HOW TO: Move from Data Silos to
Enterprise-wide Data Analytics
Stefan Spaar & Jim Hayden
The Possibilities are “Unlimited”
● Unlimited, Flat-Rate Mobile
Voice and Data Services
● Simple, All-Inclusive Pricing &
...
Big Data Paradigm Shift
IT
Structures the
data to answer
that question
IT
Delivers a platform to
enable creative
discovery...
Adopting Variety, Velocity & Volume
Persistent Data In-Motion Data
Traditional
Data
Combination of
Non-traditional/
tradit...
Cricket’s Data Evolution
Big Data Analytics Methodology
• Create a comprehensive 360
o
view of customer in order to monetize our data assets.Goal
•...
Big Data – “Goldmine”
• Location Determine the latitude and longitude of your customer at any time..
• Travel Patterns Ide...
TEOCO’s Role at Cricket
• What:
Optimize service delivery
costs &margin
• Benefits:
Cost, time and resource
reduction; ach...
Roaming activity by handset model
Call & Texting Behavior by Age
-
100
200
300
400
500
600
<18
18to24
25to34
35to44
45to54
55to64
65to74
>75
Average #Texts ...
Average Cost vs. Detailed Cost
Subscriber Call Quality by Location
Geo-Location: Usage By Age Segment
Future Applications: Subscriber Location
Pattern Analysis
Subscriber 1
Subscriber 2
Subscriber 3
Home: location 837
Work: ...
Future Applications: Mobile Advertising geo-temporal
Predict future location of subscriber relative to 3rd party locations
Lessons Learned & Next Steps
• Incremental approach beats Big Bang
• Prioritize use cases based on ROI/perceived value
• E...
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Tmw20101 hayden.j and spaar

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Tmw20101 hayden.j and spaar

  1. 1. HOW TO: Move from Data Silos to Enterprise-wide Data Analytics Stefan Spaar & Jim Hayden
  2. 2. The Possibilities are “Unlimited” ● Unlimited, Flat-Rate Mobile Voice and Data Services ● Simple, All-Inclusive Pricing & Predictable Bills ● No Contracts & No Long-Term Service Commitments ● High-Quality Feature Rich Devices ● Access to High-Quality Nationwide 3G and 4G LTE Networks ● Low-Cost Provider
  3. 3. Big Data Paradigm Shift IT Structures the data to answer that question IT Delivers a platform to enable creative discovery Business Users Explores what questions could be asked Business Users Determine what question to ask Monthly sales reports Profitability analysis Customer surveys Brand sentiment Product strategy Maximum asset utilization Big Data Approach Iterative & Exploratory Analysis Traditional Approach Structured & Repeatable Analysis
  4. 4. Adopting Variety, Velocity & Volume Persistent Data In-Motion Data Traditional Data Combination of Non-traditional/ traditional data Reuse Warehouse Data Filters incoming data Real-time Big Data Data Warehouse Variety Velocity Volume
  5. 5. Cricket’s Data Evolution
  6. 6. Big Data Analytics Methodology • Create a comprehensive 360 o view of customer in order to monetize our data assets.Goal • Combine multiple Big Data sources to allow for analytics along any dimension.Process • Incrementally leverage data produced from ROI based initiatives based on value added.Strategy
  7. 7. Big Data – “Goldmine” • Location Determine the latitude and longitude of your customer at any time.. • Travel Patterns Identify frequent routs that your customer traverses. • Application Use Distinguish the applications that customers most frequently use. • Calling Habits Associate call types and call destinations for customers. • Perceived Service Quality Understand the customer experience with Cricket service. Customer Behavior • Music Tastes Characterize customer preferences with music (Muve). • Browsing Patterns Identify the web sites that customer most frequent. • Interests Extrapolate customer interests based on search histories. Customer Preferences • Social Circles Realize how individuals interact with one another. • Customer Sentiment Evaluate customer opinions of services or products they purchase. • Influencers Highlight those individuals that persuade the habits of others. • Brand Loyalty Determine the brands that our customers choose. Social Media
  8. 8. TEOCO’s Role at Cricket • What: Optimize service delivery costs &margin • Benefits: Cost, time and resource reduction; achieved over 5x ROI • What: Optimize network availability & performance • Benefits: Maximize performance, capacity and quality • What: Optimize RAN network performance • Benefits: Maximize coverage, capacity and quality OSS/BSS Solutions Big Data Customer Analytics Insights • Who is using what service? • How much is being spent? • When was last use? • How often used? • What are common attributes attributes of customers for behavior X? • What are the most popular services, devices, plans? • End-to-end network health • What elements, services, devices were affected by network errors? • What services are seeing high error rates? • What services, devices, customers were affected by network errors? • What are the most common errors? • Where did errors happen? • Where are the heavy use hotspots & deadspots? • Where is subscriber X, and where has he been? • Billions of usage recs XDRs -- Data, SMS, MMS, AAA, 2G/3G/4G Data, Music, Roaming, etc. • Customer info • Product, service & bundles • Rate plans • Market • Hundreds of millions of events, errors, alarms • 2G, 3G & 4G network infrastructure from 3 vendors, Muve Music servers, PDSNS, etc. • Billions of 2G/3G/4G network mobile measurements from RNCs Data Sources Usage Analytics Performance Mgmt RAN Optimization
  9. 9. Roaming activity by handset model
  10. 10. Call & Texting Behavior by Age - 100 200 300 400 500 600 <18 18to24 25to34 35to44 45to54 55to64 65to74 >75 Average #Texts by Age Band - 100 200 300 400 500 600 700 <18 18to24 25to34 35to44 45to54 55to64 65to74 >75 Average #Calls by Age Band 0% 50% 100% 150% 200% 250% 300% - 100 200 300 400 500 600 700 <18 18 to 24 25 to 34 35 to 44 45 to 54 55 to 64 65 to 74 >75 Call:Text Ratio by Age Band Avg SMS Avg Calls Calls/SMS Ratio
  11. 11. Average Cost vs. Detailed Cost
  12. 12. Subscriber Call Quality by Location
  13. 13. Geo-Location: Usage By Age Segment
  14. 14. Future Applications: Subscriber Location Pattern Analysis Subscriber 1 Subscriber 2 Subscriber 3 Home: location 837 Work: location 482 Classic 9 - 5 Home: location 919 Work: location 1537 night worker Home: location 275 Work: location 278 non-standard workweek, multiple jobs Location Day of Week/Time of Day Summaries
  15. 15. Future Applications: Mobile Advertising geo-temporal Predict future location of subscriber relative to 3rd party locations
  16. 16. Lessons Learned & Next Steps • Incremental approach beats Big Bang • Prioritize use cases based on ROI/perceived value • Engage departmental sponsors • Don’t get hung up on technology • Experiment using Analytics Sandbox • The value of exploratory analytics is harder to quantify

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