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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