gartner bi placeiq presentation with kognitio
Post on 04-Dec-2014
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SPS 33Location Analytics:The Next Generation
@mphnyc
Michael HiskeyBig Data Evangelist
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BigMessy
Unstructured NoisyData
3
We do the “hard stuff” of Big Data analytics
#DataSci
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Business Users have existing interfaces
Business Intelligence Tools and Dashboards, custom‐designed internal applications, etc.
Business Analysts
Business Users
@Kognitio
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Move from dashboards to advanced analytics
create external script LM_PRODUCT_FORECAST environment rsintreceives ( SALEDATE DATE, DOW INTEGER, ROW_ID INTEGER, PRODNO INTEGER, DAILYSALES INTEGER ) partition by PRODNO order by PRODNO, ROW_IDsends ( R_OUTPUT varchar )isolate partitionsscript S'endofr( # Simple R script to run a linear fit on daily sales
prod1<-read.csv(file=file("stdin"), header=FALSE,row.names=1)colnames(prod1)<-c("DOW","ID","PRODNO","DAILYSALES")dim1<-dim(prod1)daily1<-aggregate(prod1$DAILYSALES, list(DOW = prod1$DOW), median)daily1[,2]<-daily1[,2]/sum(daily1[,2])basesales<-array(0,c(dim1[1],2))basesales[,1]<-prod1$IDbasesales[,2]<-(prod1$DAILYSALES/daily1[prod1$DOW+1,2])colnames(basesales)<-c("ID","BASESALES")fit1=lm(BASESALES ~ ID,as.data.frame(basesales))forecast<-array(0,c(dim1[1]+28,4))colnames(forecast)<-c("ID","ACTUAL","PREDICTED","RESIDUALS")
Via the Data Scientist
#DataSci
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A Platform for Advanced Analytics
• Business Applications
• Run advanced analytics in‐memory
• MPP CPU Scale‐out
• Persist data in Hadoop (and existing Data Warehouses)
Title
Subtitle subtitle subtitle subtitleContextualizing The Customer Through Location IntelligenceApril, 2014
The Mobile Consumer Challenge
• Loss of online context – Limited cookies– Anonymous usage– Short sessions / attention span– Usage of many diverse applications
• Modality: Phone influences usage & mindset– Device– Location – Time
@PlaceIQ
For organizations seeking to understand human behavior,PlaceIQ derives intelligence from activities across time,
space and devices, to uncover opportunities to learn about and connect with consumers with unrivaled clarity,
quality and relevance.
@PlaceIQ
Customer Segmentation‐based
Tell me about behaviors
Customer Segmentation‐based
Tell me about behaviors
Tile‐based
Tell me about this location
Tile‐based
Tell me about this location
The Location Contextualization Opportunity
Location‐based
Tell me about my store or my
competitor’s store
Location‐based
Tell me about my store or my
competitor’s store
@PlaceIQ
Top Brands Using PlaceIQ
AUTO RETAIL TECH/TELECOM ENTERTAINMENT
CPG
FINANCIAL
AND MORE…
@PlaceIQ
Location is Hard
Geographic Information System
Billions of Points of Interest
People are temporal
Taxonomy definitions abound
Petabyte Scale Storage and processing
Very few data points keyed to location
#analytics
GIS Rule
SELECT r.taxonomy,COALESCE(tppw.time_period_id, 6) AS period_id,rw.feature_name AS feature,rw.attribute_name AS attribute,r.target_feature_name,r.target_attribute_name,COALESCE(tppw.weight, 1.0) AS tp_weight,rw.weight AS attr_weight,rw.threshold_above,rw.threshold_below,r.offset,r.logistic,rw.instant_10
FROM rule_weights rwJOIN rules r ON r.id = rw.rule_idLEFT OUTER JOIN time_period_profile_feat_ats tppfaON (tppfa.feature_name = rw.feature_name AND
tppfa.attribute_name = rw.attribute_name)LEFT OUTER JOIN time_period_profile_weights tppwON (tppw.time_period_profile_id =
tppfa.time_period_profile_id)WHERE lower(r.taxonomy) = lower('leo')ORDER BY r.taxonomy
,period_id,feature,attribute #GISishard
Non‐Scalable Knowledge Base
Movement Data Streets Land Use Parcels
Uniquely structured data, no unifying key across datasets, difficult to implement into existing BI tools
@PlaceIQ
Location Data QualityHow do we confirm the accuracy of incoming lat/long data?
1M
Centroid DetectionDetecting devices that could appear to be at the center of a zip code or city (middle of field or body of water) as a result of inaccurate geo-coding from IP address or registration data.
Device DetectionDetecting spam devices (such as receiving 1M ad calls from one device in a short amount of time.
Transporter DetectionDetecting devices that:
• Appear to move faster than humanly possible (velocity detection)
• Remain stagnant for a period of time
• Bounce (constant movement)
Read about PlaceIQ’s hyperlocality and clusterability methodologies
Location #DQ
The PlaceIQ Solution
AnalyticsContextualizing the Customer
Customer SegmentationCreating behavioral clusters from location histories
Data / Base MapOrganizing billions of data points
Location Ingest100x100 meter tile structure
Enterprise ConnectorIntegration with CRM / Enterprise
@PlaceIQ
Location Ingest
Taxonomy4K+ categories organize
our 40+ data sources
27 Time PeriodsPeriods mapped to
moments
Nearly 1 Billion TilesUSGS 100 x 100 meter
tile grid system
PlaceIQ’s Platform Organizes Hundreds of Billions of Data Points
@PlaceIQ
Data / Base Map
@PlaceIQ
PlaceIQ Ingests a Diverse Selection of Data Sets
Residential
• Age• Income• Household Size• Children• Life Stage
• Ethnicity• Language• Building type• Auto Owned• Auto in Market
Retail & DiningGrocery, Clothing, Big Box, QSR, Buffet, Casual
EntertainmentMovies, Museums,Parks, Tourism, Bars
Consumer SpendingPurchase Data from Retail Partners
Auto & TravelDealership Lots, Airports, Hotels, Bus Stations
PIQ PrimeTimeTV Viewership from Set-Top boxes
And More…Photos, Social MediaEvents, etc.
@PlaceIQ
Hand-Made PolygonsHundreds of thousands built by
cartographers
Tile Based ScoringTiles are scored from 0 to 10
Leading PrecisionWe map to “rooftop” not
“driveway”
PlaceIQ Leads the Industry in Location Precision
@PlaceIQ
Enterprise Connector
@PlaceIQ
Customer Segmentation
@PlaceIQ
Tile‐based
Tell me about this location
Tile‐based
Tell me about this location
The Location Contextualization Opportunity
Location‐based
Tell me about my store or my competitor’s
store
Location‐based
Tell me about my store or my competitor’s
store
Customer Segmentation‐based
Tell me about behaviors
Customer Segmentation‐based
Tell me about behaviors
@PlaceIQ
Tile Analysis – the World
• Legal and financial office buildings• Hyatt hotel• Tully’s Coffee• Upscale Dining (Daniel’s Broiler and Suite) • Casual Lunchtime Dining (Joey's Bellvue and KORAL)• Luxury Retail (Nordstrom, BoConcept furniture and Elements gallery)
1. White Collar Financial Workers • M‐F 8:30am ‐ 5:30pm
2. Travelers• 6am ‐ 12am
3. Casual Lunch Dining• 12PM to 1PM
4. Upscale Dining • Sat 5‐8PM, Sun 7‐8PM• M‐F 5:30‐8PM
5. Luxury Shopper• 6am ‐ 12am
6. Mall Shopper • 6am ‐ 12am
1) RAW DATA
2) THE RULE 3) AUDIENCES
The Location Contextualization Opportunity
Location‐based
Tell me about my store or my competitor’s
store
Location‐based
Tell me about my store or my competitor’s
store
Customer Segmentation‐based
Tell me about behaviors
Customer Segmentation‐based
Tell me about behaviors
Tile‐based
Tell me about this location
Tile‐based
Tell me about this location
@PlaceIQ
Location Analysis – Your Store
Place Visit RateDo devices shownMobile ads visit keyretail locations?
PreVisitWhere do visitorsgo before they visit key retail locations?
PIQ AnalyticsWhat is unique about the movements, behaviors, and demographics of an audience?
#Location
The Location Contextualization Opportunity
Location‐based
Tell me about my store or my competitor’s
store
Location‐based
Tell me about my store or my competitor’s
store
Customer Segmentation‐based
Tell me about behaviors
Customer Segmentation‐based
Tell me about behaviors
Tile‐based
Tell me about this location
Tile‐based
Tell me about this location
@PlaceIQ
Customer Segment: Movie Goer & FC Diner
• Census• Businesses • Parks • Events • Social• Photos• Polk • Rentrak• Land use
Raw Data The Rule Customer Segments
RULEPLUS SegmentsPlus‐>SegmentX[RANGE: 6 m][FREQUENCY: 1 per month]{Dining‐>Fast_Casual_Restaurants && Entertainment‐> Movie_Theaters && HOME Segments‐> Demographic‐>Income‐>50k_74k && HOME Segments‐>Demographic‐>Income ‐>75k_99k;};
@PlaceIQ
Behavioral Graph
Big Box A Big Box B
@PlaceIQ
Big Box B Detail
@PlaceIQ
Technical Architecture
Enterprise Data
NormalizeHDFS
platform component
data Location Data
Ingest
PIQL
Enterprise Connector
Output (Visualization, Reporting)
Analytics
Base Map
Tiles Customer Behaviors
Customer Segmentation
@Kognitio
Performance Became a Massive Issue
500 node hadoop cluster =
20 hours of processing time
@PlaceIQ
Enter Kognitio
Transferred to in memory database and obtained advanced performance
on clustering, querying and multidimensional analysis
Completely unstructured approach to queries enabling you to ask questions as they come to you, get answers
returned quickly and iterate
@Kognitio
Kognitio Delivers Unrestricted Answers, Quicker
THEN NOW
500 node hadoop cluster =
20 hours of processing time
500 node hadoop cluster =
20 hours of processing time
½ terabyte system =
20 minutes
½ terabyte system =
20 minutes
@Kognitio
Why PlaceIQ?
Consumer InsightsData-Driven Q/A Process
Patented Platform Unparalleled Audiences Innovation
@PlaceIQ
Thank you.
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Recognized by Industry Analysts
Forrester Wave™: Enterprise Data Warehouse, Q4 ’13
Gartner Magic Quadrant for Data Warehouse DBMSs ‐ 2014
#GartnerBI
Booth # 421
@PlaceIQ @mphnyc @Kognitio#GartnerBI #DataSci
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