Data, Information, Knowledge and Database


Explain Data, Information, Knowledge and Database.

Answer: Data, Information, Knowledge and Database

Data :

Data can be defined as a set of isolated and unrelated raw facts, represented by values, which
have little or no meaning, simply because they lack a context for evaluation. Usually, the values are
represented in the forms of characters, numbers, or any symbol such as ‘Monica’, ‘35’, and ‘chef’.
Although these words and numbers have some meaning, it is difficult to figure out exactly what
these values signify.

Information :

It can be defined as a set of organized and validated collection of data. When the data are processed and converted into a meaningful and useful form, they are known as information. Example : ‘Monica is 35 years old and she is a chef’.

Knowledge :

It is the act of understanding the context in which the information is used. It can be based on learning through information, experience, and/or intuition. Based on the knowledge, the information can be used in a particular context.

Example : If an hotelier uses the information about Monica (she is a chef) to hire her, he is using his knowledge. Hence, knowledge can also be referred to as a personʹs capability and wisdom and how much that person knows about a particular subject. Consequently, it can be said that data constitute information, and information constitutes knowledge.

Figure below shows the relationship among data, information, and knowledge.

Database :

A database can be defined as a collection of related data from which users can efficiently retrieve the desired information. It can be anything from a simple collection of roll numbers, names, addresses, and phone numbers of students to a complex collection of sound, images, and even video or film clippings. Though they are generally computerized, instances of non‐computerized databases from everyday life can be cited in abundance. A dictionary, a phone book, a collection of recipes, and a TV guide are examples of non‐computerized databases. The examples of computerized databases include customer files, employee rosters, books catalog, equipment inventories, and sales transactions.

Difference between Hierarchical, Network and Relational models :

Hierarchical data model

  • Relationship between records is of parent child type.
  • Many‐to‐many relationship cannot be expressed in this model.
  • It is a simple, straightforward and natural method of implementing record relationships.
  • This type of model is useful only when there is some hierarchical character in the database.
  • In order to represent links among records, pointers are used. Thus relationships among records are physical.
  • Searching for a record is very difficult since one can retrieve a child only after going through record.
  • During updating deletion process, chance of data inconsistency involved.

Network data model

  • Relationship between records is expressed in the form of pointers or links.
  • Many‐to‐many relationship can also be implemented.
  • Record relationship implementation is quite complex due to the use of pointers.
  • Network model is useful for representing such records which have many‐to‐many relationships.
  • In network model also the relationship among records are physical.
  • Searching a record is easy since there are multiple access paths to a data element.
  • No problem of inconsistency exists in network model because a data element is physically located at just one place.

Relational data model

  • Relationship between  records is represented by a relation that contains a key for each record involved in the relations.
  • Many‐to‐many relationship can be easily implemented.
  • Relationship implementation is very easy though the use of a key or composite key field.
  • Relational model is useful for representing most of the real world objects and relationships among them.
  • Relational model does not maintain physical connection among records.
  • Data is organized logically in the form of rows and columns and stored in table.
  • A unique, indexed key field is used to search for a data element.
  • Data integrity maintaining methods like Normalization process, etc. are adopted for consistency.

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