1. Definition of DBMS and its Purposes(s) In Business. Definition: A database management system (DBMS) is a software package designed to define, manipulate, retrieve and manage data in a database. A DBMS generally manipulates the data itself, the data format, field names, record structure and file structure. It also defines rules to validate and manipulate this data. A DBMS relieves users of framing programs for data maintenance. Fourth-generation query languages, such as SQL, are used along with the DBMS package to interact with a database. Some dbms examples are: MySQL, SQL Server, Oracle, dBase, and FoxPro. Purpose of Database Management Systems Organizations use large amounts of data. A database management system (DBMS) is a software tool that makes it possible to organize data in a database. The standard acronym for database management system is DBMS, so you will often see this instead of the full name. The ultimate purpose of a database management system is to store and transform data into information to support making decisions. A DBMS consists of the following three elements: The physical database: the collection of files that contain the data The database engine: the software that makes it possible to access and modify the contents of the database The database scheme: the specification of the logical structure of the data stored in the database 1. To see why database management systems are necessary, let's look at a typical ``fileprocessing system'' supported by a conventional operating system. The application is a savings bank: Savings account and customer records are kept in permanent system files. Application programs are written to manipulate files to perform the following tasks: Debit or credit an account. Add a new account. Find an account balance. Generate monthly statements. Development of the system proceeds as follows: New application programs must be written as the need arises. New permanent files are created as required. but over a long period of time files may be in different formats, and Application programs may be in different languages. 2. So we can see there are problems with the straight file-processing approach: Data redundancy and inconsistency Same information may be duplicated in several places. All copies may not be updated properly.
Difficulty in accessing data o May have to write a new application program to satisfy an unusual request. o E.g. find all customers with the same postal code. o Could generate this data manually, but a long job... o Data isolation
Data in different files. o Data in different formats. o Difficult to write new application programs.
Multiple users o Want concurrency for faster response time. o Need protection for concurrent updates. o E.g. two customers withdrawing funds from the same account at the same time account has $500 in it, and they withdraw $100 and $50. The result could be $350, $400 or $450 if no protection.
Security problems o Every user of the system should be able to access only the data they are permitted to see. o E.g. payroll people only handle employee records, and cannot see customer accounts; tellers only access account data and cannot see payroll data. o Difficult to enforce this with application programs.
Integrity problems o Data may be required to satisfy constraints. o E.g. no account balance below $25.00. o Again, difficult to enforce or to change constraints with the file-processing approach.
These problems and others led to the development of database management systems.
2. Introduce the business scenario to analyze and discuss the benefits of implementing a database management system.
Database management systems are specifically designed for the storage and retrieval of (large amounts of) data. Banks handle large amounts of data. A DBMS enables them to store that data, operate on it, and retrieve it when needed, fast enough for their and their customer’s needs. That’s not the full story. Most banks use a DBMS type called “relational” (RDBMS for short), for good reasons. These reasons are abbreviated ACID, for the four properties that every RDBMS places above all else: Atomicity: When multiple changes are declared part of a single transaction, then all of them will fail or all will succeed; never a part. So if I transfer money to you and the computer fails after debiting my account but before crediting yours, then the RDBMS will ensure that, once the server is restarted, either the rest of the transaction completes or (usually) the part that was already done is undone. In other words, it can never happen that money “disappears” because my account is debited but yours is not credited. Consistency: Certain business rules can be declared within the database and then the RDBMS ensures that these will never be violated. Things such as not being able to enter a transfer order from an account number that does not exist, or not being able to enter a transfer order with a negative amount. Isolation: For each user, it appears as if they are the sole user of the database; in other words you will never “see” unfinished work from other users. When one clerk processes a transfer of $100,000 from your checking account to your savings account and another clerk looks at your value, he can either see the reality as it was before the transfer started (lot of money in the checking account, no money in the savings account), or after (empty checking account, saving account at $100,000); but never the “halfway completed” version where the money is in transfer and you appear to have suddenly lost your credit rating. (The clerk handling the transfer might see that intermediate state because that’s part of HIS transaction). Durability: Whatever happens to the computer, once a transaction is reported as complete the RDBMS ensures that the changes are permanent. So if someone transfers money from another bank to your account and your bank has a power outage just when this happens, then either it happens before the other bank gets acknowledgement (and they’ll know to wait a while and then retry); or the other bank gets confirmation and the RDBMS guarantees that the credit to your account is not lost. Without the “D” of ACID, there are some scenarios where the power can get lost when the change is in memory but not yet written to hard disk or other permanent storage, and you would lose your money.
3. Describe what business data the company would benefit from collecting and manipulating. With so much data to sort through, you need something more from your data:
You need to know it is the right data for answering your question;
You need to draw accurate conclusions from that data; and
You need data that informs your decision making process
In short, you need better data analysis. With the right data analysis process and tools, what was once an overwhelming volume of disparate information becomes a simple, clear decision point. To improve your data analysis skills and simplify your decisions, execute these five steps in your data analysis process: Step 1: Define Your Questions In your organizational or business data analysis, you must begin with the right question(s). Questions should be measurable, clear and concise. Design your questions to either qualify or disqualify potential solutions to your specific problem or opportunity. For example, start with a clearly defined problem: A government contractor is experiencing rising costs and is no longer able to submit competitive contract proposals. One of many questions to solve this business problem might include: Can the company reduce its staff without compromising quality? Step 2: Set Clear Measurement Priorities This step breaks down into two sub-steps: A) Decide what to measure, and B) Decide how to measure it. A) Decide What To Measure Using the government contractor example, consider what kind of data you’d need to answer your key question. In this case, you’d need to know the number and cost of current staff and the percentage of time they spend on necessary business functions. In answering this question, you likely need to answer many sub-questions (e.g., Are staff currently under-utilized? If so, what process improvements would help?). Finally, in your decision on what to measure, be sure to include any reasonable objections any stakeholders might have (e.g., If staff are reduced, how would the company respond to surges in demand?). B) Decide How To Measure It Thinking about how you measure your data is just as important, especially before the data collection phase, because your measuring process either backs up or discredits your analysis later on. Key questions to ask for this step include:
What is your time frame? (e.g., annual versus quarterly costs)
What is your unit of measure? (e.g., USD versus Euro)
What factors should be included? (e.g., just annual salary versus annual salary plus cost of staff benefits)
Step 3: Collect Data With your question clearly defined and your measurement priorities set, now it’s time to collect your data. As you collect and organize your data, remember to keep these important points in mind:
Before you collect new data, determine what information could be collected from existing databases or sources on hand. Collect this data first.
Determine a file storing and naming system ahead of time to help all tasked team members collaborate. This process saves time and prevents team members from collecting the same information twice.
If you need to gather data via observation or interviews, then develop an interview template ahead of time to ensure consistency and save time.
Keep your collected data organized in a log with collection dates and add any source notes as you go (including any data normalization performed). This practice validates your conclusions down the road.
Step 4: Analyze Data After you’ve collected the right data to answer your question from Step 1, it’s time for deeper data analysis. Begin by manipulating your data in a number of different ways, such as plotting it out and finding correlations or by creating a pivot table in Excel. A pivot table lets you sort and filter data by different variables and lets you calculate the mean, maximum, minimum and standard deviation of your data – JUST BE SURE TO AVOID THESE FIVE PITFALLS OF STATISTICAL DATA ANALYSIS. As you manipulate data, you may find you have the exact data you need, but more likely, you might need to revise your original question or collect more data. Either way, this initial analysis of trends, correlations, variations and outliers helps you FOCUS YOUR DATA ANALYSIS ON BETTER ANSWERING YOUR QUESTION and any objections others might have. Step 5: Interpret Results After analyzing your data and possibly conducting further research, it’s finally time to interpret your results. As you interpret your analysis, keep in mind that you cannot ever prove a hypothesis true: rather, you can only fail to reject the hypothesis. Meaning that no matter how much data you collect, chance could always interfere with your results. As you interpret the results of your data, ask yourself these key questions:
Does the data answer your original question? How?
Does the data help you defend against any objections? How?
Are there any limitation on your conclusions, any angles you haven’t considered?
If your interpretation of the data holds up under all of these questions and considerations, then you likely have come to a productive conclusion. The only remaining step is to use the results of your data analysis process to decide your best course of action. By following these five steps in your data analysis process, you make better decisions for your business or government agency because your choices are backed by data that has been robustly collected and analyzed. With practice, your data analysis gets faster and more accurate – meaning you make better, more informed decisions to run your organization most effectively.
4. Explain how a company uses a database management system to manage data collection, manipulate data and realize benefits from usage of a database management system. Collecting data When planning on how best to collect data in Step 4, it is important to be aware of the practical considerations and best practices for addressing logistical challenges organizations often face at this stage of the process. Implementing a data collection plan requires attention to matters such as:
Getting buy-in from senior leadership and key stakeholders, in or outside of the organization. This group could include boards of directors, management committees, union representatives, employees, community groups, tenants, customers and service users. Establishing a steering committee or selecting a person(s) to be consulted and held accountable for all major decisions about the data collection process, such as design, logistics, communication management, coordination and finances. Determining who will collect the data (e.g., experts or trained employees). Identifying the logistics, resources, technology and people needed to develop and implement a data collection initiative. Anticipating and addressing key stakeholder concerns and questions about the project. Designing a communication and consultation strategy that will explain the data collection initiative and encourage the highest possible participation rate. Protecting privacy and personal information by using carefully controlled procedures for collecting, storing and accessing data that comply with privacy, human rights and other legislation. Dignity and confidentiality must be respected. Minimizing the impact and inconvenience for the people affected in the workplace or service environment, which includes choosing the best time to collect the data. Aiming for flexibility to allow for changes without great expense or inconvenience. Considering a test period or a pilot phase to allow you to improve and modify data collection methods, as may be needed.
Manipulating data Whether quantitative and/or qualitative methods of gathering data are used, the analysis can be complex, or less so, depending on the methods used and the amount of data collected. Explaining the technical steps involved in analyzing and interpreting data is beyond the scope of this guide. An organization will have to determine whether it has the internal capacity and expertise to analyze and interpret data itself, or whether it will need the help of an external consultant. A smaller organization that has basic data collection needs may be able to rely on internal expertise and existing resources to interpret the meaning of gathered data. Example: An organization with 50 employees wants to find out if it has enough women working in management positions, and if there are barriers to equal opportunity and advancement. The organization counts the number of female employees it has (25), and determines how many of these employees are working in supervisory and management positions (two). A few motivated employees identify some issues of concern, like gender discrimination, that may have broader implications for the organization as a whole. After deciding to do an internal and external assessment (Step 1), and gather qualitative data using focus groups and interviews with current and past employees, senior leadership decides that barriers exist for women in the organization’s recruitment, hiring, promotion and human resources policies, processes and practices. Efforts are made to work with female employees, human resources and other staff to address these barriers. The organization makes a commitment to foster a more equitable, inclusive work environment for all employees. Retrieving Benefits from DBMS Once an organization has analyzed and interpreted the results of the data collected, it may decide to act on the data, collect more of the same type of data or modify its approach. Quantitative and qualitative information can provide a solid basis for creating an effective action plan designed to achieve strategic organizational human resources, human rights, equity and diversity goals identified through the data collection process. If an organization feels it has enough information to develop an action plan, it should consider including the following elements:
a summary of the results of the analysis and interpretation of the data identification of the barriers, gaps and opportunities that exist or may exist for Code-protected persons and other individuals/groups based on non-Code grounds steps that will be taken to address these barriers, gaps or opportunities now and in the future realistic, attainable goals with short-term and longer-term timelines input sought from stakeholders and affected communities how progress in meeting these goals will be monitored, evaluated and reported. In some cases, an organization may decide that it needs to collect more information because there are gaps in the data collected, or areas where the data is unclear or inconclusive. This may
prompt them to conduct a more detailed internal and external assessment or try another approach. 5. Discuss the components of a DBMS and the process of developing a DBMS for implementation. DBMS Components Hardware When we say Hardware, we mean computer, hard disks, I/O channels for data, and any other physical component involved before any data is successfully stored into the memory. When we run Oracle or MySQL on our personal computer, then our computer's Hard Disk, our Keyboard using which we type in all the commands, our computer's RAM, ROM all become a part of the DBMS hardware. Software This is the main component, as this is the program which controls everything. The DBMS software is more like a wrapper around the physical database, which provides us with an easy-touse interface to store, access and update data. The DBMS software is capable of understanding the Database Access Language and intrepret it into actual database commands to execute them on the DB. Data Data is that resource, for which DBMS was designed. The motive behind the creation of DBMS was to store and utilise data. In a typical Database, the user saved Data is present and meta data is stored. Metadata is data about the data. This is information stored by the DBMS to better understand the data stored in it. For example: When I store my Name in a database, the DBMS will store when the name was stored in the database, what is the size of the name, is it stored as related data to some other data, or is it independent, all this information is metadata. Procedures Procedures refer to general instructions to use a database management system. This includes procedures to setup and install a DBMS, To login and logout of DBMS software, to manage databases, to take backups, generating reports etc. Database Access Language Database Access Language is a simple language designed to write commands to access, insert, update and delete data stored in any database. A user can write commands in the Database Access Language and submit it to the DBMS for execution, which is then translated and executed by the DBMS.
User can create new databases, tables, insert data, fetch stored data, update data and delete the data using the access language. Process of developing DBMS A database is usually a fundamental component of the information system, especially in business oriented systems. Thus database design is part of system development. The following picture shows how database design is involved in the system development lifecycle. The phases in the middle of the picture (Database Design, Database Implementation) are the phases that you concentrate on in the Database Design course. The other phases are briefly described. They are part of the contents of the Systems Analysis and Design courses, for example. There are various methods of how the different phases of information system design, analysis and implementation can be done. Here the main tasks or goals are described but no method is introduced.
Database Planning The database planning includes the activities that allow the stages of the database system development lifecycle to be realized as efficiently and effectively as possible. This phase must be integrated with the overall Information System strategy of the organization. The very first step in database planning is to define the mission statement and objectives for the database system. That is the definition of:
- the major aims of the database system - the purpose of the database system - the supported tasks of the database system - the resources of the database system Systems Definition In the systems definition phase, the scope and boundaries of the database application are described. This description includes: - links with the other information systems of the organization - what the planned system is going to do now and in the future - who the users are now and in the future. The major user views are also described. i.e. what is required of a database system from the perspectives of particular job roles or enterprise application areas. Requirements Collection and Analysis During the requirements collection and analysis phase, the collection and analysis of the information about the part of the enterprise to be served by the database are completed. The results may include eg: - the description of the data used or generated - the details how the data is to be used or generated - any additional requirements for the new database system Database Design The database design phase is divided into three steps: - conceptual database design - logical database design - physical database design In the conceptual database design phase, the model of the data to be used independent of all physical considerations is to be constructed. The model is based on the requirements specification of the system. In the logical database design phase, the model of the data to be used is based on a specific data model, but independent of a particular database management system is constructed. This is based on the target data model for the database e.g. relational data model. In the physical database design phase, the description of the implementation of the database on secondary storage is created. The base relations, indexes, integrity constraints, security, etc. are defined using the SQL language. Database Management System Selection This in an optional phase. When there is a need for a new database management system (DBMS), this phase is done. DBMS means a database system like Access, SQL Server, MySQL, Oracle. In this phase the criteria for the new DBMS are defined. Then several products are evaluated
according to the criteria. Finally the recommendation for the selection is decided. Application Design In the application design phase, the design of the user interface and the application programs that use and process the database are defined and designed. Protyping The purpose of a prototype is to allow the users to use the prototype to identify the features of the system using the computer. There are horizontal and vertical prototypes. A horizontal prototype has many features (e.g. user interfaces) but they are not working. A vertical prototype has very few features but they are working. See the following picture.
Implementation During the implementation phase, the physical realization of the database and application designs are to be done. This is the programming phase of the systems development. Data Conversion and Loading This phase is needed when a new database is replacing an old system. During this phase the existing data will be transferred into the new database. Testing Before the new system is going to live, it should be thoroughly tested. The goal of testing is to find errors! The goal is not to prove the software is working well. Operational Maintenance The operational maintenance is the process of monitoring and maintaining the database system. Monitoring means that the performance of the system is observed. If the performance of the system falls below an acceptable level, tuning or reorganization of the database may be required. Maintaining and upgrading the database system means that, when new requirements arise, the new development lifecycle will be done.
6. Discuss why companies utilize various systems for domestic and global business functions. Controlling Data Redundancy In non-database systems each application program has its own private files. In this case, the duplicated copies of the same data is created in many places. In DBMS, all data of an organization is integrated into a single database file. The data is recorded in only one place in the database and it is not duplicated. Sharing of Data In DBMS, data can be shared by authorized users of the organization. The database administrator manages the data and gives rights to users to access the data. Many users can be authorized to access the same piece of information simultaneously. The remote users can also share same data. Similarly, the data of same database can be shared between different application programs. Data Consistency By controlling the data redundancy, the data consistency is obtained. If a data item appears only once, any update to its value has to be performed only once and the updated value is immediately available to all users. If the DBMS has controlled redundancy, the database system enforces consistency. Integration of Data In Database management system, data in database is stored in tables. A single database contains multiple tables and relationships can be created between tables (or associated data entities). This makes easy to retrieve and update data. Integration Constraints Integrity constraints or consistency rules can be applied to database so that the correct data can be entered into database. The constraints may be applied to data item within a single record or the may be applied to relationships between records. Data Security Form is very important object of DBMS. You can create forms very easily and quickly in DBMS. Once a form is created, it can be used many times and it can be modified very easily. The created forms are also saved along with database and behave like a software component. A form provides very easy way (user-friendly) to enter data into database, edit data and display data from database. The non-technical users can also perform various operations on database through forms without going into technical details of a fatabase. Report Writers Most of the DBMSs provide the report writer tools used to create reports. The users can create very easily and quickly. Once a report is created, it can be used may times and it can be modified very easily. The created reports are also saved along with database and behave like a software component.
Control Over Concurrency In a computer file-based system, if two users are allowed to access data simultaneously, it is possible that they will interfere with each other. For example, if both users attempt to perform update operation on the same record, then one may overwrite the values recorded by the other. Most database management systems have sub-systems to control the concurrency so that transactions are always recorded with accuracy. Backup and Recovery Procedures In a computer file-based system, the user creates the backup of data regularly to protect the valuable data from damage due to failures to the computer system or application program. It is very time consuming method, if amount of data is large. Most of the DBMSs provide the 'backup and recovery' sub-systems that automatically create the backup of data and restore data if required. Data Independence The separation of data structure of database from the application program that uses the data is called data independence. In DBMS, you can easily change the structure of database without modifying the application program.
Full description about Data Base Management System DBMS, its benefits and components of DBMS