sitnl 2015 algorithm programming made easy (marcel de bruin, sander de wildt)

Use this title slide only with an image

Algorithm programming made easy

Sander de Wildt, The Next ViewMarcel de Bruin, SAPNov 2015

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Predictive analytics

HANAAFL_PAL

Application function library –

predictive analytics libary

APL

Automated predictive insight

Predictive functions are everywhere

Automated(KXEN/SAP Infinite

Insight

Expert(SAP Predictive Analysis)

© 2013 SAP AG or an SAP affiliate company. All rights reserved. 3

Predictive analytics

HANAAFM

Application function library –

predictive analytics libary

APL

Automated predictive insight

Predictive functions are everywhere

Automated(KXEN/SAP Infinite

Insight

Expert(SAP Predictive Analysis)

AdvancedAnalytics Enterprise

Business Intelligence

Agile Visualization

Advanced Analytics

How analytics need to evolve to deliver collective insights

RawData

CleanedData

Standard Reports

Ad Hoc Reports &

OLAP

Agile Visualization

Predictive Modeling

Optimization

What happened?

Why did it happen?

What will happen?

What is the best that

could happen?

Use

r Eng

agem

ent

Maturity of Analytics Capabilities

Self Service BI

Generic Predictive Analysis

End-to-endEasy adoption

Fast implementation Business focused Enable storytelling

Col

lect

ive

Insi

ght

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RETAIL

Store Segmentation & Performance

Returner Segmentation& Targeting Product Launch

Demographic Profiling for Marketing

Markdown Optimization

Real-time PersonalizedOffers

Buyer Classification & Churn Prevention

Sales and Inventory Forecasting

Customer Sentiment Analysis

Customer Loyalty Analysis

Short-term Check-outPrediction

Market BasketAnalysis

Predictive Use Cases

© 2013 SAP AG. All rights reserved. 7

RETAIL

Store Segmentation & Performance

Returner Segmentation& Targeting Product Launch

Demographic Profiling for Marketing

Markdown Optimization

Real-time PersonalizedOffers

Buyer Classification & Churn Prevention

Sales and Inventory Forecasting

Customer Sentiment Analysis

Customer Loyalty Analysis

Short-term Check-outPrediction

Market BasketAnalysis

Predictive Use Cases

© 2013 SAP AG. All rights reserved. 8

APRIORI Algorithm

LIFT:

Confidence :How often when a customer bought A he also bought B. Milk & Bread = 2/3 because out of 3 transactions that contain Milk, only 2 contain the Milk & Bread

Support:How many times does a article combination

Occur. Milk & Bread = 2/5

Transaction product

1 Milk1 Bread2 Butter3 Beer4 Milk4 Bread4 Butter5 Milk5 Butter Support Milk & Bread

Support Milk X Support Bread = 1,6

© 2013 SAP AG. All rights reserved. 9

Predictive Use Cases

Predictive results incorporated into more applications and processes, for more users

Bridges the skills gap between human experts vs. analytical experts

Predict and act in real-time

Advanced AnalyticsConfidently anticipate what comes nextto drive better business outcomes

PREDICT

© 2014 SAP AG or an SAP affiliate company. All rights reserved.

Thank You

m.de.bruin@sap.comSolution engineer HANA/ BI

sander.de.wildt@thenextview.nlBig Data Consultant