Database Modeling And Normalization

Running head: IT & TECHNOLOGY 1

Database Modeling and Normalization

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IT & TECHNOLOGY 2

Centurion consultancy firm is the principal information consultancy firm that has been

helping many organizations in managing their systems and has been known for quite some time

now. It is this very firm that I work for and aims to help various enterprises to achieve and make

the work of human resource managers relatively easier. Recently my company was awarded a

contract by the government to help it develop its systems. Our job description under the

government agency that we were assigned was to optimize the company’s data repository to

make the systems of the firm manageable by its human resource managers. Besides, we were to

develop the company’s systems in such a way that an ad voc reporting features were installed on

the various department machinery to facilitate storage and transmission of information. Again,

we were expected to ease HR core responsibility in the firm like policy enforcement within the

business, hiring, benefits management, training, and to facilitate promotions and advertisements.

In developing an Entity-Relationship Model, there are some three key steps that one

ought to closely follow and in ensuring that the model developed suites the function that it has

been designed to do within an organization. The first step would be a comprehensive analysis of

the data. At this point all the data that is to be relayed or stored need to be identified and how

they would be efficiently be found by the operators within the system for smooth transmission

(Vexler et al, 2014). Hence at this step data requirement is of essence and must be analyzed and

assessed first. Alternatively at this step again the operations that will need to be used on the data

will also need to be identified. The essence of data identification is to facilitate its functional

requirements.

The second step is the conceptual database design. At this point, the designers try to

visualize the best plan that they would be used to develop the most appropriate design for the

storage of the available data. The technology experts are thus expected to use the semantic data

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model to come up with the sematic schema that would facilitate smooth operation of human

resource managers. The third step is the matching of the semantic schema from step two to a

company’s relational schema. It requires proper planning and coordination to align the various

systems of the organization to make them function properly. The factors that need to be consider

when developing the ERM include: the identification of the entities in the firm with, finding or

creating relationships between the various different departments, development of the most

appropriate keys that would facilitate compatibility of the multiple departmental heads to make

their jobs and innovations like advertising and promotions much easier and mapping the

attributes.

When developing an ERM the steps are to be keenly adhered to facilitate proper

functionality of the systems. In any case the steps are not followed, the enterprise risks facing

high loses due to lack of coordination of the various systems put in places. Besides, the

organization risks being intruded by outsiders and might end up losing very vital files and data. It

would also do the work of the HR to be very difficult as all the various key roles no matter how

numerous they will be done manually causing delay in the rate of output hence causing massive

losses.

In any organization, various entities dictate and require a data repository. The entities if

ranked based on their essentiality and necessity in the firm is departments, supervisors,

employees, and projects. Attempts to hold time variant data would require the use of the time ER

model. The model would control and determine the time that is needed for various activities in

the various departments to be done (Vexler et al, 2014). For policy enforcement, a unique

component is designed (policy EER). The component stores all the policies that the employees

are expected to conform by but some are made to vary from different departments. It is also this

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component that makes gives an outline of how the employees are to be recruited, vetted and

trained before being allowed to serve in the firm.

Normalization is the process by which the models used and the designs in a company are

put to the test to affirm if they are functioning correctly. In the normalization process there are

various steps that have to be considered is a perfect outcome is needed. In the first phase of the

normalcy which is commonly known as the First Normal Form (1NF). At this point, the main

rule here is that no group of data is to be repeated in any given table (Duchon et al, 2015). It thus

requires that all the entities contained therein in any team have to be one to represent a particular

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attribute in a specific group. It thus implied that an entity with a unique identifier by default

becomes the first normal form. For instance, employees are the first normal form as an entity.

The second normal form (2NF) is the rule that the primary key attributes dictate and

controls all the other minor keys attributes. It always deals with the conditions only and only if

the entity identifier has more than one attribute, and the small attributes depend squarely on the

primary identifier. Like if the employees are the primary identifiers with composite identifiers

like salary and, time and output.

The third normal form (3NF) states that no minor attributes are made independent

however this form is typically violated when there are dependent minor key attributes that are

transitive. The transitive dependency is created by moving the attributes that are dependent

IT & TECHNOLOGY 6

towards those that are new to establish a relationship that is binding on them, and the new one is

made the identifier. The assumptions at this stage are that the departmental entities consider with

the main projects’ entities and so they are compatible (Finn et al, 2015). It is for this very reason

that the departmental systems are linked with those from other departments to make the system

useful.

In the organization, there are various entities that are used to sustain a personal solution. All

these entities show independence to make the transmission of information easier within the

various firm departments.

Database management system has undergone a significant evolution ever since the first

time that the management system was introduced in the sixties. The historical development and

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growth of the database management systems, as well as the logical data models, have gone

through four phases. The first phase is the hierarchical stage where the first database

management system was developed. It used to have inverted lists and network systems which

were rectified in the 1990s to come up with the object-oriented database management system.

Phase two was marked with the invention of SQL products. It also included the use of non SQL

products, and all were invented in the 1990s. The third phase came with the innovation of the

models that supported online analytical processing. It later came up with the invention of

DBMSs that are specialized that has also been in use to today (Kawai et al, 2015). The final

phase was marked by the introduction of the NoSQL in the 20th century to support the use of Big

Data in various organizations, various non-relational data and all the graphical analysis that is

needed at some points by some companies.

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Technologically, data is required to be stored for future use within the systems, and this is

why there are that are used, and they include the cloud, hard disks and so on. The data are

conveyed electronically in these mediums, protected and then kept for future use. Access to them

requires re-downloading from where they are stored and then making them available for use to

those who need them.

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Reference

Vexler, V. A., Bazhenov, R. I., & Bazhenova, N. G. (2014). Entity-relationship model of adult

education in regional extended education system. Asian Social Science, 10(20), 1.

Skarin, B., Duchon, A., Allopenna, P., & Dejordy, R. (2015). U.S. Patent No. 9,123,022.

Washington, DC: U.S. Patent and Trademark Office.

Finn, C. S., Crooks, S. S., McCALLIE, D. P., & O'larte, D. J. (2014). U.S. Patent No. 8,639,520.

Washington, DC: U.S. Patent and Trademark Office.

Eustice, K. F., Lawson, N., Yetisgen-Yildiz, M., Kawai, K., Perkowitz, M., Ferrel, P. J., &

Lazarus, J. D. (2015). U.S. Patent No. 9,183,535. Washington, DC: U.S. Patent and

Trademark Office.

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