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Slide 1: Chapter 1 The Data Warehouse
Slide 2: Data Warehouse Basics • Objective of the book – Provide engineering methodology for the design of a data warehouse • Data warehouse is not an end to itself but part of the BI infrastructure • Business Intelligence (BI) – data warehouse or data mart, On-Line Analytical Processing (OLAP), and data mining
Slide 3: Business Intelligence loop • Figure 1-1 – Operational environment – Data Warehouse/Data Mart – Decision Support Systems (DSS)
Slide 4: Business Intelligence • Why? – Lack of BI is an enormous competitive disadvantage • The business of chess – What shall I do next?=Strategic thinking=chess – Strategy = Business mission statement – Business strategist : predict behavior of business nouns – Business environment is full of unknowns
Slide 5: Business Intelligence (Cont’d) • Organizations with purely operational systems – unable to make meaningful information out of volumes of data • BI – helps develop strategy – must be able to anticipate future conditions – need to understand the past
Slide 6: Parts of the Data Warehouse • Key to understanding data warehouse – see how the parts interact with one another • Parts of the DW (Figure 1-2) – – – – – Operational Environment Independent Data Mart : src is operational env. Extraction : passive/active DW Extraction Store Transformation/Cleansing : scrubbing
Slide 7: Parts of the Data Warehouse (Cont’d) • Parts of the DW (Cont’d) – – – – – – – Extraction Log External Source Data Administrator : quality of data Central Repository Metadata : what, where, encoding, relationships Data : multidimensional/relational DB Dependent Data Mart : DW is source of data
Slide 8: Features of the Data Warehouse • A Data Warehouse is a subject oriented, integrated, nonvolatile, time variant collection of data in support of management’s decision – W.H. Inmon
Slide 9: Features of the DW (Cont’d) • Subject Orientation (Figure 1-3) – Transaction-oriented systems structure data in a way that optimizes processing of transactions (normalization) – DW is concerned with the business nouns (customers, products, sales, etc.) – Operational data is distributed across multiple applications – DW gathers all data in one place
Slide 10: Features of the DW (Cont’d) • Integration – forms a single cohesive environment – data transformation, data cleansing (Figure 1-4) – Data cleansing • Removing errors from the input stream • A good cleansing process can improve quality of operational environment • Debate on appropriate action when detecting errors: correct in operational environment as well? • Cannot detect all errors
Slide 11: Features of the DW (Cont’d) • Integration (Con’td) – Data Transformation • Receives input streams and transform into one consistent format • Issue of defining inconsistencies (Table 1-1) • Description • Encoding • Units of Measure • Format
Slide 12: Features of the DW (Cont’d) • Nonvolatile – Once data is written, it remains unchanged in the DW – Figure 1-5 – Virtual read-only database system – DB can eliminate background processes used for recovery (ex : redo log)
Slide 13: Features of the DW (Cont’d) • Time-Variant Collection of Data – Adds time dimension to the data warehouse – Creates snapshot of the organization – Can view patterns and trends over time
Slide 14: Features of the DW (Cont’d) • Supporting Management’s Decision – DW user is the Business strategist – Static reports generated by IT dept. can no longer satisfy business strategist – Requires appropriate timely performance – Design user interface for business strategist
Slide 15: Decision Support Systems • DSS extends from the extraction of the data through the DW to the presentation to the business strategist • Spectrum of DSS tools : Figure 1-6 • Reporting – The higher the level of the business strategist, the higher level of summarization required. – Enterprise-class reporting : Rapid development, Easy maintenance, Easy distribution, Internet Enabled.
Slide 16: Decision Support Systems(Cont’d) • On-Line Analytical Processing – Leverages the time-variant characteristics for strategist to look both back and ahead in time – MOLAP (Multi-dimensional OLAP) – ROLAP (Relational OLAP) – HOLAP (Hybrid OLAP) – Typical OLAP interface (Figure 1-7) – Rotation, roll-up, drill-down – Support “what-if” analysis - manipulate variables
Slide 17: Data Mining • Data mining allows us to see the hidden picture • 2 types : Classification and estimation • Classification : segment into different classes • Estimation : estimate some numerical value based on a subject’s characteristics • Use subset of data : size depends on deviation of data characteristics
Slide 18: Data Mining (Cont’d) • Decision Trees (Figure 1-8) – Decision is represented with a box and each alternative with a circle – Branches are labeled with probability • Neural Networks (Figure 1-9) – Mimics structure of the human brain – Each neuron processes the info. It receives and passes its results down the line
Slide 19: Data Mining (Cont’d) • Genetic Modeling – Suited for categorizing – Survival of the fittest – Randomly place data into desired categories and members that are not well suited move to other categories
Slide 20: Summary • Why build DW? Business strategist can make a plan for organization to thrive • DW is a subject oriented, integrated, nonvolatile, time variant collection of data in support of management’s decisions. – W.H. Inmon