Solved by verified expert:Constructively respond to the attached three items. It seems my reponses are never constructive enough.
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In my experience, I spend a great deal of time on analysis when planning out
System Development Life Cycles. Analysis is the detailed review of each piece of
data and information provided for the overall plan and mapping out of specific
relationships that the information has with one another.
As a step in the conceptual data model, analysis holds a key role in confirming
compatibility of relationships in databases and information systems. The conceptual
data model notates all of the attributes, entities, and rules as well as the
relationships between each of them. This detailed review is essential in the analysis
and planning phases of a project as when data and information are incorrect or
incomplete in those stages, it is sure to be at the completion point as well.
Understanding the scope during the planning stage truly reduces the requirement of
returning multiple times in the higher level conceptual data model, as the logical and
physical models generally review specific portions of the database in greater detail.
When working with clients, both internal and external, I find starting with the
conceptual data model, paying special attention to analysis, will most often place my
team in the best position for success in exceeding the clients expectiations.
I chose to write about planning phase which is the most significant phase
of Systems Development Life Cycle (SDLC). During the planning phase, business users and
analyst defines the project scope by deriving the objectives of the development. As
described in the textbook, they gather the emerging business needs and analyze the
existing databases to select the required business functionalities that will be added
along with the new requirements (Hoffer, Ramesh, & Topi, 2016). In this way, the
enterprise data model is being created by gathering the new business requirements,
and justifying the need for the existing functionalities/related data to add along with the
new requirements to the development.
Once the enterprise data model is finalized by the business, analyst produces the
conceptual data model that is the preliminary version of the new database. This
conceptual data model provides the overall concept of the database development. This
model elaborates all the entities, attributes covered under each entity, the relationship
between the entities, constraints, business rules, and define the categories of entities
concerning the data that it holds (Hoffer et al., 2016). Hence, this detail-level conceptual
data model is evolving from the high-level enterprise data model that defines the overall
scope of the new database requirements to support the existing and emerging business
functionalities.
The management estimates the cost, duration, and outcome/deliverables of the
development from the project scope derived by the planning phase (Morris, n.d.) . Rest
of the project phases heavily depends on this planning phase. The efficient planning is
critical for improving the chances of the successful development process (Hoffer et al.,
2016).
I have working experience with a couple of implementation cycles in my subject
area where most of my clients used agile model for the implementation. Agile model is
the combination of an iterative and incremental model in which project team works on
the components of the development regarding delivering the build in each iteration and
enhances/adjust the features of the build in the iteration (Ghahrai, 2016). This model
keeps providing the builds in each iteration until the build prepared to satisfy the
business requirement. The agile model has five subsequent steps in each iterative
cycle; they are planning, requirement analysis, designing, building, and testing. If the
build did not pass the test cases of users or the users want to introduce additional
features to the build, then it will be enhanced through subsequent iterations. It is the
realistic approach in modern development projects, but it requires active collaboration
between the project team and business users (Ghahrai, 2016).
References
Ghahrai, A. (2016, March). Overview of SDLC Methodologies in Software
Testing. Retrieved from
https://www.testingexcellence.com/sdlc-methodologies-advantages-disadvantages/
Hoffer, J. A., Ramesh, V., & Topi, H. (2016). Modern database management. Boston:
Pearson.
Morris, K. (n.d.) Steps in the System Development Life Cycle. Retrieved from
http://smallbusiness.chron.com/steps-system-development-life-cycle-43241.html
Out of the five major phases of the System Development Life Cycle, I think the
Analysis Phase carries a great deal of importance.
It’s the second phase of SDLC where the team considers the functional
requirements of the project. This is the point where the
project team is needed to determine end-user requirements with the help of
client focus groups (Morris, n.d.). This phase helps in
understanding who will use the product, how they will use it, and if there are
any special customer requirements are involved. The
the analysis phase is also known as Requirement Gathering Phase where all
the user requirements are documented in a Business
Requirement Document, which is then finalized and signed by the client.
In order to thoroughly analyze the business situation for a better
understanding of the requirements, conceptual data modeling is
preliminarily developed during this phase (Hoffer, Ramesh, & Topi, 2015).
The analyst is responsible to produce a detailed data
the model that showcases the overall structure of the organizational data. The
conceptual data model includes every data attribute, all
categories of data, every relationship between data entities, and every rule that
indicates the integrity of data (Hoffer, Ramesh, &
Topi, 2015).
One additional format of the SLDC Methodology:
As we all know, the mentioned waterfall model is the most straightforward of
the structured SLDC methodologies. But, this model
works best when all the requirements are well collected and there is only a
very little room for flexibility. Another SLDC
methodology, known as the Agile Model is now considered to be a very
realistic development approach. The ultimate goal of this
model is to create faster quality delivery through automation and
collaboration among the customers, operations, and
development (Agile Methodology, n.d.).
References
Agile Methodology. (n.d.). Retrieved from Agile Methodology:
http://agilemethodology.org/
Hoffer, J. A., Ramesh, V., & Topi, H. (2015). In V. R. Jeffrey A.
Hoffer, MODERN DATABASE MANAGEMENT (p. 20). Pearson.
Morris, K. (n.d.). Steps in the System Development Life Cycle. Retrieved from
Chron: http://smallbusiness.chron.com/steps-system-developmentlife-cycle-43241.html
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