KNIME for Deterministic Analytics by Business Users Dr. Arne Beckhaus, Head of Big Data

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1 Bitte decken Sie die schraffierte Fläche mit einem Bild ab. Please cover the shaded area with a picture. (24,4 x 7,6 cm) KNIME for Deterministic Analytics by Business Users Dr. Arne Beckhaus, Head of Big Data Chassis & Safety

2 Continental Corporation Overview 2016 Since 1871 with headquarters in Hanover, Germany Sales of 40.5 billion 220,137 employees worldwide 427 locations in 56 countries Tires 26% ContiTech 14% Sales by division in % Chassis & Safety 22% Powertrain 18% Interior 20% Status: December 31,

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4 Why KNIME? Open Source Open Source Open Source innovation and integration wide spread in organization future proof and customizable, technologically & commercially (Gartner Magic Quadrant) 4

5 business user 5

6 choice of KNIME

7 Deterministic Analytics 80:20 rule of data science It s not ETL, it s data wrangling or even Deterministic Analytics instead of predictive analytics Analyze a wide search scope, e.g. 20,000 part nrs. Calculate a user-defined KPI per case for ranking Draw charts holding the details of each case From reactive single checks in Excel to proactive full coverage in KNIME 7

8 deterministic analytics choice of KNIME

9 Target Group Users in business functions, no IT background Typically Excel-savvy Big Data for them: bigger than Excel deterministic analytics Also interested in automating repetitive tasks such as reporting 9

10 Crowd vs. Competence Center Business User Power User Data Scientist 10

11 Crowd vs. Competence Center Empowered Business Users in all Functions Data Science Competence Center 11

12 deterministic analytics business users choice of KNIME

13 Training Domain related training workflows 13

14 Training Domain related training workflows Three levels Entry Level Data Wrangling Advanced Level Data Wrangling Entry Level Data Science Per level: 4 consecutive weeks with a 3 hour block each 300 participants so far, diversity! 14

15 Training: e.g. GroupBy 15

16 Training: e.g. GroupBy 16

17 Training: e.g. GroupBy 17

18 Training: e.g. Convert Layered Header Lecture 18

19 Training: e.g. Convert Layered Header Lecture 19

20 Training: e.g. Convert Layered Header Lecture 20

21 Training: e.g. Convert Layered Header Lecture 21

22 Training: e.g. Convert Layered Header Lecture 22

23 Training: e.g. Convert Layered Header Lecture 23

24 training deterministic analytics *.knwf business users choice of KNIME

25 Nodes used in Typical Projects (e.g. Logistics) Vendor Call-offs (690) Advance Shipm. (126) SC consistency (356) 25

26 Supply Chain Consistency Analysis Customer demand (e.g. 2,400 of part nr ) via bill of materials: required raw material (e.g. 4,800 of material ) vs. ordered raw material (e.g. 4,700 of = shortage of 100 parts) considering initial stock level (e.g. 250 of = surplus of 150 parts) More complexity in reality: time dimension added, thousands of material numbers, re-use of raw material in different products, cross BU, more than one supplier per material, etc. 26

27 Supply Chain Consistency Analysis 27

28 training deterministic analytics projects business users

29 Thank you for your attention! Dr. Arne Beckhaus Head of Big Data & Digital Transformation, Division C&S March