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Learning BI Project Realized by : Amine BOUHARB
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OutlineIntroduction
Data warehouse
Olap & reporting
Big data analysis
Data mining & web mining
Conclusion
IntroductionHow do I make decisions in my business? How does the company make decisions? Using intuition? You have to make business decisions based on reality (facts and numbers!)
Aggregate Data Present Data Enrich Data Inform Decision 3
Introduction
1-Aggregate Data
Data Base Data Warehouse Data Mart ETL Tools Integration Tools
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Introduction
2-Present Data
Reporting ToolsOLAP CubesDashboardsStatics ReportsMobile Reporting
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Introduction
3-Enrich Data
Big Data AnalysisData MiningText MiningTime Series
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Introduction
4-Inform a Decision
KPI’sData Mining PredictionsBig Data Analysis
Predictions
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Data WarehouseDim Article Dim Enterprise
Dim Time
Dim Purchasing Document
Dim Supplier
Fact Reception Fact Purchasing
orderDim Division
Dim Shop
Delivered quantityOrdered quantityMissing quantityConfirmed quantity
Predictive delivery in daysReception time in daysOrdered quantity
OLAP & Reporting
First data visualization by different dashboards and KPI’s
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OLAP & Reporting : The BIG Challenge
“Data isn’t like your kids, you don’t have to pretend to love
them equally.”
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• Improve our relationships with suppliers and assess their performance
• Improve the quality of services offered by our suppliers and give alternative suppliers
• Doing some benchmarking of sales between companies, years and places
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OLAP & Reporting : Results
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OLAP & Reporting : Results
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OLAP & Reporting : Decisions
Previous suppliers New Suppliers Material group provided
• Atlanta Electronic Suppy
• Electronics component dist
• Gateway supply
• Tucker industries
• NJ Electronics
• Sunny tetral3
• Jotashi SN5000
• Sec Multisync XV15
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OLAP & Reporting : Results
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OLAP & Reporting : Decisions
Trusted suppliers Suppliers by revenue
• IDES LIEFERANT• Jones LTD• SKF Americas• TIEFLAND GLASS AG
• TIEFLAND GLASS AG• ALU Cast• SKF Kugelmeir KGaA• SMI Supplier
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Big Data Analysis : What can be useful for us?
Web server logs
Internet clickstream data
Social media content
Social network activity reports
Customer emails and survey responses
Mobile –phone call detail records
Machine data captured by
sensors connected to the internet of
things
Big Data Analysis : What are our goals?
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Collecting the data from different sources in order to achieve some analysis goals like : Evaluating the e-reputation of
SAP in the social network Uncover hidden patterns,
unknown correlations. Discover customer preferences
and other useful business information.
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Big Data Analysis : Twitter Word Cloud
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Big Data Analysis : Tweets Geolocations
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Big Data Analysis : What were the decisions?
After analysing the geolocations map , we noticed that the number of tweets is greater in Germany and UK , and lower in Russia , France and Italy
SAP should focus on enlarging advertisements in Russia , France and Italy to attract them and get more interactions from them
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Data Mining Web
Mining
Time Series
Data Mining
Data Analyses
Data from Data Warehouse
Data from Web Sites &Social Networks
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Data Mining : Time Series
The prediction of the products’ quantity that we’ll buy for the next year
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Data Mining : Time Series
• We infer from the chart that purchasing process of products dramatically decrease from the second month to August then it’ll increase sharply till the end of the year
• This analysis is useful to foretell the budget of the next year and also to have an idea about the stockpile fluctuation
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Data Mining
Forks Manufacturing GmbH Tiedemeier Entsorgung GmbH Meir Logistics GmbH C.E.B. Berlin IDES AG New GL - (InterCompany Acco)53
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The most scored suppliers
Score
Companies
Germany USA Japan. Others
74% of the companies are based in Germany
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Data Mining : Web Mining
Comments
SharesLikes
Data Mining : Web Mining
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Data Mining : Web Mining
www.sap.com
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Data Mining : Web Mining
ConclusionData Quality & Accuracy
Data Consistency
Data Timeliness
“Get the right information to the right people at the right time”
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