#geodesummit - using geode as operational data services for real time mobile experience
TRANSCRIPT
UsingGeodetoEnhanceCustomerExperience
2 Copyright © Capgemini 2015. All Rights Reserved
Whatproblemarewetryingtosolve?
Everyretailer’sproblemtosolve…
Personalized Service
Mobile check-
out
Optimized Staffing, Tasks and Training
Real-time Inventory Tracking
Assortment and Price Differentiation
Endless Aisle
Facilities Control
Fault Detection
DynamicLayout
Platform Data SecurityAnalytics
Virtual Wall
Dynamic Labels
Smart Digital Signage
Customer Devices
END POINT CAPABILITY PROCESS CORE PROCESS END POINT CAPABILITY
Knowledgeable Sales Associates
Heating and Lighting
Robotics
POC and Mobility
Clienteling Apps
Customer
Employee
Physical Store
Product
Customer Profiling
Traffic, Purchase Patterns
In-store Interactions
Personalized Recommendations
Whatretailer’s“Loyalty2.0”mightbegrapplingwith…
1. Omni-Channel, Seamless Commerce
2. Insights & Data 3. Marketing Resource
Management 4. Applied Innovation
5. Unified View of the Customer
NextGeneraFonLoyaltyProgramsrequirements…
Theholygrailofcustomerloyalty
Delivering insights at the point of action in
“human time”
3 Copyright © Capgemini 2015. All Rights Reserved
Somekeydimensionsoftheproblem…
§ RetailershavemanyBigandFastDatachallenges...§ Datavolumes–numberofcompaniesstoring100+TBisgrowing…
…buttheyareanalyzingonlyafrac%onoftheirdataassets
§ Mostretailershave10’sto100’sofsystemschurningoutdatainrealFme-plusexternaldatasources(socialmedia,weather,demographic,spaFal)thattheyshouldbeusing……buttheystruggletostoreandintegratedata,andmanagedatacomplexity>today,BATCHrules
0
50
100
Structured Semi-Structured Unstructured
Not Analyzed Analyzed
Volume
Velocity
Variety
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Justimagine:urbanlivingin2025andtherequirementsoftheretailenvironment.
ConsumerengagementTakingpartinadialoguewithconsumers,jusFfyingtheirtrustintheindustryTransparencyKeepingconsumersinformedaboutproducts’keya`ributes,ingredients,nutrientsandprovenanceaswellastheirenvironmentalandsocietalimpactsThelastmileofdistribu%on–bothtotheretailstoreandtotheconsumerReconsideringtheassumpFonthatitisanareawherecompaniesoperateindependentlyofeachother,andexploringopportuniFestocollaboratetoimprovespeed,efficiencyandconsumersaFsfacFon
EnabledbyModularizedTechnologyBusinessagilityandrapidcollaboraFonrequirethetransformaFonfromrigidandpurpose-builtITstructuresintomulF-use,component-basedtechnologycapabiliFeswhichallowforeasyassemblyordisassemblyaccordingtobusinessneeds.
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Everythingchangesbeforetomorrow
Click Call Read
Click
Location
Push
Engage
Lifestyle
Location
Demand
?
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Whytomorrowistoolate
I’m at the store in Charlotte
Human-Time Social Analytics
Store Master
Customer Master
Human-Time Business Analytics
Customer Transactions
Product Master
Store ABC
Jane Doe
Jane
Hey Shovel fans: Offer
Code!
Great: On our way!
Jane’s Friends
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Geode Cluster
HowweareusingGeodeforthis
Data Lake
Geode Geode Geode Geode Geode Geode
Write Back Batch Analytics Services Reference Data
Enterprise History Cache
Fast S
peed C
onsumer
Data
Human-Time analytics
Bulk Enterprise Data
8 Copyright © Capgemini 2015. All Rights Reserved
Advantagesofano-RDBMSarchitecture
§ Geode/Gemfire handles the transactional integrity § Data Lake stores the history
§ Developer model is much more suited to Java developers than RDBMS
§ Enables multiple other engines to be added to the Batch part § Neo4J for network/graph analytics
§ Cost
9 Copyright © Capgemini 2015. All Rights Reserved
Whatcouldtomorrowlooklike?
The Present Tomorrow? Retailers send consumers coupon books generated on a monthly basis.
Retailers figure out consumer shopping patterns, and deliver offers the day before the consumer plans to shop. No coupons – the consumer has an electronic wallet that pops up in-store.
Customers get printed coupons when they go through the check out line.
Consumers scan products as they shop, interact with “recommender” systems they like, and skip the check out line. Apple Pay. - Recipe Help
“It looks like you’re making chili – but you didn’t pick up any kidney beans – did you forget?”
- Healthy Choices “I see you just selected potato chips – would baby carrots be a better choice?”
- Market Share Shifting “There’s an extra 20% off if you choose Doritos instead of Lays today.”
- Managing Perishable Store Inventory “I know you like lettuce – there’s special pricing if you buy today.” Hint: Store ordered too much lettuce, it’s going to spoil if not sold soon.
Customers push their carts up one aisle and down the other.
“I see that it’s been several weeks. Have you been busy? Since you are one of our best customers, would it help if we were to suggest some items to tide you over, and deliver them by Uber right now?” “The weather forecasters are predicting a blizzard. Do you need some extra supplies? Do you need bottled water for baby formula? We can deliver…”
10 Copyright © Capgemini 2015. All Rights Reserved
EnterpriseBigDataReferenceArchitecture–BusinessDataLake
Hadoop – Foundational Elements
REST
HTTP/S
Stream
SPARK-ML (Formerly Mahout)
Classification Clustering Collaborative Filtering
Processing & Computation
In-Memory Process
SPARK R
SPARK
YARN
Business Data Lake Zones
Interactive
Analytics
Visualization & Reporting
Analytical Tools,
Simulation & Languages
Enterprise Solutions
(API – XML – Json)
Localized Data Sources
Deep Learning Machine Learning, Evolutionary/Genetic Programming, Complex Event Processing & Enterprise Automation
Tachyon Load & Refine
Scoop Flume
Hive NFS
WebHDFS
Pig
Speed Layer Zone 0 Zone 1 Zone 5
Zone 2 Zone 3 Zone 4
Cleansed
Ideation Sandbox Enriched
Detailed Modeled
Aggregate Modeled Stagin
g
Metadata Security
Knox Ranger Kerberos
Provision, Monitor, Manage
Ambari Zoo Keeper
Schedule
Oozie
Deployment Choice Linux Windows On-Premises Cloud
Hue
NO SQL- Slider
HIVE - TEZ Phoenix
HBase Solr (Search)
Landing
SOURCES
Geolocation
IT Systems
Sensor & Machine
Server Logs
Web & Social
Clickstream
Unstructured
Apache Geode
Thenewdigitaldivide
• Thenewdigitaldivide:thegapbetweenconsumer’sdigitalbehaviorsandexpectaFons–incontrasttothereadinessandabilityofretailerstodeliveronthedesiredexperiences.– Percentageofshoppers100%connected–growingquickly– ExpectaFons–evolvingrapidly– “Showrooming”–e.g.fearthatdigitaldrivesconsumersonline(it’samyth)– Micro-characterisFcs–geographic,demographic,ethnic,social,age,gender– Abilityofretailerstoharnesstechnologythat–
• PermitsapplicaFonstobebuiltquickly,dismantledquickly• Deliversconsumerbehavior-influencingacFonsinrealFme• Drillsdowntoindividualconsumers• Integratesdata–frominsideandoutsidetheenterprise
13 Copyright © Capgemini 2015. All Rights Reserved
….andhowcouldwedothis?
Unleash Data and Insights as-a-service
Make Insight-driven Value a Crucial
Business KPI
Empower your People with Insights at the
Point of Action
Develop an Enterprise Data Science Culture
Master Governance, Security and Privacy of your
Data Assets
Enable your Data Landscape for the Flood coming from Connected
People and Things
Embark on the Journey to Insights within your
Business and Technology Context
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