IMPORTANCE OF DATA
Every company of organization or institute well arranged data for any kind of processing or to manage its information system. This is available data in normally in form of database. The collection of coherent, meaningful data is called a database. Collection of core and data needs to have a point of reference to be understood as student information, employees, personal information etc. Students information may be:
(1) Name. (2) Roll No.
(3) class. (4) Subjects
An address book is the simplest example of the database. The 'name' 'postal address' 'phone number'. Are the data that feels that database.
Database is not only the collection of data, they are also appeared in the form of table of information, because, the data is just simply a data until it is organised in a meaningful way.
TYPES OF DATA
1.STRUCTURED DATA:
Structure data is considered the most 'traditional' form of data storage, since the earlier version of database management system (DBMS) by able to store, process and access structured data.
Structure data is data the adheres predefined data model and is therefore straight-forward type of analysis. Structured data confirm to a tabular format with the relationship between the different row and column. Common example of structured data Excel files for SQL database. Each of these have structure row and column that even be sorted.
Structured data depends on the existence of a data model -a model of how data can be stored, processed, and accessed. Because of data model, each field is discrete and can be accessed separately or jointly along with data from other fields. This make structured data extremely powerful structure: it is possible to quickly aggregated data from various location in the database. Structured data is further divided into two major categories described as follows:
1. Qualitative Data:
Quantitative data, as the name suggests is one which deals with quantity or number. It refers to the database compute the value and counts, and and also can be expressed in numerical terms is called qualitative data. In statistics most of the analysis are conduct using this data.
Quantitative data may be used in computation and statistical test. It is concerned with measurements like height,weight, volume,length size humidity speed is etc. The tabular and diagrammatic presentation of data is also possible in the form of chart graphs tables etc further, the quantitative data can be classified as discrete or continuous data.
2. Qualitative data.
Qualitative data refers to the data that provide insight and understanding about a particular problem. It can be approximate but cannot be computed. Hence, the researcher should possess complete knowledge about the type of characteristics, prior to the collection of data.
The nature of data is descriptive and so it is a bit difficult to analysis it. This type of data can be classified into categories, on the basis of physical attributes and properties of the object. The data is integrated has spoken or written narratives rather than numbers. It is concerned with the data that is observable in term of smell, appearance, taste, feel, texture, gender, nationality, and so on. The methods of collecting qualitative data are are :
• Focus Group
• Archival material like newspapers
2. SEMI-STRUCTURED DATA
Semi structured data is the form of structured data that does not conform with the formal structure of data model associated with relational data basis or other forms of data tables, but Nonetheless contain text or other makers to separate semantic elements and importance of records and fields within the data therefore it is also known as self describing structure.
Examples of semi-structured data include JSON and XML forms of semi structured data.
The reason that this category exists (between structured and unstructured data) because semi structured data is considerably easier to analysis then unstructured data. Many big Data solutions and tools have the ability to ‘read’ and process either JSON or XML. This reduces the complexity to analyse structured data, compared to unstructured data.
3. UNSTRUCTURED DATA:
Unstructured data is information that either does not have a predefined data model or is not organised in pre-defined manner. Understand information is typically text-heavy, but may contain data such as dates, number and facts as well. This results in irregularities and ambiguities that make it difficult to understand using traditional programs as compared to data stored in structured database. Common examples of understand data include audio, video files or No – SQL databases.
The ability to store and process unstructured data has greatly grown recent year, with many new technologies and tools coming to the market that are able to store specialized types of unstructured data. MongoDB, for example, is optimized to stored documents. Apache Giraph, as an opposite example, is optimized for storing relationships between nodes.
The ability to analyse unstructured data is especially relevant in the context of Big Data, since a large part of data in organizations is unstructured data is one of main driers behind the quick growth of Big Data.
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