meghadoot mobile company case study

Meghadoot Mobile Company Background The development and design of a data warehouse is inextricably linked to the business needs of an enterprise.. Meghadoot Mobile Company MMC is a ficti

Meghadoot Mobile Company Background

The development and design of a data warehouse is inextricably linked to the business needs of

an enterprise Meghadoot Mobile Company (MMC) is a fictitious company that has a need for an analytical data warehouse

Meghadoot Mobile Company has been in business since 1990 Like every Mobile Service

Provider, the company is searching for new business in an already saturated market

Distribution channels have been traditionally through direct sales and Franchise outlets Recently, MMC started offering products through the Internet

You explore MMC’s business needs more fully as you progress through the design process

Throughout the remaining practices in this course, you refer to MCC’s issues and requirements to help you make critical design decisions

Business Situation

In January 2005 and later due to WTO agreement implementation there will be drastic changes at MMC business Due to increased competition it is expected that Sales will be lower than they have been in the previous years The flagship products may become out-dated and may not do well in market

The vice president of sales and marketing wants to understand what is driving the business He wants to know the reason for sales spikes Sales volumes that may be generated through the Internet, but wants to know whether this channel is introducing new clients to MMC or whether it

is acquiring other channels

The president is concerned because the aggregate margins may shrink and therefore may hit the bottom line and top lines of the company

Technology is changing very fast and may see more and more domestic and foreign competitors

Business Objectives

In strategic discussions, managers articulate the objectives and goals for the company, for the supply side–Inventory Management, Planning and Operations–and for the demand side–sales and marketing MMC has chosen to focus on the demand side for the initial warehouse

implementation

Information Requirements

Through interviews and the requirements-building process, you have identified the following information needs:

 Analyze industry trends and target specific market segments

 Identify best and worst sellers

 Identify sales opportunities through custom package offerings or by capitalizing on buying trends

 Identify product trends and develop a channel strategy.

 Analyze sales over multiple promotional cycles

 Analyze sales channels and increase profits

These provide a guideline to your design both in the data that should be included and the

arrangement of that data to support this analysis

Meghadoot Mobile Company Value Proposition

After much discussion and evaluation, the management of MMC identified its greatest business opportunities that could be addressed through a data warehouse

The proof-of-concept deliverable for MMC wants to analyze Customer Behavior to understand the following issues:

Product Strategy: What products are selling? What products should we offer?

Customer Identification: Who are our customers, what are they buying

Objective

In this practice you learn how to identify source data for your data warehouse While discovering the source databases, you adjust your warehouse to schema to accommodate the limitations and constraints of the data that is available

In this practice, you:

 Design the logical model for the data warehouse for MMC

 Identify the subject area for MMC data warehouse application

 Design a conceptual model

 Create the logical model

Next you will design the physical model for the data warehouse for MMC

 Identify the attributes for the data warehouse logical model

 Map the source data from the OLTP model to the data warehouse model

 Identify any data transforms

Review

Creating the logical database design involves:

 Choosing primary keys

 Mapping entities to tables

 Mapping attributes to columns

 Mapping relationships to foreign keys

 Choosing subtype options (and identifying any inheritance properties)

 Choosing optimality

 Choosing candidate index columns

Note: Although indexes are not strictly part of logical design, in reality they are usually identified

at this stage

Begin your design by identifying the subject areas of your business to be implemented in the first (current) increment Consider only the business view of the information requirements Your perspective needs to be independent of any implementation constraints or concerns at this stage

Subject areas are building blocks that are uniquely identifiable and usable across the business These subjects need to be founded on long-term, stable business concepts

Note: The subject areas become the dimensions of the star model.

The logical design may consider implementation consequences Changes need to be made to the logical model for technical reasons, such as:

 Scope of the data warehouse

 Business entities and attributes

 Business rules, domain, cardinality

 Relationships between business entities

 Referential integrity rules

MCC Subject Areas

From the MCC business case, the following subject areas have been identified for the conceptual model:

 Customer

 Time

 Place

 Age

 Gender

 Consumption level

 Customer type

 Customer occupation

 Credit level

 Business brand

 Business category

Although it may be tempting to create the entities for the warehouse from existing systems, you need to define the entities from the subject areas identified by your specific analysis of business requirements These entities become the dimensions of your warehouse model

The next step in creating your conceptual model is to identify the relationship within each entity and between entities The relationships are important in this model, as your starting point

Identifying the Attributes

Now that you have identified the entities and relationships between them from the outlined business requirements you can now determine in your what sort of conceptual model, information about each entity is of interest The attributes are the descriptors in the entity Attributes may also become primary and foreign key values

The data warehouse may contain:

 Descriptive attributes such as name, Occupation, and Credit Limit

 Key attributes such as order and people identifiers

 Level indicators

Key values are determined in your next step

Note: The attributes of entities ultimately become the table columns in the logical model

Identifying the Critical Business Measures

The critical measures of the business are the facts by which the business is evaluated Consider

different ways the business might need to be viewed as you define these measures For example consider:

 Internal measurements such as salesperson quota, or percentage of default Payments

 Customer measures such as discounts extended, or days between order and receipt

 Marketing information that may include demographic data, or industry trends

The key performance measures represent the direct results of a business event and not an analysis about what the event means

Note: These measures typically become the attributes of the fact table.

Defining the Keys

Your entity relationship model now contains the:

 Relationships between entities

 Attributes for each entity

 A list of measures

 Derived data attributes to hold calculated values

Map Source System to Subject Area

As you consider which system can provide the warehouse with data, conduct a life cycle mapping

of sources to the subject areas For example, looking at the Order Entry System for Global Computing Company, you see that the Customer subject area is created, updated, and read through this system

You should assess each source system for its viability to act as an accurate, continuing source of data by evaluating the source system’s:

 Application type, whether package or custom

 Architecture, whether open or proprietary

 Age

 Database size and growth pattern

 Maintenance history

 Size of user base and criticality of data

 Audit quality

These measures indicate the long term stability of the system as a data provider for the warehouse