MIS631 - Midterm
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Stevens Institute Of Technology *
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500
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Information Systems
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Dec 6, 2023
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Midterm –
MIS631 Name : Bhargava Naidu Gulla You are to answer any five (5) of the following eight questions. Your submission should be an essay of 5-10 pages (space and a half)) in total. The material for these questions is found in the text (Chapters 1-5) and/or the lectures (primarily the text). 1. What are the advantages and disadvantages of the relational model? 2. What is the role of the data model in the database design process? 3. Describe a specific challenge in the creation of a data model, and how to potentially overcome it. 4. Identify and explain three (3) potential negative outcomes that could result from by an inaccurate data model. 5. Identify and explain three (3) potential negative outcomes that could result from failing to understand user data requirements. 6. When designing a database, what are the advantages and disadvantages of using a prototype? 7. Identify and explain potential advantages and disadvantages of including senior business managers and executives in data modeling exercises. 8. Critique an aspect of the text. Identify one (1) topic and/or explanation that could have been improved, in your opinion. Reference specific pages from the text that support your answers as needed. You may use other external sources as well, but all externally-sourced material must be accompanied by a footnote. This is an individual assignment and you may not collaborate with class members or other individuals to complete it. Answers must be uploaded as a PDF or Word file, no other file formats will be accepted. Good luck!
1.
What are the advantages and disadvantages of the relational model?
Relational models are one of the most important standards in data processing today and are extensively used for design and implementation of commercial databases. A relational model is organized as a two-dimensional table, with rows representing records of an entity, and columns signifying the attributes describing that entity. Uniformity is maintained within each column, as it holds data of a singular type. The model is anchored by a primary key, a unique identifier for each row within its table, and may include foreign keys, which are attributes used to establish links between different tables. A relation has one primary key, which must be unique in that table and foreign keys which are an attribute that is placed in a relation to represent a relationship.
Advantages: •
Concurrency Control:
One area in which relational databases shine is concurrency control. They maintain the integrity of the data while enabling numerous users to view the database at once.
•
Management of Transactions
: Due to their advanced transaction management skills, several activities can be handled as a single unit, with each operation having the same chance of success or failure.
•
Normalization of Data
: Data normalization is the process of organizing data to lessen redundancy and enhance data integrity, and it is encouraged by the relational model.
•
Data accuracy
: The relational model provides primary keys, foreign keys, and other restrictions as means of guaranteeing data integrity. This guarantees the data's accuracy and dependability. This helps ensure that data is accurate even when it is updated or altered.
•
Independence of Data
: One of the relational model's main advantages is data independence. alterations to the schema logical level, such changing a table, don't call for modifications to the application or routes of access to the data.
•
Adjustability and Expandability
: Because query flexibility is provided by the relational architecture, complicated queries ability to be built to retrieve data from several tables. It is scalable to manage numerous concurrent users and substantial data volumes.
Disadvantages: •
Achievement:
Relational databases could not function as well as other models, such NoSQL databases, for specific kinds of queries or data-intensive activities, especially when it comes to managing real-time processing and large data sets.
•
Difficulty in Handling Big and Complicated Databases:
Although the relational model has a straightforward notion, its construction can get complicated. when working with several tables and relationships, or when working with huge datasets.
•
Static Schema
: One restriction that may apply is the need for a set schema before data is stored applications with changing data models or those that require greater flexibility.
•
Price:
It can be expensive to implement strong relational database management systems. There's the price as well connected to management and upkeep by trained staff members.
(References: https://www.thecrazyprogrammer.com/2021/09/advantages-and-disadvantages-
of-relational-database.html
)
2.
What is the role of the data model in the database design process? The data model stands as a foundational element in the database design lifecycle, acting as a schematic for the database's construction, deployment, and ongoing management. This model offers a high-level overview of the structural needs of the database, including the relationships among data points, the integrity constraints that protect the data, and the data types that define the type of data that needs to be stored. A well-designed data model supports the information needs of the apps
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that communicate with the database, providing an ideal framework for effective data extraction and reliable data management.
There are three primary categories of data models, and the degree of operational detail largely determines the purpose of each category. The data model gets richer in context and information with each level of data modeling. An approximate conceptual data model is a draft that includes the necessary entities or concepts along with their connections.
The second step of data modeling is called logical data modeling, or information modeling drafting. The information needed for a specific is represented in the given business sector.
The database-specific context is provided by a physical data model, which elaborates on the logical and conceptual models developed before. Physical data models are therefore frequently used as the guide for a suggested database.
•
Visualize data from anywhere at any time
: Organizations can utilize Erwin DM to visualize their data, whether it is unstructured or structured, and regardless of whether it is kept in a data warehouse or relational database or the cloud—all through a single user interface.
•
Automate the creation of database schema and data models:
Users of Erwin DM gain from enhanced automation features that save them time and increase effectiveness and lowering mistakes.
•
Centralize the creation and administration of models:
The integrated perspective for conceptual, logical, and physical data models, Erwin DM assist in closing knowledge gaps between technical and business parties.
•
Promote accountability, teamwork, and data literacy:
Boost decision-
making and data intelligence throughout the company by utilizing the stakeholder’s capacity to use, comprehend, and rely on pertinent data.
•
Boost application development agility
: By combining and creating hybrid architectures—traditional, cloud, on-premises, and Big Data—into your applications.
Cut expenses and risks:
Risks are decreased, and data definitions and structures are standardized through automation costs, as well as the ability to test updates and new applications prior to release into production.
(References: https://www.quora.com/How-is-data-modeling-different-from-database-design )
3.
Describe a specific challenge in the creation of a data model, and how to potentially overcome it.
Data modeling is a process that involves defining, structuring, and organizing data elements as well as establishing the relationships between them to meet business requirements. However, as organizations generate and capture an ever-increasing volume of data, data modelers face the significant challenge of scaling their data models to accommodate this growth. This phenomenon, known as data growth, presents several specific hurdles.
•
Intensity and Speed
: The volume and velocity of data are growing exponentially, making it difficult for standard data modeling techniques to keep up. The complexity of the model increases with the size of the data collection, which frequently results in higher system load and poorer performance.
•
Changing Structures of Data: Data becomes more complex as it grows. The data model must be updated and enlarged on a regular basis due to the emergence of new data kinds and sources, which can be a laborious procedure.
•
Achievement:
Performance problems with data manipulation and querying might arise from large data volumes. This may be the result of excessive I/O operations, improper indexing, and query design, all of which can lead to latency and bad user experience.
Strategies to Overcome Challenges
•
Data Modeling in Modules:
Managing complexity can be facilitated by using a modular approach to data modeling. This entails developing unique models that can be independently created and scaled for various business elements.
•
Archiving and Tiering Data
: Use data tiering techniques to shift less-
frequently-accessed data to less expensive storage options. One way to lessen the strain on primary data storage is to archive old data.
•
Utilizing Dispersed Systems
: Make use of distributed databases, which are more effective than conventional relational database systems at handling enormous amounts of data. By spreading the data across several nodes, technologies like distributed computing platforms like Apache Hadoop and NoSQL databases like MongoDB and Cassandra are made to scale out.
•
Data Management Automation
: To automate data management processes that might otherwise become unmanageable with massive data sets, such as data quality checks, de-duplication, and compression, use machine learning and AI tools.
•
Governance of Data:
Robust data governance principles guarantee that only pertinent and high-quality data is retained, which can aid in managing data expansion. Policies for data disposal, quality control, and retention may fall under this category.
•
Consistent Performance Evaluations
: To find inefficiencies and bottlenecks in the underlying databases and data model, conduct performance audits. Over time, performance loss can be avoided with this proactive strategy.
(References: https://www.upgrad.com/blog/major-challenges-of-big-data/# )
4.
Identify and explain three potential negative outcomes that could result from by an inaccurate data model. An inaccurate data model is one that fails to correctly capture or project the trends and intricacies present in the data it aims to mimic or predict. The roots of this inaccuracy are varied, encompassing issues like substandard data quality, the phenomenon of overfitting or underfitting, baseless presumptions, or the selection of unsuitable modeling methods. There are numerous potential negative repercussions associated with the dependence on a flawed data model.
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Poor Decision Making
The most direct consequence of an inaccurate data model is the facilitation of poor decision-making processes. Decision-makers often rely on data models to understand complex realities, forecast future trends, and evaluate the potential impacts of their decisions. If a model does not accurately reflect reality, any decisions based on that model are likely to be flawed.
Strategic Failures
: In business, for example, if a model inaccurately predicts market demand, a company might overproduce, leading to excess inventory costs, or underproduce, resulting in missed opportunities and customer dissatisfaction.
Policy Mistakes
: In public policy, models that inaccurately predict the outcomes of interventions can lead to misguided policies that fail to address the issues they are meant to or, worse, exacerbate existing problems.
Erosion of Trust
Inaccurate data models can lead to an erosion of trust among stakeholders. This can be particularly damaging when models are used to communicate information to the public or within an organization.
Public Mistrust:
In healthcare, for example, models that inaccurately predict the spread of a disease could lead to public confusion and mistrust in health advisories and government agencies.
Organizational Credibility:
Within a corporation, persistent reliance on faulty models can erode employee confidence in management and reduce faith in data-
driven initiatives.
Legal and Regulatory Repercussions
Inaccurate models can lead to violations of legal and regulatory standards, particularly in sectors where decisions are heavily regulated, such as healthcare, finance, and environmental policy.
Non-compliance Penalties:
Companies may face penalties if models used for regulatory reporting, such as those related to emissions or financial solvency, are inaccurate.
Litigation Risks:
There can be a heightened risk of litigation if stakeholders, such as investors or customers, are misled due to inaccurate models, leading to financial or personal harm.
(References:
https://www.unleashedsoftware.com/blog/5-major-consequences-poor-quality-
data-how-avoid )
5.
Identify and explain potential advantages and disadvantages of including senior business managers and executives in data modeling exercise.
A Senior Business Manager recruits, trains, coaches and develops talent to their full potential with exceptional quantitative/analytical competencies, as well as project management skills and leadership skills. Manage and set priorities of analyst, assign projects, and allocate resources, communicate business performance and project progress to management & business partners. Data modeling is a process used to define and analyze data requirements needed to support the business processes within the scope of corresponding information systems in organizations.
Advantages:
•
Strategic Alignment:
Senior managers can ensure that the data models are aligned with the broader strategic goals of the organization. This alignment is crucial for creating models that not only serve immediate needs but also contribute to long-term objectives.
•
Knowledge and expertise:
Executives frequently possess a plethora of information regarding the business operations of the company, the state of the market, and the competitive environment. Their knowledge can help the modeling process, improving the relevance and stability of the models.
•
Resource Allocation:
Because they have the power to dedicate the money and personnel required to support the data modeling activities, their engagement can help with resource allocation.
Disadvantages:
•
Time Restrictions
: Due to their busy schedules, senior managers and executives may find it inefficient to devote all their time to intricate data modeling exercises when they could be using it for more high-level strategic endeavors. •
Potential Over-Simplification:
To speed up decision-making, executives may push for models that are unduly simplified, which could fail to capture the essential complexity of the data and produce subpar results.
•
Decision-Making:
The involvement of upper management might possibly inject bias into the modeling process since team members would be more likely to agree with the executives' viewpoints, which could lead them to ignore important information or other strategies.
•
Danger of Misalignment:
Although CEOs have a broad perspective, they might not have the in-depth operational understanding that technical specialists or middle managers do.
(References: https://www.bizspace.co.uk/news/senior-management-team-roles )
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