BI from open sourceswith

GT data mining

By: Edith Ohri, DatalertHome of GT data mining

edith@datalert.co.il

AboutEdith Ohri – Developer of GT data mining and founder of Datalert startup for early detection. Industrial & Management Eng. from the Technion, MSc from NY Polytech.Management member of IE group in Association of Engineers and Architects in Israel, and a Liaison to Israel Society for Quality.

GT applications include:

SMU Singapore – early detection of top students and dropouts.RAFAE”L – root cause of late deliveries in PurchasingSCD Israel – root cause of a quality issue in productionDetection of earthquakes seismology patterns of behavior - Israel

27 Oct 2015 BI from open sources with GT data mining. All rights reserved © Edith Ohri 2015 Slide 2

The challenge

How to exploit free data?

27 Oct 2015 BI from open sources with GT data mining. All rights reserved © Edith Ohri 2015 Slide 3

Open data integrity issues

1. Unsupervised records2. Interdependencies3. “Long tail”4. “Overfitting”5. BIG DATA concerns,

such as inconsistencies and dynamics...

27 Oct 2015 BI from open sources with GT data mining. All rights reserved © Edith Ohri 2015 Slide 4

The GT data mining solutionGT is about patterns detection* in unsupervised complex data, including rare patterns and newly emerging ones.

*Patterns always provide further resolution, associations and insight.

s27 Oct 2015 BI from open sources with GT data mining. All rights reserved © Edith Ohri 2015 Slide 5

Some of GT new principles1. They shall not clean input data!

2. Always prefer unsupervised data!

3. Include exceptions;

4. Include the data environment;

5. No pre-assumption about data behavior… * Consider variables as interdependent unless proved

otherwise;

6. Conclusions have to be explicit and fully traceable.

27 Oct 2015 BI from open sources with GT data mining. All rights reserved © Edith Ohri 2015 Slide 6

Example: predicting market prices

The target: pricing of new products, based on historic price lists.

The client doubts if analysis can help at all, since there is no data on competitors prices and clients’ behavior. Currently, Marketing determines prices by trial-and-error.

*See how GT resolve that issue in slide #13

27 Oct 2015 BI from open sources with GT data mining. All rights reserved © Edith Ohri 2015 Slide 7

Cont. example – predicting prices, input data

The input contains ~20,000 lines and 22 inter -dependent variables (YET NO data about competitors or clients behavior)

27 Oct 2015 BI from open sources with GT data mining. All rights reserved © Edith Ohri 2015 Slide 8

Cont. example – predicting prices, patterns

GT identifies the 3 product families and 9 subgroups - Inserts, Tools and Solid Carbide, and in them 5 sgr defined by specific functions: 6Q CUT-Grip, 30B Turning-with-hole, 21T Milling-new, 3 sgr defined by their typical Prod.Type, Grade, Shape and Size, and 1 Exceptions sgr.

27 Oct 2015 BI from open sources with GT data mining. All rights reserved © Edith Ohri 2015 Slide 9

Cont. example – predicting prices, key factors

Key-factors in addition to the already existing definitions of Sales-group and Item group:₋ Type of product₋ Geometric shape₋ Type of Chipbreaker₋ Grade₋ Product Radius (Length though has no effect..) *GT arrives to similar key factors on two independent sets.

27 Oct 2015 BI from open sources with GT data mining. All rights reserved © Edith Ohri 2015 Slide 10

Non-quantitative

Cont. example – predicting prices, test

Projecting prices with GT formulas:

27 Oct 2015 BI from open sources with GT data mining. All rights reserved © Edith Ohri 2015 Slide 11

Sub-group

Price by formula

Actual price $

Description Item

Gr732 44.11 45.74 38U drilling Inserts 5505456Gr750 7.52 9.84 1E self-grip Inserts 6002918Gr736 18.10 18.88 12n~A do-grip Inserts 6002410Gr736 16.17 15.96 12n~A do-grip Inserts 6095285

…

Anticipated prices are very close to true prices, see full simulation in next slide

Cont. example – predicting prices, simulation

27 Oct 2015 BI from open sources with GT data mining. All rights reserved © Edith Ohri 2015 Slide 12

Cont. example – predicting prices, GT insights

Answer to Slide-7: pricing can be done without data on competitors and clients, by reverse-engineering their old item price lists. It means that we may know the competitors’ prices more than themselves!

PS, GT groups characteristics also help improve existing Sales-group and Item-group definitions.

27 Oct 2015 BI from open sources with GT data mining. All rights reserved © Edith Ohri 2015 Slide 13

Cont. example – predicting prices, GT Benefits

1. Shorter time to market.

2. Improved specifications of new products.

3. Marketing competitive edge.

4. Extra windfall from data...

27 Oct 2015 BI from open sources with GT data mining. All rights reserved © Edith Ohri 2015 Slide 14

GT features Sum-upDiscovery, Root-causes

Early detection

Automation, fits all platforms

Low cost application method

Fast adaptation to changes

Scalability (by fast & affordable adaptation).27 Oct 2015 BI from open sources with GT data mining. All rights reserved © Edith Ohri 2015 Slide 15

ThanksEdith Ohri

Home of GT data mining

*Imported pictures are from free web sources.27 Oct 2015 BI from open sources with GT data mining. All rights reserved © Edith Ohri 2015 Slide 16