Knowledge/Data Management. MIS 4133 Software Systems

Transcription

1 Knowledge/Data Management MIS 4133 Software Systems

2 Outline Managing Data Technical Aspects Managerial Aspects Data Warehousing Data Mart Data Mining Knowledge Management

3 Why Manage Data? Organizations could not function long without critical business data Cost to replace data would be very high Time to reconcile inconsistent data may be too long Data often needs to be accessed quickly Data should be: Cataloged Named in standard ways Protected Accessible to those with a need to know Maintained with high quality

4 Technical Aspects of Managing the Data Resource The Data Model Data modeling involves: Methodology, or steps followed to identify and describe data entities Notation, or a way to illustrate data entities graphically Entity-relationship diagram (ERD) Most common method for representing a data model and organizational data needs Captures entities and their relationships Entities things about which data are collected Attributes actual elements of data that are to be collected

5 Technical Aspects of Managing the Data Resource The Data Model: ERD NOTE: Entities are Customer, Order, and Product. Attributes of the Customer entity could be customer last name, first name, street, city,

6 Technical Aspects of Managing the Data Resource Database Architecture Database shared collection of logically related data, organized to meet needs of an organization Database Architecture way in which the data are structured and stored in the database Six basic database architectures: 1.Hierarchical (top-down organization) 2.Network (high-volume transaction processing) 3.Relational (data arranged in simple tables) 4.Object-oriented (data and methods encapsulated in object classes) 5.Object-relational (hybrid of relational and object-oriented) 6.Multidimensional (used by data warehouses)

7 Technical Aspects of Managing the Data Resource Tools for Managing Data Database Management System (DBMS) support software used to create, manage, and protect organizational data A DBMS helps manage data by providing seven functions: 1. Data storage, retrieval, update 2. Backup 3. Recovery 4. Integrity control 5. Security control 6. Concurrency control 7. Transaction control

8 Technical Aspects of Managing the Data Resource Tools for Managing Data Data Dictionary (DD) central encyclopedia of data definitions and usage information a database about data Contains: Definition of each entity, relationship, and data element Display formats Integrity rules Security restrictions Volume and sizes List of applications that use the data

9 Technical Aspects of Managing the Data Resource Database Programming Query language a 4 GL, nonprocedural programming language to obtain data from a database, often provided by the DBMS SQL query language example: SELECT field(s) FROM table(s) WHERE criteria SELECT ORDER#, CUSTOMER#, CUSTNAME, ORDER-DATE FROM CUSTOMER, ORDER WHERE ORDER-DATE > 04/12/05 AND CUSTOMER.CUSTOMER# = ORDER.CUSTOMER#

10 Managerial Issues in Managing Data Principles in Managing Data The need to manage data is permanent Data can exist at several levels Application software should be separate from the database Application software can be classified by how they treat data 1. Data capture 2. Data transfer 3. Data analysis and presentation Application software should be considered disposable Data should be captured once There should be strict data standards

11 Managerial Issues in Managing Data Data Management Policies Organizations should have policies regarding: Data ownership Data administration

12 Data Warehousing Establishment and maintenance of a large data storage facility containing data on all (or at least many) aspects of the enterprise Provides users data access and analysis capabilities without endangering operational systems a subject-oriented, integrated, time-variant, nonvolatile collection of data used to support the strategic decision-making process for the enterprise. the central point of data integration for business intelligence A database that stores large amounts of historical business data Contains multiple databases

13 Data Warehousing Martin et al. 2005

14 Data Warehouse Configuration Timing of Advertising Customer Segment Year North Territory Southeast Territory Southwest Territory East Territory #1 #2 #3 #4 Product Lines Marketing Database Sales Database Customer Database Product Database Haag et al. 2013

15 Overview of Data Warehousing Infrastructure ODS operational data store ETL extract, transform and load OLTP online transaction processing OLAP online analytical processing Mailvaganam 2004

16 Data Mart Part of the data warehouse Provides a more focused piece of the data warehouse Still contains multiple databases

17 Data Mining Data Mining uses different technologies to search for (mine) nuggets of information from data stored in a data warehouse or data mart Data mining software examples: Oracle 9i Data Mining and Oracle Data Mining Suite SAS Enterprise Miner Decision techniques used: Decision trees Linear and logistic regression Clustering for market segmentation Rule induction Nearest neighbor Genetic algorithms

18 Data Mining Uses: Cross-selling Customer churn Customer retention Direct marketing Fraud detection Interactive marketing Market basket analysis Market segmentation Payment or default analysis Trend analysis

19 Kinds of Analyses Most Basic Query and Reporting Usually performed by functional managers Use predefined queries More Complex OLAP and Statistical Analysis Designed for business analysts Looks at data across multiple dimensions Provides summary info, with drill down capabilities Provides relationships between factors Most Complex Data Mining Analyze very large data sets Highlights hidden patterns

20 Cleaning Data Possible Steps? Define rules in advance Identify homonyms and synonyms Homonyms two or more different items with the same identifier Synonyms the same items with more than one identifier Use data-profiling software Highlights errors and inconsistencies Use fuzzy matching software Looks for duplicates and errors introduced by keyboard entry Automatic editing software Checks for reasonableness and data consistency Britain s defense dept data cleansing project cost $11 million over four years, and has saved the Ministry of Defense $36 million

21 Data Warehouse Uses CRM Consolidate customer data Identify areas of customer satisfaction and frustration Fraud detection Product repositioning analysis Profit center discovery Corporate asset management Etc.

22 Questions to Ask in the Data Warehouse Planning Stage? What data is needed to make business decisions? Which business units will use it? What kind of data analysis will be done? How granular will the data be and what is the oldest data to be archived in it? What are the security requirements?

23 Reasons for Implementing a DW A relatively small amount of knowledge of the technical aspects of database technology is required to write and maintain queries and reports. It speeds up the writing and maintaining of queries and reports by technical personnel. It is a repository of cleaned up TPSs data. It is easier to query and report data from multiple TPSs, from external data sources, and/or from data that must be stored for query / report purposes only. It creates a repository of TPS data that contains data from a longer span of time than can efficiently be held in a TPS.

24 Reasons for NOT Implementing a DW DW systems can complicate business processes significantly. DW can have a learning curve that may be too long for impatient firms. DW can become an exercise in data for the sake of the data. In certain organizations, ad hoc end user query / reporting tools do not take. Many strategic applications of DW have a short life span and require the developers to put together a technically inelegant system quickly. There is a limited number of people available who have worked with the full DW system project life cycle. DW systems can require a great deal of maintenance which many organizations cannot or will not support. Sometimes the cost to capture data, clean it up, and deliver it in a format and time frame that is useful for the end users is too much of a cost to bear.

25 Data Warehousing ROI If done correctly, then: Cost savings Increases in revenues Increase in analysis of marketing DBs to cross-sell products Less computer storage on the mainframe Ability to identify and keep the most profitable customers, while getting to better idea of who they really are Changes users jobs (faster access to more accurate data; better customer service)

26 Disadvantages of a Data Warehouse Expense Time Money Operational DBs are sufficient Requires more IT support

27 Data Warehousing Examples Harrah s Entertainment Uses a data warehouse to track and analyze customer spending in its casinos Continental Airlines Uses a data warehouse to forecast passenger bookings, detect fraud, manage customers, etc.

28 Knowledge Management Involves strategies and processes of identifying, creating, capturing, organizing, transferring, and leveraging knowledge to help compete Relies on recognizing knowledge held by individuals and the firm 2 Types of Knowledge: Tacit Knowledge knowledge that resides in an employee s mind but has not yet been documented Explicit Knowledge knowledge that has been documented

29 Knowledge Management Systems System for managing organizational knowledge Technology or vehicle that facilitates the sharing and transferring of knowledge so that valuable knowledge can be reused Enables people and organizations to enhance learning, improve performance, and produce long-term competitive advantage

30 A Knowledge Management Framework Customer Capital Human Capital Structural Capital McNurlin and Sprague 2006

31 References Daniel, D. (2007). The Secret to Successful Business Intelligence: A Top-Notch Data Warehouse, CIO.com, [accessed: October 2, 2015]. Davenport, T.H. (2002). Just-in-Time Delivery comes to Knowledge Management, Harvard Business Review, pp Greenfield, L. (2004). The Data Warehousing Information Center, [accessed: October 4, 2013]. Haag, S., Cummings, M. and McCubbrey, D.J. (2013). Management Information Systems for the Information Age, 9 th edition, McGraw-Hill Companies, Inc. Imhoff, C., Galemmo, N. and Geiger, J.G. (2003). Mastering Data Warehouse Design, Wiley Publishing, Inc., Indianapolis, Indiana. Kankanhalli, A., Tan, B.C.Y. and Wei, K.-K. (2005). Understanding seeking from electronic knowledge repositories: an empirical study, Journal of the American Society for Information Science and Technology (56:11), pp

32 References Laudon, K.C. and Laudon, J.P. (2006). Management Information Systems: Managing the Digital Firm, Pearson Education, Inc., Upper Saddle River, New Jersey. Levinson, M. (2007). Knowledge Management Definition and Solutions, CIO.com, [accessed: October 2, 2015]. Mailvaganam, H. (2004). Introduction to Metadata, Data Warehousing Review, [accessed: October 4, 2013]. Martin, E.W., Brown, C.V., DeHayes, D.W., Hoffer, J.A. and Perkins, W.C. (2005). Managing Information Technology, 5 th edition, Pearson Education, Inc., Upper Saddle River, New Jersey. McNurlin, B.C. and Sprague, Jr., R.H. (2006). Information Systems Management in Practice, 7 th edition, Pearson Education, Inc., Upper Saddle River, New Jersey. Wheatley, M. (2004). Operation Clean Data, [accessed: January 25, 2005].