Data modeling in the context of Requirement

Data analysis is the process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. In today's business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively.

Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. In statistical applications, data analysis can be divided into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA). EDA focuses on discovering new features in the data while CDA focuses on confirming or falsifying existing hypotheses. Predictive analytics focuses on the application of statistical models for predictive forecasting or classification, while text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, a variety of unstructured data. All of the above are varieties of data analysis.

An information silo, or a group of such silos, is an insular management system in which one information system or subsystem is incapable of reciprocal operation with others that are, or should be, related. Thus information is not adequately shared but rather remains sequestered within each system or subsystem, figuratively trapped within a container as grain is trapped within a silo: there may be much of it, and it may be stacked quite high and be freely available within those limits, but it has no effect outside them.

Information silos occur whenever a data system is incompatible, or not integrated, with other data systems. This incompatibility may occur in the technical architecture, in the application architecture, or in the data architecture of a data system. Such data silos are proving an obstacle for businesses wishing to use data mining to make productive use of their data. However, since it has been shown that established data-modeling methods are the root cause of the data-integration problem, most data systems are at least incompatible in the data-architecture layer.

In software engineering,a class diagramin the Unified Modeling Language (UML) is a type of static structure diagram that describes the structure of a system by showing the system's classes, their attributes, operations (or methods), and the relationships among objects.

The class diagram is the main building block of object-oriented modeling. It is used for general conceptual modeling of the structure of the application, and for detailed modeling, translating the models into programming code. Class diagrams can also be used for data modeling. The classes in a class diagram represent both the main elements, interactions in the application, and the classes to be programmed.

A geographic data model, geospatial geographical measurements, or simply data from modules in the context of geographic information systems (GIS), is a mathematical and digital structure for representing phenomena over the Earth. Generally, such data modules represent various aspects of these phenomena by means of statistical data measurement, including locations, change over time. For example, the vector graphic data model represents geography as collections of points, lines, and arrays, and the elimination data model represent geography as space matrices that store numeric values. Data models are implemented throughout the GIS ecosystem, including the software tools for data management and spatial analysis, data stored in very specific languages of GIS file formats specifications and standards, and specific designs for GIS installations.

While the unique nature of spatial information has led to its own set of model structures, much of the process of data modeling is similar to the rest of information technology, including the progression from conceptual models to logical models, and the difference between generic models and application-specific design.

A data model is an abstract model that organizes elements of data and standardizes how they relate to one another and to the properties of real-world entities. For instance, a data model may specify that the data element representing a car be composed of a number of other elements which, in turn, represent the color and size of the car and define its owner.

The corresponding professional activity is called generally data modeling or, more specifically, database design.Data models are typically specified by a data expert, data specialist, data scientist, data librarian, or a data scholar. A data modeling language and notation are often represented in graphical form as diagrams.

The database schema is the structure of a database described in a formal language supported typically by a relational database management system (RDBMS). The term "schema" refers to the organization of data as a blueprint of how the database is constructed (divided into database tables in the case of relational databases). The formal definition of a database schema is a set of formulas (sentences) called integrity constraints imposed on a database. These integrity constraints ensure compatibility between parts of the schema. All constraints are expressible in the same language. A database can be considered a structure in realization of the database language. The states of a created conceptual schema are transformed into an explicit mapping, the database schema. This describes how real-world entities are modeled in the database.

"A database schema specifies, based on the database administrator's knowledge of possible applications, the facts that can enter the database, or those of interest to the possible end-users." The notion of a database schema plays the same role as the notion of theory in predicate calculus. A model of this "theory" closely corresponds to a database, which can be seen at any instant of time as a mathematical object. Thus a schema can contain formulas representing integrity constraints specifically for an application and the constraints specifically for a type of database, all expressed in the same database language. In a relational database, the schema defines the tables, fields, relationships, views, indexes, packages, procedures, functions, queues, triggers, types, sequences, materialized views, synonyms, database links, directories, XML schemas, and other elements.

An entity–relationship model (or ER model) describes interrelated things of interest in a specific domain of knowledge. A basic ER model is composed of entity types (which classify the things of interest) and specifies relationships that can exist between entities (instances of those entity types).

In software engineering, an ER model is commonly formed to represent things a business needs to remember in order to perform business processes. Consequently, the ER model becomes an abstract data model, that defines a data or information structure that can be implemented in a database, typically a relational database.