Complexities of Separating Data in an ERP Environment

Complexities of Separating
Data in an ERP Environment

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After completion of this presentation, you will be able to:
 Objective 1: Discover the reasons for data separation.
 Objective 2: Understand data architecture in an
Enterprise Resource Planning (ERP) System.
 Objective 3: Explain how to effectively separate the
data.
Learning Objectives

Who Is eprentise?
In 2007 eprentise was founded
on its original product, FlexField
 Enables customers to make
unprecedented changes to their financial
chart of accounts while maintaining
transactional history and data integrity.
In 2009 we introduced our Consolidation,
Divestiture, and Reorganization products
 Transformational software which can copy, change, filter, or
merge all elements of Oracle EBS financial systems to
address ever-changing business needs, such as regulatory
compliance and growth opportunities.
In 2020 we began expanding to new markets with our
C Collection analytics suite, and our Audit Automation software
 Automated Audit provides finance teams with
drill-down data from a balance sheet report into
the transaction-level detail. The software covers
hundreds of substantive procedures for the
entire enterprise domain and builds in consistent
audit processes and workflows across the
organization.
 C Collection analytics provides transparency and
identifies potential problem areas with
transactional data. This allows users to reduce
costs, leverage opportunities across the
enterprise, improve business processes, and
increase the confidence level of the users in their
data, processes, and operations.
Transformation to Optimization
One-time usage to subscription model

Companies Need to Change Their Oracle® E-Business Suite
Without Reimplementing
 Consolidate Multiple EBS Instances
 Change Underlying Structures and
Configurations
 Chart of Accounts, Other Flexfields
 Merge or Split Ledgers, Operating
Units, Legal Entities, Inventory
Organizations
 Calendars, Currency, Costing
Methods
 Asset Revaluation, Inventory
Valuation
 Separate Data for a Divestiture
Reduce Operating Costs and Increase
Efficiencies
eprentise Can… …So Our Customers Can:
Adapt to Change
Avoid a Reimplementation
Reduce Complexity and Control Risk
Improve Business Continuity, Service
Quality and Compliance
Streamline Operations with Visibility
to All Parts of the Business
Establish Data Quality Standards and
a Single Source of Truth
Finished But Not Done®

 Data – The Most Valuable Business Asset?
 Reasons for Data Separation
 ERP Data Architecture
 Effectively Separating Data
 Data Separation Case Studies
 Separating data in different instance – Divestiture
 Separating data in same instance – Re Organization.
 Questions
Agenda

Objective 1
Discover the reasons for data separation.

 Is Data the New Oil of Data Economy?
 Data and oil have similar properties
 “Data-fication” of information
 Types of Data
 Structured Data – Tables/ Excel- ERP
 Unstructured Data – Documents, Photographs, Videos, ECG reports
 Sources of Data
 Human generated data
 Machine generated data aka Internet of things
 Data Usage
 Business operations –order entry , invoice generation , reporting etc.
 Business analytics – market trends, market-based analysis
 AI and ML – patterns, data mining, build models to predict a future outcome
 Data monetization
Data – The Most Valuable Business Asset

Technology change and growth and usage of data over time

Huge Data – Challenges
 Top 5 Key challenges handling huge data
 Common definitions and usage
 Lack of proper understanding of massive data
 Data growth issues
 Relevance/importance of data points changing over time
 Integrating data from a variety of sources
 Data security
 Data access
 Data separation

 An act of segregating data to produce information that
meets a business requirement
 Simple uses – requires none to some knowledge of
underlying ERP data model:
 Specifying report parameters when running a report
 Specifying a WHERE clause criteria in a query when selecting data from a
database table
 Complex uses – requires extensive knowledge of underlying
ERP data model:
 In the case of a divestiture, identify which customers and suppliers will go
with the buyer organization
 When archiving data from an ERP instance, identify which data entities will be
impacted
 Separate business operations for regulatory or statutory requirements
 Financial auditing
Data Separation

 Reporting:
 Specific reporting requirements from a subset of data
 Business event:
 Buying or selling part of an organization
 Compliance:
 Different legal requirements for different parts of the organization
 Governance:
 Different controls or security requirements within the organization
 Archive/Purge:
 Archive/purge historical data
 Masking:
 Mask HR and other sensitive data based on regulations such as PCI compliance, etc. – how to
identify what to mask?
Why Separate Data?

Interesting Data Fact
On average, every human
created at least 1.7 MB of
data per second in 2020.[1]
[1] https://techjury.net/blog/how-much-data-is-created-every-day/#gref

Objective 2
Understand data architecture in an ERP system

ERP Technical Architecture
User Interface
Desktop/Client Tier
Application Logic
Application Tier
Application Data &
Database Logic
Internet
Database Tier

ERP Functional Structure
Employees
Managers and
Stakeholders
Central
Database
Reporting
Applications
Human
Resource
Management
Applications
Financial
Applications
Manufacturing
Applications
Inventory
and Supply
Applications
Human
Resource
Management
Applications
Service
Applications
Sales and
Delivery
Applications
CRM
and Customer
Service Reps
Customers Back-office
Administrators
And Workers
Suppliers

 Finance and accounting
 Investment, cost, asset, capital and debt management
 Budgets, profitability analysis and performance reports
 Sales and delivery
 Handles pricing, availability, orders, shipments and billing
 Inventory and supply management
 Process planning, BOM, inventory costing, MRP, allocates resources, schedules,
Purchase Orders and inventory
 Human resources
 Workforce planning, payroll & benefits and organizational charts
ERP Modules – 4 Main Functional Areas

Data stored at Multiple Levels – EBS View

Organization Components
Business Group: The consolidated enterprise, a major division,
or an operations company.
Ledger: A financial entity comprised of a chart of accounts, calendar, and
functional currency. Oracle General Ledger secures information by ledger.
Legal Entity: The level at which fiscal or tax reports are prepared. Maintains the
legal lines of credit information of the company.
Operating Unit: Tied to a legal entity, an operating unit can only reference one
ledger and one currency. Most master data such as customers, suppliers, bank
and transaction data of AP, PO, AR, OM, CM are secured by operating unit. In
R12, a LE can be in many OUs, and an OU may have many legal entities. An OU
must have a Default Legal Context (DLC), which will be a particular legal entity.
You can assign that DLC legal entity to multiple OUs.
Inventory Organization: An organization that tracks inventory transactions by
item. Oracle INV, BOM, Engineering, WIP, Master Scheduling/MRP, Capacity, and
Purchasing Receiving all secure data by this type of organization. At least one
Master Inventory Org needs to be defined to maintain item information.

Data Segregation in ERP
 Business Group
 Segregates HR and employee data
 Ledger
 Segregates financial data
 Operating Unit
 Segregates master data (customers, suppliers, bank account) and transaction data
for modules such as Payables, Receivables, Purchasing, Order Management, etc.
 Inventory Organization
 Segregates inventory related data for modules such as Inventory, Material
Requirements Planning, Bills of Materials, Work in Process, etc.

Organization Structure – Example 1
LE 2
LE 1
Primary
Ledger
IO 4
OU 1
IO 1 IO 5 IO 6
IO 2 IO 3
MIO
OU 2 OU 3
Secondary
Reporting
Ledger

Organization Structure – Example 2
US Ledger
US LE
Chicago
OU
Japan Ledger
Japan
LE
Corporate
BG
Canada Ledger
Canada
LE
East
OU
Tokara
OU
Inventory (IO)
Calgary
Manufacturing
(IO)
Warehouse (IO)
West
OU
Quebec
Manufacturing
(IO)
Montreal
Inventory
(IO)

 The same data structures must hold data to support many business models
 The data structure might contain data attributes that are context sensitive
 Highly normalized data in the database
 Different user experiences requires different data types:
 Conventional structured data
 Unstructured data
 Video files
 Sound files
 Image files
 Other file formats (PDF, Word, EXCEL, etc.)
 Social media
 Data or process integrations with other upstream or downstream systems
Is More Flexibility = Low Complexity?

The majority of the
world’s data has come
about in only the past two
years as indicated by data
growth statistics.
[1]

Objective 3
Explain how to effectively separate the data

Data Separation – Divestiture Case Study
• Pre-sale planning is the key to successful divestiture
• Carve-out criteria types
 By Ledger
 By OU
 By Company
 By Product Line
• According to an executive survey done by Deloitte these are
the top 5 priorities for the sellers:
 Developing carve-out financial statements
 Performing a detailed valuation analysis
 Analyzing potential deal structures
 Considering tax and legal structure
 Establishing an incentive plan for target management

Data Separation – Divestiture Case Study
ERP - OU1,
OU2, OU3
ERP - OU1
Buyer (Child)
Carve Out Criteria
ERP –
OU2, OU3
Seller (Parent) ERP Instance
Prior Divestiture
Buyer (Child) ERP Instance
Post Divestiture
Seller (Parent) ERP Instance
Post Divestiture
Seller (Parent)
Carve Out Criteria

Divestiture Case Study – Seller Pre-divestiture
LE 2
LE 1
Primary
Ledger
IO 4
OU 1
IO 1 IO 5 IO 6
IO 2 IO 3
MIO
OU 2 OU 3
Secondary
Reporting
Ledger
Customers
Suppliers
Price Lists
Employees

Divestiture Case Study – Post-divestiture
Buyer Instance
LE 1
Primary
Ledger
OU 1
IO 1 IO 2 IO 3
MIO
Secondary
Reporting
Ledger
Cust
Supp
Price
Empl
Seller Instance
LE 2
IO 4 IO 5 IO 6
OU 2 OU 3
MIO
Primary
Ledger
Secondary
Reporting
Ledger
omers
liers
Lists
oyees

 Starts at a higher-level data entity such as operating unit
or inventory organization and then further drills down to
a lower level that meets the business requirements:
 List of all the outbound shipments from a specific warehouse
for a given month
 List of all customer invoices and related payments for a
customer under a specific operating unit
 List of all the purchase orders created to ship inventory to a
specific warehouse
 List of all the asset account balances for a given financial ledger
as of a given period
Top-down Data Separation

Top-down – Separate Data Based on OU
Operating Unit (OU)
Items Suppliers Warehouse
Purchase Order
Purchase Items
Receipts
7
8
6
4
1 (Start)
5
2
3
9

 Starts at a lower-level data entity/attribute, such as an
item or transaction date, and then further rollup to a
higher level that meets requirements:
 List of all the outbound shipments for a given item
 List of all the customers whole received a shipment for a given
item
 List of all suppliers who shipped specific items to any
warehouse after a specific date
 List of all customer receivables for a specific transaction date
period that uses an accounting code for a specific company
Bottom-up Data Separation

Bottom-up – Separate Data Based on Item
Operating Unit (OU)
Items Suppliers Warehouse
Purchase Order
Purchase Items
Receipts
1 (Start)
2
4
5
6
7
3

In 2024, the number of
emails will be about 361
billion every day.[1]

 Understanding of data entities, using:
 Published data dictionary
 ER diagrams
 Metadata from the database
 Other available sources such as custom documentation, internal
knowledgebase, etc.
 Techno-Functional knowledge of the data - how different
business functions relate to the data
 For example, understand purchasing and its connection with
payables and financial accounting
 Understanding of the tools available in the market
Pre-requisites for Making Good Decisions with Data
Separation

Data Separation – Reorganization Case Study
ERP - OU1,
OU2, OU3
Reorganization Criteria
ERP Instance
Prior Reorganization
ERP Instance
Post Reorganization
ERP - OU1,
OU2, OU3

Re-organization Case Study – Pre-reorganization
LE 2
LE 1
Primary
Ledger - USD
IO 4
OU 1
IO 1 IO 5 IO 6
IO 2 IO 3
MIO
OU 2 OU 3
Secondary
Reporting
Ledger
Customers
Suppliers
Price Lists
Employees

Re-organization Case Study – Post -reorganization
LE 2
LE 1
Primary
Ledger(EUR)
IO 4
OU 1
IO 1 IO 5 IO 6
IO 2 IO 3
MIO
OU 2 OU 3
Secondary
Reporting
Ledger - EUR
Customers
Suppliers
Price Lists
Employees
Primary
Ledger (USD)
Secondary
Reporting
Ledger - EUR

 Cloud Solutions: SaaS, PaaS, IaaS
 Single functional domain SaaS tools have been in the market
for 10+ years now and are very mature (Salesforce.com)
 PaaS and IaaS are very mature now
 Multiple functional domain SaaS tools such as ERP are fairly
new to the market and are increasingly being adopted by
customers and the product offering is also being constantly
enhanced by the customer
 Customer can utilize APIs to extract data from Cloud ERP but
have limited ability to directly access the data using external
SQL/data integration tools
Data Separation in the Cloud

Questions?

Thank You!
- One World, One System, A Single Source of Truth -
Anil Kukreja
Managing Director, eprentise India
Chief Technology Officer, eprentise
Hosted by eprentise®
Questions and Responses shared on EBS Answers:
linkedin.com/groups/4683349/
www.eprentise.com | www.CrystallizeAnalytics.com

Complexities of Separating Data in an ERP Environment

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