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Variations in Database Systems - Colorado Technical University

Future database system implementation plan

Question: What fundamental differences exist between object­ oriented and object ­relational database systems and Web ­based database systems?

Solution:  

Difference between object-oriented and object-relational database systems

A relational database stores data by means of tables while object oriented database stores data as objects. Data are read and stored inclusive of the actions. The advantage with relational model is that it helps in linking data and retrieving the essential data irrespective of the large volumes present quickly. Web based database systems follow object relational system. Relational database is known to handle relatively simpler data unlike the object oriented system and this is the reason for wide application of relational database system when the entities are similar and grouped together to arrive at the results.

Question: Would these differences impact your retail store?

Solution:

Impact on the retail store

These differences would certainly impact in terms of storage and retrieval processes associated with the data stored in the data warehouse.

Question: Details of what changes would need to be introduced to the database if it was used to build a data mart or a data warehouse.

Solution:

Changes to database to build data warehouse

The brick and mortar store that has a physical entity also has an online store and relational data of the customers are stored in the database. Since the volume of data is expected to be high, it is important to construct a data warehouse or a data mart.

Should we plan to construct a warehouse, the first step is to create operational databases with the help of relational data (Botelho, 2018). Further, it is essential to diversify the relational sources and apply schema-on-write to normalize the available data. In general, a relational database is helpful in processing the transactions but restricts to one source. This has to be diversified. The subsequent change will be to curate data at a central server so that accessibility becomes easier and simpler. Instead of consistent flow of data, the database will be tweaked to store historical data in the central server and use for key business decisions.

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Question: What considerations would need to be made if the database were to become a distributed database.

Solution:

Considerations for a distributed database

Similar to the points mentioned in the previous section, the existing relational database system will be centralized without affecting the data relationships. The next step is to improve data redundancy. In general, a relational database is creating keeping redundancy avoidance in mind. However, a distributed database would completely prefer multiple copies of data as the control and accessibility are made easier.

Finally, the data in regular database will be split into different parts without affecting the original relations so that tuples can be accessed without any data loss and new data can be added ensuring a lossless join.

Question: What specific types of business intelligence could be gathered from the database?

Solution:

Business intelligence types from database

From the created database, it is possible to gather three types of business intelligence features - predictive analytics, reporting and data mining (Selecthub, 2018).

The whole idea of the firm is to simplify the data access and perform statistical analysis with the acquired datasets. This is entirely possible with the database created. First of all, the datasets are distributed and it is possible to take decisions based on tuple. Secondly, it ensures better control of the stakeholders towards the data and finally, there is a possibility for centralized reporting.

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Question: How would this information assist in the decision ­making process for your retail store?

Solution:

Influence on decision making process

With the help of the business intelligence types mentioned in the previous section, it is possible to take decisions for the subsequent financial years. The predictive analytics can indicate the customer behaviour and financial performance in the coming quarters that can influence the overall productivity of the firm. Further, this can give accurate information on the performance of the firm due to quality tools and features.

Question: How would your retail store benefit from data warehousing in the following areas?

Solution:

Benefits of data warehousing on retail store

3-year ROI

Before we detail the ROI, it is important to see where the costs can be cut down.

-          Information management

-          Data analysis

-          Multiple decision support tools

-          Operational efficiency

-          Hardware and software costs

-          Procurement

-          Period of business cycle

The keepers for the business can be cut down by at least 20%. Again, 30% of the time spent by the managers is cut down. Further, it cuts down the user's time spent in the system by 50%. This eventually multiplies ROI of the organization by 20% in the first year, 40% in the second year and up to 70% in the third year since major changes in the organization will be made (Kelley, 2001).

Competitive advantage

It is possible to take accurate decisions and report the market events without having to take external consultations. The accurate predictions can give a better sense of direction to the organization thereby resulting in market leadership.

Increased productivity of decision makers

Decision makers need not have to spend several hours in collating the reports and calculating the investments and returns. In just a click, it is now possible to understand the needs of the customers and revise the decisions for a better financial year ahead. Most importantly, decision makers gain better control over the data and internal business processes.

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Question: How would you address the following data warehousing problems if they occurred in your retail store?

Solution:

Solution to data warehousing problems

Required data never captured

To ensure that this never happens, it is important to choose the right metrics and create a data governance team that is cross functional. The data capture processes will have to be assessed on a regular basis.

High demand for disk space

If having a dedicated disk space is a challenge, it is a good idea to go for cloud based solutions that offer on demand data storage services. This will eventually eliminate the disk space costs.

Hidden problems with source systems

This happens during a data import in the original source system. A common instance where hidden problems develop is when humans enter the data and result in manual errors. Automated data entry and processing can be helpful in avoiding the hidden problems associated with source systems.

Influence of this part on goals of case study

As the chosen organization deals largely with data, this part of the case analysis will help in understanding how the data can be captured and used effectively. Further, it gives a clarity on how the chosen database type can benefit the decision makers and maximize the overall productivity of the organization.

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