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Learning BI Project Realized by : Amine BOUHARB

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Page 1: Learning BI Project

Learning BI Project Realized by : Amine BOUHARB

Page 2: Learning BI Project

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OutlineIntroduction

Data warehouse

Olap & reporting

Big data analysis

Data mining & web mining

Conclusion

Page 3: Learning BI Project

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

Page 4: Learning BI Project

Introduction

1-Aggregate Data

Data Base Data Warehouse Data Mart ETL Tools Integration Tools

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Page 5: Learning BI Project

Introduction

2-Present Data

Reporting ToolsOLAP CubesDashboardsStatics ReportsMobile Reporting

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Page 6: Learning BI Project

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

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OLAP & Reporting

First data visualization by different dashboards and KPI’s

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Page 10: Learning BI Project

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

Page 17: Learning BI Project

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

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Data Mining : Web Mining

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Data Mining : Web Mining

www.sap.com

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Data Mining : Web Mining

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ConclusionData Quality & Accuracy

Data Consistency

Data Timeliness

“Get the right information to the right people at the right time”

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