The Warranty Data Lake – After, Inc.
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The document outlines the concept of a warranty data lake, which is a large storage repository designed to collect and analyze various data types from different areas of the warranty chain, enhancing program improvements. It highlights the advantages of data lakes over traditional data warehouses, particularly their ability to handle diverse data formats and support agile processes suited for evolving business needs. Furthermore, it emphasizes the importance of analyzing unstructured and spatial data to unlock valuable insights and improve warranty program performance.
The Warranty Data Lake – After, Inc.
- 2. The Warranty Data Lake How Big Data and Data Science Can Drive Program improvements March 2016 Richard Vermillion, CEO of After, Inc. and Fulcrum Analytics, Inc.
- 3. Who is After, Inc.? Just What Is a “Data Lake”? Why a Warranty Data Lake? Warranty Data Science • Customer Data • Claims Data • Spatial Data • Telematics • Parts/Supplier/Lot Data • Shipment, Engineering, Dealer Data What does it all mean? Agenda
- 4. Founded in 2005 as a division of Fulcrum Analytics, we leverage our dual-expertise in data analytics and consumer marketing to help companies establish world-class warranty businesses. We help manage program risk and optimize sales and marketing programs from start to finish — absorbing almost all of the management and technology burden. As your partner, we are singularly committed to creating the greatest possible value for you and your customers. Who is After, Inc.?
- 5. What is a Data Lake? TRADITIONAL REPOSITORIES Large, well-organized data warehouses That deliver some data to retail data marts Much larger, more loosely organized lakes That you can sample at will DATA LAKES
- 6. What is a Data Lake? A ”Data Lake” is a large storage repository and processing engine Data Lakes are built on a “Big Data” platform like the Hadoop stack • Volume • Velocity • Variety Focus on variety of data they hold than just volume or velocity: • Flat files • Relational • Hierarchical • Unstructured, free-form text • Graph/network • Real-time Also distinguished by how they are built and loaded • Focus on agility • Iterative development to meet changing business needs • Support innovation • Destroy silos
- 7. Not an agile process Data Warehouse or Mart INNOVATION KILLER Generate Business & Technical Requirements Design Conformed Data Model Develop ETL for Each Data Source Build Reports & Connect BI Tools Too Limited • Drop fields that don’t fit • Aggregate away detail • Only answer questions you anticipated Too Slow • Waste time fully understanding each data source • Business requirements change by the time it’s built Too Expensive • Costly commercial hardware/software • Especially if you aren’t sure each data source is valuable
- 8. Supports an agile process Data Lake INNOVATION ENABLER Load Data As-Is Conform and Model What You Need Iterate ELT for Each Data Source Access All Data Types with One Toolset Not Limited • Keep all the fields in their original format • Keep the detail data • Pose and answer questions you never anticipated Not Slow • Easy to get started, just copy raw data in • Incremental changes as business requirements change Not Expensive • Cheap storage, open source tools • Defer hard work until you have a use for the data
- 9. Build to optimize warranty, extend to impact other areas Why a Warranty Data Lake? Warranty Chain is unusual, if not unique, in how many areas of the business it touches: • Customer – sales, usage, service, satisfaction • Engineering – quality, reliability, design • Service – repairs, claims • Distribution – dealers, retailers • Finance – insurance, reserves, credit Spans front-office and back-office Many relevant, but siloed, data sets Large opportunities for analytical improvements Large (usage) data sets on the horizon
- 10. By collecting multiple types of data, we can reduce reliance on lagging indicators (e.g., claims paid) and also identify, or develop new leading indicators for program performance. Why a Warranty Data Lake? Key analytical questions a warranty data lake can help answer: • Claim reserve and loss modeling • Early warning for losses • Claim & service triage optimization, “no fault found” minimization • Fraud and/or service anomaly detection • Customer satisfaction warning signs • Sales & marketing effectiveness • Field usage and relation to service events • Recall cost estimation
- 11. Unstructured Data & Text Claim notes include more details than codes… but less structure Codes impose structure and simplify analysis • But only for problems you anticipated and created codes for Text mining and natural language processing can unlock the secrets in claim and repair notes Visualization techniques such as word trees and clouds enable free text exploration
- 12. Unstructured Data & Text Tweets, product reviews and other user-generated content also can be collected and analyzed Sentiment analysis, topic modeling, keyword & semantic analysis Bayesian classifiers (like your spam filter) can be trained to spot quality complaints and failure reports Possible early warning of service problems
- 13. Spatial Data Spatial data (precipitation, temperature, crop production, land use, etc.) can be a proxy for usage For example, snow accumulationData spatially correlated with claims Can be predictive of frequency
- 14. Usage Data & Telematics The real ‘big data’ of warranty (high volume, high velocity) Usage data, telematics, “Internet of Things”, sensor data Real-time streaming or collected at service event Tracking Device (GPS + Altimeter + Accelerometer) with post- processing can identify: • Mileage and usage time • Hard driving and hard braking incidents • Inefficient shifting • Idle time • Geography and location of usage • Altitude changes More accurately map usage cycles to calendar time (relevant for warranty) Segment light vs. heavy usage and model differing expected losses •
- 15. Graph Data Graph data models the world as nodes connected by edges Developed for web search (pages connected by hyperlinks) and social network analysis (users connected by friendship) Can capture the complex relationships between parts, assemblies and sub-assemblies Store part explosions and proximity natively as directed graphs Capture failure sets of parts serviced together (causal or not)
- 16. Customer and Marketing Data Product registration data Demographic & third-party data Marketing event data Promotion participation Direct mail participation and response Email sends, opens, click-through Customer service calls and dispositions Clickstream data Mobile app engagement Consumer research and survey responses Price and messaging test results
- 17. Other Useful Data Claim Workflow Details Unit Shipment Data Inventory Data Dealer Information (locations, ownership, firmographics, etc.) Retail Unit Sales, Pricing and Discounting Damaged Product Images (especially for ADH claims) ESP Terms & Conditions (often unstructured).
- 18. What does it all mean? A Data Lake provides a repository and processing engine to gather all relevant data for analysis Handling variety is the key to breaking down data silos A Warranty Data Lake can bring together front-office and back- office data to optimize program performance and improve loss projections No one is going to build a warranty data lake with every data source discussed, but at After, we see companies beginning to build – and incrementally add to – starter data lakes Create the corporate data asset to drive 21st century program improvement.
- 19. Richard Vermillion, CEO After, Inc. + Fulcrum Analytics, Inc. https://www.linkedin.com/in/rvermillion rvermillion@afterinc.com www.afterinc.com 800.374.4728 212.651.7000 70 West 40th Street, 10th Floor New York, NY 10018 afterinc.com Learn more