Business Intelligence and Data Processing: Tools, Techniques, and Decision Making, High school final essays of Computer science

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Business

Intelligence

Learning Outcomes

By the end of this unit students will be able to: LO1. Discuss business processes and the mechanisms used to support business decision-making. LO2. Compare the tools and technologies associated with business intelligence functionality. LO3. Demonstrate the use of business intelligence tools and technologies. LO4. Discuss the impact of business intelligence tools and technologies for effective decision-making purposes and the legal/regulatory context in which they are used.

“ Factual information (such as measurements or statistics) used as a basis for reasoning, discussion, or calculation.”

What is data?

Why do we need Data ????

• (^) Improve people’s life
• (^) Make informed decision
• (^) Get the results you want
• (^) Find solutions to the problem etc

DATA INPUT/ENTRY

• Input and storage of text and numbers from a document into an electronic system.
• (^) Done by means of automated computer software or manual entry, depending on the type of document.
• When data sources include handwriting, it gets trickier to utilize automated processing methods.
• (^) Handwritten data requires manual data entry which is more expensive and time consuming than automated software.

DATA PROCESSING

• Manipulation of data by a computer.
• (^) Data processing occurs when data is collected and translated into usable information.
• It includes the conversion of raw data to machine-readable form, flow of data through the CPU and memory to output devices, and formatting or transformation of output.
• (^) Its usually performed by Data Scientists.
• (^) Data processing needs to be done correctly, as it should not affect the end-product or data output.
• (^) The data processing starts with data in its raw form and converts into a more readable format (graphs, documents etc.)

STAGES OF DATA PROCESSING

• Data collection - Data is pulled from available sources, including data lakes and data warehouses.
• (^) Data preparation - The stage at which raw data is cleaned up and organized for the stage of data processing.
• (^) Data input - The clean data is then entered into its destination and translated into understandable language.
• (^) Processing - Processing is done using machine learning algorithms, though the process itself may vary slightly depending on the source of data being processed.
• (^) Data output/interpretation - It is translated, readable, and often in the form of graphs, videos, images, plain text, etc.).
• Data storage - After all of the data is processed, it is then stored for future use.

DATA SECURITY CONSIDERATIONS

• Basically, to protect programs and data in computers and communication systems against unauthorized access, modification, destruction, disclosure or transfer
• (^) It could be accidental or intentional by building physical arrangements and software checks.
• (^) It refers to an individual's or an organization's right to refuse or limit the collection and use of data about illegal access.
• (^) Data security necessitates system administrators constructing physical arrangements and software checks to reduce unwanted access to systems.

DATA SECURITY CONSIDERATIONS

Various methods that keeps data safe and secured:

1. Assuring the data's integrity.
2. Assuring the data's confidentiality.
3. Data loss or destruction should be avoided at all costs. How can we Protect the Data?

• (^) Backups
• (^) Archival Storage
• (^) Disposal of Data

ARCHIVAL STORAGE

• Data archiving is the process of storing data in a secure location for long periods of time.
• (^) The information might be saved in a secure area and retrieved as needed.
• The archived data is still important to the company and may be needed in the future.
• (^) Data archives are also indexed and searchable, allowing files and parts of files to be quickly identified and retrieved.
• (^) Data archiving is a method of lowering primary data storage usage and associated costs.

ARCHIVAL STORAGE

• Data archives has many different forms:

1. Archive material is stored online on disk systems, where it is easily accessible.
2. Using data archiving software, offline data storage copies archive data on tape or other removable media.
3. Another option for archiving is cloud storage. Example: Amazon Glacier

DISPOSAL OF DATA

• (^) The method of erasing data recorded on tapes, hard disks, and other electronic media so that it is fully unreadable, unusable, and inaccessible for unlawful reasons is known as data destruction or data disposal.
• It also assures that the company keeps data records for as long as they are required.
• (^) When they are no longer needed, properly eliminates them or disposes of them in another manner, such as by transferring them to an archive service.

BENEFITS - DISPOSAL OF DATA

• (^) It prevents the business from incurring needless storage expenditures by utilizing office or server space to store records that are no longer required.
• (^) Because there is less to search for, finding and retrieving information is easier and faster.

CRITICAL HANDLING - DISPOSAL OF DATA

• (^) The sheer size of a legacy record needs more attention.
• (^) The functions are being transferred to another authority, and data records are being disposed of as part of the transition.

STRUCTURED DATA

• (^) Structured data is data whose elements are addressable for effective analysis.
• (^) It has been organized into a formatted repository that is typically a database.
• (^) It concerns all data which can be stored in database SQL in a table with rows and columns.
• (^) They have relational keys and can easily be mapped into pre-designed fields. Example: Relational data.

SEMI - STRUCTURED DATA

• (^) Semi-structured data is information that does not reside in a relational database but that has some organizational properties that make it easier to analyze.
• (^) With some processes, you can store them in the relation database (it could be very hard for some kind of semi-structured data), but Semi-structured exist to ease space. Example : XML data.