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


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"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. "

Published in: Business
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L'évolution du métier du DAF induite par la transformation digitale

  1. 1. L'évolution du métier du DAF induite par la transformation digitale
  2. 2. Mathilde Bluteau Patrice Trousset @MathildeBluteau Directrice financière Microsoft France @ptrousse DSI Microsoft France
  3. 3. Demystifying Machine Learning
  4. 4. “Information about transactions, at some point in time, will become more important than the transactions themselves.” Walter Wriston, former CEO of Citicorp
  5. 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. 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. 7. Data is pervasive. Action is elusive Decision automation Decision support
  8. 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. 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. 10. Machine Learning Process Business Scenarios
  11. 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. 12. Transform data to intelligent action Decision automation Decision support Value
  13. 13. Advanced Analytics provides the answer EXAMPLE CUSTOMER QUESTIONS
  14. 14. 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
  15. 15. = V1 + V2 + V3 + V4 …. Vn .4 .2 .3 .5 .9 .1 .2 .7 .4 .2 .3 .8 .4 Dataset Creation Predict a Class Classification
  16. 16. 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
  17. 17. Step-by-Step
  18. 18. Step-by-Step
  19. 19. Step-by-Step
  20. 20. DEMO
  21. 21. 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%.”
  22. 22. 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)
  23. 23. 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
  24. 24. N° 26
  25. 25. @microsoftideas @microsoftpme N° 27
  26. 26. N° 28 Notez cette session Et tentez de gagner un Surface Book Doublez votre chance en répondant aussi au questionnaire de satisfaction globale * Le règlement est disponible sur demande au commissariat général de l’exposition. Image non-contractuelle