"A l’heure de la surinformation et de la multitude des données, de plus en plus d'outils sont à disposition des Directeurs financiers. Comment intégrer le ""Digital Work"" pour la direction financière, un retour d'expérience avec Mathilde Bluteau Chief Financiel Officer pour Microsoft France autour de l'optimisation de la collaboration & l'impact des outils d'analyse prédictive sur les organisations. "

1. L'évolution du métier du DAF induite par la
transformation digitale

2. Mathilde Bluteau
Patrice Trousset
@MathildeBluteau
Directrice financière Microsoft France
@ptrousse
DSI Microsoft France

3. Demystifying Machine
Learning

4. “Information about transactions, at some point
in time, will become more important than the
transactions themselves.”
Walter Wriston,
former CEO of Citicorp

5. Source: Drew Conway
The study of the extraction of knowledge from
data (Wikipedia)
Extracting, creating, and processing data to turn
it into business value
What is Data Science?

6. Scorecards and Reports
The State of Analytics
Advanced Analytics
Visual Analytics
Statistical Learning
Machine Learning
Business Intelligence
Interactive Dashboards
Data Mining
What happened
Why did it happen
What will happen
What will happen if we do this
Static reports
Dashboards
Predictions
Recommendations

7. Data is pervasive. Action is elusive
Decision automation
Decision support

8. ML answers questions. Be precise.
Questions that can be answered with name or number
Vague Questions
What can the data tell me?
What should I do?
How can I increase revenue?
Precise Questions
How many Xbox consoles will we sell
during Christmas in France?
Which customer is likely to leave for a
competitor?

9. Four Questions Machine Learning can Answer
Regression Anomaly Detection
Predict a number Find unusual items
Clustering
Find groups/patterns
What will product revenue
be in Jan in France?
What is propensity of
customer to churn?
Find similar customers
who use cloud products.
Identify fraudulent
expense report filings.
Predict a Class
Classification
How Many? What category? In what group? Is it weird?

10. Machine Learning Process
Business Scenarios

11. Agent allocation
Warehouse efficiency
Smart buildings
Predictive maintenance
Supply chain optimization
User segmentation
Personalized offers
Product
recommendation
Fraud detection
Credit risk
management
Sales forecasting
Demand forecasting
Sales lead scoring
Marketing mix
optimization
Advanced Analytics scenarios
Pricing Strategy
Risk & Compliance
Financial forecasting

12. Transform data to intelligent action
Decision automation
Decision support
Value

15. Advanced Analytics provides the answer
EXAMPLE CUSTOMER QUESTIONS

16. Situation:
A competitor is targeting Microsoft customers and trying to
convert them to their own solution
Business Impact:
• Each customer loss to this competitor is $1 mil. lost lifetime revenue
• Each 1% of market share lost is $190 mil.
Question:
Can we predict the next customer conversion to this competitor before it
happens?
Business Problem and Question
Predict a Class
Classification

17. = V1 + V2 + V3 + V4 …. Vn
.4 .2 .3 .5 .9 .1 .2 .7 .4 .2 .3 .8 .4
Dataset Creation
Predict a Class
Classification

18. 1. Collect historical data
2. Clean, prepare and explore the data
3. Split data into training set and test set
4. Choose appropriate ML algorithm
5. Apply algorithm to training data
6. Score test data based on model
7. Evaluate effectiveness of model
Define the business problem you want to solve.
Step-by-Step
Data
Clean and Prepare
Algorithm
Train the model
Score the model
Evaluate Results
Split Training/Test

19. Step-by-Step

20. Step-by-Step

21. Step-by-Step

22. DEMO

23. Financial Forecasting
Harvard Business Review, August 2016
“Forecasting is the third rail of business. Few companies are really good at
it, and there can be big penalties for being wrong. In fact, a survey of more
than 500 senior executives showed that only 1% of companies hit their
financial forecast over three years, and only one out of five are within 5%.
Overall, companies were off by 13%, impacting shareholder value by 6%.”

24. Machine Learning Forecasting: Project Delphi
Challenges in Finance:
 Inefficient planning and forecasting process (slow)
 Productivity: man hours required to generate a forecast
 Accuracy
 Human bias in forecasts erodes executive trust
VP Machine Learning agreed to pilot a project with Microsoft Finance in Central Finance
Goals
Provide a strong unbiased and automated baseline forecast to FP&A professionals who can
apply their domain expertise and adjust it to create a final revenue forecast
More frequent forecasts to give finance ability to respond to the business (enabled through
automation)

25. What we learned
• Continuous improvement system (new data sources, model refinement etc.)
• Strong partnership with finance and data scientists with shared goal of accuracy
• Finance has important business insights to help inform feature selection.
• Some one time events cannot be learned by machine. It remains critical for
business to judge the final forecast.
• IT required enhanced security for enterprise financial data in the cloud.
We built it into platform
• Explain-ability of results is crucial for adoption. Build driver-trees. Educate finance
on machine learning

26. N° 26

27. @microsoftideas @microsoftpme
N° 27

28. N° 28
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