Journey of SAP Analytics Cloud - Amazon Web Services
Transcript of Journey of SAP Analytics Cloud - Amazon Web Services
Journey of SAP Analytics Cloud
Sri YalamanchiliAnalytics Lead
Florida Crystals/ASR Group
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Key Outcomes/Objectives
1. Transformation Journey2. Roll out Strategy3. Change Management
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Agenda
• Introduction• Data and Analytics Strategy• Data and Analytics Architecture• Use Cases• Lessons Learned
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Florida Crystals/ ASR Group at a Glance
▪ Privately owned, vertically integrated producer of cane sugar and rice products.
▪ The company also has energy, transportation, tourism and real estate operations.
▪ Employees: 18,000 worldwide
Key Facts
▪ 190,000 acres in Florida▪ 250,000 acres in Dominican
Republic
▪ 71,300 acres in Belize▪ 39,000 acres in Mexico
Footprint in North America and Europe
Farming Refining Sales
Florida Crystals, ASR Group & Affiliates -- Vertically-Integrated From the Farm to the Table
Milling
▪ 5 sugar mills
▪ 12 million tons cane per
year
▪ World’s largest cane sugar refiner
▪ 11 sugar refineries worldwide
▪ 6 million tons of refined sugar
▪ Sell to consumer, specialty, industrial and food service channels
▪ 10 consumer brands
▪ 1,000 products
▪ Focus on product and packaging innovation
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Data and Analytics Strategy
Our data and analytics strategy aligns platforms with the best features to meet different objectives.
Strategy Operational Reports
ManagementDashboards
Data Discovery
AdvancedAnalytics
▪ Everyday reports
▪ Easy filtering
▪ Transaction reporting
▪ Support routine tasks
▪ Enterprise KPIs
▪ Business performance
▪ Certified analytical components and stories
▪ Ad hoc, seamless and visual data exploration
▪ Raw, normalized and blended data
▪ Deep data insights
▪ Applied data science
▪ Statistical and computational methods
▪ BW/BO Portal
▪ Analysis for Office
▪ Analytics Cloud
▪ Analytics Cloud
▪ Excel
▪ BOBJ Web Intelligence
▪ Analysis for Office
▪ Analytics Cloud
▪ R
▪ Python
▪ Analytics Cloud
▪ Analysis for Office
▪ Data Lake: Cloud Tools
▪ Analytics Cloud
▪ Tableau (EPM only)
▪ Data Lake: Cloud Tools
▪ Analysis for Office
▪ Analytics Cloud
▪ Data Lake: Cloud Tools
▪ Data Lake: R / Python
▪ Data Lake: Cloud Tools
▪ Analytics Cloud Predictive
Current Activity
Future Activity
FutureToolset(Italics –
Primary)
Objectives
CurrentToolset(Italics –
Primary)
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Data and Analytics Architecture
Our data and analytics architecture is driven by our data and analytics strategy
PDF PFC EWM Belize AribaConcur IBP C4COther ERP
Services
Success
FactorsMDG
Replication
Tables
Custom
Schemas
Virtual
TablesBW Schema
FunctionalViews
F it for
purpose
V iews
A d-hoc
WorkspaceBase
DSOs
Applicatio
n DSOs
Maste
r Data
BW
Workspace
BEx Query
Open
DSO
Data staging container
(cleansing and confirmation)
Reporting and Visualization
Operational Reports Management Dashboards Data Discovery Advanced Analytics
SAP BW on HANA SAP HANA Model
SAP HANA DB
Corporate Data LakeEnterprise Data Warehouse
SAP Ecosystem (on premise or cloud systems) and other ERP Data
ftp drop box
File Interfaces
• SLT/SDI• SDA
• CPI (HCI)• ODP/BW extractors
• Batch• ODBC/JDBC
/OData• SDA Non-enterprise,
unstructured data
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Before TransformationStatic reports in BW using Web Application Designer
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Before TransformationBusiness Objects reports and dashboards using Web Intelligence
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After Transformation
Management Dashboard Use Case
▪ Trend analysis on Total Cane yield per Acre
▪ Number of acres and varieties distributed by soil type
ValueProvide KPIs on acreage harvested and yield per acre in the past 6 years for each soil type
Purpose
Production Analysis by Soil Type
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After Transformation
Advanced Analytics Use Case
▪ Generates planting and rotation recommendations based on predictive analysis of prior years’ yield
▪ Geospatial capabilities help visualize the exact locations in the recommendations
ValueProvide insights into cane rotation, planting and harvesting plan by specific acreage location
Purpose
Farm Rotation Analysis
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SAC at Florida Crystals
SAC is a core component of our Analytics tool strategy which will expand as SAC matures
▪ Enterprise reporting capabilities
▪ Consistent features across data sources
▪ Ad-hoc analysis/self service capabilities vs other tools
▪ Advance analytics capabilities
▪ Non-SAP integration
▪ App Design capabilities
Future Opportunities▪ Integration with SAP sources (BW, S4, ECC,
cloud, IBP)
▪ Strategic future of analytics for an SAP enabled enterprise (embedded analytics, standard content)
▪ Availability and simplicity of chart types, drag and drop capabilities
▪ Geo spatial capabilities
▪ Basic predictive capabilities/ Predictive for citizen data scientist
Why we use SAC?
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Lessons Learned
Governance
▪ Identify viewers, analysts and power users by line of business
▪ Define custom security roles
▪ Organize content by Enterprise and Workspace versions by packages/folders
▪ Assign access by packages and TEAMS
Tailored training content
▪ Features differ based on data source
▪ Blending capability dependency on HANA DB version
▪ Limitations on number of records for IBP
Enablement of self service analytics
▪ Maintain data catalog
▪ Design sessions based on use case
▪ Set expectations on data prep
Questions?
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Thank you.
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