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Driving Business outcomes with
Data Science Asha Poulose Johnson
CIOGE - Power Services India
Driving Business outcomes with
Data science
ASHA POULOSE JOHNSON CIO, GE POWER SERVICES, INDIA
Data Science – How can it drive business outcomes ??
Prediction Models
Forecast field resource needs based on historic actuals and projected work
Identify receivables with high risk of late payment
↑ CFOA ↑ Field engineer assigned time
Recommendations
Identify price increase recommendations based on purchase affinity and elasticity
Customer segmentation based on shared attributes for targeted marketing campaign
↑ Operating profit
Optimization
Determine optimal upgrade plan to minimize lifecycle cost through parts sharing
↑ Contract productivity
Data Enrichment
Enrich data sets with additional attributes derived from algorithm-driven relationships (e.g. text mined comments)
↑ Data quality
Pricing
Marketing
Sourcing
Finance
Commercial
Field Services
Parts
Equip Business teams with predictive data insights to help drive Productivity, Cost out, Cash, market share and growth
Business acumen
Decision science
Data mgmt Data
analysis + =
Competencies Domains Outcomes
Model development Using 2010-2014 historical data … build a decision tree by customer that identifies factors correlating to late payment. Highly correlated factors include: • Customer past delay • Payment terms • Investment code
High risk receivables prediction
3
Problem statement: Large percentage of customer invoices were paid past due-a) .
Goal: Create a model to identify future receivables with a high likelihood of being paid past due to drive proactive action.
Approach & validation Outcome
(a- Past due is defined as invoice closed >5 days past due date
Model validation Testing against Jan-Apr 2015 data yielded ~80% accuracy for invoices identified as high risk-b)
(b- High risk is defined as those invoices identified with a delayed payment probability of 0.8-1
Example: February 2015 Total invoices 2552
High Risk: 385 (15%)
Low Risk: 2,167 (85%)
False positive: 57 (15%)
Actual delay: 328 (85%)
Delivery mechanism Dynamic, automated Tableau dashboard that empowers commercial and collections team by allowing visibility to receivables and filtering on delay probability, amount, customer and region.
Early <15 days late
15-30 days late 30-45 days late
Invoice Payment pattern
Data Enrichment Solutions
Product Family Classification Buyer Analysis Substitutable Parts
Grouping parts based on part description Helpful in Mergers & Acquisitions Identify the best supplier for each part groups Forecasting at part groups level and helps in supplier renegotiations
Identify Part numbers based on part description & other available information Build savings / deflation report for buyers (cost analysis based on part number identified) Identify which buyers are doing well 120,000 records / year. 40% records
classified with 80% accuracy. ~ 6 weeks
Identify similar parts based on description Suggest alternative parts which is used for similar purpose but which can be bought at lower cost
Build foundational capability: • Enrich data • Solve for Master data challenges • Address data gaps • Assess and improve data quality • Identify missing information • Apply categorization
Price comparison websites : Compare products based on description. Different text across different sources. How to identify all these three point to same product and compare the features?
Application
Problem Solving Approach
Identify Potential Data
sources
Preprocess Data (Clean, quality
checks)
Exploratory Data Analysis
Statistical Models/ Machine Learning algorithm
Visualization Communicate
Report Findings
Build Tool/ App / Data Product
Integrate with enterprise ecosystem
Multiple iterations in short
cycles
Applicable to selected problems
Business Insights
Take Action / Outcome
Community Focused
Volunteer Driven
Knowledge Share
Accelerated Learning
Collective Excellence
Distilled Knowledge
Shared, Non Conflicting Goals
Validation / Brainstorm platform
Mentor, Guide, Coach
Satisfied, Empowered Professional
Richer Industry and Academia
About Information Excellence Group
Progress Information Excellence
Towards an Enriched Profession, Business and Society
About Information Excellence Group
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Progress Information Excellence Towards an Enriched Profession, Business and Society
Progress Information Excellence Towards an Enriched Profession, Business and Society
