msbi basics

7/31/2019 MSBI basics

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Raugh kimball In simplest terms Data Warehouse can be defined as collection of Data marts.

-Data marts : Subjective collection of Data.

Bill Inmon

A data warehouse is a subject-oriented, integrated, timevariant,and nonvolatile

collection of data in support of managements decision-making process.

ERPwill Run the Business

- like how Tyres Run the CarBI (Reports,Data mining,Dashboards,kpis)will help you to take business decisions basedon your historical data.

- like Steering, mirrors, breaks,

dashboards will help, how smoothly you canrun the Car or reach the Destination.


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Which are ourlowest/highest margincustomers ?

Who are my customersand what products

are they buying?

Which customers

are most likely to goto the competition ?

What impact willnew products/serviceshave on revenue

and margins?

What product prom-

-otions have the biggestimpact on revenue?

What is the mosteffective distributionchannel?

In What way a Data warehouse helps any Business

Lets say A producer wants to know.


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Data, Data everywhere yet ... I cant find the data I need

data is scattered over the network many versions, subtle differences

I cant get the data I need

need an expert to get the data

I cant understand the data I found

available data poorly documented

I cant use the data I found

results are unexpected

data needs to be transformed from one

form to other


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A single, complete and

consistent store of data

obtained from a variety of

different sources made availableto end users in a what they can

understand and use in a

business context.

[Barry Devlin]


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What are the users saying...

Data should be integrated across

the enterprise

Summary data has a real value to

the organization

Historical data holds the key to

understanding data over time

What-if capabilities are required


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A process of transforming

data into information and

making it available to users

in a timely enough manner

to make a difference

[Forrester Research, April 1996]

Data

Information


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Data Warehousing --

It is a process

Technique for assembling and

managing data from various sources

for the purpose of answering

business questions. Thus makingdecisions that were not previous

possible

A decision support database

maintained separately from the

organizations operational database


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Data Mining works with

Warehouse Data

Data Warehousing provides the

Enterprise with a memory

Data Mining provides the

Enterprise with intelligence


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We want to know ...

Given a database of 100,000 names, which persons are the least likely todefault on their credit cards?

Which types of transactions are likely to be fraudulent given thedemographics and transactional history of a particular customer?

If I raise the price of my product by Rs. 2, what is the effect on my ROI?If I offer only 2,500 airline miles as an incentive to purchase rather than5,000, how many lost responses will result?

If I emphasize ease-of-use of the product as opposed to its technicalcapabilities, what will be the net effect on my revenues?

Which of my customers are likely to be the most loyal?

Data Mining helps to extract such information


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Base Product

$ 25K $ 40K $ 25K

Oracle 10gIBM DB2


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Base Product

Manageability

(included)

$ 25K $ 40K $ 25K$ 56K $ 35K

Tuning$3K

Diagnostics$3K

Partitioning$10K

PerformanceExpert$10K


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Base Product

Manageability

(included)

$ 25K $ 35K$ 154.5K$ 56K$ 116K

BusinessIntelligence

OLAP$20k

Mining$20k

BI Bundle$20k

DB2 OLAP$35K

DB2Warehouse$75K

Cube Views$9.5K


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Base Product

Manageability

(included)

$ 25K $ 154.5K$ 164.5K$ 232K$ 116K


High Availability

Data Guard$116K Recovery

Expert$10k

$116K


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Base Product

Manageability

(included)

High Availability


Multi-core

$348k -$464k$ 232K$ 25K $ 164.5K$ 329K

$164.5K$116K -$232K


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Additional BenefitNumber of Users

What

happened?

Why did

it happen?

What will

happen?

What happened

why and how?


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OLTP Online Transaction ProcessingOLAP Online Analytical ProcessingMOLAP Multidimensional OLAPROLAP Relational OLAPHOLAP Hybrid OALP

Dimensions De-normalized master tablesAttributes Columns of DimensionsHierarchies sequential order of attributesFacts (Measure group) Transactions tables in DWHFact (Measures)Cubes Multidimensional storage of Data

KPIs Key performance indicatorDashboards combination of reports,kpis,chartsData Marts Subjective Collection of DataSCDs Slowly changing DimensionsPerspectives Child Cube


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Operational

Data Sources

Data-Migration

Middleware (Populations-Tools)

DataStorage

Repository

Data

AnalysisReporting, OLAP,

Data Mining


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Stage DB

Optional

ROLAP

OLTP

MOLAP

O L A P

Integration Services Analysis

Services

Reporting

Services

SSAS

SSRS

SSISData Marts

CUBE


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1. OLTP (on-line transaction processing)

2. Day-to-day operations: purchasing,inventory, banking, manufacturing, payroll,registration, accounting, etc.

1. OLAP (on-line analytical processing)

2. Data analysis and decision making

3. The tables are in the Normalized form. 3. The tables are in the De-Normalizedform.

5. For Designing OLTP we used datamodeling.

5. For Designing OLTP we usedDimension modeling.OLAP is classified into two i.e.,MOLAP & ROLAP

4. We Called the Storage objects asTables. i.e., All the masters and theTransactions are stored in the tables.

4. We Called the Storage objects asDimension and Facts. i.e., All the mastersAre dimension and the Transactions are

Facts.


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Product

Prod_Id

Prod_Name

Base_Rate

Cat_IdCategory

Cat_Id

Cat_Name

Cat_Desc

Group_IdGroupGroup_Id

Group_Name

Group_Desc

Product_Dim

Prod_Id

Prod_Name

Base_Rate

Cat_Name

Cat_Desc

Group_Name

Group_Desc

Topics Later We will Cover

2. Slowly changing Dimensions

1. Types of Dimensions

3. Hierarchies

Normalized Tables De-Normalized Tables


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SalesOrder_Fact

Cust_Id

Prod_Id

Order_Date

Delivery_Date

Unit_Price

Qty

Total_Amount

Tax

SalesOrderDetails

Cust_Id

SalesPerson

Prod_Id

Order_Date

Booked_Date

Delivery_Date

Unit_Price

Qty

Tax

Created_By Qty*Unit_Price+Tax=Total AmountUsually calculate all the calculationsbefore storing into OLAP

Referencekeys of

Dimensions

Numericfieldscalled as

Fact ormeasure


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Prod_Dim

Prod_Id

Cust_Dim

Cust_Id

Time_Dim

Date

Year

Month

Org_Dim

Org_Id

SalesOrder_Fact

Cust_Id

Prod_Id

Order_Date

Delivery_Date

Org_Id

Unit_PriceQty

Total_Amount

Tax

STAR Schema


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Product_Dim

Prod_Id

Prod_Name

Base_Rate

Cat_Name

Cat_Desc

Group_Name

Group_Desc

SalesOrder_Fact

Cust_Id

Prod_Id

Order_Date

Delivery_Date

Unit_Price

Qty

Total_AmountTax


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1. Dimensions will have onlyrelation with the Fact.

(Normalized model)

1. Dimension will have arelation other than Fact. (De-

Normalized model)

2. One to many or One toOne relation will Occur.

2. Used for many to manyrelation.

3. Performance is fast butrequired huge storage space.

3. Performance is Low butrequired Less storage space.


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