Learning BI Project

transcript

Learning BI Project Realized by : Amine BOUHARB

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

Introduction

2-Present Data

Reporting ToolsOLAP CubesDashboardsStatics ReportsMobile Reporting

Introduction

3-Enrich Data

Big Data AnalysisData MiningText MiningTime Series

Introduction

4-Inform a Decision

KPI’sData Mining PredictionsBig Data Analysis

Predictions

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

OLAP & Reporting : The BIG Challenge

“Data isn’t like your kids, you don’t have to pretend to love

them equally.”

• 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

OLAP & Reporting : Results

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

OLAP & Reporting : Results

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

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?

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.

Big Data Analysis : Twitter Word Cloud

Big Data Analysis : Tweets Geolocations

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

Data Mining Web

Mining

Time Series

Data Mining

Data Analyses

Data from Data Warehouse

Data from Web Sites &Social Networks

Data Mining : Time Series

The prediction of the products’ quantity that we’ll buy for the next year

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

Data Mining

Forks Manufacturing GmbH Tiedemeier Entsorgung GmbH Meir Logistics GmbH C.E.B. Berlin IDES AG New GL - (InterCompany Acco)53

The most scored suppliers

Companies

Germany USA Japan. Others

74% of the companies are based in Germany

Data Mining : Web Mining

Comments

SharesLikes

www.sap.com

ConclusionData Quality & Accuracy

Data Consistency

Data Timeliness

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

Learning BI Project

Documents