Predictive Analytics and Proactive Service. Dr. Detlef Nauck Research & Innovation, BT

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1 Predictive Analytics and Proactive Service Dr. Detlef Nauck Research & Innovation, BT

2 Big Data Improving Data Plumbing

3 Operational Data Stove Pipe Challenge

4 Analytics No Silver Bullet

5 Autonomics Self-aware Processes

6 Decision Support Prediction is not enough

7 Objective Improving business decisions by revolutionising the way we analyse and make use of data

8 Big Data Benefits store and process at scale with commodity hardware store once process many times data agility schema flexibility complement existing data infrastructure Data economics More value per byte stored Lower the barriers to innovation 8

9 Autonomic Systems Increased responsiveness Selfconfiguring Selfhealing Business resiliency Selfoptimising Selfprotecting Operational Efficiency Secure information and resources

10 Motivation for Data Analytics Something has gone wrong and you want to fix it Gain insight. data mining process mining survey analysis react

11 Aperture Process Mining in BT Web-based Automatically discovers real process from workflow data Helps process optimisation teams Visualisation of process flows and statistical information Used across BT Important tool for transformational journey

12 Business Process Mining

13 Visual Analytics - SATURN

14 Predictive Analytics Pre-empt problems Optimise decisions Forecast to plan. data analysis process mining survey analysis churn/complaints early life failure demand react anticipate

15 Predicting Customer Behaviour Interactive models of customer behaviour Data from customer service processes and customer feedback Identification of Drivers What If Scenarios Target Setting Used in-house: Churn prevention, complaint prevention

16 Predict Early Life Failures Will a phone line that was switched off for an extended period fail in the first 28 days after being restarted? Predictive information changes the line start process in Openreach. X% 0.5X% Implementation Evaluation

17 Fault Model Simulations Weather Impact Generate multiple rainfall scenarios (full analysis uses 1000 scenarios), and use fault model to generate multiple plausible fault rate scenarios rainfall scenarios - weekly rain (mm) fault rate scenarios (faults per 1000 lines per year) Apr-12 Jul-12 Oct-12 Jan-13 Apr Apr-12 Jul-12 Oct-12 Jan-13 Apr-13

18 Forecasting Operational What If Scenarios volumes backlogs task times Policies people numbers cycle times history + forecasts validate against historic + provide forecasts

19 Predictive Dashboards 2012 Highly Commend

20 Pro-active Discovery You only find what you are looking for data analysis process mining survey analysis react churn early life failure demand anticipate analyst drives

21 Pro-active Discovery I don t know what I am looking for but I will know when I see it Machine makes suggestions Discovery machine guides data analysis process mining survey analysis react churn early life failure demand anticipate analyst drives

22 IDEAL Automatically Detecting Changing Patterns

23 Autonomic Business Processes Detect Act Diagnose Autonomic Operations: Act automatically in anticipation of events

24 Autonomic Network Management Rule mining to predict failures or degradation Key: Stages involved in prediction process Stages that implement predictions in real-time Stages that update historical data

25 USE CASES Network Autonomics EFFECT Link failures Link failures Router metrics QoS degradation Social TV Proactive provisioning Service flows unaffected by network failures Customer experience unaffected during high network load Customers receive content seamlessly Cross-layer correlation Router metrics Session admission Guaranteed QoS on admitted services Service metrics Multicast routing Dynamic capacity management

26 Open Innovation what to expect We are running a large open innovation programme with universities and customers around the globe Innovation centres in China and UAE Innovation scouting in Silicon Valley Some Highlights from MIT: Data Tamer data curation Data Wrangler data cleaning and transformation (http://vis.stanford.edu/wrangler/) MapD: massively parallel database platform (http://worldmap.harvard.edu/tweetmap/).

27 Data Wrangler (http://vis.stanford.edu/wrangler/)

28 Data Wrangler (http://vis.stanford.edu/wrangler/)

29 Data Wrangler (http://vis.stanford.edu/wrangler/)

30 Data Wrangler (http://vis.stanford.edu/wrangler/)

31 MapD (http://worldmap.harvard.edu/tweetmap/).

32 MapD (http://worldmap.harvard.edu/tweetmap/).

33 MapD (http://worldmap.harvard.edu/tweetmap/).

34 MapD (http://worldmap.harvard.edu/tweetmap/).

35

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