ryan price director digital analytics at avanade - ryan... · ryan price director digital analytics...

Ryan PriceDirector Digital Analytics at Avanade

Machine Learning & Big Data

Avanade Analytics

©2017 Avanade Inc. All Rights Reserved. <Highly Confidential> See Avanade’s Data Management Policy

Ryan PriceDirector, Digital Analytics, Netherlands

[email protected]+31 63 926 3470

Past

Present

Future

Commander

Director & Project Manager

Director, Digital AnalyticsMBA

https://avanade.sharepoint.com/sites/policies/Policies2/Data%20Management/1431_DataManagement.pdf


Meet Avanade

9 consecutive Microsoft Alliance Partner of the Year wins

$2B+ in revenue18,500 Microsoft certified professionals



Meet Avanade Analytics

3000+ man years of Analytics experience

AvanadeData & Analytics

Agile delivery thru variety of delivery models: Onshore, Near-shore, Offshore & Composite

Industry & Technology accelerators to jumpstart & accelerate delivery

43% of our customers are in the Global 500

Analytics for: Internet of Things

Managed Services for On-Premise, Cloud and Hybrid environments

Analyticsas a Service

Analytics Application Development Factory

AnalyticsInnovationLab

Industry leadingAvanade Modern Analytics PlatformOn Azure

70 Locations

22Countries

550+ Analytics customers across all industries

4000+ Analytics professional WW1000+ Data Engineers & Scientists100+ Big Data Consultants

Analytics: driving digital innovation and business outcomes

5



Lloyd’s Coffee HouseNo. 16 Lombard Street, London



Future-oriented mathematical and statistical analyses used to drive positive change & improve business performance.

Techniques used include data mining, machine learning, predictive analytics, simulation, and optimization.

Advanced Analytics: Data Driven Decision Making

Prepare, don’t react.



• Biostatistics for medical data• Data Science for clickstream/web data• Machine Learning for big data, video, etc.• Natural Language Processing for text and audio data• Signal Processing for IOT• Business Analytics (Customer Analytics) for data about customers• Econometrics for economics data• Etc., etc.

Advanced Analytics goes by many names…

It’s basically all the same thing, with slightly different approaches

and different outcome goals.



Types of Analytics

Descriptive Analytics

Diagnostic Analytics

Predictive Analytics

Prescriptive Analytics

Cognitive Analytics

What happened?

Why did it happen?

What will happen?

How can we make it happen?

Deep learning and decision automation

Past Present Future



Descriptive Analytics

Diagnostic Analytics

Predictive Analytics

Prescriptive Analytics

Cognitive Analytics



The “Big Data Phenomenon” is core to advanced analyticsData is growing exponentially and is expected to increase in excess of 32 ZB by 2020. Mobile, social, and cloud based data sources are rapidly changing and becoming more important in the decision process. As a result, the rate at which data is being produced is far exceeding our traditional ability to extract value from it.



Data Pipelines Data Lake ExperienceAnalytics

A four step approach to advanced analytics and machine learning



Advanced analytics can drive outcomes across business functions and industries


Client Experiences

Leading Dutch FS ProviderMortgage churn, credit card fraud and customer lifetime value

A foundational Customer Data & Analytics platform with 3 modules on Advanced Analytics, Reporting & Dashboards and Campaign Management for international, banking and insurance group divisions

Multiple machine learning models to predict customers at the highest risk of churn, identify and block fraudulent credit card transactions, create lifetime value segments and drive more effective loan underwriting

Marketing campaigns to focus on the customers at a high risk of churn by lifetime segment or to underwrite more effectively based on triggers identified

Churn percentage reduced by nearly 50%, leading to a retention of millions of dollars of mortgage business, effective blocking of fraudulent credit card transactions while continuing to service the account

Premium Car BrandPredictive Maintenance on camshaft drive chain failures

Realized cost savings through predictive maintenance

Increased Customer Satisfaction

Big Data Platform to ingest, analyze and process massive car sensor data

Predict extensions of camshaft drive chains and identify potential root causes

Recommend predictive maintenance action to replace or adjust camshaft driving chains during routine workshop

Reduce visits to save costs, warranty cases and increase customer satisfaction

16

Large Oil & Gas CompanyPrediction of equipment failure to reduce costly, unscheduled downtime

Collecting and Ingesting IOT, streaming and historic data from 10,000 wells across 700 facilities and 500 fields

Predictive and real time event detection for pump failure in hours, days or weeks based on historic and current streaming sensor information from ESP lifted wells

Helped the client understand the precursors of failures and optimize logistical maintenance scheduling around well maintenance and repair

3 months to value with a 10% reduction in downtime and 20% reduction in operational costs

17

Technologies: Microsoft Azure, Data Factory, SQL, Azure Machine Learning, Power BI

<Confidential> See Avanade’s Data Management Policy©2017 Avanade Inc. All Rights Reserved.

Using biometrics to add science to Williams Martini racing pit stops

https://www.youtube.com/watch?v=U7wEsFrGcWM16


http://players.brightcove.net/2103045685001/ryfVT8tf_default/index.html?videoId=4109302667001

https://www.youtube.com/watch?v=U7wEsFrGcWM

How long is the journey to value?

<Restricted> See Avanade’s Data Management Policy©2017 Avanade Inc. All Rights Reserved.

What assets do we use to drive rapid value?

3 weeks Insight

Discovery Approach



Why predictive analytics on boiler failures?



The starting point was Baxi’s existing boiler failure forecast

87% Bi-Annual Forecasts

A Single UK Forecast



Success Factor 1: Add forecast granularity which couldn’t be done before

A Same day Urgent faults for highest priority customers

B Next day Safety Critical faults for all customers and Urgent faults for medium priority customers

C Within next 3 working days Urgent faults for lowest priority customers

D At the earliest opportunity (convenient to Customer and BCS), aim within 5 working days

Non-urgent faults for all customers and Services for Adhoc Chargeable or Heatguard customers



Success Factor 2: Show insights not previously known

From Data to Insight

Uncovering the “Aha”

moments that matter!



Success Factor 3: Rank triggers that drive the boiler failure forecast



Success Factor 4: Drive ROI and value through significantly improved accuracy

87%


How did we get there?An agile, three week, insight discovery based collaborative approach

Mobilise

Enriching Data & Designing Modelling Approaches

Making it Real & Learning Continuously

Setting You up for Success

Kick off workshop

Finalpresentation

& value

Making it real – first

results

Days 1-2 Days 5-9Days 3-4 Days 10-14 Days 17-18Days 15-16 Day 19

First triggers lead to new approach to weather data

Final models, automation & handover

Collaborative sessions at critical

milestones


Rapid insight comes from foundational hypothesis

Cranky Mondays

Pay for Better Service

Sunny in London, Freezing in Edinburgh

2

3






4

Its Cold!1



If you don’t have enough data, look externally

Source: https://www.wunderground.com/



A blended forecasting approach combines all hypothesis & 450 machine learning models

1. Base seasonal and non seasonal models

2. Including weather

3. Day of the week models

4. Steps 1-3 for urgent vs. non urgent requests

All previous forecasts by SLA groups

Forecasts for Mondays only, Tuesdays only, etc.

Daily Forecast with Weather

Base Daily Forecast

One Final Forecast per Day

14 UK Regions = approx. 450 models!

20% of Base Model Forecast

40% of Weather Model Forecast

40% of Day of the Week Model Forecast



Ranking Forecast Triggers

Ranking Parameter Forecasting Error Reduction

1 Fog 6.4%2 Total Expiring Warranties 5.9%3 Average Wind Speed 5.5%4 Snow 5.5%5 Average Humidity 5.4%6 Total Appliances Sold 5.4%7 High Humidity 5.3%8 High Wind 5.3%9 Rain 5.2%10 Average Sea Level Pressure 5.2%

11 Difference between total sold and expiring warranties 5.2%12 Thunder 5.2%13 High Sea Level Pressure 5.1%14 Low Sea Level Pressure 5.1%15 Low Temperature 5.1%



Previously unknown insights: the “Aha” moment for Baxi

1 2 3 4 5

Fog and wind speed are top

triggers driving job requests

Weather does not impact

every region in the same way

Temperature does not impact job requests in any significant

way

Non-urgent requests are

not affected by weather

A regional forecast is not

only possible, it also increases

accuracy



A new forecast accuracy of 98%

Forecast Accuracy uplift of 11%



Revisiting Success Factors

Increased forecast granularity






Insights not known before

Ranking Parameter Error Reduction1 Fog 6.4%

2 Total Expiring Warranties 5.9%

3 Average Wind Speed 5.5%4 Snow 5.5%5 Average Humidity 5.4%

Ranking of forecast triggers

98% accuracy, 11% uplift



Avanade’s Modern Analytics Platform ensures model deployment and re-usability

Automated forecasting models

Free weather data API

Models are in Azure designed to easily transition to production

Current Strategic Planning Infrastructure

Run Forecast

Get Weather

Upload Job Data


Questions?

ryan price director digital analytics at avanade - ryan... · ryan price director digital analytics...

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