IS 2080C Professor Rapine Week 4 Notes
IS 2080C Professor Rapine Week 4 Notes IS 2080C
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This 6 page Class Notes was uploaded by Brady Zuver on Wednesday September 14, 2016. The Class Notes belongs to IS 2080C at University of Cincinnati taught by Prof.Rapien in Fall 2016. Since its upload, it has received 30 views. For similar materials see IS 2080C in Business at University of Cincinnati.
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Date Created: 09/14/16
IS 2080C Professor Rapine Week 4 Notes Chapter 3 Data and Knowledge Management (Continued) and Plug IT in 3 Big Data Companies collect more data than they can analyze and use 1. Managing Data a. Database Relationships i. Database Management Systems (DBMS): A set of programs that provide users with tools to create and manage a database ii. Relational Database Model: Based on two dimensional tables that contain records (in rows) and attributes (in columns) iii. Data Model: A diagram that represents the relationships between entities in the database iv. Entity: A person, place, thing or event v. Record: Describes an entity in an instance and refers to each row in a relational table vi. Attribute: Each characteristic or quality of a specific entity (can be fluid) vii. Primary Key: A field in a database that can uniquely identify each record to be used viii. Secondary Key: Has some identifying information, but does not identify the record with complete accuracy (Same as foreign Key) 1. Foreign Key: a field (or group) that uniquely identifies a row of another table 2. Used as a link between two tables b. Entity-Relationship Modeling i. Database designers plan the database design in a process called (ER Modeling) ii. ER Diagrams consist of entities (tables), attributes (can change for the most part) and relationships iii. Relationships described by: 1. Cardinality: Maximum number of times an instance of one entity can be associated with an instance of another entity (1 to infinity) 2. Modality: Minimum number of times two entities can be associated (0 or 1) 3. Examples of this on slide 21 in notes c. Normalization i. Occurs when attributes in the table depend only on the primary key 1. Minimum redundancy a. Ex: Put in an M number (unique) 2. Maximum data integrity a. Ex: your M number is unique to only you 3. Best Processing performance a. Designed to align primary keys and foreign keys ii. Data only primarily related is stored in that table iii. Why Normalize? 1. Minimize redundancy 2. Maximize data integrity a. Select from a drop down instead of entering i. Guaranties integrity 3. Processing efficiency a. Swipe a card at Kroger, normalized database has all information associated with that card stored 2. Big Data a. Defining Big Data i. Gartner: Defines as diverse, high-volume, high-velocity information assets that require new forms of processing to enable enhanced decision making, insight discovery, and process optimization (DEFINITION FROM www.gartner.com) ii. Big Data Institute: Describes as vast databases that exhibit variety, include structured, unstructured and semi-structural data, are generated at high velocity with uncertain pattern, that does not fit neatly into traditional, structured, relational databases, and can be captured, processed, transformed and analyzed in a reasonable amount of time only by sophisticated information systems. (DEFINITION from www.the- bigdatainstitute.com) b. Characteristics of Big data i. Volume: a very large volume of data 1. Huge amounts, Titter getting 10-15 million tweets an hour 9sometimes per minute) and shows trending topics ii. Velocity: The rate that data flows is increasing and is critical 1. Increases the speed of the feedback between company and customers iii. Variety: formats change rapidly 1. Can contain satellite imagery, broadcast audio streams, digital music files, Web page content 2. Google has to look at google searches, tweets, what is being watched and other sources very quickly c. Issues with Big data i. Untrusted data sources ii. Dirty Data: inaccurate, incomplete, incorrect, duplicate or flawed data 1. General information, cannot use primary key immediately iii. Changes in Big Data: Data quality can change, or the data itself can change 1. Caused by conditions under which the data captured can change 2. Data coming in one way, and then is coming in from a different way, constantly coming in. d. Managing Big Data i. Can reveal valuable patterns, trends, and information previously hidden 1. Tracking spread of disease, tracking crime, and detecting fraud a. Chicago used twitter to track crime i. People were tweeting about the crime, where and when they happened etc. ii. First Step: Integrate information silos into a database environment and develop data warehouses for decision making 1. Information Silo: Information system that does not communicate with other, related information systems in an organization iii. Second Step: making sense of their increasing data 1. How do I clean it and organize data? iv. NoSQL databases are being used to process big data 1. NoSQL Database: can manipulate structured/unstructured data, inconsistent/missing data providing an alternative for firms that have more and different kinds of data (Big Data) in addition to the traditional, structured data that fit neatly into the rows and columns of relational databases. 2. Can categorize videos, blogs, etc. e. Putting Big Data to Use i. Making big data available 1. Data needs to be available for relevant stakeholders so the company can gain value 2. Let employees use it ii. Enabling Organizations to Conduct Experiments 1. Conducting experiments allows companies to improve their performance 2. Cops in Chicago had to work with the data iii. Micro-Segmentation of Customers 1. Dividing customers into groups of shared characteristics iv. Creating New Business Models 1. Allows companies to monitor and use data collected to improve the way the business is operated v. Organizations Can Analyze Far More Data 1. Sometimes organizations can look at all data and not need to sample as much 2. Can look at trends in order to sell to trends 3. Data Warehouse and Data Marts a. Data Warehouse: A storage system of historical data that is used to support decision making in an organization (large scale) i. Created by using transactional data from all divisions of business ii. Data Mart: A small scale data warehouse, designed for the individual departments b. Characteristics of Data Warehouses and Data Marts i. Organized by subject such as Customer, Vendor, Product, Price Level and Region ii. Use online analytical processing (OLAP) 1. Integrated: Collected from multiple systems and integrated around subjects 2. Time Variant: Maintain historical data 3. Nonvolatile: Data cannot be changed or updated a. Historical data is never changed because it is historical b. Data is only inserted 4. Multidimensional: Tend to use a multidimensional data structure a. Categorized i. Age, Gender, Race, Family Size c. Data Warehouse vs. Database i. Data Warehouse: Captures Data, clean data, Data does not change (only inserted or deleted), Used for mining data fir historical trends and patterns ii. Generic warehouse environment 1. Source Systems: Systems that provide a source for organizational data 2. Data Integration: Taking data from a system and using it with other systems and putting them together in a proper way 3. Storing the Data: Set up into a large scale data warehouse 4. Metadata: Characteristics used to better understand customers 5. Data Quality: The data must meet the users’ needs a. Users’ needs need to be met so they trust the data b. Can be improved with data-cleansing software or improving the quality at the source system level 6. Governance: What is meant by legal expense, what is defined as customer 7. Users: Who is using (Developers, workers, Analysts, Managers, etc.) iii. Benefits of Data warehousing 1. Data can be quickly and easily accessed through web browsers a. From phone, tablet, computer 2. Extensive data analysis can be conducted a. Horizontal data from supply to production to customer service etc. b. Can consolidate all data 3. United view of organizational data d. Value and Characteristics of Quality Information i. Decisions are only as good as the information used to make the decision ii. High-accuracy information hold characteristics such as 1. Accuracy 2. Completeness: Must have all data 3. Consistency: ex. Street, Str. ST, st. (NOT CONSISTENT) 4. Lack of Redundancy 5. Timeliness iii. Error can come from many sources 1. Intentionally inaccurate information to protect privacy a. Census: Lack of filling out, Putting wrong info 2. Different entry standards and formats a. Some forms include tax, others do not 3. Abbreviated or erroneous information by accident or to save time a. 5 Street, Fifth Street S. Street, South Street 4. External information contains inconsistencies, inaccuracies or errors 4. Knowledge Management a. Concepts and Important Definitions i. Knowledge Management: A process that helps manipulate important knowledge 1. Knowledge: Information that is contextual, relevant and useful a. Can also be called Intellectual Capital/Assets ii. Explicit Knowledge: More objective, rational and technical knowledge 1. Policies, procedural guides, reports, etc. iii. Tacit Knowledge: The cumulative store of subjective or experiential learning 1. Experiences, insights, expertise, trade secrets, understanding etc. iv. Knowledge Management System: The use of modern information technologies to systemize, enhance and expedite intra-firm and inter-firm knowledge management 1. Intended to help an organization cope with turnover, rapid change and downsizing by making the expertise of the organization’s human capital widely accessible 2. Knowledge management systems – software and processes used to systematize, enhance, and expedite knowledge management 3. Six Steps a. Create Knowledge: created as people find new ways to do things or develop know-how i. External knowledge is sometime brought in b. Capture Knowledge: Knowledge needs to be identified as valuable and represented in a reasonable manner c. Refine Knowledge: Knowledge must be placed into context so it is actionable i. Tacit qualities (human insights) need to be added to explicit facts d. Store Knowledge: Knowledge must be stored in a reasonable format and somewhere that can be accessed by others in the organization. e. Manage Knowledge: Knowledge must be kept current by being reviewed regularly to verify its relevance and accuracy f. Disseminate Knowledge: Must be made available to anyone in the organization who needs it anywhere, anytime b. Knowledge Management: the process that helps organizations manipulate knowledge in the organization’s memory, usually unstructured i. Types of Knowledge 1. Explicit: Knowledge that can be articulated and written (Know-what) 2. Tacit: Knowledge that is difficult to encode, and one cannot be fully written down (know-how) c. KMS Challenges i. How do we represent different types of knowledge? 1. Explicit 2. Tacit ii. How do we organize it? iii. How do we get people to contribute their knowledge? 1. Human nature causes resistance a. Why would I give all my knowledge? It would make me useless to them.
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