Journey from Descriptive Analytics to Predictive Analytics - Journey from Descriptive... · May 7...
Transcript of Journey from Descriptive Analytics to Predictive Analytics - Journey from Descriptive... · May 7...
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May 7 – 9, 2019
Journey from Descriptive Analytics to Predictive Analytics
Balaji Sundaram, BI Analyst, Benjamin Moore & CoSree Rajitha Indraganti, Lead BW Analyst, Benjamin Moore & Co
Session ID # 83598
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About the Speakers
Speaker Name
• Balaji Sundaram - Business Intelligence Analyst
• 5 years of experience in BI
• Certified Data Scientist
Speaker Name
• Sree Rajitha Indraganti –Business Warehouse Lead
• 10 years of experience in SAP BW
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• Introduction
• Reporting System Architecture
• Descriptive to Predictive Analytics
• Benefits Realized
• Roadmap
Agenda
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Benjamin Moore & Co. Company Overview• Benjamin Moore & Co., a Berkshire Hathaway company, was founded in 1883. • One of North America's leading manufacturers of premium quality residential,
commercial and industrial maintenance coatings, Benjamin Moore & Co. maintains a relentless commitment to innovation and sustainable manufacturing practices.
• The Benjamin Moore premium portfolio spans the brand’s flagship paint lines including Aura, Regal Select, Natura and ben. The Benjamin Moore & Co. family of brands includes specialty and architectural paints from Coronado, Corotech, Lenmar and Insl-x.
• Benjamin Moore & Co. coatings are available primarily from its more than 5,000 locally owned and operated paint and decorating retailers.
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Benjamin Moore & Co. Company Overview
“Benjamin Moore’s primary goal: turn out the best paint in the world and have the best retailer organization in the world”
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Wonder What Your
Customer really
wants
Give them before they
could ask
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Low
High
Semantic Layer-Based Platforms
Visual-Based Data Discovery Platforms
Modern BI and Analytics Platforms
3 to 5 Years
IT-LedDescriptive
Business-LedDescriptive/Diagnostic
PervasiveAutodescriptiveDiagnosticPredictive, Prescriptive
Months Days/Hours Instant/In-Line
Time to Advanced Insight
Pe
rvas
ive
ne
ss o
f M
L-En
able
d A
dva
nce
d In
sigh
t o
n A
ll D
ata
Today
Source: Gartner
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From Traditional Reporting Process
QUERY
DATABASE (BW/HANA)
RESULT
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Reporting Process Towards…
PAST DATA
RESULT NEW DATA MODEL
TRAINING
ACTION
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Old Vs New Architecture
EDW Oracle
Non-EDW
Oracle Loads
Stage
Hist
Hist
SAP Data Services /
Workbench
SAP BW/HANA
CRM
Ecommerce
DB2
Flat Files
SAP HANA
Sybase
Non-EDW Oracle Loads
Stage
Informatica
Oracle
CRM
Ecommerce
DB2
Flat Files
Stage
Old New
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Reporting System Architecture – Pre SAP Initiative
Flat Files Legacy System
3rd Party Systems
Web Intelligence
Data Marts
Tables and Views
Staging
SAP Business Objects BI Enterprise 4.2 SP03
Web Intelligence
Oracle
Data Marts
Tables and Views
Staging
ReportingLayer
DataWarehouseLayer
SourceSystemLayer
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• Project life cycle – Phase 1 (SAP Data)
• Began July 2015
• Sprint 1 go live Jan 2016
• Sprint 2 Phase 1 go live Jan 2017
• Sprint 2 Phase 2 go live Dec 2017
– Phase 2 (Non SAP data)• Began October 2017
• Sprint 1 go live April 2018
• Sprints 2 and 3 go live May 2018
• Sprint 4 to be completed June 2018
• Delivered using Agile methodology for SAP BW and Scrum approach for HANA
• Each sprint had a series of associated RICEFs– Each RICEF had a series of associated tasks/ effort
– Delivered End to End to a final Business Objects report
• Daily standups with visual progress tracking
Project Delivery – Tracking / Keeping Pace
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Benjamin Moore & Co. embarked on a business transformation effort – Prism – with a multiphase implementation of SAP ECC, beginning in 2014, with these primary objectives:
• Advance and standardize business processes to support future growth
• Enable enhanced analytics and data-driven decision-making
Program Background: Prism
Order to Cash
Procureto Pay
Recordto Report
Forecastto Stock
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On PremiseHANA Enterprise Cloud
Reporting System Architecture – Phase 1 (2015 – 2017)
Flat Files
3rd Party Systems
SAP BW 7.4 SP11 on HANA
BEx Queries
Composite Providers
Open ODS Views InfoObjectsAdvanced DSOs
SAP ECC 6.0 SP07 on HANA
Standard and Generic Data Sources
FM Extractors CDS Views
ABAP Dictionary Tables
ReportingLayer
DataWarehouseLayer
SourceSystemLayer
SAP Business Objects BI Enterprise 4.2 SP03
Web Intelligence
Dashboards and VisualizationsQlikSense
Oracle
Data Marts
Tables and Views
Staging
Non SAP
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2015 2017 2018 2019 20202016
• BW BEx Queries used as foundation for WebI
reports, via BICS connection, eliminating the
need for Universes
• BOBJ Web Intelligence as the reporting front end
interface for all pre-built and ad-hoc user reporting
• WebI integrated with third-party tool to provide
flexible and dynamic broadcasting of reports
• Ad-hoc: Users can copy and modify reports in
personal folders or create new reports
Reporting Flow
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2015 2017 2018 2019 20202016
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• Change Management
Challenges2015 2017 2018 2019 20202016
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• Data Literacy
Challenges
Business
• Knows what the
data means
• Knows how to interpret
the data to make
decisions
• DOESN'T know how to
think about data
technically (organize,
classify, etc.)
IT
• Knows how to build and
manage data systems
• Knows how to build and
update reports
• DOESN'T know what
the data means or what
decisions should be
made from it
2015 2017 2018 2019 20202016
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2015 2017 2018 2019 20202016
• More Training Sessions
• Increased number of Powers users
• Increased number of Business users
• Faster reporting than before
• Additional IT resources
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– Self Service Analytics
was getting matured
– Descriptive Analytics and
Ad-Hoc reporting
increased
In Favor2015 2017 2018 2019 20202016
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• Heavy Usage of Excel
2015 2017 2018 2019 20202016
Challenges
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• Heavy Usage of Excel
• Inconsistency in MetricsCHAOS DOUBT
Anyone and Everyone can
manipulate the dataCan we trust our data?
Different People were getting
different resultsAre our conclusions accurate?
2015 2017 2018 2019 20202016
Challenges
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• Heavy Usage of Excel
• Inconsistency in Metrics
• Data Trust
2015 2017 2018 2019 20202016
Challenges
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Low
High
Semantic Layer-Based Platforms
Visual-Based Data Discovery Platforms
Modern BI and Analytics Platforms
3 to 5 Years
IT-LedDescriptive
Business-LedDescriptive/Diagnostic
PervasiveAutodescriptiveDiagnosticPredictive, Prescriptive
Months Days/Hours Instant/In-Line
Time to Advanced Insight
Pe
rvas
ive
ne
ss o
f M
L-En
able
d A
dva
nce
d In
sigh
t o
n A
ll D
ata
Today
2015 2017 2018 2019 20202016
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2015 2017 2018 2019 20202016
• First need from Business
o Forecast the need for Raw
Materials
• Business Involved
o Procurement and Supply
Chain
• Partially Satisfy the requirement using - Excel and SAP BO
Predictive Analytics
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2015 2017 2018 2019 20202016
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2015 2017 2018 2019 20202016
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• Not dynamic in generating results
• Not powerful enough to handle large data sets
• Capable of performing only algorithms
Challenges2015 2017 2018 2019 20202016
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• Business users realized the need and advantages of predictive Analytics
• Preliminary Evaluation and POC’s
o SAP Predictive Analytics
o Text Analytics via HANA Libraries
o Text Analytics via HDInsight and
Qliksense
2015 2017 2018 2019 20202016
In Favor
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Reporting System Architecture – Phase2
HANA Enterprise Cloud
SAP BW 7.4 SP11 on HANA
BEx Queries
Composite Providers
Open ODS Views InfoObjectsAdvanced
DSOs
SAP ECC 6.0 SP07 on HANA
Standard and Generic Data Sources
FM Extractors CDS Views
ABAP Dictionary Tables
ReportingLayer
DataWarehouseLayer
SourceSystemLayer
SAP Business Objects BI Enterprise 4.2 SP03
Web Intelligence
Dashboards and VisualizationsQlikSense
HANA Enterprise Cloud
IQ
Flat Files
3rd Party Systems
SAP HANA Enterprise
Calculation Views
HOT DATA
Non SAP
DLM Cold Data
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Benefits Realized
• High Performance: power of HANA for better performance
• Integration: SAP and Non-SAP on a single HANA platform for better data integration, support and maintenance
• Predictive and agile analytics with historical sales data
• Improved use of previously unused data
• Multi temperature data management using DLM
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• Known Customer Needs
o Publish forecast results in DSR App
o Publish retailer churn analysis SMD App
• Proof of Concept
o Real Time Predictive Analytics
o Integrated R with Qliksense
o Integrated Rapidminer with Qliksense
o Integrated HANA with R & Rapidminer
• Business Unit
o Presented Live Demo to FP&A and Pricing Team
2015 2017 2018 2019 20202016
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2015 2017 2018 2019 20202016
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2015 2017 2018 2019 20202016
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Data Scientist
Database
(HANA, Azure, Big
Data)
Developer
End User
Social
Media
Analysis
Retailer/
Contractor
Churn Analysis
Advanced Analytics
Qliksense
R
Trained ModelsNew Customized
Models (throughAPIs)
Applications
Forecast
Analysis
2015 2017 2018 2019 20202016
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Low
High
Semantic Layer-Based Platforms
Visual-Based Data Discovery Platforms
Modern BI and Analytics Platforms
3 to 5 Years
IT-LedDescriptive
Business-LedDescriptive/Diagnostic
PervasiveAutodescriptiveDiagnosticPredictive, Prescriptive
Months Days/Hours Instant/In-Line
Time to Advanced Insight
Pe
rvas
ive
ne
ss o
f M
L-En
able
d A
dva
nce
d In
sigh
t o
n A
ll D
ata
Today
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• Retailer Churn
• Price Elasticity
• Forecast Gallons
Business Requirement for AI2015 2017 2018 2019 20202016
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2015 2017 2018 2019 20202016
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Lets looks at some NumbersStores closed after renovating – We could have saved $175k
2015 2017 2018 2019 20202016
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Lets looks at some NumbersStores closed after renovating – We could have saved $175k
Gallons were lost due to the churned customers
2015 2017 2018 2019 20202016
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Lets looks at some NumbersStores closed after renovating – We could have saved $175k
Gallons were lost due to the churned customers
would have been the profit margin from all discontinued stores
2015 2017 2018 2019 20202016
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First Predictive Model• Develop the model within Enterprise
• Predictive Model – R/ Python
• Semi-supervised Learning
o Clustering the Retail Outlets (Unsupervised Learning)
o Random Forest for building the tree (Supervised Learning)
• AI Platform – SAP Leonardo
• Visualization – Qliksense
R/ Python
SAP Leonardo
Qliksense
2015 2017 2018 2019 20202016
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First Predictive Model
Churn Model
Sales
Accounts Receivable
CRMDemograp
hics/ External
Promotions
TidbitsU.S. companies lose
$136.8 billion per year due to
avoidable consumer switching.- CallMiner
TidbitsChurn can increase
by up to 15% if businesses fail to
respond to customers over social media.
- Gartner
2015 2017 2018 2019 20202016
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First Predictive Model
Confusion Matrix – Preliminary Results based on
data through 2017
Actual Discontinue
Actual Active
Predicted Discontinue
34 52
Predicted Active
7 1742
Total 41 1794
LabelNo – DiscontinueYes - Active
2015 2017 2018 2019 20202016
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The work in progress model predicted 74% of Churned “Paint and Decorating”
Retailers out of all the churned “Paint and Decorating” Retailers in 2018
2015 2017 2018 2019 20202016
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The work in progress model predicted 74% of Churned “Paint and Decorating”
Retailers out of all the churned “Paint and Decorating” Retailers in 2018
Actual Discontinue
Actual Active
Predicted Discontinue
74% 0
Predicted Active
26% 0
2015 2017 2018 2019 20202016
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Where are we heading to…2015 2017 2018 2019 20202016
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Our Vision2015 2017 2018 2019 20202016
Low
High
Semantic Layer-Based Platforms
Visual-Based Data Discovery Platforms
Modern BI and Analytics Platforms3 to 5 Years
IT-LedDescriptive
Business-LedDescriptive/Diagnostic
PervasiveAutodescriptiveDiagnosticPredictive, Prescriptive
Months Days/Hours Instant/In-LineTime to Advanced Insight
Pe
rvas
ive
ne
ss o
f M
L-En
able
d A
dva
nce
d
Insi
ght
on
All
Dat
a
Today
Source: Gartner
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Q&AFor questions after this session, contact us at
Balaji SundaramEmail: [email protected]: https://www.linkedin.com/in/balaji-
sundaram-994b4665/
Sree Rajitha IndragantiEmail: [email protected]
LinkedIn: https://www.linkedin.com/in/sree-rajitha-indraganti-7430158a/
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