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