dataZen Product Info Management
                     dataZen
Product Information
Management
Whitepaper
www.chainsys.com
Table of Contents
dataZen: Featured Domains 2
Products Information Management 3
Why use Product Information Management 3
PRODUCT INFORMATION MANAGEMENT ILLUSTRATION 5 
Screen Shots: 6
Data Governance process for Products: 8
Screen Shots: 9
MDM IMPLEMENTATION MODELS: 11
Roles within Product MDM 12
Support Endpoints (Partial) 13
Charlie Massoglia
VP & CIO, Chain-Sys Corporation.
ChainSys Corporation
Former CIO for Dawn Food Products For 13+ years.
25+ years experience with a variety of ERP systems.
Extensive experience in system migrations &
conversions. Participated in 9 acquisitions ranging
from a single US location to 14 sites in 11 countries.
Author of numerous technical books, articles,
presentations, and seminars globally.
2
™
Featured Domains
Products Customers Suppliers
Equipment Finance Hierachies
Bills of Materials Formula Receipe
Routings Pricelists Custom
3
Products Information
Management
Products Information Management (PIM) enables you to keep a “Single Source of Truth” for all your 
Products data. It offers data quality solutions to “Get it Clean” and “Keep it Clean” of Products data. 
The flexible data governance workflow enables you to govern the Product’s lifecycle from Onboarding 
to making additional changes in the most efficient manner. Lastly Product 360 is available to achieve 
insightful data around products and related transactions.
Why use Product
Information Management?
Realize the Clean and ML simplifies Simplify
 Zen of Single Complete   Attribution, Product 
 Source of Product data minimal Onboarding 
Truth  ready for you manual errors process
Improve Sell more Enhance 
Product using Supplier Achieve the 
experience eCommerce Collaboration 80/20 rule
™
4
Product Information
Management Illustrations
Data Quality process for Products: Get it Clean and Keep it Clean
Problem1: Existing Customer data in SAP or Oracle or other enterprise applications are Bad. Need to 
cleanse and apply the changes back to enterprise Application.
Problem2: Migration of Legacy applications to the Modern Enterprise Applications. Part of this exercise 
would like to Consolidate, Cleanse and Standardize the Customer’s and Partner’s data and then migrate.
Problem3: After migration into the modern application, want a Single Source of truth using a clean 
Governance process for onboarding new prospects and customer accounts, and making changes to the 
complex customer data either small or bulk quantity easily. Enforce SLA for all activities and measure it. 
Want to keep the data always clean, setup active and passive governance policies to correct the problems 
with Human approval and sometimes automated as well.
ChainSys dataZen solves your data quality problems completely by applying advanced machine learning 
algorithms. Strong background data quality engine is the magic wand for your success. ChainSys has 
cleansed successfully data for major enterprises including: General Electric, Siemens, Expedia, Amazon,
Canon, Agilent Technologies to name a few of the 500+ projects accomplished so far.
Our Goal is to create a “Clean Data Enterprise”. 
PIM 
Golden Hub
Workflow Automated Automated Manual
Execution Process Process
™
Bulk Extract Match & Merge Data Cleanse Data Approve
1 2 3 4
data_steward q_planner q_planner inv_manager
Oracle Item Master: SAP Material Master: 
Data Quality Demo Data Quality Demo
5
Screen Shots:
Match and Merge Review and Approve Workbench 
Functional Dependency Profiling Results
™
6
Data Quality Workflow Configurations: It’s really fun to work on this!!
Data Quality done in multiple simplified Bucketed steps to give you clean and complete data:
7
Data Governance process for Products: 
Configure your Governance workflow with a simple drag and drop interface. Reduce the time for 
onboarding and improve the quality of data stewardship with achieving SLA, which is out of the 
box feature. A simple to complex workflow needs can be easily configured using the ChainSys 
Governance workflow engine. A clear reporting and notifications are provided to see the current 
status of each of your requests.
q_requester Collection q_finance
Material 
Request Collection
q_planner
Approval gtc teamNo-match Inventory_approval
Approval
q_procurement Approval
Matches
with Hub Approval Inventory_manager
import Types of standard
Request
Inventory_manager
Regular
Approval
Merge
Review
™
q_planner
gtc team
Approval
q_finance gtc team
Collection Approval
Oracle Item Master SAP Material Master
Data Governance Demo Data Governance Demo
8
Screen Shots:
Notification screen for New, Modify Customer Requests, and Completed Requests.
Workflow Process Steps and Current Status:
Smart Data Platform™
9
Screen Configured using Standard Layout for Customer Master for Data Request / Stewards / Owners:
Online Validations with Standard Rules and allows to Customize rules
Data Governance Workflow Configurations: Its Finally Easy!!
™
10
MDM Implementation Models:
dataZen support Registry, Consolidation, Co-existence and Centralized MDM models for 
implementations. Let’s see which model applies when?
Registry Consolidation
When you want to store only the unique columns When you want to build an MDM model for 
from multiple systems for synchronization. Analytical purpose.
Co-Existence Model Centralized Model
When there is a need for Centralization plus When there is only need for centralization in 
De-centralization of data ownership and data ownership and stewardship
stewardship actions.
Registry Model Consolidation Model
ERP ERP
PLM COTS
dataZen dataZen
PLM EDM
PIM PIM
COTS EDM
Co-Existence Model Centralized Model
ERP ERP
PLM COTS
dataZen dataZen
PLM EDM
PIM PIM
EDM COTS
11
Roles within Product MDM
Its important to form a strong MDM team to create a strong Data driven organization. Here are 
some tips for the same. We will provide you more detailed recommendations during the 
implementation period.
Administrators Business Users
Architect: Responsible for configuring the Data Requestors: Responsible for requesting 
dataZen application to the requirements for for new master products or modify existing 
Data Quality Rules, Governance Workflows products. 
and Integrations. Makes the changes in 
development, check-in the objects into the 
versioning tool, migrates the changes into the Data Stewards: Responsible for collecting 
production instance of dataZen. Either IT or a additional data required for completing the 
Power User can take this role. product attribution. Domain wise stewards 
would be great to have like Finance, Materials 
System Administrator: Responsible for management, Sales, Manufacturing, Planning, 
applying patches, shut-down, restart of the Maintenance/MRO, Service, Self-service etc.
dataZen applications, backups, recovery etc. 
IT DBA’s are generally given this role
Data Owners: Responsible for reviewing the 
actions performed by the requestors and 
stewards and approve or reject or send for 
rework against each request.
Customer Master Domain Supplier Master Domain
12
Supported Endpoints ( Partial )
Oracle Sales Cloud, Oracle Marketing Cloud, Oracle Engagement Cloud, 
Oracle CRM On Demand, SAP C/4HANA, SAP S/4HANA, SAP BW, Cloud 
SAP Concur, SAP SuccessFactors, Salesforce, Microsoft Dynamics 365, Applications
Workday, Infor Cloud, Procore, Planview Enterprise One
Oracle E-Business Suite, Oracle ERP Cloud, Oracle JD Edwards, 
Oracle PeopleSoft, SAP S/4HANA, SAP ECC, IBM Maximo, Workday, Enterprise 
Microsoft Dynamics, Microsoft Dynamics GP, Microsoft Dynamics Nav, Applications
Microsoft Dynamics Ax, Smart ERP, Infor, BaaN, Mapics, BPICS
Windchill PTC, Orale Agile PLM, Oracle PLM Cloud, Teamcenter, SAP PLM, 
SAP Hybris, SAP C/4HANA, Enovia, Proficy, Honeywell OptiVision, PLM, MES &
Salesforce Sales, Salesforce Marketing, Salesforce CPQ, Salesforce Service, CRM
Oracle Engagement Cloud, Oracle Sales Cloud, Oracle CPQ Cloud, 
Oracle Service Cloud, Oracle Marketing Cloud, Microsoft Dynamics CRM
Oracle HCM Cloud, SAP SuccessFactors, Workday, ICON, SAP APO and IBP, HCM & Supply 
Oracle Taleo, Oracle Demantra, Oracle ASCP, Steelwedge Chain Planning
Oracle Primavera, Oracle Unifier, SAP PM, Procore, Ecosys, Project Management 
Oracle EAM Cloud, Oracle Maintenance Cloud, JD Edwards EAM, IBM Maximo & EAM 
OneDrive, Box, SharePoint, File Transfer Protocol (FTP), Oracle Webcenter, Enterprise Storage 
Amazon S3 Systems
HIVE, Apache Impala, Apache Hbase, Snowflake, mongoDB, Elasticsearch,
SAP HANA, Hadoop, Teradata, Oracle Database, Redshift, BigQuery Big Data
mangoDB, Solr, CouchDB, Elasticsearch No SQL Databases
PostgreSQL, Oracle Database, SAP HANA, SYBASE, DB2, SQL Server, 
MySQL, memsql Databases
IBM MQ, Active MQ Message Broker
Java, .Net, Oracle PaaS, Force.com, IBM, ChainSys Platform Development 
Platform
13
One Platform for your
End to End Data Management needs 
Data Migration Data Quality Management Data Analytics
Data Reconciliation Data Governance Data Catalog
Data Integaration Analytical MDM Data Security & Compliance
www.chainsys.com