of commercial timber on such and such a day of such and such a year, according to information from such and such an exchange, was so many dollars per ton” (trade data);
c) “the state budget deficit in such and such a country amounted to so and so billions of dollars in such and such a year” (financial datum);
d) “at such and such a moment in time, the automatic laboratory heading towards Jupiter deviated from the calculated trajectory by so many degrees, so many thousands of kilometers in such and such a direction” (data from the field of space technology).
From a technological point of view, some experts usually define the concept of “data” as information that is stored in databases and processed application programs, or information presented as a sequence of characters and intended for processing in a computer, i.e. data includes only that part of knowledge that is formalized to such an extent that formalized processing procedures can be carried out on it using various technical means.
Data is information presented in a formalized form suitable for automatic processing with possible human participation. Data is information written (encoded) in the language of the machine. Data are individual facts that characterize objects, processes and phenomena in subject area, as well as their properties.
There is a difference between information and data; Data can be considered as signs or recorded observations that for some reason are not used, but only stored. Therefore, in this moment Over time, they do not influence behavior or decision-making. However, data turns into information if such an impact exists.
For example, the main body of data for a computer consists of features that do not affect behavior. Unless this data is organized appropriately and reflected in the form of an output so that the manager acts in accordance with it, it is not information. They remain data until the employee accesses them in connection with the implementation of certain actions or in connection with some decision that he is obliged to make.
Data turns into information when its meaning is realized. It can also be said that when it is possible to use data to reduce uncertainty about something, data turns into information.
Data Life Cycles
Like matter and energy, data can be collected, processed, stored, and changed in form. However, they have some features. First of all, data can be created and disappeared. For example, data on an extinct animal may disappear when a piece of coal with its prints is burned. Data may be erased, lose accuracy, etc. Data can be characterized by a life cycle (Fig. 1.9), in which three aspects are of primary importance - generation, processing, storage and retrieval.
Reproduction and use of data can occur at different points in their life cycle and are therefore not shown in the diagram.
Rice. 1.9. Life cycle of data
When processed on a computer, data is transformed, conditionally going through the following stages:
1) data as a result of measurements and observations:
2) data on material media information (tables, protocols, reference books);
3) data models (structures) in the form of diagrams, graphs, functions;
4) data in the computer in a data description language;
5) databases on computer media.
Data Models
The data model is the core of any database. The appearance of this term in the early 70s of the twentieth century is associated with the works of the American cybernetics E.F. Codd, which reflected the mathematical aspect of the data model used in the sense of data structure. In connection with the needs for the development of data processing technology in the theory of automated information banks (ABI), in the second half of the 70s, the instrumental aspect of the data model appeared; the content of this term included restrictions imposed on data structures and operations with them.
In a modern interpretation data model is defined as a set of rules for generating data structures in databases, operations on them, as well as integrity constraints that determine permissible connections and data values, and the sequence of their changes.
Thus, a data model represents a set of data structures, integrity constraints, and data manipulation operations. Based on this, we can formulate the following working definition: A data model is a set of data structures and processing operations.
Currently, there are three main types of data models: hierarchical, network and relational. Hierarchical data model organizes data in the form of a tree structure and is the implementation of logical connections: generic relations or “whole - part” relations. For example, the structure of higher education educational institution is a multi-level hierarchy (see Fig. 1.10).
Rice. 1.10. Example of a hierarchical structure
A hierarchical (tree) database consists of an ordered set of trees; more precisely, from an ordered set of multiple instances of the same type of tree. In this model source elements generate other elements, and these elements in turn generate the following elements. Each child element has only one parent element. Organizational structures, lists of materials, tables of contents in books, project plans, meeting schedules, and many other sets of data can be presented in a hierarchical form.
The main disadvantages of this model are: a) the complexity of displaying the relationship between objects of the “many to many” type; b) the need to use the hierarchy that was the basis of the database during design. The need for constant reorganization of data (and often the impossibility of this reorganization) has led to the creation of more general model– network.
The network approach to data organization is an extension of the hierarchical approach. This model differs from hierarchical in that each child element can have more than one parent element. An example of a network data model is shown in Figure 1.11.
Because a network database can directly represent all kinds of relationships inherent in the data of the relevant organization, this data can be navigated, explored and queried in every possible way, i.e. network model not bound by just one hierarchy. However, in order to make a request to a network database, you need to delve deeply into its structure (have the schema of this database at hand) and develop your own mechanism for navigating the database, which is a significant drawback of this database model.
Rice. 1.11. Example network structure
One of the disadvantages of the data models discussed above is that in some cases, with a hierarchical and network representation, the growth of the database can lead to a violation of the logical representation of the data. Such situations arise when new users, new applications and types of requests appear, taking into account other logical connections between data elements. The relational data model avoids these disadvantages.
A relational database is one in which all data is presented to the user in the form of rectangular tables of data values, and all operations on the database are reduced to manipulations with tables.
A table consists of columns (fields) and rows (records); has a name that is unique within the database. The table reflects the type of real world object (entity), and each of its rows represents a specific object. Thus, the Sports section table contains information about all children involved in a given sports section, and its rows represent a set of attribute values for each specific child. Each table column is a collection of values for a specific attribute of an object. The Weight column, for example, represents the totality of all weight categories of children involved in the section. The Gender column can only contain two different meanings: "husband." and "feminine." These values are selected from the set of all possible values for an object's attribute, called the domain. Thus, the values in the Weight column are selected from the set of all possible child weights.
Each column has a name, which is usually written at the top of the table. These columns are called fields tables. When designing tables within a specific DBMS, it is possible to select for each field its type, those. define a set of rules for its display, as well as determine the operations that can be performed on the data stored in this field. Sets of types may vary between different DBMSs.
The field name must be unique in the table, but different tables can have fields with the same name. Any table must have at least, one field; The fields are located in the table in accordance with the order in which their names appeared when it was created. Unlike fields, strings do not have names; their order in the table is not defined, and their number is logically unlimited. The lines are called records tables.
Since the rows in the table are not ordered, it is impossible to select a row by its position - there is no "first", "second", or "last" among them. Any table has one or more columns, the values of which uniquely identify each of its rows. This column (or combination of columns) is called a primary key. In the Sports section table, the primary key is the Full Name column. (Fig. 1.12).
This choice primary key It has significant drawback: it is impossible to register two children in a section with the same value in the Full Name field, which in practice is not so rare. That is why an artificial field is often introduced to number records in the table. Such a field, for example, could be a journal number for each child, which can ensure the uniqueness of each entry in the table. If a table satisfies this requirement, it is called attitude(relation).
Rice. 1.12. Relational data model
Relational Models data can usually support four types of relationships between tables:
1) One to one(example: one table stores information about schoolchildren, another table stores information about schoolchildren’s vaccinations).
2) One to Many(example: one table stores information about teachers, another table stores information about students for whom these teachers are class teachers).
3) Many to One(as an example, we can offer the previous case, considering it from the other side, namely from the side of the table in which information about schoolchildren is stored).
4) Many to Many(example: orders for the supply of goods are stored in one table, and in another - companies executing these orders, and several companies can be combined to fulfill one order /
Relational representation of data has a number of advantages. It is understandable to a user who is not a programming specialist, allows you to easily add new descriptions of objects and their characteristics, and has great flexibility when processing queries.
Questions and tasks
1. Define the concept of “data”.
2. What is the data life cycle?
3. What data models do you know?
4. List the advantages and disadvantages of each data model.
INFORMATION PROCESSES
Module 1 (1.5 credits): Introduction to Economic Informatics
Topic 1.1: Theoretical foundations of economic informatics
Topic 1.2: Technical means of information processing
Topic 1.3: System Software
Topic 1.4: Service software and algorithmic basics
Economic informatics and information
1.1. Theoretical foundations of economic informatics
1.1.2. Data, information and knowledge
Basic concepts of data, information, knowledge.
TO basic concepts that are used in economic informatics include: data, information and knowledge. These concepts are often used interchangeably, but there are fundamental differences between these concepts.
The term data comes from the word data - fact, and information (informatio) means explanation, presentation, i.e. information or message.
Data is a collection of information recorded on a specific medium in a form suitable for permanent storage, transmission and processing. Transformation and processing of data allows you to obtain information.
Information is the result of data transformation and analysis. The difference between information and data is that data is fixed information about events and phenomena that is stored on certain media, and information appears as a result of data processing when solving specific problems. For example, various data are stored in databases, and upon a certain request, the database management system provides the required information.
There are other definitions of information, for example, information is information about objects and phenomena environment, their parameters, properties and condition, which reduce the degree of uncertainty and incomplete knowledge about them.
Knowledge– this is recorded and practice-tested processed information that has been used and can be repeatedly used for decision-making.
Knowledge is a type of information that is stored in a knowledge base and reflects the knowledge of a specialist in a specific subject area. Knowledge is intellectual capital.
Formal knowledge can be in the form of documents (standards, regulations) regulating decision-making or textbooks, instructions describing how to solve problems.
Informal knowledge is the knowledge and experience of specialists in a certain subject area.
It should be noted that there are no universal definitions of these concepts (data, information, knowledge), they are interpreted differently.
Decisions are made based on the information received and existing knowledge.
Making decisions- this is the choice of the best, in a certain sense, solution option from a set of acceptable ones based on the available information.
The relationship between data, information and knowledge in the decision-making process is presented in the figure.
Rice. 1.
To solve the problem, fixed data is processed on the basis of existing knowledge, then the information received is analyzed using existing knowledge. Based on the analysis, all are offered feasible solutions, and as a result of choice, one decision that is best in some sense is made. The results of the solution add to knowledge.
Depending on the scope of use, information can be different: scientific, technical, management, economic, etc. For economic informatics, economic information is of interest.
At the very beginning of this chapter, it is necessary to emphasize the differences between the concepts of data and information. There is a difference between them and quite a significant one. According to information theory, data should be understood as all that information that is collected and, most importantly, subjected to special processing in order to extract from it (including through calculations) only those information that will be necessary and useful for solving a given problem. specific task. This processed data will constitute information. And raw information is usually called data. Thus - such an analogy is quite appropriate here - data can be likened to some kind of ore, and information can be likened to useful substances extracted from it. Data is always associated with an excess of information, while information is always associated with the necessary sufficiency. Information, in other words, is what contributes to the growth of knowledge; it always bears the stamp of novelty and represents new information. But if we focus on market research, information is not just any new information. Ultimately, this is new information that is perceived and evaluated by the relevant services (specialists) in order to perform specific professional actions.
Data transfer and information appearance
Numerous transformations of data on the path 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 its way to the recipient is initially lost in the physical channels of its transmission in the form of so-called physical noise (for example, when conducting a survey in marketing research, some questionnaires turned out to be incorrectly filled out and are removed from further processing). The data that reaches the recipient (received) may not be fully understood and perceived by him due to, say, an insufficient level of knowledge. Misunderstood and unperceived data passes by the recipient's consciousness in the form of semantic noise. And finally, some of the data perceived by the recipient may simply be ignored due to the fact that it turns out to be redundant or simply unsuitable for the tasks being solved. In the form of pragmatic noise, this part of the message also passes by the recipient's consciousness. The remaining part of the data represents the actual information that can be used in solving practical problems. It is clear that at the assessment stage, according to Yasin’s scheme, data processing occurs, the necessary computational procedures, comparisons, etc. are carried out.
In practice, the concepts data and information are often identified with each other, i.e. replace one with the other, 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 quite acceptable. In this book, the concept of information will be used much more often than the concept of data, although sometimes the author will also use the concept of data. The explanation here is simple: it’s all about established traditions. In marketing, when talking about the rationale for decisions, the term information is more often used, even if we're talking about about the choice of justification for this necessary information(i.e. the information itself) from their large arrays (i.e. from data arrays). And the term data is used mainly during the initial collection of any information. There is no big contradiction with information theory here, and therefore the established tradition is not violated.
BASIC POINTS
1. In everyday life, people often use the term “information”, putting into it a simple meaning - “message”. When they say: “We don’t have enough information”, “I give information!”, “This is scientific information”, then intuitively the term “information” means a fairly wide range of meanings: “body of knowledge”, “data”, “concepts”, “ presentations", "news", "information".
There is no generally accepted scientific formulation of the term “information” yet. The proposed options suffer from incompleteness, often vagueness and imprecision. In this case, in science it is customary to build a definition by listing and describing the properties of an object or phenomenon.
Let's consider the basic properties of information. As an initial, working definition, we will take the most common one: information is any information (1). Here the words “information” and “information” are considered as synonyms. However, there are quite a lot of situations where information does not provide information. Thus, A.P. Chekhov in the story “Literature Teacher” puts into the mouth of the hero, teacher Ippolit Ippolitovich, a phrase that has become a symbol of communicative banality: “The Volga flows into the Caspian Sea, and horses eat oats and hay.” This information is true, but it does not contain information. Important point in understanding the essence of the phenomenon being studied: this message does not carry information, it contains a well-known fact.
Not all information is informative, but only that which carries something important, new, and valuable for the recipient. It is the recipient of the message who decides whether to consider the message informative or not. Considering the above, we can clarify the previous formulation: information is information that either has significance (value) for the recipient or acquires it (2). Let us clarify a number of positions:
information exists in certain conditions, it is connected with them, there is a source of information, an informant object that can disseminate some information;
information has unequal value from the point of view of the users receiving it;
the receiver of the information makes a selection, dividing it into informative and useless (the latter are called noise).
Information in human communication always makes sense, based on the gap in knowledge between speakers.
A public relations specialist or journalist must understand that his message will be perceived as informative only when it is relevant or presents facts in a new way and significantly arouses interest.
It is legitimate to talk about the subjective value of information. Not all people will perceive the same information as meaningful to them. Information about the exchange rate of world currencies is of significant value (informative) for a businessman, for the owner of a currency, but those who are not involved in foreign exchange transactions will treat it indifferently. Information is functionally related to the recipient's goals. In ordinary understanding, the arrival of a message is associated with certain events. It is events that are the source of a message that contains or does not contain information.
The process of information exchange plays an important role in the life of any creature. The ability to transmit or receive information in the broadest sense is a criterion of life. A message about changes in conditions in the environment of existence is detected by a living organism as either favorable or dangerous, requiring certain reactions. The concept of information is so comprehensive that some scientists include it in the definition of life, for example N. Wiener.
Information is involved in acts of communication with the outside world. Communication is a connection, an exchange of information.
Thus, communication, information, vitality are concepts of the same circle.
Another property of information. Having lost its novelty, information disappears. We do not re-read the primer, since everything in it is known and uninformative
Hence some preliminary conclusions:
information is the unknown, the uncertain,
subjective information disappears after it is perceived by the user.
Uncertainty and information content are related by mathematical relationships: the greater the uncertainty, the more informative the message.
So, information has two contradictory properties:
this is a certain amount of data that exists objectively, independently, and can be measured (for example, data in a computer; volume, number of printed characters in a book);
The information value and usefulness of this data is determined by the fact that it will be understandable to people and will be able to expand and clarify their knowledge. Therefore, assessing the “information content” of specific data is subjective; it depends on the amount of knowledge of a particular individual. It is a fact that the ratio 2x2 = 4 is a real discovery for a first-grader, but after a while this information becomes commonplace for him.
In the 20th century In connection with the development of scientific knowledge, the concept of information has deepened significantly. Information began to be viewed as something independent within the framework of a new science, cybernetics, which studies management processes. Cybernetics proves that information participates in the processes of control and development of any systems (living organisms or automatic devices) that ensure stability and survival. Based on the original cybernetic ideas, philosophers are trying to provide a broad justification for their views on the properties of information as a philosophical category. In philosophical science, two leading ideas have emerged: differently explanatory properties of information and its characteristics.
Adherents of one school (B.V. Biryukov, I.B. Novnk, A.D. Ursul and others) qualify information as a property of any material objects. According to the followers of this direction (they are sometimes called attributes of stam and), information can be extracted from any material object of life. inanimate nature. Matter is considered as a repository of “dead information”. The information is contained objectively, but in a hidden Hades. As part of natural objects, there is always a certain structure (a set of parts, when the connections between components), which can be known. Therefore, such information is called hidden, structural (sometimes related). Only an observer, a person, can extract information. He processes it, encodes and recodes it to transfer information from object to subject. So, information is an actively and purposefully used part of knowledge (3).
The meaning of activity lies in direct or indirect (for example, through interpersonal contact in communication, etc.) interaction with the object of knowledge. Information becomes in demand only if there is an intelligent being, a recipient of information, capable of comprehending the content of the stored message. Information is updated when a cognizing, thinking subject appears, affects the receptors of his senses, causes appropriate reactions, decision-making, and is involved in behavior management. This process (information extraction) is individual. For example, an artist admires a special shade in the color of a sitter’s eyes, and a doctor sees signs of a dangerous disease in these same shades of eye color.