3.Planning & Project management 2/23/2012 3.Planning & Project management/D.S.Jagli

3.Planning & Project management: topics to be covered How is it different? Life-cycle approach The Development Phases Dimensional Analysis Dimensional Modeling Star Schema Snowflake Scheme 2/23/2012 3.Planning & Project management/D.S.Jagli

3.Planning & Project management Reasons for DWH projects failure Improper planning Inadequate project management Planning for Data ware house is necessary. Key issues needs to be planned Value and expectation Risk assessment Top-down or bottom –up Build or Buy Single vender or best of breed Business requirement ,not technology Top management support Justification 2/23/2012 3.Planning & Project management/D.S.Jagli

3.Planning & Project management Example for DWH Project Outline for overall plan Introduction Mission statement Scope Goals& objectives Key issues & Options Value& expectations Justification Executive sponsorship Implementation Strategy Tentative schedule Project authorization 2/23/2012 3.Planning & Project management/D.S.Jagli

3.1 How is it different? DWH Project Different from OLTP System Project DWH Distinguish features and Challenges for Project Management Data Acquisition Data Storage – Info . Delivery- 2/23/2012 3.Planning & Project management/D.S.Jagli

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3.2 The life-cycle Approach Fig: DW functional components and SDLC 2/23/2012 3.Planning & Project management/D.S.Jagli

DWH Project Plan: Sample outline 2/23/2012 3.Planning & Project management/D.S.Jagli

3.3 DWH Development Phases 2/23/2012 3.Planning & Project management/D.S.Jagli

3.3 DWH Development Phases Project plan Requirements definition Design Construction Deployment Growth and maintenance Interleaved within the design and construction phases are the three tracks along with the definition of the architecture and the establishment of the infrastructure 2/23/2012 3.Planning & Project management/D.S.Jagli

3.4 Dimensional Analysis A data warehouse is an information delivery system. It is not about technology, but about solving users’ problems and providing strategic information to the user. In the phase of defining requirements, you need to concentrate on what information the users need, not on how you are going to provide the required information. 2/23/2012 3.Planning & Project management/D.S.Jagli

Dimensional Analysis Usage of Information Unpredictable In providing information about the requirements for an operational system, the users are able to give you precise details of the required functions, information content, and usage patterns Dimensional Nature of Business Data Even though the users cannot fully describe what they want in a data warehouse, they can provide you with very important insights into how they think about the business. 2/23/2012 3.Planning & Project management/D.S.Jagli

Managers think in business dimensions : example 2/23/2012 3.Planning & Project management/D.S.Jagli

Dimensional Nature of Business Data 2/23/2012 3.Planning & Project management/D.S.Jagli

Dimensional Nature of Business Data 2/23/2012 3.Planning & Project management/D.S.Jagli

Examples of Business Dimensions 2/23/2012 3.Planning & Project management/D.S.Jagli

Examples of Business Dimensions 2/23/2012 3.Planning & Project management/D.S.Jagli

INFORMATION PACKAGES—A NEW CONCEPT a novel idea is introduced for determining and recording information requirements for a data warehouse. This concept helps us to give a concrete form to the various insights, nebulous thoughts, and opinions expressed during the process of collecting requirements. The information packages, put together while collecting requirements, are very useful for taking the development of the data warehouse to the next phases. 2/23/2012 3.Planning & Project management/D.S.Jagli

Requirements Not Fully Determinate Information packages enable us to: Define the common subject areas Design key business metrics Decide how data must be presented Determine how users will aggregate or roll up Decide the data quantity for user analysis or query Decide how data will be accessed Establish data granularity Estimate data warehouse size Determine the frequency for data refreshing Ascertain how information must be packaged 2/23/2012 3.Planning & Project management/D.S.Jagli

An information package. 2/23/2012 3.Planning & Project management/D.S.Jagli

Business Dimensions business dimensions form the underlying basis of the new methodology for requirements definition. Data must be stored to provide for the business dimensions. The business dimensions and their hierarchical levels form the basis for all further phases. 2/23/2012 3.Planning & Project management/D.S.Jagli

Dimension Hierarchies/Categories Examples: Product: Model name, model year, package styling, product line, product category, exterior color, interior color, first model year Dealer: Dealer name, city, state, single brand flag, date first operation Customer demographics: Age, gender, income range, marital status, household size, vehicles owned, home value, own or rent Payment method: Finance type, term in months, interest rate, agent Time: Date, month, quarter, year, day of week, day of month, season, holiday flag 2/23/2012 3.Planning & Project management/D.S.Jagli

Key Business Metrics or Facts The numbers the users analyze are the measurements or metrics that measure the success of their departments. These are the facts that indicate to the users how their departments are doing in fulfilling their departmental objectives. 2/23/2012 3.Planning & Project management/D.S.Jagli

Example: automobile sales The set of meaningful and useful metrics for analyzing automobile sales is as follows: Actual sale price MSRP sale price Options price Full price Dealer add-ons Dealer credits Dealer invoice Amount of down payment Manufacturer proceeds Amount financed 2/23/2012 3.Planning & Project management/D.S.Jagli

3.5 DIMENSIONAL MODELING Star Schema Snowflake Scheme 2/23/2012 3.Planning & Project management/D.S.Jagli

FROM REQUIREMENTS TO DATA DESIGN The requirements definition completely drives the data design for the data warehouse. A group of data elements form a data structure. Logical data design includes determination of the various data elements that are needed and combination of the data elements into structures of data. Logical data design also includes establishing the relationships among the data structures. 2/23/2012 3.Planning & Project management/D.S.Jagli

FROM REQUIREMENTS TO DATA DESIGN The information package diagrams form the basis for the logical data design for the data warehouse. The data design process results in a dimensional data model 2/23/2012 3.Planning & Project management/D.S.Jagli

From requirements to data design. 2/23/2012 3.Planning & Project management/D.S.Jagli

Dimensional Modeling Basics: Formation of the automaker sales fact table. 2/23/2012 3.Planning & Project management/D.S.Jagli

Formation of the automaker dimension tables. 2/23/2012 3.Planning & Project management/D.S.Jagli

Concept of Keys for Dimension table Surrogate Keys A surrogate key is the primary key for a dimension table and is independent of any keys provided by source data systems. Surrogate keys are created and maintained in the data warehouse and should not encode any information about the contents of records; Automatically increasing integers make good surrogate keys. The original key for each record is carried in the dimension table but is not used as the primary key. Surrogate keys provide the means to maintain data warehouse information when dimensions change. Business Keys Natural keys Will have a meaning and can be generated out of the data from source system or can be used as is from source system field

The criteria for combining the tables into a dimensional model. The model should provide the best data access. The whole model must be query-centric. It must be optimized for queries and analyses. The model must show that the dimension tables interact with the fact table. It should also be structured in such a way that every dimension can interact equally with the fact table. The model should allow drilling down or rolling up along dimension hierarchies. 2/23/2012 3.Planning & Project management/D.S.Jagli

The dimensional model :a STAR schema With these requirements, we find that a dimensional model with the fact table in the middle and the dimension tables arranged around the fact table satisfies the condition 2/23/2012 3.Planning & Project management/D.S.Jagli

Case study: STAR schema for automaker sales. 2/23/2012 3.Planning & Project management/D.S.Jagli

E-R Modeling Versus Dimensional Modeling DW meant to answer questions on overall process DW focus is on how managers view the business DW focus business trends Information is centered around a business process Answers show how the business measures the process The measures to be studied in many ways along several business dimensions OLTP systems capture details of events transactions OLTP systems focus on individual events An OLTP system is a window into micro-level transactions Picture at detail level necessary to run the business Suitable only for questions at transaction level Data consistency, non-redundancy, and efficient data storage critical 2/23/2012 3.Planning & Project management/D.S.Jagli

E-R Modeling Versus Dimensional Modeling Dimensional modeling for the data warehouse. E-R modeling for OLTP systems 2/23/2012 3.Planning & Project management/D.S.Jagli

THE STAR SCHEMA 2/23/2012 3.Planning & Project management/D.S.Jagli

Star Schemas Data Modeling Technique to map multidimensional decision support data into a relational database. Current Relational modeling techniques do not serve the needs of advanced data requirements 2/23/2012 3.Planning & Project management/D.S.Jagli

Star Schema Facts Dimensions Attributes Attribute Hierarchies 4 Components Facts Dimensions Attributes Attribute Hierarchies 2/23/2012 3.Planning & Project management/D.S.Jagli

Facts Numeric measurements (values) that represent a specific business aspect or activity. Stored in a fact table at the center of the star scheme. Contains facts that are linked through their dimensions. Updated periodically with data from operational databases 2/23/2012 3.Planning & Project management/D.S.Jagli

Dimensions Qualifying characteristics that provide additional perspectives to a given fact DSS data is almost always viewed in relation to other data Dimensions are normally stored in dimension tables 2/23/2012 3.Planning & Project management/D.S.Jagli

Attributes Dimension Tables contain Attributes Attributes are used to search, filter, or classify facts Dimensions provide descriptive characteristics about the facts through their attributed Must define common business attributes that will be used to narrow a search, group information, or describe dimensions. (ex.: Time / Location / Product) No mathematical limit to the number of dimensions (3-D makes it easy to model) 2/23/2012 3.Planning & Project management/D.S.Jagli

Attribute Hierarchies Provides a Top-Down data organization Aggregation Drill-down / Roll-Up data analysis Attributes from different dimensions can be grouped to form a hierarchy 2/23/2012 3.Planning & Project management/D.S.Jagli

Concept of Keys for Star schema Surrogate Keys The surrogate keys are simply system-generated sequence numbers and is independent of any keys provided by source data systems. They do not have any built-in meanings. Surrogate keys are created and maintained in the data warehouse and should not encode any information about the contents of records; Automatically increasing integers make good surrogate keys. The original key for each record is carried in the dimension table but is not used as the primary key. Business Keys Primary Keys Each row in a dimension table is identified by a unique value of an attribute designated as the primary key of the dimension. Foreign Keys Each dimension table is in a one-to-many relationship with the central fact table. So the primary key of each dimension table must be a foreign key in the fact table.

Star Schema for Sales Dimension Tables Fact Table 2/23/2012 3.Planning & Project management/D.S.Jagli

Star Schema Representation Fact and Dimensions are represented by physical tables in the data warehouse database. Fact tables are related to each dimension table in a Many to One relationship (Primary/Foreign Key Relationships). Fact Table is related to many dimension tables The primary key of the fact table is a composite primary key from the dimension tables. Each fact table is designed to answer a specific DSS question 2/23/2012 3.Planning & Project management/D.S.Jagli

Star Schema The fact table is always the larges table in the star schema. Each dimension record is related to thousand of fact records. Star Schema facilitated data retrieval functions. DBMS first searches the Dimension Tables before the larger fact table 2/23/2012 3.Planning & Project management/D.S.Jagli

Star Schema : advantages Easy to understand Optimizes Navigation Most Suitable for Query Processing 2/23/2012 3.Planning & Project management/D.S.Jagli

THE SNOWFLAKE SCHEMA 2/23/2012 3.Planning & Project management/D.S.Jagli

THE SNOWFLAKE SCHEMA Snowflaking” is a method of normalizing the dimension tables in a STAR schema. 2/23/2012 3.Planning & Project management/D.S.Jagli

Sales: a simple STAR schema. 2/23/2012 3.Planning & Project management/D.S.Jagli

Product dimension: partially normalized 2/23/2012 3.Planning & Project management/D.S.Jagli

When to Snowflake The principle behind snowflaking is normalization of the dimension tables by removing low cardinality attributes and forming separate tables. In a similar manner, some situations provide opportunities to separate out a set of attributes and form a subdimension. 2/23/2012 3.Planning & Project management/D.S.Jagli

Advantages and Disadvantages Small savings in storage space Normalized structures are easier to update and maintain Disadvantages Schema less intuitive and end-users are put off by the complexity Ability to browse through the contents difficult Degraded query performance because of additional joins 2/23/2012 3.Planning & Project management/D.S.Jagli

??? Thank you 2/23/2012 3.Planning & Project management/D.S.Jagli