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CMIS U5
| Question | Answer |
|---|---|
| What are two major challenges managers face when making decisions? | Information overload and poor data quality |
| What is information overload? | An overabundance of irrelevant or excessive data that makes decision-making difficult |
| Why can information systems both help and hinder decision-making? | They organize data but can also create information overload |
| What is data quality? | The accuracy, consistency, completeness, and usefulness of data |
| What is dirty data? | Incorrect or poorly entered data |
| Examples of dirty data | Incorrect phone numbers or missing customer information |
| What is inconsistent data? | Data stored differently across systems or records |
| Why is poor data quality dangerous? | It can lead to poor decisions and incorrect analysis |
| What is data granularity? | The level of detail in data |
| What problem occurs when data is too detailed or too general? | It becomes harder to make useful decisions |
| What does OLTP stand for? | Online Transaction Processing |
| What is OLTP? | Systems that collect and process transaction data during daily operations |
| Examples of OLTP systems | Bank transactions, online purchases, and ticket sales |
| What is the purpose of OLTP systems? | To efficiently enter, process, and store operational data |
| What are the two types of OLTP processing? | Immediate processing and batch processing |
| What is immediate processing in OLTP? | Transactions are processed instantly |
| Example of immediate OLTP processing | Ticketmaster preventing duplicate ticket sales |
| What is batch processing in OLTP? | Transactions are collected and processed later together |
| What does OLAP stand for? | Online Analytic Processing |
| What is OLAP? | Systems that analyze data collected from OLTP systems |
| What is the purpose of OLAP systems? | To support decision-making and identify trends and patterns |
| What are measures in OLAP? | The data items being analyzed |
| Examples of OLAP measures | Total sales and purchase date |
| What are dimensions in OLAP? | Descriptive categories used to organize data |
| Examples of OLAP dimensions | Region and product type |
| What are facts in OLAP? | Quantitative values |
| Example of an OLAP fact | Sales equal $500K |
| What is the main difference between OLTP and OLAP? | OLTP runs day-to-day operations while OLAP analyzes data for decisions |
| What is the data resource challenge? | When organizations collect data but fail to use it effectively for decision-making |
| Why should organizations treat data as a resource? | Because data is a valuable organizational asset |
| What is a Business Intelligence (BI) system? | A system that provides information to improve decision-making |
| How do BI systems provide competitive advantage? | They help organizations make faster and better decisions |
| What can BI systems help organizations do? | Identify trends, improve efficiency, and understand customers better |
| What is a data warehouse? | A facility that prepares, stores, and manages data for reporting and analysis |
| Why do organizations use data warehouses? | To separate business analysis from day-to-day operational systems |
| What does a data warehouse contain? | Organized data, related data, metadata, and outside purchased data |
| What is metadata? | Data about data |
| Examples of metadata | Data sources and formatting information |
| What is a data mart? | A smaller data collection focused on a specific business function or problem |
| Examples of data marts | Marketing data marts and inventory data marts |
| What is the difference between a data warehouse and a data mart? | A warehouse stores organization-wide data while a data mart focuses on one area or department |
| What is data mining? | Using statistical techniques to find patterns and relationships in large databases |
| What is unsupervised data mining? | Data mining where no model is created beforehand |
| Example of unsupervised data mining | Cluster analysis of customer groups |
| What is supervised data mining? | Data mining where a model is created before analysis begins |
| Example of supervised data mining | Banks predicting loan defaults |
| What are neural networks used for? | Predictions and classifications |
| What is market-basket analysis? | Finding products commonly purchased together |
| Example of market-basket analysis | Pancake mix and maple syrup |
| What is a pivot table? | A tool used to summarize and organize large amounts of data quickly |
| What can pivot tables help users do? | Calculate totals, averages, counts, and identify trends |
| How can businesses use pivot tables? | To analyze sales, products, regions, and trends |
| What are the benefits of pivot tables? | Fast analysis, flexibility, easy use, and better decision-making |
| What problems can occur with pivot tables? | Dirty data, misinterpretation, scalability problems, and user skill dependence |
| Why are pivot table results sometimes misleading? | Because incorrect data or wrong fields may be used |
| What is scalability? | The ability to handle very large amounts of data |
| Why are user skills important when using pivot tables? | Results depend on how well the user analyzes and organizes data |
Created by:
user-2019771