SlideShare a Scribd company logo
October 2019
Predicting Banking Customer Needs with
an Agile Approach to Analytics in the Cloud
Who is presenting today …
2
Milan BerkaJakub Mašek
- Machine learning engineer at
DataSentics, working for Moneta’s
DataSquad
- Spark-certified developer
- Roles:
- Building the analytical platform
- Productionalizing the usecases
- Evangelize Spark across the
company
- Leader of DataSquad at MONETA
- Experienced data science manager
- Roles:
- Partnering with the different
departments across the bank
- Helping finding them the ML
opportunities
- Managing the process
milan.berka@datasentics.com
www.linkedin.com/in/milan-berka/
jakub.masek@moneta.cz
www.linkedin.com/in/jakub-mašek-
19631155
Agenda
Background:
• Who is MONETA Money Bank a what is the role of Datasentics
• Moneta’s journey into the cloud
• Creation of Data Squad
Building the analytical platform:
• Setting up an analytical environment in the cloud fully utilizing AWS and Databricks
• Hurdles along the way
Use-cases:
• Utilizing online data in digital marketing and customer value management
• Optimization of branches/ATM
Next steps, Q&A
§ Major Czech banking institution
§ 4th in size, 1st in innovation
§ 1 mio clients; 181 branches; 650 ATMs
§ 3.000 employees
§ Undergoing digital transformation
§ Collecting innovation awards
§ Smart Banka (mobile app)
§ Digital products
§ Migration to the cloud
Moneta Money Bank - Czech bank for Czech people
… almost forgot to mention „Tom“ - our advertising star
Make data science and machine learning have a real
impact on organizations across the world - demystify
the hype and black magic surrounding AI/ML and bring
to life transparent production-level data science
solutions and products delivering tangible impact and
innovation.
DataSentics - European Data Science Center of Excellence
based in Prague
- Machine learning and cloud data engineering
boutique
- Helping customers build end-to-end data solutions in
cloud
- Incubator of ML-based products
- 50 specialist (data science, data/software engineering)
- Partner of Databricks & Microsoft
Moneta and it’s journey to the cloud
2018
2019
2020
2021
10% cloud-based
30+% cloud-based
50+% cloud-based
Optimal cloud
hosting
Growing Platform
as a Service
• Primary Datacenter
migration
• Cloud design & initiation
• First set of application
migrated to Amazon
Cloud
• PaaS, SaaS and
Containers
• Automation embedded
into the key processes
• Second Datacenter
migration
• AS400 refresh/hosting
• Software and
Infrastructure
harmonization
• Platform as a Service,
implemented for the
selected capabilities
• Use the most optimal
hosting strategy for each
application
• Further infrastructure and
application optimization
• Hosted fixed telephony
• Software as a Service
implemented for the
selected capabilities
Birth of Datasquad as a new analytical DNA supporting the
cloud journey and making „digital“ into real
New analytical worldOld analytical world
-Tools:
-On-premise Oracle data warehouse
with limited computational power
-On-premise SAS for modelling
-Data: Mainly offline (transactions, …)
-Tools:
- Cloud-based, elastic and scalable –
unlimited resources
- Data in Datalake
- Spark, Python, R
-Data:
- offline (internal data)
- online (web-browsing data, digital
marketing data, …)
Datasquad is pioneering the new analytical world
DATALAKE
PLATFORM
DATA TEAM EVANGELIZATION
& SERVICE
DATA SCIENCE
SOLUTIONS
- POC; MVP
- Products
- Frameworks
- onboarding
- Evangelize Spark
and new
technologies
Main goal: utilize cloud services as much as possible
Technology:
§ Storage: AWS S3 with auto-encryption
§ ETL: AWS Glue
§ Access Management: AWS IAM + ADFS
§ Analytical service: Databricks
§ Security measures: AWS S3 auto encryption, AWS
EBS auto-encryption, Databricks SSO, Databricks
without access to internet, hashing of all sensitive data
Building the analytical platform
Analytical platform in the cloud
Datalake structure
Data:
§ Adform data (terabytes)
§ Web data (terabytes)
§ Geo-data (gigabytes)
§ Branches/ATM data (gigabytes)
§ Onboarding/fraud data (gigabytes)
§ Transactions (terabytes)
Use-cases
“Online” data
Web analytics data
(AdobeAnalytics/GA)
Campaign data (Adform)
Real estate market data
“Offline” data
Branch/ATM performance
Sales data
Onboarding data
CVM data
Feature Store CVM STORY
DIGITAL STORY
RISK STORY
BRANCH / ATM STORY
FRAUD / AML STORY
Use-cases
“Online” data
Web analytics data
(AdobeAnalytics/GA)
Campaign data (Adform)
Real estate market data
Feature Store CVM STORY
DIGITAL STORY
RISK STORY
BRANCH / ATM STORY
FRAUD / AML STORY
“Offline” data
Branch/ATM performance
Sales data
Onboarding data
CVM data
If we look at a typical customer journey for a
consumer loan, we see a relevant touchpoint
gap, an opportunity for us to address …
15
… and we already have a plan in
motion to address this opportunity
Digital Story
Digital marketing
cost analysis
1
Moneta Ad Quality2
Ad Targeting users
in „think“ phase
3
„Think“ phase predictors
in CVM campaigns
4
If we look at a typical customer journey for a
consumer loan, we see a relevant touchpoint
gap, an opportunity for us to address …
16
… and we already have a plan in
motion to address this opportunity
Digital Story
Digital marketing
cost analysis
1
Moneta Ad Quality2
Ad Targeting users
in „think“ phase
3
„Think“ phase predictors
in CVM campaigns
4
USE CASE: Digital marketing cost analysis
17
→ WE HAVE PROVEN, THAT DISPLAY ADS DRIVE SALES INDIRECTLY
1
THERE IS OBVIOUS POTENTIAL IN THE „THINK“ PHASE
DATA WE USED
• Advertising data (what user, on which specific
website/page/context, for how long has seen or interacted with
our Ads, for how much)
• Moneta Website behavior
• Marketing costs
WHAT WE DID
• We implemented an attribution model to prove how online ad
impressions (not clicks!) drive sales. An attribution model
shows how each market channel drives conversions. Here we
wanted to see what contribution each channel makes to
closing consumer loans.
NEXT STEPS
• Incrementally start to reallocate more budget to Online Ads
(upper funnel – think phase) and evaluate impact on efficiency
BUSINESS CASE
• Increase digital sales for the same media spending. By
better split between Online Ads and Search
Marketing channel Costs (units) Cost efficiency
Performance - Adform 1 11,3
Brand - Adform 17 6,6
Performance - remarketing 23 2,4
Performance - display 26 1,2
Performance – search 1
115 1
Performance - social 0,75 0,5
Brand – youtube 0,4 0,18
1 Performance – search chosen as a reference with cost effeciency ratio 1
USE CASE – Moneta Ad Quality
18
2
→ DIFFERENT COST PER VISIBLE MINUTE
ACROSS DIFFERENT WEBSITES
WE CAN INCREASE AD VISIBILITY TO USERS IN
THINK PHASE
DATA WE USED
• Advertising data (what user, on which specific
website/page/context, for how long has seen or
interacted with our Ads, for how much)
WHAT WE DID
• We see an ENORMOUS difference in visible time
of online Ads. Cost per 1 visible minute in Online
differs from 15 to 35 CZK in
NEXT STEPS
• Create engine to optimize Online Ads buying
(buy more visible ads)
BUSINESS CASE
• We should be able to buy at least 20% more
media time for the same budget
Analytical output - Cost per visible minute
→ ADJUSTING ADFORM BY DISADVANTAGING
DOMAINS WITH EXPENSIVE VISIBLE MINUTES
Adform implementation – multipliers
autoweb.cz 0.75
autozine.cz 0.8
autozive.cz 0.9
avizo.cz 0.85
babinet.cz 0.95
babyweb.cz 0.65
banger.cz 0.85
banky.cz 0.85
bazarbox.cz 0.7
behani.cz 0.85
bejvavalo.cz 0.85
bezrealitky.cz 0.65
biatlonmag.cz 0.8
biginzerce.cz 0.7
bike-mania.cz 0.85
...
...
API
Quality
model
19
Locality (L) attractiveness is given by
surrounded points of interests
To measure attractiveness, weights of individual
points of interests need to be set
MONETA wants to compare localities in terms of
business KPI - possible bank performance
200
m
eters
L
• Total attractiveness of the measured point is given by the sum
of partial weights
• Two possible scenarios how to set the weights:
By expert (e.g. Bank 50; Bus station 15 …) having
dimensionless index
Data Science approach (machine learnig) - using
internal data to set KPI and having interpretable resuls
1 2 181
Branch Story
Moneta needs to independently evaluate every single
locality or branch network cross the country …
v Assumption v Target variablev Approach
20
→ PRAGUE – EXPOSED AREAS BY PREDICTED PERFORMANCE INDEXWE CAN PREDICT PERFORMANCE IN ANY LOCALITY IN CZ
DATA WE USED
• Geospatial data - points of interests
• Population statistics
• Internal data – performance of our existing branches; costs; #
FTEs; ATM performance
WHAT WE DID
• We wanted to evaluate every single location in CZ in terms of
footfall. The closest equivalent to footfall is visitors' rate which
is measured only for 15% of our network. But visitors' rate is
strongly corelated with business KPI - performance rate -
which was finally used as a proxy variable for our model. We
are now able to predict possible banking performance of any
observed location.
MODEL VARIABLES
• # of transportation in 200m
• # of food in 200m
• # of competitors and highly exposed areas
• City population
Branch Story use case
Use-case deep dive: DSID = Enabler for the Digital attribution
model
Problem: we have many identifiers (internal id, phone, website cookie, Adform cookie) of
a person/client, which shows at different times at different places – how do we connect
all these into a single ID?
I1
I2
I3
W1
W2
W3
W4
W5
A1
A2
A3
Use-case deep dive: DSID = Enabler for the Digital attribution
model
Answer:
GraphFrames!
Use-case deep dive: DSID = Enabler for the Digital attribution
model
WebsiteID InternalID
W1 I1
W2 I1
W3 NULL
WebsiteID AdformID
W1 A1
W2 A2
W3 A3
InternalD Phone
I1 999999
I2 999999
I3 019645
df3.filter(not_fake(col(‘Phone’))
df1.withColumn(‘src’, ‘WebsiteId’)
df1.withColumn(‘dst’, ‘InternalId’)
df2.withColumn(‘src’, ‘WebsiteId’)
df2.withColumn(‘dst’, ‘AdformId’)
df3.withColumn(‘src’, ‘InternalId’)
df3.withColumn(‘dst’, ‘Phone’)
df = df1
.union(df2)
.union(df3)
.distinct()
Use-case deep dive: DSID = Enabler for the Digital attribution
model
src dst
W1 I1
W2 I1
W3 NULL
W1 A1
W2 A2
W3 A3
I3 019645
vertices = df
.selectExpr(‘src AS id’)
.union(df.selectExpr(‘dst AS id’))
edges = df
g = GraphFrame(vertices, edges)
df_connected = g.connected_components()
Use-case deep dive: DSID = Enabler for the Digital attribution
model
id Component
W1 1
W2 1
W3 2
I3 3
I1 1
A1 1
A2 1
A3 2
019645 3
plus further adjustements:
• filter business clients
• disjoint the groups with two or more internal ids
• …
= DSID
Statistics:
- Number of vertices (ids): 14 969 170
- Number of edges: 30 029 363
- Running time: ~20 min
Next steps
26
- Major goal: Continue with democratizing of the platform, the ultimate goal is to have a self-serving data
platform
- Continue with the use-cases and moving them to production
- Implement company-wide feature store
- Employ new technologies (in particular - Spark Structured Streaming)
Question
How many members does Data Squad have?
5.5
(3 from Moneta, 2.5 from DataSentics)
Wrap up
29
Even with the small team you can do big things …
Achieving this - you need to have supportive environment
and you need to be disruptive to drive changes and show the added value to prove that:
… „data is really the new oil for your company“
Safety always first
Data science is about data AND science – doing science is always linked with blind paths – be patient and
keep going!
Thank you for your attention

More Related Content

What's hot

Real-Time Fraud Detection at Scale—Integrating Real-Time Deep-Link Graph Anal...
Real-Time Fraud Detection at Scale—Integrating Real-Time Deep-Link Graph Anal...Real-Time Fraud Detection at Scale—Integrating Real-Time Deep-Link Graph Anal...
Real-Time Fraud Detection at Scale—Integrating Real-Time Deep-Link Graph Anal...
Databricks
 
GraphTour - ING - Fighting insanity
GraphTour - ING - Fighting insanityGraphTour - ING - Fighting insanity
GraphTour - ING - Fighting insanity
Neo4j
 
Pinterest - Big Data Machine Learning Platform at Pinterest
Pinterest - Big Data Machine Learning Platform at PinterestPinterest - Big Data Machine Learning Platform at Pinterest
Pinterest - Big Data Machine Learning Platform at Pinterest
Alluxio, Inc.
 
Building a Just in Time Data Warehouse by Dan Morris and Jason Pohl
Building a Just in Time Data Warehouse by Dan Morris and Jason PohlBuilding a Just in Time Data Warehouse by Dan Morris and Jason Pohl
Building a Just in Time Data Warehouse by Dan Morris and Jason Pohl
Spark Summit
 
Building an ML Tool to predict Article Quality Scores using Delta & MLFlow
Building an ML Tool to predict Article Quality Scores using Delta & MLFlowBuilding an ML Tool to predict Article Quality Scores using Delta & MLFlow
Building an ML Tool to predict Article Quality Scores using Delta & MLFlow
Databricks
 
Spark Summit Keynote by Seshu Adunuthula
Spark Summit Keynote by Seshu AdunuthulaSpark Summit Keynote by Seshu Adunuthula
Spark Summit Keynote by Seshu Adunuthula
Spark Summit
 
How Starbucks Forecasts Demand at Scale with Facebook Prophet and Databricks
How Starbucks Forecasts Demand at Scale with Facebook Prophet and DatabricksHow Starbucks Forecasts Demand at Scale with Facebook Prophet and Databricks
How Starbucks Forecasts Demand at Scale with Facebook Prophet and Databricks
Navin Albert
 
No REST till Production – Building and Deploying 9 Models to Production in 3 ...
No REST till Production – Building and Deploying 9 Models to Production in 3 ...No REST till Production – Building and Deploying 9 Models to Production in 3 ...
No REST till Production – Building and Deploying 9 Models to Production in 3 ...
Databricks
 
Scaling Production Machine Learning Pipelines with Databricks
Scaling Production Machine Learning Pipelines with DatabricksScaling Production Machine Learning Pipelines with Databricks
Scaling Production Machine Learning Pipelines with Databricks
Databricks
 
Spark and the Enterprise by Tony Baer
Spark and the Enterprise by Tony BaerSpark and the Enterprise by Tony Baer
Spark and the Enterprise by Tony Baer
Spark Summit
 
Scalable, Fast Analytics with Graph - Why and How
Scalable, Fast Analytics with Graph - Why and HowScalable, Fast Analytics with Graph - Why and How
Scalable, Fast Analytics with Graph - Why and How
Cambridge Semantics
 
The Art of Intelligence – A Practical Introduction Machine Learning for Oracl...
The Art of Intelligence – A Practical Introduction Machine Learning for Oracl...The Art of Intelligence – A Practical Introduction Machine Learning for Oracl...
The Art of Intelligence – A Practical Introduction Machine Learning for Oracl...
Lucas Jellema
 
Journey to Creating a 360 View of the Customer: Implementing Big Data Strateg...
Journey to Creating a 360 View of the Customer: Implementing Big Data Strateg...Journey to Creating a 360 View of the Customer: Implementing Big Data Strateg...
Journey to Creating a 360 View of the Customer: Implementing Big Data Strateg...
Databricks
 
Commercial Analytics at Scale in Pharma: From Hackathon to MVP with Azure Dat...
Commercial Analytics at Scale in Pharma: From Hackathon to MVP with Azure Dat...Commercial Analytics at Scale in Pharma: From Hackathon to MVP with Azure Dat...
Commercial Analytics at Scale in Pharma: From Hackathon to MVP with Azure Dat...
Databricks
 
Augmenting Machine Learning with Databricks Labs AutoML Toolkit
Augmenting Machine Learning with Databricks Labs AutoML ToolkitAugmenting Machine Learning with Databricks Labs AutoML Toolkit
Augmenting Machine Learning with Databricks Labs AutoML Toolkit
Databricks
 
Unlocking Value in Device Data Using Spark: Spark Summit East talk by John La...
Unlocking Value in Device Data Using Spark: Spark Summit East talk by John La...Unlocking Value in Device Data Using Spark: Spark Summit East talk by John La...
Unlocking Value in Device Data Using Spark: Spark Summit East talk by John La...
Spark Summit
 
Graph representation learning to prevent payment collusion fraud
Graph representation learning to prevent payment collusion fraudGraph representation learning to prevent payment collusion fraud
Graph representation learning to prevent payment collusion fraud
DataWorks Summit
 
Using Hadoop for Cognitive Analytics
Using Hadoop for Cognitive AnalyticsUsing Hadoop for Cognitive Analytics
Using Hadoop for Cognitive Analytics
DataWorks Summit/Hadoop Summit
 
Automated Production Ready ML at Scale
Automated Production Ready ML at ScaleAutomated Production Ready ML at Scale
Automated Production Ready ML at Scale
Databricks
 
Tapjoy: Building a Real-Time Data Science Service for Mobile Advertising
Tapjoy: Building a Real-Time Data Science Service for Mobile AdvertisingTapjoy: Building a Real-Time Data Science Service for Mobile Advertising
Tapjoy: Building a Real-Time Data Science Service for Mobile Advertising
SingleStore
 

What's hot (20)

Real-Time Fraud Detection at Scale—Integrating Real-Time Deep-Link Graph Anal...
Real-Time Fraud Detection at Scale—Integrating Real-Time Deep-Link Graph Anal...Real-Time Fraud Detection at Scale—Integrating Real-Time Deep-Link Graph Anal...
Real-Time Fraud Detection at Scale—Integrating Real-Time Deep-Link Graph Anal...
 
GraphTour - ING - Fighting insanity
GraphTour - ING - Fighting insanityGraphTour - ING - Fighting insanity
GraphTour - ING - Fighting insanity
 
Pinterest - Big Data Machine Learning Platform at Pinterest
Pinterest - Big Data Machine Learning Platform at PinterestPinterest - Big Data Machine Learning Platform at Pinterest
Pinterest - Big Data Machine Learning Platform at Pinterest
 
Building a Just in Time Data Warehouse by Dan Morris and Jason Pohl
Building a Just in Time Data Warehouse by Dan Morris and Jason PohlBuilding a Just in Time Data Warehouse by Dan Morris and Jason Pohl
Building a Just in Time Data Warehouse by Dan Morris and Jason Pohl
 
Building an ML Tool to predict Article Quality Scores using Delta & MLFlow
Building an ML Tool to predict Article Quality Scores using Delta & MLFlowBuilding an ML Tool to predict Article Quality Scores using Delta & MLFlow
Building an ML Tool to predict Article Quality Scores using Delta & MLFlow
 
Spark Summit Keynote by Seshu Adunuthula
Spark Summit Keynote by Seshu AdunuthulaSpark Summit Keynote by Seshu Adunuthula
Spark Summit Keynote by Seshu Adunuthula
 
How Starbucks Forecasts Demand at Scale with Facebook Prophet and Databricks
How Starbucks Forecasts Demand at Scale with Facebook Prophet and DatabricksHow Starbucks Forecasts Demand at Scale with Facebook Prophet and Databricks
How Starbucks Forecasts Demand at Scale with Facebook Prophet and Databricks
 
No REST till Production – Building and Deploying 9 Models to Production in 3 ...
No REST till Production – Building and Deploying 9 Models to Production in 3 ...No REST till Production – Building and Deploying 9 Models to Production in 3 ...
No REST till Production – Building and Deploying 9 Models to Production in 3 ...
 
Scaling Production Machine Learning Pipelines with Databricks
Scaling Production Machine Learning Pipelines with DatabricksScaling Production Machine Learning Pipelines with Databricks
Scaling Production Machine Learning Pipelines with Databricks
 
Spark and the Enterprise by Tony Baer
Spark and the Enterprise by Tony BaerSpark and the Enterprise by Tony Baer
Spark and the Enterprise by Tony Baer
 
Scalable, Fast Analytics with Graph - Why and How
Scalable, Fast Analytics with Graph - Why and HowScalable, Fast Analytics with Graph - Why and How
Scalable, Fast Analytics with Graph - Why and How
 
The Art of Intelligence – A Practical Introduction Machine Learning for Oracl...
The Art of Intelligence – A Practical Introduction Machine Learning for Oracl...The Art of Intelligence – A Practical Introduction Machine Learning for Oracl...
The Art of Intelligence – A Practical Introduction Machine Learning for Oracl...
 
Journey to Creating a 360 View of the Customer: Implementing Big Data Strateg...
Journey to Creating a 360 View of the Customer: Implementing Big Data Strateg...Journey to Creating a 360 View of the Customer: Implementing Big Data Strateg...
Journey to Creating a 360 View of the Customer: Implementing Big Data Strateg...
 
Commercial Analytics at Scale in Pharma: From Hackathon to MVP with Azure Dat...
Commercial Analytics at Scale in Pharma: From Hackathon to MVP with Azure Dat...Commercial Analytics at Scale in Pharma: From Hackathon to MVP with Azure Dat...
Commercial Analytics at Scale in Pharma: From Hackathon to MVP with Azure Dat...
 
Augmenting Machine Learning with Databricks Labs AutoML Toolkit
Augmenting Machine Learning with Databricks Labs AutoML ToolkitAugmenting Machine Learning with Databricks Labs AutoML Toolkit
Augmenting Machine Learning with Databricks Labs AutoML Toolkit
 
Unlocking Value in Device Data Using Spark: Spark Summit East talk by John La...
Unlocking Value in Device Data Using Spark: Spark Summit East talk by John La...Unlocking Value in Device Data Using Spark: Spark Summit East talk by John La...
Unlocking Value in Device Data Using Spark: Spark Summit East talk by John La...
 
Graph representation learning to prevent payment collusion fraud
Graph representation learning to prevent payment collusion fraudGraph representation learning to prevent payment collusion fraud
Graph representation learning to prevent payment collusion fraud
 
Using Hadoop for Cognitive Analytics
Using Hadoop for Cognitive AnalyticsUsing Hadoop for Cognitive Analytics
Using Hadoop for Cognitive Analytics
 
Automated Production Ready ML at Scale
Automated Production Ready ML at ScaleAutomated Production Ready ML at Scale
Automated Production Ready ML at Scale
 
Tapjoy: Building a Real-Time Data Science Service for Mobile Advertising
Tapjoy: Building a Real-Time Data Science Service for Mobile AdvertisingTapjoy: Building a Real-Time Data Science Service for Mobile Advertising
Tapjoy: Building a Real-Time Data Science Service for Mobile Advertising
 

Similar to Predicting Banking Customer Needs with an Agile Approach to Analytics in the Cloud

Flink Forward Berlin 2017: Bas Geerdink, Martijn Visser - Fast Data at ING - ...
Flink Forward Berlin 2017: Bas Geerdink, Martijn Visser - Fast Data at ING - ...Flink Forward Berlin 2017: Bas Geerdink, Martijn Visser - Fast Data at ING - ...
Flink Forward Berlin 2017: Bas Geerdink, Martijn Visser - Fast Data at ING - ...
Flink Forward
 
Digital Transformation in Automotive Industry Chinese-German CAR Symposium
Digital Transformation in Automotive Industry Chinese-German CAR SymposiumDigital Transformation in Automotive Industry Chinese-German CAR Symposium
Digital Transformation in Automotive Industry Chinese-German CAR Symposium
accenture
 
Systematix_Credential_Presentation_latest (1)
Systematix_Credential_Presentation_latest (1)Systematix_Credential_Presentation_latest (1)
Systematix_Credential_Presentation_latest (1)
Alex Hunt
 
Heyhumming_Tech Capabilities_Draft-updated - Read-Only.pptx
Heyhumming_Tech Capabilities_Draft-updated  -  Read-Only.pptxHeyhumming_Tech Capabilities_Draft-updated  -  Read-Only.pptx
Heyhumming_Tech Capabilities_Draft-updated - Read-Only.pptx
ssusercf1a23
 
Change the face of your partner model... Use Cloud, don't get left behind
Change the face of your partner model...  Use Cloud, don't get left behindChange the face of your partner model...  Use Cloud, don't get left behind
Change the face of your partner model... Use Cloud, don't get left behind
asehgal
 
Improve Store Expansion (Territory Management Featuring)
Improve Store Expansion (Territory Management Featuring)Improve Store Expansion (Territory Management Featuring)
Improve Store Expansion (Territory Management Featuring)
Esri España
 
Clicks, Conversions and Crawls
Clicks, Conversions and CrawlsClicks, Conversions and Crawls
Clicks, Conversions and Crawls
Michelle Robbins
 
Marketech Agency Credentials
Marketech Agency CredentialsMarketech Agency Credentials
Marketech Agency Credentials
Ram N Kumar
 
Nicholas Gorski: Real-time revenue science at Twitter
Nicholas Gorski: Real-time revenue science at TwitterNicholas Gorski: Real-time revenue science at Twitter
Nicholas Gorski: Real-time revenue science at Twitter
David Garrison
 
Fast Data for Competitive Advantage: 4 Steps to Expand your Window of Opportu...
Fast Data for Competitive Advantage: 4 Steps to Expand your Window of Opportu...Fast Data for Competitive Advantage: 4 Steps to Expand your Window of Opportu...
Fast Data for Competitive Advantage: 4 Steps to Expand your Window of Opportu...
VoltDB
 
Deep Link Analytics Empowered by AI + Graph + Verticals
Deep Link Analytics Empowered by AI + Graph + VerticalsDeep Link Analytics Empowered by AI + Graph + Verticals
Deep Link Analytics Empowered by AI + Graph + Verticals
TigerGraph
 
Online Advertising Theatre; Shattering Campaign Effectiveness; Campaign as a ...
Online Advertising Theatre; Shattering Campaign Effectiveness; Campaign as a ...Online Advertising Theatre; Shattering Campaign Effectiveness; Campaign as a ...
Online Advertising Theatre; Shattering Campaign Effectiveness; Campaign as a ...
TFM&A
 
Designing Outcomes For Usability Nycupa Hurst Final
Designing Outcomes For Usability Nycupa Hurst FinalDesigning Outcomes For Usability Nycupa Hurst Final
Designing Outcomes For Usability Nycupa Hurst Final
WIKOLO
 
Session 2183 Profile hub - The Etisalat Story
Session 2183   Profile hub - The Etisalat StorySession 2183   Profile hub - The Etisalat Story
Session 2183 Profile hub - The Etisalat Story
Arvind Sathi
 
Web Service Search Engines - Enabling Of Service Commerce
Web Service Search Engines - Enabling Of Service CommerceWeb Service Search Engines - Enabling Of Service Commerce
Web Service Search Engines - Enabling Of Service Commerce
miczar
 
Big Data and Data Analytics to grow Auto Manufacturer Business
Big Data and Data Analytics to grow Auto Manufacturer BusinessBig Data and Data Analytics to grow Auto Manufacturer Business
Big Data and Data Analytics to grow Auto Manufacturer Business
Sheetal Gangakhedkar
 
How the World's Leading Independent Automotive Distributor is Reinventing Its...
How the World's Leading Independent Automotive Distributor is Reinventing Its...How the World's Leading Independent Automotive Distributor is Reinventing Its...
How the World's Leading Independent Automotive Distributor is Reinventing Its...
NUS-ISS
 
Deep.bi - Real-time, Deep Data Analytics Platform For Ecommerce
Deep.bi - Real-time, Deep Data Analytics Platform For EcommerceDeep.bi - Real-time, Deep Data Analytics Platform For Ecommerce
Deep.bi - Real-time, Deep Data Analytics Platform For Ecommerce
Deep.BI
 
CWIN17 san francisco-eunice cardenas-datastax - real-time cx for today's righ...
CWIN17 san francisco-eunice cardenas-datastax - real-time cx for today's righ...CWIN17 san francisco-eunice cardenas-datastax - real-time cx for today's righ...
CWIN17 san francisco-eunice cardenas-datastax - real-time cx for today's righ...
Capgemini
 
Real Time Customer Experience for today's Right-Now Economy
Real Time Customer Experience for today's Right-Now EconomyReal Time Customer Experience for today's Right-Now Economy
Real Time Customer Experience for today's Right-Now Economy
DataStax
 

Similar to Predicting Banking Customer Needs with an Agile Approach to Analytics in the Cloud (20)

Flink Forward Berlin 2017: Bas Geerdink, Martijn Visser - Fast Data at ING - ...
Flink Forward Berlin 2017: Bas Geerdink, Martijn Visser - Fast Data at ING - ...Flink Forward Berlin 2017: Bas Geerdink, Martijn Visser - Fast Data at ING - ...
Flink Forward Berlin 2017: Bas Geerdink, Martijn Visser - Fast Data at ING - ...
 
Digital Transformation in Automotive Industry Chinese-German CAR Symposium
Digital Transformation in Automotive Industry Chinese-German CAR SymposiumDigital Transformation in Automotive Industry Chinese-German CAR Symposium
Digital Transformation in Automotive Industry Chinese-German CAR Symposium
 
Systematix_Credential_Presentation_latest (1)
Systematix_Credential_Presentation_latest (1)Systematix_Credential_Presentation_latest (1)
Systematix_Credential_Presentation_latest (1)
 
Heyhumming_Tech Capabilities_Draft-updated - Read-Only.pptx
Heyhumming_Tech Capabilities_Draft-updated  -  Read-Only.pptxHeyhumming_Tech Capabilities_Draft-updated  -  Read-Only.pptx
Heyhumming_Tech Capabilities_Draft-updated - Read-Only.pptx
 
Change the face of your partner model... Use Cloud, don't get left behind
Change the face of your partner model...  Use Cloud, don't get left behindChange the face of your partner model...  Use Cloud, don't get left behind
Change the face of your partner model... Use Cloud, don't get left behind
 
Improve Store Expansion (Territory Management Featuring)
Improve Store Expansion (Territory Management Featuring)Improve Store Expansion (Territory Management Featuring)
Improve Store Expansion (Territory Management Featuring)
 
Clicks, Conversions and Crawls
Clicks, Conversions and CrawlsClicks, Conversions and Crawls
Clicks, Conversions and Crawls
 
Marketech Agency Credentials
Marketech Agency CredentialsMarketech Agency Credentials
Marketech Agency Credentials
 
Nicholas Gorski: Real-time revenue science at Twitter
Nicholas Gorski: Real-time revenue science at TwitterNicholas Gorski: Real-time revenue science at Twitter
Nicholas Gorski: Real-time revenue science at Twitter
 
Fast Data for Competitive Advantage: 4 Steps to Expand your Window of Opportu...
Fast Data for Competitive Advantage: 4 Steps to Expand your Window of Opportu...Fast Data for Competitive Advantage: 4 Steps to Expand your Window of Opportu...
Fast Data for Competitive Advantage: 4 Steps to Expand your Window of Opportu...
 
Deep Link Analytics Empowered by AI + Graph + Verticals
Deep Link Analytics Empowered by AI + Graph + VerticalsDeep Link Analytics Empowered by AI + Graph + Verticals
Deep Link Analytics Empowered by AI + Graph + Verticals
 
Online Advertising Theatre; Shattering Campaign Effectiveness; Campaign as a ...
Online Advertising Theatre; Shattering Campaign Effectiveness; Campaign as a ...Online Advertising Theatre; Shattering Campaign Effectiveness; Campaign as a ...
Online Advertising Theatre; Shattering Campaign Effectiveness; Campaign as a ...
 
Designing Outcomes For Usability Nycupa Hurst Final
Designing Outcomes For Usability Nycupa Hurst FinalDesigning Outcomes For Usability Nycupa Hurst Final
Designing Outcomes For Usability Nycupa Hurst Final
 
Session 2183 Profile hub - The Etisalat Story
Session 2183   Profile hub - The Etisalat StorySession 2183   Profile hub - The Etisalat Story
Session 2183 Profile hub - The Etisalat Story
 
Web Service Search Engines - Enabling Of Service Commerce
Web Service Search Engines - Enabling Of Service CommerceWeb Service Search Engines - Enabling Of Service Commerce
Web Service Search Engines - Enabling Of Service Commerce
 
Big Data and Data Analytics to grow Auto Manufacturer Business
Big Data and Data Analytics to grow Auto Manufacturer BusinessBig Data and Data Analytics to grow Auto Manufacturer Business
Big Data and Data Analytics to grow Auto Manufacturer Business
 
How the World's Leading Independent Automotive Distributor is Reinventing Its...
How the World's Leading Independent Automotive Distributor is Reinventing Its...How the World's Leading Independent Automotive Distributor is Reinventing Its...
How the World's Leading Independent Automotive Distributor is Reinventing Its...
 
Deep.bi - Real-time, Deep Data Analytics Platform For Ecommerce
Deep.bi - Real-time, Deep Data Analytics Platform For EcommerceDeep.bi - Real-time, Deep Data Analytics Platform For Ecommerce
Deep.bi - Real-time, Deep Data Analytics Platform For Ecommerce
 
CWIN17 san francisco-eunice cardenas-datastax - real-time cx for today's righ...
CWIN17 san francisco-eunice cardenas-datastax - real-time cx for today's righ...CWIN17 san francisco-eunice cardenas-datastax - real-time cx for today's righ...
CWIN17 san francisco-eunice cardenas-datastax - real-time cx for today's righ...
 
Real Time Customer Experience for today's Right-Now Economy
Real Time Customer Experience for today's Right-Now EconomyReal Time Customer Experience for today's Right-Now Economy
Real Time Customer Experience for today's Right-Now Economy
 

More from Databricks

DW Migration Webinar-March 2022.pptx
DW Migration Webinar-March 2022.pptxDW Migration Webinar-March 2022.pptx
DW Migration Webinar-March 2022.pptx
Databricks
 
Data Lakehouse Symposium | Day 1 | Part 1
Data Lakehouse Symposium | Day 1 | Part 1Data Lakehouse Symposium | Day 1 | Part 1
Data Lakehouse Symposium | Day 1 | Part 1
Databricks
 
Data Lakehouse Symposium | Day 1 | Part 2
Data Lakehouse Symposium | Day 1 | Part 2Data Lakehouse Symposium | Day 1 | Part 2
Data Lakehouse Symposium | Day 1 | Part 2
Databricks
 
Data Lakehouse Symposium | Day 2
Data Lakehouse Symposium | Day 2Data Lakehouse Symposium | Day 2
Data Lakehouse Symposium | Day 2
Databricks
 
Data Lakehouse Symposium | Day 4
Data Lakehouse Symposium | Day 4Data Lakehouse Symposium | Day 4
Data Lakehouse Symposium | Day 4
Databricks
 
5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop
5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop
5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop
Databricks
 
Democratizing Data Quality Through a Centralized Platform
Democratizing Data Quality Through a Centralized PlatformDemocratizing Data Quality Through a Centralized Platform
Democratizing Data Quality Through a Centralized Platform
Databricks
 
Learn to Use Databricks for Data Science
Learn to Use Databricks for Data ScienceLearn to Use Databricks for Data Science
Learn to Use Databricks for Data Science
Databricks
 
Why APM Is Not the Same As ML Monitoring
Why APM Is Not the Same As ML MonitoringWhy APM Is Not the Same As ML Monitoring
Why APM Is Not the Same As ML Monitoring
Databricks
 
The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix
The Function, the Context, and the Data—Enabling ML Ops at Stitch FixThe Function, the Context, and the Data—Enabling ML Ops at Stitch Fix
The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix
Databricks
 
Stage Level Scheduling Improving Big Data and AI Integration
Stage Level Scheduling Improving Big Data and AI IntegrationStage Level Scheduling Improving Big Data and AI Integration
Stage Level Scheduling Improving Big Data and AI Integration
Databricks
 
Simplify Data Conversion from Spark to TensorFlow and PyTorch
Simplify Data Conversion from Spark to TensorFlow and PyTorchSimplify Data Conversion from Spark to TensorFlow and PyTorch
Simplify Data Conversion from Spark to TensorFlow and PyTorch
Databricks
 
Scaling your Data Pipelines with Apache Spark on Kubernetes
Scaling your Data Pipelines with Apache Spark on KubernetesScaling your Data Pipelines with Apache Spark on Kubernetes
Scaling your Data Pipelines with Apache Spark on Kubernetes
Databricks
 
Scaling and Unifying SciKit Learn and Apache Spark Pipelines
Scaling and Unifying SciKit Learn and Apache Spark PipelinesScaling and Unifying SciKit Learn and Apache Spark Pipelines
Scaling and Unifying SciKit Learn and Apache Spark Pipelines
Databricks
 
Sawtooth Windows for Feature Aggregations
Sawtooth Windows for Feature AggregationsSawtooth Windows for Feature Aggregations
Sawtooth Windows for Feature Aggregations
Databricks
 
Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink
Redis + Apache Spark = Swiss Army Knife Meets Kitchen SinkRedis + Apache Spark = Swiss Army Knife Meets Kitchen Sink
Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink
Databricks
 
Re-imagine Data Monitoring with whylogs and Spark
Re-imagine Data Monitoring with whylogs and SparkRe-imagine Data Monitoring with whylogs and Spark
Re-imagine Data Monitoring with whylogs and Spark
Databricks
 
Raven: End-to-end Optimization of ML Prediction Queries
Raven: End-to-end Optimization of ML Prediction QueriesRaven: End-to-end Optimization of ML Prediction Queries
Raven: End-to-end Optimization of ML Prediction Queries
Databricks
 
Processing Large Datasets for ADAS Applications using Apache Spark
Processing Large Datasets for ADAS Applications using Apache SparkProcessing Large Datasets for ADAS Applications using Apache Spark
Processing Large Datasets for ADAS Applications using Apache Spark
Databricks
 
Massive Data Processing in Adobe Using Delta Lake
Massive Data Processing in Adobe Using Delta LakeMassive Data Processing in Adobe Using Delta Lake
Massive Data Processing in Adobe Using Delta Lake
Databricks
 

More from Databricks (20)

DW Migration Webinar-March 2022.pptx
DW Migration Webinar-March 2022.pptxDW Migration Webinar-March 2022.pptx
DW Migration Webinar-March 2022.pptx
 
Data Lakehouse Symposium | Day 1 | Part 1
Data Lakehouse Symposium | Day 1 | Part 1Data Lakehouse Symposium | Day 1 | Part 1
Data Lakehouse Symposium | Day 1 | Part 1
 
Data Lakehouse Symposium | Day 1 | Part 2
Data Lakehouse Symposium | Day 1 | Part 2Data Lakehouse Symposium | Day 1 | Part 2
Data Lakehouse Symposium | Day 1 | Part 2
 
Data Lakehouse Symposium | Day 2
Data Lakehouse Symposium | Day 2Data Lakehouse Symposium | Day 2
Data Lakehouse Symposium | Day 2
 
Data Lakehouse Symposium | Day 4
Data Lakehouse Symposium | Day 4Data Lakehouse Symposium | Day 4
Data Lakehouse Symposium | Day 4
 
5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop
5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop
5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop
 
Democratizing Data Quality Through a Centralized Platform
Democratizing Data Quality Through a Centralized PlatformDemocratizing Data Quality Through a Centralized Platform
Democratizing Data Quality Through a Centralized Platform
 
Learn to Use Databricks for Data Science
Learn to Use Databricks for Data ScienceLearn to Use Databricks for Data Science
Learn to Use Databricks for Data Science
 
Why APM Is Not the Same As ML Monitoring
Why APM Is Not the Same As ML MonitoringWhy APM Is Not the Same As ML Monitoring
Why APM Is Not the Same As ML Monitoring
 
The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix
The Function, the Context, and the Data—Enabling ML Ops at Stitch FixThe Function, the Context, and the Data—Enabling ML Ops at Stitch Fix
The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix
 
Stage Level Scheduling Improving Big Data and AI Integration
Stage Level Scheduling Improving Big Data and AI IntegrationStage Level Scheduling Improving Big Data and AI Integration
Stage Level Scheduling Improving Big Data and AI Integration
 
Simplify Data Conversion from Spark to TensorFlow and PyTorch
Simplify Data Conversion from Spark to TensorFlow and PyTorchSimplify Data Conversion from Spark to TensorFlow and PyTorch
Simplify Data Conversion from Spark to TensorFlow and PyTorch
 
Scaling your Data Pipelines with Apache Spark on Kubernetes
Scaling your Data Pipelines with Apache Spark on KubernetesScaling your Data Pipelines with Apache Spark on Kubernetes
Scaling your Data Pipelines with Apache Spark on Kubernetes
 
Scaling and Unifying SciKit Learn and Apache Spark Pipelines
Scaling and Unifying SciKit Learn and Apache Spark PipelinesScaling and Unifying SciKit Learn and Apache Spark Pipelines
Scaling and Unifying SciKit Learn and Apache Spark Pipelines
 
Sawtooth Windows for Feature Aggregations
Sawtooth Windows for Feature AggregationsSawtooth Windows for Feature Aggregations
Sawtooth Windows for Feature Aggregations
 
Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink
Redis + Apache Spark = Swiss Army Knife Meets Kitchen SinkRedis + Apache Spark = Swiss Army Knife Meets Kitchen Sink
Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink
 
Re-imagine Data Monitoring with whylogs and Spark
Re-imagine Data Monitoring with whylogs and SparkRe-imagine Data Monitoring with whylogs and Spark
Re-imagine Data Monitoring with whylogs and Spark
 
Raven: End-to-end Optimization of ML Prediction Queries
Raven: End-to-end Optimization of ML Prediction QueriesRaven: End-to-end Optimization of ML Prediction Queries
Raven: End-to-end Optimization of ML Prediction Queries
 
Processing Large Datasets for ADAS Applications using Apache Spark
Processing Large Datasets for ADAS Applications using Apache SparkProcessing Large Datasets for ADAS Applications using Apache Spark
Processing Large Datasets for ADAS Applications using Apache Spark
 
Massive Data Processing in Adobe Using Delta Lake
Massive Data Processing in Adobe Using Delta LakeMassive Data Processing in Adobe Using Delta Lake
Massive Data Processing in Adobe Using Delta Lake
 

Recently uploaded

A gentle exploration of Retrieval Augmented Generation
A gentle exploration of Retrieval Augmented GenerationA gentle exploration of Retrieval Augmented Generation
A gentle exploration of Retrieval Augmented Generation
dataschool1
 
Senior Software Profiles Backend Sample - Sheet1.pdf
Senior Software Profiles  Backend Sample - Sheet1.pdfSenior Software Profiles  Backend Sample - Sheet1.pdf
Senior Software Profiles Backend Sample - Sheet1.pdf
Vineet
 
Drownings spike from May to August in children
Drownings spike from May to August in childrenDrownings spike from May to August in children
Drownings spike from May to August in children
Bisnar Chase Personal Injury Attorneys
 
一比一原版澳洲西澳大学毕业证(uwa毕业证书)如何办理
一比一原版澳洲西澳大学毕业证(uwa毕业证书)如何办理一比一原版澳洲西澳大学毕业证(uwa毕业证书)如何办理
一比一原版澳洲西澳大学毕业证(uwa毕业证书)如何办理
aguty
 
Interview Methods - Marital and Family Therapy and Counselling - Psychology S...
Interview Methods - Marital and Family Therapy and Counselling - Psychology S...Interview Methods - Marital and Family Therapy and Counselling - Psychology S...
Interview Methods - Marital and Family Therapy and Counselling - Psychology S...
PsychoTech Services
 
How To Control IO Usage using Resource Manager
How To Control IO Usage using Resource ManagerHow To Control IO Usage using Resource Manager
How To Control IO Usage using Resource Manager
Alireza Kamrani
 
一比一原版加拿大渥太华大学毕业证(uottawa毕业证书)如何办理
一比一原版加拿大渥太华大学毕业证(uottawa毕业证书)如何办理一比一原版加拿大渥太华大学毕业证(uottawa毕业证书)如何办理
一比一原版加拿大渥太华大学毕业证(uottawa毕业证书)如何办理
uevausa
 
Call Girls Hyderabad (india) ☎️ +91-7426014248 Hyderabad Call Girl
Call Girls Hyderabad  (india) ☎️ +91-7426014248 Hyderabad  Call GirlCall Girls Hyderabad  (india) ☎️ +91-7426014248 Hyderabad  Call Girl
Call Girls Hyderabad (india) ☎️ +91-7426014248 Hyderabad Call Girl
sapna sharmap11
 
Sample Devops SRE Product Companies .pdf
Sample Devops SRE  Product Companies .pdfSample Devops SRE  Product Companies .pdf
Sample Devops SRE Product Companies .pdf
Vineet
 
一比一原版(lbs毕业证书)伦敦商学院毕业证如何办理
一比一原版(lbs毕业证书)伦敦商学院毕业证如何办理一比一原版(lbs毕业证书)伦敦商学院毕业证如何办理
一比一原版(lbs毕业证书)伦敦商学院毕业证如何办理
ywqeos
 
Call Girls Hyderabad ❤️ 7339748667 ❤️ With No Advance Payment
Call Girls Hyderabad ❤️ 7339748667 ❤️ With No Advance PaymentCall Girls Hyderabad ❤️ 7339748667 ❤️ With No Advance Payment
Call Girls Hyderabad ❤️ 7339748667 ❤️ With No Advance Payment
prijesh mathew
 
Call Girls Lucknow 0000000000 Independent Call Girl Service Lucknow
Call Girls Lucknow 0000000000 Independent Call Girl Service LucknowCall Girls Lucknow 0000000000 Independent Call Girl Service Lucknow
Call Girls Lucknow 0000000000 Independent Call Girl Service Lucknow
hiju9823
 
reading_sample_sap_press_operational_data_provisioning_with_sap_bw4hana (1).pdf
reading_sample_sap_press_operational_data_provisioning_with_sap_bw4hana (1).pdfreading_sample_sap_press_operational_data_provisioning_with_sap_bw4hana (1).pdf
reading_sample_sap_press_operational_data_provisioning_with_sap_bw4hana (1).pdf
perranet1
 
[VCOSA] Monthly Report - Cotton & Yarn Statistics March 2024
[VCOSA] Monthly Report - Cotton & Yarn Statistics March 2024[VCOSA] Monthly Report - Cotton & Yarn Statistics March 2024
[VCOSA] Monthly Report - Cotton & Yarn Statistics March 2024
Vietnam Cotton & Spinning Association
 
Q4FY24 Investor-Presentation.pdf bank slide
Q4FY24 Investor-Presentation.pdf bank slideQ4FY24 Investor-Presentation.pdf bank slide
Q4FY24 Investor-Presentation.pdf bank slide
mukulupadhayay1
 
一比一原版加拿大麦吉尔大学毕业证(mcgill毕业证书)如何办理
一比一原版加拿大麦吉尔大学毕业证(mcgill毕业证书)如何办理一比一原版加拿大麦吉尔大学毕业证(mcgill毕业证书)如何办理
一比一原版加拿大麦吉尔大学毕业证(mcgill毕业证书)如何办理
agdhot
 
Template xxxxxxxx ssssssssssss Sertifikat.pptx
Template xxxxxxxx ssssssssssss Sertifikat.pptxTemplate xxxxxxxx ssssssssssss Sertifikat.pptx
Template xxxxxxxx ssssssssssss Sertifikat.pptx
TeukuEriSyahputra
 
一比一原版悉尼大学毕业证如何办理
一比一原版悉尼大学毕业证如何办理一比一原版悉尼大学毕业证如何办理
一比一原版悉尼大学毕业证如何办理
keesa2
 
06-18-2024-Princeton Meetup-Introduction to Milvus
06-18-2024-Princeton Meetup-Introduction to Milvus06-18-2024-Princeton Meetup-Introduction to Milvus
06-18-2024-Princeton Meetup-Introduction to Milvus
Timothy Spann
 
06-20-2024-AI Camp Meetup-Unstructured Data and Vector Databases
06-20-2024-AI Camp Meetup-Unstructured Data and Vector Databases06-20-2024-AI Camp Meetup-Unstructured Data and Vector Databases
06-20-2024-AI Camp Meetup-Unstructured Data and Vector Databases
Timothy Spann
 

Recently uploaded (20)

A gentle exploration of Retrieval Augmented Generation
A gentle exploration of Retrieval Augmented GenerationA gentle exploration of Retrieval Augmented Generation
A gentle exploration of Retrieval Augmented Generation
 
Senior Software Profiles Backend Sample - Sheet1.pdf
Senior Software Profiles  Backend Sample - Sheet1.pdfSenior Software Profiles  Backend Sample - Sheet1.pdf
Senior Software Profiles Backend Sample - Sheet1.pdf
 
Drownings spike from May to August in children
Drownings spike from May to August in childrenDrownings spike from May to August in children
Drownings spike from May to August in children
 
一比一原版澳洲西澳大学毕业证(uwa毕业证书)如何办理
一比一原版澳洲西澳大学毕业证(uwa毕业证书)如何办理一比一原版澳洲西澳大学毕业证(uwa毕业证书)如何办理
一比一原版澳洲西澳大学毕业证(uwa毕业证书)如何办理
 
Interview Methods - Marital and Family Therapy and Counselling - Psychology S...
Interview Methods - Marital and Family Therapy and Counselling - Psychology S...Interview Methods - Marital and Family Therapy and Counselling - Psychology S...
Interview Methods - Marital and Family Therapy and Counselling - Psychology S...
 
How To Control IO Usage using Resource Manager
How To Control IO Usage using Resource ManagerHow To Control IO Usage using Resource Manager
How To Control IO Usage using Resource Manager
 
一比一原版加拿大渥太华大学毕业证(uottawa毕业证书)如何办理
一比一原版加拿大渥太华大学毕业证(uottawa毕业证书)如何办理一比一原版加拿大渥太华大学毕业证(uottawa毕业证书)如何办理
一比一原版加拿大渥太华大学毕业证(uottawa毕业证书)如何办理
 
Call Girls Hyderabad (india) ☎️ +91-7426014248 Hyderabad Call Girl
Call Girls Hyderabad  (india) ☎️ +91-7426014248 Hyderabad  Call GirlCall Girls Hyderabad  (india) ☎️ +91-7426014248 Hyderabad  Call Girl
Call Girls Hyderabad (india) ☎️ +91-7426014248 Hyderabad Call Girl
 
Sample Devops SRE Product Companies .pdf
Sample Devops SRE  Product Companies .pdfSample Devops SRE  Product Companies .pdf
Sample Devops SRE Product Companies .pdf
 
一比一原版(lbs毕业证书)伦敦商学院毕业证如何办理
一比一原版(lbs毕业证书)伦敦商学院毕业证如何办理一比一原版(lbs毕业证书)伦敦商学院毕业证如何办理
一比一原版(lbs毕业证书)伦敦商学院毕业证如何办理
 
Call Girls Hyderabad ❤️ 7339748667 ❤️ With No Advance Payment
Call Girls Hyderabad ❤️ 7339748667 ❤️ With No Advance PaymentCall Girls Hyderabad ❤️ 7339748667 ❤️ With No Advance Payment
Call Girls Hyderabad ❤️ 7339748667 ❤️ With No Advance Payment
 
Call Girls Lucknow 0000000000 Independent Call Girl Service Lucknow
Call Girls Lucknow 0000000000 Independent Call Girl Service LucknowCall Girls Lucknow 0000000000 Independent Call Girl Service Lucknow
Call Girls Lucknow 0000000000 Independent Call Girl Service Lucknow
 
reading_sample_sap_press_operational_data_provisioning_with_sap_bw4hana (1).pdf
reading_sample_sap_press_operational_data_provisioning_with_sap_bw4hana (1).pdfreading_sample_sap_press_operational_data_provisioning_with_sap_bw4hana (1).pdf
reading_sample_sap_press_operational_data_provisioning_with_sap_bw4hana (1).pdf
 
[VCOSA] Monthly Report - Cotton & Yarn Statistics March 2024
[VCOSA] Monthly Report - Cotton & Yarn Statistics March 2024[VCOSA] Monthly Report - Cotton & Yarn Statistics March 2024
[VCOSA] Monthly Report - Cotton & Yarn Statistics March 2024
 
Q4FY24 Investor-Presentation.pdf bank slide
Q4FY24 Investor-Presentation.pdf bank slideQ4FY24 Investor-Presentation.pdf bank slide
Q4FY24 Investor-Presentation.pdf bank slide
 
一比一原版加拿大麦吉尔大学毕业证(mcgill毕业证书)如何办理
一比一原版加拿大麦吉尔大学毕业证(mcgill毕业证书)如何办理一比一原版加拿大麦吉尔大学毕业证(mcgill毕业证书)如何办理
一比一原版加拿大麦吉尔大学毕业证(mcgill毕业证书)如何办理
 
Template xxxxxxxx ssssssssssss Sertifikat.pptx
Template xxxxxxxx ssssssssssss Sertifikat.pptxTemplate xxxxxxxx ssssssssssss Sertifikat.pptx
Template xxxxxxxx ssssssssssss Sertifikat.pptx
 
一比一原版悉尼大学毕业证如何办理
一比一原版悉尼大学毕业证如何办理一比一原版悉尼大学毕业证如何办理
一比一原版悉尼大学毕业证如何办理
 
06-18-2024-Princeton Meetup-Introduction to Milvus
06-18-2024-Princeton Meetup-Introduction to Milvus06-18-2024-Princeton Meetup-Introduction to Milvus
06-18-2024-Princeton Meetup-Introduction to Milvus
 
06-20-2024-AI Camp Meetup-Unstructured Data and Vector Databases
06-20-2024-AI Camp Meetup-Unstructured Data and Vector Databases06-20-2024-AI Camp Meetup-Unstructured Data and Vector Databases
06-20-2024-AI Camp Meetup-Unstructured Data and Vector Databases
 

Predicting Banking Customer Needs with an Agile Approach to Analytics in the Cloud

  • 1. October 2019 Predicting Banking Customer Needs with an Agile Approach to Analytics in the Cloud
  • 2. Who is presenting today … 2 Milan BerkaJakub Mašek - Machine learning engineer at DataSentics, working for Moneta’s DataSquad - Spark-certified developer - Roles: - Building the analytical platform - Productionalizing the usecases - Evangelize Spark across the company - Leader of DataSquad at MONETA - Experienced data science manager - Roles: - Partnering with the different departments across the bank - Helping finding them the ML opportunities - Managing the process milan.berka@datasentics.com www.linkedin.com/in/milan-berka/ jakub.masek@moneta.cz www.linkedin.com/in/jakub-mašek- 19631155
  • 3. Agenda Background: • Who is MONETA Money Bank a what is the role of Datasentics • Moneta’s journey into the cloud • Creation of Data Squad Building the analytical platform: • Setting up an analytical environment in the cloud fully utilizing AWS and Databricks • Hurdles along the way Use-cases: • Utilizing online data in digital marketing and customer value management • Optimization of branches/ATM Next steps, Q&A
  • 4. § Major Czech banking institution § 4th in size, 1st in innovation § 1 mio clients; 181 branches; 650 ATMs § 3.000 employees § Undergoing digital transformation § Collecting innovation awards § Smart Banka (mobile app) § Digital products § Migration to the cloud Moneta Money Bank - Czech bank for Czech people
  • 5. … almost forgot to mention „Tom“ - our advertising star
  • 6. Make data science and machine learning have a real impact on organizations across the world - demystify the hype and black magic surrounding AI/ML and bring to life transparent production-level data science solutions and products delivering tangible impact and innovation. DataSentics - European Data Science Center of Excellence based in Prague - Machine learning and cloud data engineering boutique - Helping customers build end-to-end data solutions in cloud - Incubator of ML-based products - 50 specialist (data science, data/software engineering) - Partner of Databricks & Microsoft
  • 7. Moneta and it’s journey to the cloud 2018 2019 2020 2021 10% cloud-based 30+% cloud-based 50+% cloud-based Optimal cloud hosting Growing Platform as a Service • Primary Datacenter migration • Cloud design & initiation • First set of application migrated to Amazon Cloud • PaaS, SaaS and Containers • Automation embedded into the key processes • Second Datacenter migration • AS400 refresh/hosting • Software and Infrastructure harmonization • Platform as a Service, implemented for the selected capabilities • Use the most optimal hosting strategy for each application • Further infrastructure and application optimization • Hosted fixed telephony • Software as a Service implemented for the selected capabilities
  • 8. Birth of Datasquad as a new analytical DNA supporting the cloud journey and making „digital“ into real New analytical worldOld analytical world -Tools: -On-premise Oracle data warehouse with limited computational power -On-premise SAS for modelling -Data: Mainly offline (transactions, …) -Tools: - Cloud-based, elastic and scalable – unlimited resources - Data in Datalake - Spark, Python, R -Data: - offline (internal data) - online (web-browsing data, digital marketing data, …)
  • 9. Datasquad is pioneering the new analytical world DATALAKE PLATFORM DATA TEAM EVANGELIZATION & SERVICE DATA SCIENCE SOLUTIONS - POC; MVP - Products - Frameworks - onboarding - Evangelize Spark and new technologies
  • 10. Main goal: utilize cloud services as much as possible Technology: § Storage: AWS S3 with auto-encryption § ETL: AWS Glue § Access Management: AWS IAM + ADFS § Analytical service: Databricks § Security measures: AWS S3 auto encryption, AWS EBS auto-encryption, Databricks SSO, Databricks without access to internet, hashing of all sensitive data Building the analytical platform
  • 12. Datalake structure Data: § Adform data (terabytes) § Web data (terabytes) § Geo-data (gigabytes) § Branches/ATM data (gigabytes) § Onboarding/fraud data (gigabytes) § Transactions (terabytes)
  • 13. Use-cases “Online” data Web analytics data (AdobeAnalytics/GA) Campaign data (Adform) Real estate market data “Offline” data Branch/ATM performance Sales data Onboarding data CVM data Feature Store CVM STORY DIGITAL STORY RISK STORY BRANCH / ATM STORY FRAUD / AML STORY
  • 14. Use-cases “Online” data Web analytics data (AdobeAnalytics/GA) Campaign data (Adform) Real estate market data Feature Store CVM STORY DIGITAL STORY RISK STORY BRANCH / ATM STORY FRAUD / AML STORY “Offline” data Branch/ATM performance Sales data Onboarding data CVM data
  • 15. If we look at a typical customer journey for a consumer loan, we see a relevant touchpoint gap, an opportunity for us to address … 15 … and we already have a plan in motion to address this opportunity Digital Story Digital marketing cost analysis 1 Moneta Ad Quality2 Ad Targeting users in „think“ phase 3 „Think“ phase predictors in CVM campaigns 4
  • 16. If we look at a typical customer journey for a consumer loan, we see a relevant touchpoint gap, an opportunity for us to address … 16 … and we already have a plan in motion to address this opportunity Digital Story Digital marketing cost analysis 1 Moneta Ad Quality2 Ad Targeting users in „think“ phase 3 „Think“ phase predictors in CVM campaigns 4
  • 17. USE CASE: Digital marketing cost analysis 17 → WE HAVE PROVEN, THAT DISPLAY ADS DRIVE SALES INDIRECTLY 1 THERE IS OBVIOUS POTENTIAL IN THE „THINK“ PHASE DATA WE USED • Advertising data (what user, on which specific website/page/context, for how long has seen or interacted with our Ads, for how much) • Moneta Website behavior • Marketing costs WHAT WE DID • We implemented an attribution model to prove how online ad impressions (not clicks!) drive sales. An attribution model shows how each market channel drives conversions. Here we wanted to see what contribution each channel makes to closing consumer loans. NEXT STEPS • Incrementally start to reallocate more budget to Online Ads (upper funnel – think phase) and evaluate impact on efficiency BUSINESS CASE • Increase digital sales for the same media spending. By better split between Online Ads and Search Marketing channel Costs (units) Cost efficiency Performance - Adform 1 11,3 Brand - Adform 17 6,6 Performance - remarketing 23 2,4 Performance - display 26 1,2 Performance – search 1 115 1 Performance - social 0,75 0,5 Brand – youtube 0,4 0,18 1 Performance – search chosen as a reference with cost effeciency ratio 1
  • 18. USE CASE – Moneta Ad Quality 18 2 → DIFFERENT COST PER VISIBLE MINUTE ACROSS DIFFERENT WEBSITES WE CAN INCREASE AD VISIBILITY TO USERS IN THINK PHASE DATA WE USED • Advertising data (what user, on which specific website/page/context, for how long has seen or interacted with our Ads, for how much) WHAT WE DID • We see an ENORMOUS difference in visible time of online Ads. Cost per 1 visible minute in Online differs from 15 to 35 CZK in NEXT STEPS • Create engine to optimize Online Ads buying (buy more visible ads) BUSINESS CASE • We should be able to buy at least 20% more media time for the same budget Analytical output - Cost per visible minute → ADJUSTING ADFORM BY DISADVANTAGING DOMAINS WITH EXPENSIVE VISIBLE MINUTES Adform implementation – multipliers autoweb.cz 0.75 autozine.cz 0.8 autozive.cz 0.9 avizo.cz 0.85 babinet.cz 0.95 babyweb.cz 0.65 banger.cz 0.85 banky.cz 0.85 bazarbox.cz 0.7 behani.cz 0.85 bejvavalo.cz 0.85 bezrealitky.cz 0.65 biatlonmag.cz 0.8 biginzerce.cz 0.7 bike-mania.cz 0.85 ... ... API Quality model
  • 19. 19 Locality (L) attractiveness is given by surrounded points of interests To measure attractiveness, weights of individual points of interests need to be set MONETA wants to compare localities in terms of business KPI - possible bank performance 200 m eters L • Total attractiveness of the measured point is given by the sum of partial weights • Two possible scenarios how to set the weights: By expert (e.g. Bank 50; Bus station 15 …) having dimensionless index Data Science approach (machine learnig) - using internal data to set KPI and having interpretable resuls 1 2 181 Branch Story Moneta needs to independently evaluate every single locality or branch network cross the country … v Assumption v Target variablev Approach
  • 20. 20 → PRAGUE – EXPOSED AREAS BY PREDICTED PERFORMANCE INDEXWE CAN PREDICT PERFORMANCE IN ANY LOCALITY IN CZ DATA WE USED • Geospatial data - points of interests • Population statistics • Internal data – performance of our existing branches; costs; # FTEs; ATM performance WHAT WE DID • We wanted to evaluate every single location in CZ in terms of footfall. The closest equivalent to footfall is visitors' rate which is measured only for 15% of our network. But visitors' rate is strongly corelated with business KPI - performance rate - which was finally used as a proxy variable for our model. We are now able to predict possible banking performance of any observed location. MODEL VARIABLES • # of transportation in 200m • # of food in 200m • # of competitors and highly exposed areas • City population Branch Story use case
  • 21. Use-case deep dive: DSID = Enabler for the Digital attribution model Problem: we have many identifiers (internal id, phone, website cookie, Adform cookie) of a person/client, which shows at different times at different places – how do we connect all these into a single ID? I1 I2 I3 W1 W2 W3 W4 W5 A1 A2 A3
  • 22. Use-case deep dive: DSID = Enabler for the Digital attribution model Answer: GraphFrames!
  • 23. Use-case deep dive: DSID = Enabler for the Digital attribution model WebsiteID InternalID W1 I1 W2 I1 W3 NULL WebsiteID AdformID W1 A1 W2 A2 W3 A3 InternalD Phone I1 999999 I2 999999 I3 019645 df3.filter(not_fake(col(‘Phone’)) df1.withColumn(‘src’, ‘WebsiteId’) df1.withColumn(‘dst’, ‘InternalId’) df2.withColumn(‘src’, ‘WebsiteId’) df2.withColumn(‘dst’, ‘AdformId’) df3.withColumn(‘src’, ‘InternalId’) df3.withColumn(‘dst’, ‘Phone’) df = df1 .union(df2) .union(df3) .distinct()
  • 24. Use-case deep dive: DSID = Enabler for the Digital attribution model src dst W1 I1 W2 I1 W3 NULL W1 A1 W2 A2 W3 A3 I3 019645 vertices = df .selectExpr(‘src AS id’) .union(df.selectExpr(‘dst AS id’)) edges = df g = GraphFrame(vertices, edges) df_connected = g.connected_components()
  • 25. Use-case deep dive: DSID = Enabler for the Digital attribution model id Component W1 1 W2 1 W3 2 I3 3 I1 1 A1 1 A2 1 A3 2 019645 3 plus further adjustements: • filter business clients • disjoint the groups with two or more internal ids • … = DSID Statistics: - Number of vertices (ids): 14 969 170 - Number of edges: 30 029 363 - Running time: ~20 min
  • 26. Next steps 26 - Major goal: Continue with democratizing of the platform, the ultimate goal is to have a self-serving data platform - Continue with the use-cases and moving them to production - Implement company-wide feature store - Employ new technologies (in particular - Spark Structured Streaming)
  • 27. Question How many members does Data Squad have?
  • 28. 5.5 (3 from Moneta, 2.5 from DataSentics)
  • 29. Wrap up 29 Even with the small team you can do big things … Achieving this - you need to have supportive environment and you need to be disruptive to drive changes and show the added value to prove that: … „data is really the new oil for your company“ Safety always first Data science is about data AND science – doing science is always linked with blind paths – be patient and keep going!
  • 30. Thank you for your attention