a warm welcome from - women in big data...tina rosario (sap), nahia orduña (vodafone), astrid...

A Warm Welcome From

&

#SAPBWNMUC

#SAPBWN

#wibd

➢ Opportunities and trends in Big Data Careers and Women In Big Data initiative:

global and local

Tina Rosario (SAP), Nahia Orduña (Vodafone), Astrid Neumann (Mercateo)

➢ Buzzword Wars – Predictive vs. Machine Learning

Sarah Detzler (SAP)

➢ Sales Enablement, Strategy & Content: “Leading through Change

Gretchen Nemechek (SAP)

#SAPBWNMUC

#SAPBWN

#wibd

➢ Opportunities and trends in Big Data Careers and Women In Big Data initiative:

global and local

Tina Rosario (SAP), Nahia Orduña (Vodafone), Astrid Neumann (Mercateo)

➢ Buzzword Wars – Predictive vs. Machine Learning

Sarah Detzler (SAP)

➢ Sales Enablement, Strategy & Content: “Leading through Change

Gretchen Nemechek (SAP)

#SAPBWNMUC

#SAPBWN

#wibd

Inspire

Connect

Grow

Tina Rosario - EMEA Lead

Astrid Neumann & Nahia

Orduna – Munich Chapter

Big Data Trends

6/28/2019 5

✓ Platforms

✓ Intelligence

✓ Streaming

✓ Data Citizens

✓ Regulations

Big Data = Big Opportunities

Demand for Big Data talent will likely exceed

supply by 60-70% by 2020 according to a

McKinsey study

Companies are struggling to find qualified

employees - not just technical opportunities

- women provide untapped potential.

Women prefer to enter professions where

they can have a beneficial impact on people

and society

6/28/2019 6

Female Representation

Technology jobs (globally) <19%

Leadership positions in technology <5%

Big Data users and decision makers <7%

Diversity in Big Data

6/28/2019 7

WiBD Munich Chapter

6/28/2019 Women in Big Data 8

Who we are

NahiaSenior Manager Analytics/Digital IntegrationVodafone

Astrid Senior Talent Manager /IT RecruitingMercateo

PatProduct ManagerIntel

KendyBusiness Development ManagerThales

KatharinaBusiness Consultant Dassault Systemes

28 June 2019 9

Where are the career opportunities in Data?

Digital HR Specialist

Analytics Manager

Data Scientist

Digital Finance Manager

6/28/2019 Women in Big Data Forum 10

Where is Big Data

• Marketingmarketing automation

• HR analytics of applications

• Financesmarter investment decisions

• Sales & Business Developmentanalyzing the average sales cycle

• ITPredict IT faults

www.womeninbigdata.org

• Events in Munich (Eventbrite)• Coaching, Big Data topics

• Global webinars

• Training offers

• Speaker and Blog opportunities

• Women in Big Data Munich LinkedIn group (local)

• Women in Big Data Forum LinkedIn group (global)

6/28/2019 11

Activities of Women in Big Data - Munich

Women in Big Data

6/28/2019 Women in Big Data 12

Our Sponsors and partners

Let´s have an awesome night

➢ Opportunities and trends in Big Data Careers and Women In Big Data initiative:

global and local

Tina Rosario (SAP), Nahia Orduña (Vodafone), Astrid Neumann (Mercateo)

➢ Buzzword Wars – Predictive vs. Machine Learning

Sarah Detzler (SAP)

➢ Sales Enablement, Strategy & Content: “Leading through Change

Gretchen Nemechek (SAP)

#SAPBWNMUC

#SAPBWN

#wibd

PUBLIC

Dr. Sarah Detzler, SAP

June, 2019

Buzzword WarsPredictive vs. Machine Learning

16PUBLIC© 2019 SAP SE or an SAP affiliate company. All rights reserved. ǀ

Deep Learning & Machine Learning, Predictive Analytics, Data Science …

DL ⊂ ML ⊂ AI

DS = ML ∪ PA ∪ OR ∪ ETC

ML ⊂ DS

ML ∩ PA ≠ ∅

17PUBLIC© 2019 SAP SE or an SAP affiliate company. All rights reserved. ǀ

IntegrationModels

Data

Predictive Projects – The Main Pillars

18PUBLIC© 2019 SAP SE or an SAP affiliate company. All rights reserved. ǀ

▪ Customer

▪ Products

▪ Transactions

▪ Facilities/

equipment

▪ Demand

▪ Risk

▪ Churn

▪ Marketing

▪ Finance

▪ Maintenance/

Service

Historical DataTypical and recurring

behavior/pattern

Prediction and

Initiatives

Predictive Analytics: How does it work?

19PUBLIC© 2019 SAP SE or an SAP affiliate company. All rights reserved. ǀ

Benefit

• Automate the identification of relevant maintenance plans to avoid errors

• Automate the calculation of optimal maintenance intervals

• Estimation of survival rates to provide Service Level Agreements-based plans

• Shorten the time for error pattern detection and analysis of causes from several hours to several seconds

• Overall equipment effectiveness through optimized maintenance plans

Preventive maintenance plan optimization

Current Problem

• In order to ensure a high level of overall equipment effectiveness, the minimization of equipment failures is crucial

• Preventive maintenance plans are based on operator experience and at the level of equipment level

• Lack of IT support to perform root cause analysis of equipment failures and failure patterns

• Lack of information and IT support in order to prevent preventive maintenance frequencies in order to minimize equipment failures

20PUBLIC© 2019 SAP SE or an SAP affiliate company. All rights reserved. ǀ

Use Case:

Better understanding of errors and warnings that appear at the same

time.

Provide recommendations for future maintenance

Examples of data relevant for the predictive model

• Unique Production cycle ID*

• Error Code*

• Timestamp

• Short description

https://blogs.sap.com/2018/05/28/another-view-on-link-analysis-are-

error-log-files-social/

Suggestions for suitable data

Example: Machine error analysis

21PUBLIC© 2019 SAP SE or an SAP affiliate company. All rights reserved. ǀ

Which of our error or warnings appear together in production cycles?

22PUBLIC© 2019 SAP SE or an SAP affiliate company. All rights reserved. ǀ

Our Errors and Warnings …

23PUBLIC© 2019 SAP SE or an SAP affiliate company. All rights reserved. ǀ

… and errors and warnings during each production cycle

24PUBLIC© 2019 SAP SE or an SAP affiliate company. All rights reserved. ǀ

Focus on the network

in which production cycles appeared more than one error or warning

25PUBLIC© 2019 SAP SE or an SAP affiliate company. All rights reserved. ǀ

How often did each error or warning pair appear together?

The more often, the stronger the connection and the thicker the line

26PUBLIC© 2019 SAP SE or an SAP affiliate company. All rights reserved. ǀ

Visual rearrangement already shows possible recommendations

27PUBLIC© 2019 SAP SE or an SAP affiliate company. All rights reserved. ǀ

Error and warning communities provide additional context and guidance

on which recommendation to choose

28PUBLIC© 2019 SAP SE or an SAP affiliate company. All rights reserved. ǀ

Bridge nodes between error and warning communities

can open new group to errors and warnings in a specific production cycle

Dr. Sarah Detzler, sarah.Detzler@sap.com

Senior Presales Specialist

https://blog-sap.com/analytics/author/sarahdetzler/

Thank you.

➢ Opportunities and trends in Big Data Careers and Women In Big Data initiative:

global and local

Tina Rosario (SAP), Nahia Orduña (Vodafone), Astrid Neumann (Mercateo)

➢ Buzzword Wars – Predictive vs. Machine Learning

Sarah Detzler (SAP)

➢ Sales Enablement, Strategy & Content: “Leading through Change

Gretchen Nemechek (SAP)

#SAPBWNMUC

#SAPBWN

#wibd

Leading Through ChangeGretchen NemechekSVP Global EnablementSAP Customer Experience

LET GO

LOOSEN

EMBRACE

TRANSPARENT

GROWTH

OPTIMISM

Learn to LET GO.

That is the key to happiness.

- Buddha

THANK YOU!Gretchen NemechekSVP Global EnablementSAP Customer Experience

Twitter: @gnemechek

FAVORITE RESOURCES

• Cy Wakeman – No Ego Podcast

• Brene Brown – Anatomy of Trust

• Amy Cuddy – Body Language Ted Talk

• Women’s Meditation Network

Have fun networking!

&

#SAPBWNMUC

#SAPBWN

#wibd