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Paul K Chen

Chapter 4

Data Warehouse Project Planning & Management

Data Warehouse Fundamentals

Chapter 4 - Objectives

Review types of development models Review the essentials of system development life cycle

and project management functions Discuss project team organization, roles, and

responsibilities Review data warehouse project scope document Consider the warning signs and success factors Distinguish between data warehouse projects and

OLTP system projects Discuss Data Warehouse deployment

Types of Development Models

The Waterfall Development Model

The Spiral Model

The Iterative Development Model

The Waterfall Development Model

Characteristics: Encouraging to gather and define system requirements.

Breaking the complex mission of development into several logical steps (, analysis, design, code, test, and so forth) – Divide and conquer approach.

Ensuring each step is executed properly with good quality deliverable, validation, entry, and exit criteria for each step.

The Waterfall Development Model

Advantages:

Enabling tracking of project progress more accurately and uncovering possible slippages early.

Focusing the organization that develops the software system to be more structured and manageable.

Disadvantages:

The process could become too rigid to be efficient and effective.

The Spiral Model

Developed by Boehm in 1988

Characteristics: Relying heavily on prototyping and risk management vs. the

document-driven approach of the waterfall approach.

Foe each portion of the project and for each of its levels of elaboration, the same sequence of steps (cycle) is involved. For instance, the concept of software requirements, to design, and implementation, each involves a spiral cycle.

The Spiral Model

Approach: The first step of each cycle is to identify the objective of the

portion of the product being elaborated, the alternative means of implementation of the portion of the product, and the constraints imposed on the application of the alternatives.

The next step is to evaluate the alternatives relative to the objectives and constraints and to identify the associated risks and resolve them.

In addition to prototyping for risk analysis, the spiral model also simulations, models, and benchmarks in order to reach the beat alternatives.

Waterfall Approach vs. Spiral Approach

Structured Development: Analysis, design and coding take place in The traditional waterfall way. Each step is isolated from the other. (Waterfall Approach)

A D P

A D

P(Spiral Approach)

Object-oriented development: One multifaceted model is used from Concept to code. Because one underlying model is used, teams apply Analysis, design

And programming

Concurrently.

The Iterative Development Model

Characteristics:

Begin with a subset of the requirements and develop a subset of the product that satisfies the essential needs of the users.

Based on the analysis of each immediate product, the requirements and design are modified over a series of iterations to provide a system to the user that meets evolving customer needs with improved design based on feedback and testing.

Combine with prototyping with the strength of the classical waterfall model.

Supporting the iterative development was the small team approach in which each team assumed the full responsibility of the system.

System Development Life Cycle – A brief overview

It is a systematic approach to solving business problem. It’s divided into seven phases:

Identifying problems, opportunities, and objectives Determining system requirements Analyzing system needs Designing the recommended systems Developing and documenting software Testing and maintaining the system Implementing and evaluating the systems

System Development Life Cycle – A Brief Overview

Why should a system development project be segmented in phases?

Project Management– easier to understand and manage its deliverables and track its progress

Resources – Better utilize the resources related to technology, skills, and time Risk –Minimize commitment and cost in case the project

restarts.

Project Management Functions

Initiate project

Project planning

Establishing project

Organization

Start the project by assessing the

opportunity

Determining tasks, schedule, and

allocating resources

Defining project charter and issuing

The statement of work

Organizing staff by function, tools

and environment

Project Management Functions (cont’d)

Administration

Evaluation and control

Termination

On-going project reporting And administrative work

Monitor project progress by cost, product, and schedule

Wrapping up the task by doing project summary and archives

Five Major Project Fundamentals For System Analysts

The five project fundamentals the system analysts must handle are:

Project initiation

Determining project feasibility

Project scheduling

Activity planning and control

Managing system analysis team members

Project Initiation

Projects are initiated for two broad reasons:– Problems that lend themselves to systems

solutions– Opportunities for improvement through

» Upgrading systems

» Altering systems

» Installing new systems

Project Feasibility

A feasibility study assesses the operational, technical, and economic merits of the proposed project

There are three types of feasibility:

– Technical feasibility

– Economic feasibility

– Operational feasibility

Technical Feasibility

Technical feasibility assesses whether the current technical resources are sufficient for the new system

If they are not available, can they be upgraded to provide the level of technology necessary for the new system

Economic Feasibility

Economic feasibility determines whether the time and money are available to develop the system

Includes the purchase of

– New equipment

– Hardware

– Software

Operational Feasibility

Operational feasibility determines if the human resources are available to operate the system once it has been installed

Users that do not want a new system may prevent it from becoming operationally feasible

Determining Project Feasibility (Key Issues)

Value and Expectations

Risk Assessment

Top-down or Bottom-up

Build or Buy

Single Vendor or Best-of-Breed

Tools for Planning & Scheduling Activities

Gantt Chart & PERT (Program Evaluation and Review Techniques) diagram; Spreadsheet

Computer-based project scheduling

Such as: Microsoft Project; Computer Associates’ CA-Super Project

Gantt Chart

A

B

C

D

E2 4 95Time

10

20

40

30

50

A 4

B, 2

C, 5

D,3

E, 6

15

Gantt vs. PERT Diagram

Circles called events

The longest path is called critical path.

Activity Planning and Control

Beginning to plan a project by breaking it these three

major activities :

Analysis Design Implementation

Activity Planning and Control

Refining the planning and scheduling of analysis activities

by adding detailed tasks and establishing the following

milestones:

Data Gathering Data Flow & Decision analysis Proposal Preparation

Data Warehouse Project Team: Roles and Responsibilities

Executive Sponsor – Direction, support, arbitration Project Manager – Assignment, monitoring, control User Liaison Manager – Coordination with user group  Lead Architect – Architecture Design Business Analyst – Requirement definition Data Modeler – Relational and Dimensional Modeling Data Warehouse Administrator –DBA Quality Assurance Analyst – Quality control for warehouse data Testing Coordinator – Program, system and tool testing End-user Application Specialist – Confirmation of data meanings/relationships Development Programmer – in-house programming and scripts

    

Steps in Ascertaining Hardware and Software Needs

Inventory computer hardware already in the organization Estimate both current and projected workload for the system Evaluate the performance of hardware and software using

some predetermined criteria Choose the vendor according to the evaluation Acquire the hardware and software from the selected vendor Acquire the hardware and software in conformance with

your enterprise architecture The acquisition of the hardware and software must be

justified by a business process required of either short-term (tactical) or long-term(strategic) goals.

Data Warehouse Project Scope Document

I Executive Summary -- Business needsII Project Background -- How did the project start? -- Who is the sponsor?III Project Definition -- Project Objectives -- Project Organization -- Project Critical Success Factor -- Measurements of Success

Data Warehouse Project Scope Document

IV Project Scope What’s in the Data Warehouse? What’s not in the Data Warehouse? Samples of Queries & Reports

V Methodology and Approach Methodology Employed Techniques Employed

Data Warehouse Project Scope Document

VI Project Cost/BenefitsVII Project Schedule, Budget and Resources -- The plan should include the following milestones: Logical Data Modeling Data Warehouse Data Modeling Data Warehouse Physical Model Source System of Record Extraction/Transformation Program Populated Data Warehouse Populated Metadata End User Access Application End User Training Ongoing Support Plan

Data Warehouse Project Scope Document

VIII Project Planning Assumptions and Issues

-- Project Assumptions

-- Project Risks

-- Project Contingencies

IX Expected Follow-on Projects

Summary

Project management consists of these four essential elements:

Planning (an iterative process) Determining the deliverables Estimating efforts and cost Projecting the resources

Organizing Assembling the team Defining and establishing the structure of the team Creating a productive environment

Summary

Controlling the project Monitoring the progress Reporting performance and variables Adjusting resources

Leading the project Emphasizing human factors—motivation; Team spirit; Delegation

Consider the warning signs and success factors

Warning Sign Indication Action

The Data Requirements definition phase is well the target date.

Need to write too many in-house programs.

Users not cooperating to provide details of data.

Suffering from “analysis paralysis”.

Selected third party tools running out of steam.

Possible turf concerns over data ownership.

Stop the capturing of unwanted inf. Remove any problems by meeting with users. Set firm final target date.

If there is time and budget, get different tools.

Otherwise increase programming staff.

Work with executive sponsor to resolve the issue.

Consider the warning signs and success factors (cont’d)

Warning Sign Indication Action

Users not comfortable with the query tools.

Continuing problems with data brought over to the staging area.

Users not trained adequately.

Data transformation and mapping not complete.

First ensure that the selected query tool is appropriate. Then provide additional training.

Revise all data transformation and integration routines. Ensure that no data is missing. Include the user representative in the verification process.

Data Warehouse Project Different From OLTP System Project

Data Acquisition Data Storage Inf. Delivery

Large Number of sources

Many disparate sources

Different computing platforms

Outside sources

High initial load

Ongoing data feeds

Storage of large data volumes

Rapid growth

Need for parallel processing

Data storage in staging area

Multiple index types

Several index files

Several user types

Queries stretched to limits

Multiple query types

Web-enabled

Multidimensional analysis

OLAP functionality

Metadata management

Data Warehouse Project Different From OLTP System Project

Data Acquisition Data Storage Inf. Delivery

Data replication considerations

Difficult data integration

Complex data transformations

Data cleaning

Storage of newer data types

Archival of old data

Compatibility with tools

RDBMS & MDDBMS

Interfaces to DSS applications

Feed into data mining

Multi-vendor tools

Major Deployment Activities

Complete User Acceptance

Finish final testing of all aspects of user interface including system performance.

Perform Initial Loads

Load dimension tables followed by the fact tables. Create aggregate tables.

Major Deployment Activities (cont’d)

Get User Desktops Ready

Install all the needed desktop tools. Test each client machine.

Complete Initial User Training

Train the users on data warehouse concepts, relevant contents, and data access tools.

Major Deployment Activities (cont’d)

Institute Initial user Support

Set up support to assist the users in basic usage, answer questions, and hold hands.

Deploy in stages

Divide the deployment into manageable stages in agreement with users.

Deploy in Stages

Top-down approach Deploy the overall enterprise data warehouse (E-R model)

followed by the dependent data marts, one by one. Bottom-up approach Gather departmental requirements, plan and deploy the

independent data marts, one by one.

Practical approach Deploy the subject data marts (dimensional model), one by

one, with fully confirmed dimensions and facts, according to preplanned sequence.

Considerations for A Pilot

Proof-of Technology: Intended only to prove new technology for IT.

Comprehensive Test: Only intended for IT to test all infrastructure/architecture. Proof-of-concept: Small-scale, works with limited data, not suitable for

integration

Types of pilot deployment:

Considerations for A Pilot (cont’d)

User tool appreciation: Only intended for users to test and become familiar with tools.

Broad Business: Early deliverable with broader scope, may be integrated.

Expandable Seed: Manageable and simple, but

designed for integration.