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IntroductionIntroduction
to DSSto DSS
&&Data WarehousingData Warehousing
ConceptsConceptsTATA
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Atul Gandre
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ContentsContents
• What is DSS ?What is DSS ?• DSS architecture and its componentsDSS architecture and its components
•Extraction, Transformation & LoadingExtraction, Transformation & Loading•Data Access & AnalsisData Access & Analsis
• Data martsData marts• Data !iningData !ining
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What is DSS?What is DSS?
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ManagementManagement
ObjectivesObjectives• $ncreased profits• $mpro%ed margins
• educed o%erheads• Larger mar'et share
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Typical Questions a DecisionTypical Questions a Decision
Maer may as !!!Maer may as !!!“ Give peformance of all TV’s, over the past 3years ”
• Sho) Sales * %olume, %alue and margin contri*ution
• + different time periods
• + product model• + region
• ompare " ears sales month)ise
• ompare sales %-s tpe of promotion, * region
• +est distri*utors - Worst distri*utors• !argin Analsis, ost +rea'up
• apacit utilisation o%er time
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• Sales %alue
• 1 of target met
• 1 o%er last ear
• + region
• + product
“ Top !" / Bottom !" #ales men this year “ y
Typical Questions a DecisionTypical Questions a Decision
Maer may as !!!Maer may as !!!
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• + month - 4uarter - ear
• + product - product group - all products
• 5or a region - all regions
• an'ing o%er the last 62 months
• eco%er of dues
“ #ho$ the Top %" &ustomers “
Typical Questions a DecisionTypical Questions a Decision
Maer may as !!!Maer may as !!!
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Can TransactionCan Transaction
systems ans"er suchsystems ans"er such#ueries $#ueries $ The pro*lems 8
• 9ighl normalised structures ma'e 4ueries more complex
• $ncrease in complexit of 4ueries due to 8
Aggregation, Summarisation, an'ing, umulations, unning totals,omparison
/arious dimensions
• Little historical data stored on:line for comparison
• 9igh resource utilisation )ill result in slo) response to
complex 4ueries
• Difficult in ma'ing ad:hoc 4ueries
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DSS - a definition DSS - a definition
Decision Support Systems use computers tofacilitate the decision main! process of semistructured tass" These systems are desi!ned not
to replace mana!erial #ud!ement $ut to supportit and mae the decisions more effecti%e" DSShelps mana!ers react &uicly to chan!in! needs"
' ( H Inmon
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DSS % a paradigm orDSS % a paradigm or
analysisanalysis• ather strategic information
• onstant prototpe mode
• Detailed and summaried data• edundanc allo)ed
• Data is normall loaded
• Amount of data used in a process is
large
• Ser%es the managerial communit
' ( H Inmon
Transactional Applications DSS Applications
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DSS architecture and its DSS architecture and its
componentscomponents
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• Data )arehouse architecture
• Data extraction, transformation and loading
• Data access and analsis@
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!!!DSS 'rchitecture!!!DSS 'rchitecture
The DSS Architecture consists of@@@
Extraction of data from %arious operational sstems
on different platforms, then transforming and
loading to the Data Warehouse
• The Data Warehouse contains historical data as )ell
as current data@
• The data in the Data Warehouse is accessed * the
front:end tools
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Data Warehouse % theData Warehouse % the
heart o DSSheart o DSS The Data Warehouse is that portion of an
o%erall architected data en%ironment that
ser%es as the single integrated source ofdata for decision support sstems B
' ( H Inmon
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Data Warehouse (Data Warehouse (
'nother de)nition 'nother de)nitionA Data Warehouse is a• su*Cect:oriented,
• integrated,
• time %ariant and
• non:%olatile
collection of data in support of
managements decision:ma'ing process@B
-- ( H Inmon
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** Characteristics o aCharacteristics o a
Data WarehouseData Warehouse• The DW pro%ides access to
corporate - organiational data
• The data in the DW is consistent
• The data in the DW can *e separated and com*ined *
means of e%er possi*le measure in the *usiness
• The DW is )here data is pu*lishedB
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DW Data Model CharacteristicsDW Data Model Characteristics
• Data centric not process *ased
• Simple to understand
• 5lexi*le to add-modif
• Design reflects *usiness information
•uer dri%en design
• Denormalised
$ntuiti%e and eas to use
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The Dimensional ModelThe Dimensional Model
• The E< of a ompan sas :
• We sell products in %arious markets and )e
measure our performance o%er timeB Time
Product
Market
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The Dimensional ModelThe Dimensional Model
• Each ell in the cu*e contains *usiness
measures for a particular com*ination of
• Product , Market and Time
•
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East (est North
1234
5234
6237
8237
1237
0u$y
Emerald
Saffire
Sales0e%enue
• North
• Emerald
• 5234
DW Data modelling - DW Data modelling -
Multidimensionality : An example Multidimensionality : An example
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DW Data modelling :DW Data modelling :
Star SchemaStar SchemaSales
Dimension ta*les
Time
egion
0roduct
ustomer
5act ta*les
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Star Schema FeaturesStar Schema Features
•
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Snowflake DesignSnowflake Design
• Sno)fla'e refers to normalising dimension
ta*les
• reating Outrigger ta*les containing• containing descriptions of codes in dimension ta*le
• containing additional attri*utes
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Snowflake Schema ExampleSnowflake Schema Example
Sales Fact
Product
Seller
Supplier
Location
Time
Store
District
Region
Month
Day
Season
Sales Assoc.
Sales Dept.
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Data Modelling StepsData Modelling Steps
Starting point : End users and source data
• $dentif a su*Cect area
• 5ind out the Gfacts• Associate the facts )ith the *usiness dimensions
• Define the attri*utes in the dimensions
• Decide on the le%el of detail : the granularit• Decide the Gsummarise and purge period
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Data extraction transformation Data extraction transformation
and upload and upload
Operational
Systems Data Warehouse !"S
Data !xtraction Data
Transformation
Data #pload
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Data +,tractionData +,traction
The Extract program :
• ummages through a file or data*ase
• Hses some criteria for selection• $dentifies 4ualified data and• Transports the data o%er onto another
file or data*ase@
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Data +,traction %Data +,traction %
CleanupCleanup• estructuring of records or fields• emo%al of
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Data transormationData transormation
• $ntegrating dissimilar data tpes• hanging codes• Adding a time attri*ute• Summarising data
• alculating deri%ed %alues• Denormalising data
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Data loadingData loading
• $nitial and incremental loading
• Hpdation of metadata
• Hpdation of log
• oll*ac' in case of loading errors
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+T- Tools+T- Tools
• $nformatica
• Ardent DataStage
•
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Data 'ccess &Data 'ccess &
'nalysis 'nalysis
• Ease of na%igation across screen• /alue addition * *etter information presentation
I graphs, charts and mapsJ
• 9ighlighting exception information *
Alarms and Alerts
• Drill:do)n - roll:up through successi%e le%els of
data
• What :if analsis
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.eporting Tools.eporting Tools
• !icrostrateg
• +usiness
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What is O-'/$What is O-'/$
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O-'/ CharacteristicsO-'/ Characteristics
• Al)as in%ol%es interacti%e 4uer and analsis of the
data@ The interaction is usuall multiple passes
• $n%ol%es drilling do)n into successi%el lo)er le%els
of detail data
• $n%ol%es roll:ups to higher le%els of summariation
and aggregation@
•
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Data martsData marts
• Architecture
• haracteristics
• Example
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E)ternal
Sources
Data
E)traction*
Scru$$in!* +
Transformation
Data Sources
,Operational
Systems-
EIS
OLA.
Data /inin!
Data Access
Data mart in the DSS 'rchit Data mart in the DSS 'rchit
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Data
(arehouse
O0
AI
Data /art
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• Data marts are scaled:do)n and lessexpensi%e %ersions of data )arehouses
• Data marts utilie large:scale data
)arehousing concepts on a smaller, more focussed le%el
• Data marts are focussed at departmental
users• Decentralised approach
What are Data marts $What are Data marts $
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What is data mining$What is data mining$
Data !ining, the extraction of hidden
information from large data*ases@ $t is a po)erful ne) technolog )ith great potential
to help companies focus on the most
important information in the data )arehouse@
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Data miningData mining
capabilitiescapabilities• Disco%er of un'no)n patterns
• 0rediction of trends and *eha%iors• Disco%er of anomalies in data
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