Computer Applications in Business Aarthy ppt.pdf
- 2. DATA-MEANING ✔The word data is the plural of the word datum, which means fact. ✔Therefore, data means collection of facts.(Numbers, Text) ✔Data can be considered as the raw material of information. ✔The data may be either numerical or non numerical such as sales report, inventory figures, etc., or non numerical like customer names, addresses, etc., ✔The term "data" typically refers to raw, unprocessed facts, figures, observations, or symbols. It's essentially the building blocks of information.
- 3. TYPES OF DATA • Qualitative data: classification characteristics of nature of things such as good, blue etc., • Quantitative data: It is expressed in terms of measurable quantities such as 100 tons, 98.4 degrees F etc., • Numeric Type: This type of data may also be an integer(+,-) or fractions. • Floating point representation: it consists of 3 components namely: a) Mantissa(Fractions- logarithm) b) Radix or Base (number system) c) Exponent (expressing large numbers) Besides above it also includes alphabetic data and alphanumeric data.
- 4. PROPERTIES OF DATA ✔ Accuracy- true/ inaccurate leads to incorrect conclusions and decisions ✔ Completeness-all necessary information/incomplete data leads to biased results. ✔ Consistency- uniform/inconsistent cause errors in data entry ✔ Reliability-accuracy and consistency ✔ Relevance- relevant to particular matter or situation/irrelevant ✔ Timeliness-timely data helpful to update/outdated data will not be relevant.
- 5. Digitalization of Data and Information ✔ Conversion- text, images, audio, video ✔ Storage-Digital data is stored in electronic storage devices such as hard drives, magnetic tapes, and optical discs, pen drives. ✔ Processing-using computers and digital processing techniques. This includes tasks such as data analysis, manipulation, transformation, and computation. ✔ Transmission- Digital data can be transmitted over communication networks such as the internet, intranets, and wireless networks. Exchange of information between devices and systems located in different geographical locations.
- 6. ✔Accessibility-accessed and retrieved quickly and efficiently ✔Search and Retrieval -allows users to quickly locate and access specific information from large datasets or repositories. ✔Security- Digital data can be protected using encryption, access controls, authentication mechanisms, and other security measures. ✔Archiving and Preservation-archived and preserved for long-term storage and access. (back up)
- 7. S/N o. Operation Data Information 1. Typing of students name, Matriculation number and scores in computer science Characters like alphabets (A-Z, a-z), digits (0-9), or special characters ( +,-,*,/) Set of characters (words) like 23CMA001 Aadhira 2. Computation of a class average science in computer science Each student's test score in computer science The class average score in Computer science Ex.60 The Table below shows example of data being used as information
- 8. DATA INFORMATION Data is raw, an unchanged fact Information is an organised and sorted fact It serves as input into the computer system It serves as an output from the computer system Observation and recording are done to produce data Analysis of data are done to obtain information Data is the lowest level of knowledge Information is the second level of knowledge Data by itself is not significant Information is significant
- 9. DATA PROCESSING ✔ The process of converting raw data into meaningful information through a series of operations is known as data processing. ✔ Data processing is any process that uses a computer program to enter data and summarize, analyze or otherwise convert data into useful information. ✔ These operations can include data validation, sorting, aggregation, calculation, analysis, and presentation. ✔ Data processing can be performed manually or automatically using computers and specialized software.
- 10. ✔The main objective of data processing is to give accurate and relevant information to the decision makers, so they can make high quality decisions quickly. ✔Mere data cannot solve any problem. ✔The solution can be arrived at by organizing and manipulating the data. ✔Data processing is the conversation of data into more useful form. ✔That is transmission of data into meaningful information is called data processing.
- 11. SCOPE OF DATA 1. Types of Data A database might store text data, numerical data, dates, images, audio files, video files, etc. 2. Volume of Data The amount of data stored in the database. It could range from small databases with a few megabytes of data to large databases with terabytes or even petabytes of data. 3. Granularity of Data Granularity refers to the level of detail or specificity of the data stored in the database. Some databases may store highly granular data with detailed information for each individual record, while others may store aggregated or summarized data.
- 12. 4.Accessibility Who can access the data in the database and under what circumstances. Access control mechanisms and security measures determine the scope of data accessibility. 5.Retention Period It relates to how long data is retained in the database before it is archived or purged. The retention period may vary depending on legal requirements, business needs, or data management policies.
- 13. 5.Geographical Scope: For distributed databases, the scope may include the geographical distribution of data across multiple locations or regions. 6.Temporal Scope: This refers to the timeframe or period covered by the data stored in the database. It could include historical data, real-time data, or a combination of both.
- 14. DATA PROCESSING CYCLE INPUT DATA PROCESSING OUTPUT DATA Record, Checked and Prepared E.g: Details of hours worked by INPUT PROCESSING by Computer Information E.g: Payslips MAINTAINED DATA E.g: Relatively permanent details of employees
- 15. OBJECTIVES OF DATA PROCESSING •Handle voluminous data •Qualitative and Quantitative information •Appropriate and timely information •Storage and retrieval of data •Helps in decision making •Improving productivity •Maintaining performance at optimum level •Effective Office Management •Automation of workflow
- 16. KINDS OF DATA PROCESSING ✔Manual data processing -Human being converts the data into information(Abacus, Slide rule, Napier bones etc.,) ✔Mechanical data processing - A person uses and controls various machines to get the work done.(Calculators Tabulators etc.,) ✔Electronic data processing - Data is processed by either analog or Digital computer.
- 17. STEPS OF DATA PROCESSING • Processing of data on a computer has become a part and parcel of everyday life. • Especially in commerce and industry it becomes inevitable. The following are the basic steps: ✔ Preparation of source documents - Collection of data - What type of data are required for obtaining the desired output ✔ Input of data - After extracting the necessary data from the source documents it should be transposed into a suitable form that is acceptable to the computer. - Great care should be taken not to make wrong entries.
- 18. ✔ Manipulation of data - Shifting, sorting and rearranging the given input. - Check the correctness of the data entered. ✔ Output the information - The main purpose of data processing is to provide meaningful information to the decision maker. - The output must be easy for the user to comprehend - The person involved must be very clear what information is needed and in what form he likes to have it. ✔ Storage of information -The data process need to be kept for future use. - All the processed data will need some form of secondary storage. - When storing the data it is always necessary to maintain a backup.
- 19. STAGES INVOLVED IN DATA PROCESSING/TRANSFORMATION OF DATA TO DECISION RELEVANT INFORMATION • Data Collection Gathering raw data from various sources such as sensors, surveys, databases, or web scraping. • Data Entry Inputing collected data into a computer system for further processing. • Data Cleaning Identifying and correcting errors, inconsistencies, or missing values in the data. • Data Transformation Converting raw data into a format suitable for analysis. • Data Analysis Examining the processed data to discover patterns, trends, correlations, or insights.
- 20. •Data Visualization Presenting the analyzed data in visual formats such as charts, graphs, or maps to facilitate understanding and decision-making. •Data Interpretation Drawing conclusions or making inferences based on the analyzed data to support decision-making or problem-solving.
- 21. Ex:-Transforming Customer Feedback Data •Data Collection: Gather customer feedback from surveys, social media, and support tickets. •Data Cleaning: Remove spam, correct typos, and handle missing responses. •Data Integration: Combine feedback from different platforms into a single dataset. •Data Transformation: Categorize feedback into themes (e.g., product quality, customer service). •Data Analysis: •Descriptive: Calculate the percentage of feedback mentioning each theme. •Predictive: Identify common factors leading to negative feedback.
- 22. •Data Interpretation: Relate feedback themes to specific product features or service aspects. •Information Presentation: Create a dashboard showing feedback trends over time. •Decision-Making Support: Recommend product improvements or customer service training. •Feedback and Refinement: Monitor the impact of changes on future feedback and adjust strategies accordingly.
- 23. Practical Data Processing Applications in Business •Process Control •Accounting •Payroll preparation •Sales analysis •Inventory Management •Office Automation •Insurance and stock broking •Managerial aid