Conceptual Modeling Design

Running Head: DATABASE DESIGN 2

2

DATABASE DESIGN

Database Design

Institution

Name

Date

Musical artist managers sometimes find it hard to organize shows for their artists. Having taken a look at some of these issues there is a proposal to use certain applications developed by companies to help manage artists in a much easier way. here I propose the use of the back on-stage database.

This helps artist managers in various ways. being an application, the information that will be required is for the manager to sign up with the app then fill in the detail about the company and the artist. It helps in booking gigs for the clients, has inquiry follow-ups this includes keeping invoices with inline payments (Zhou, 2019). This is meant for the managers and their staff members who are taking part in preparing events for the artists. the back on stage needs information in the artist this includes the name and the type of music they produce, the targeted crowd to help during finding gigs for the artists, amount paid in the events, and number of the artist

The system should be able to capture the marketing of the artist should be sure to reach the targeted audience for the artist. should be reliable where the event should certain returns in terms of cash flow and music sales and promotions (Zhou, 2019). the database should cover artist performance schedules, venues, communication between the manager artists and the audience. payouts this is in terms of promotions, such as t-shirts caps, and hoods it also builds hype during artist performance. the database will store music released, schedule performance and tours. the best database should store information conceding the manager's duties and the client's needs (Zhou, 2019). The database should help the manager plan and reduce the expenditure in paying event managers and people to help put up or book events.

References

Zhou, N. (2019). Database design of regional music characteristic culture resources based on improved neural network in data mining. Personal and Ubiquitous Computing, 24(1), 103–114. https://doi.org/10.1007/s00779-019-01335-9