Data Ownership: Most companies and organizations have this notion that data governance should be taken care of , by the Information Technology department, because IT owns the system which stores the data. The owner of the data is responsible for providing attributes to the data and answerable to any questions regarding data. The people answerable to these kinds of data are generally the ones involved in defining business rules, data cleaning and consolidation.? Data Stewardship:? Data stewards should be favorably those people who are familiar with the data. It is often seen that there is need to deploy several people, to handle and correct data, whereas a single data steward could have done the same job. Since the data being handled involves organizational level data, it is important that there are governance rules for this process.? If there is some certain rule in the data which causes large data volumes to fail, this rule should be fixed while data cleansing. So it is important to take care of the amount of clean data sent to the stewards, since we are not aware of which rules might trigger what amount of data.? Choice of data stewards is again a difficult selection. Data Security:? Although the master data is data on organization level, but there is some confidentiality level linked to it.? Not every employee has the authorization to view its aspects. Security rules can be applied to the data. The various departments in the organization must set different rules to the data they own. They need to grant permissions to these rules , so that the user can view the data. A large company can have data sourced out of many regions. It is to be ensured that they are responsible to correct only their own data.? Data survivorship: There are some guidelines which are set up by data governance. These rules can often change over hthe time according to new data sources being added. The changes made to the data , are communicated to the organization so that data stewards and users can understand the process. So from a data steward's point of view, it is important to apply security rules to the people who are involved in data handling and correction. This is a result of how data governance and data security can be applied while implementing MDM.? ?

1. IÉSEG School of Management, Campus de Lille
Master Data Management
Jatan Dewgun Lucas Bonnett Sabir Akhtar Victor Ernoult

2. MON SOMMAIRE
What is Master
Data ?
Why is MDM
needed ?
How to
implement
MDM ?
Use Cases
Conclusion/
Suggestions

3. What is MDM?
• Definition
The
The set of disciplines and methods to ensure the currency,
meaning, quality, and deployment of a company's reference
data within and across subject areas.
Jill Dyche
VP, SAS Best Practices

4. What is MDM?
• What is Master Data ?
A combination of Reference data (external data) & internal data
of a company that are strategic and critical.
Reference data : Data used to categorize the Master data (ex:
country codes, gender codes, marital status..)
Internal Data: Data on customers, products, employees,
physical assets, suppliers, accounting information...and so on.
Master Data
Reference
data

5. Introduction to the 4Vs

6. Why is MDM needed
Demand Gen's Report - 2018 Database Strategies & Contact Acquisition Survey
Challenges faced
The more a business grows, the more diverse the data gets:
• Multiple versions of the data
• Data errors
• Untrusted data
• Inability to draw insights
According to Demand Gen companies' main challenges in data are:
• Old / Outdated data (83%)
• Lack of time / resources (71%)
• Lack of data on current customers (64%)

7. Why is MDM needed
MDM benefits
• Metrics and analysis
• Data integrity / quality
• Data Governance
Allows GDPR
compliance
Improve operational efficiency
Allow automation of data processes
Provide a 360° view of the customer
Save time and resources

8. How to implement MDM
Data Ownership:
 Data ownership can be a difficult job.
 Data governance is not the responsibilty of IT department
 The owner of the data is responsible for:
- providing attributes to the data
- answerable to any questions regarding data.

9. How to implement MDM
Data Stewardship:
 Data stewardship equals data ownership.
 Data stewardship is giving a person or a group of people the task of correcting data present in a Master Data
Management process.
• Systematic process
• Move aside the incorrect data and keep it aside in a repository.
• Keep data in repository until data is corrected.

10. How to implement MDM
Data Security:
Although the master data is data on organization level, but there is some confidentiality level linked to it.
• Authorization to select employees.
• Security rules can be applied to the data.
So from a data steward's point of view, it is important to apply security rules to the people who are involved in data
handling and correction. This is a result of how data governance and data security can be applied while implementing
MDM.

11. How to implement MDM
Data Matching:
A very important term which one may come across in Master data Management is Golden Record. The Golden Record
serves as the most proper and accurate version of the data.
It may be often seen that an account may have 2 versions of data sources.
How do you figure out:
Which is most current data?
Which is the most accurate data?

12. Key Use Cases Of MDM
Enhanced 360°
Customer View
Operations
Analysis
Security
Solutions
Data
Warehouse
Augmentation
Master Data
Management
Big Data
Exploration

13. Kraft – Heinz Success Story
CHALLENGES
- Complex Database of
Brands, Products, Product
Images spread across 5
systems.
- Ensuring accurate and
detailed product information
available
- Support to customers and
other consumers with the
information.
BACKGROUND
- 5th Largest Food and
Beverage company in the
world.
- Co-Headquartered in
Pittsburgh and Chicago.
- 8 Major brands ($1 bln
each) + 200 other brands.
SOLUTION
- Master Data Management
tool "STEP" by StiboSystems.
- STEP as a product hub.
- STEP provides E-commerce
ready information

14. Die Bayersiche Success Story
CHALLENGES
- Ease of Access to Field Sales
Units.
- Growing Business.
- Performance Requirements
needed to be met as they
scaled.
- 24/7 support system for the
field sales teams.
BACKGROUND
- Mid Size; Insurance
Company.
- 500+ Employees.
- Subsidiary of die
Bayerische Verischerung.
- Project under Delvin
GmBH
SOLUTION
- MDM Tool: Neo4j.
- Flexibly search through
customer and insurance
policies data.
- Paradigm shift for
Insurance Industry.
- Performance Increase.
- Support as the business
scales.
- Porting Metadata to MDM
system.

15. Suggestions and Conclusion
MDM is a core
structure, not
a one-time
project
Organizational
Structure
helps with
Data
Governance
Focus on ROI
Continuous
Involvement
of Senior
Management

16. REFERENCES
Service – 2015 – email_du_service@ieseg.fr
Demand Gen's Report - 2018 Database Strategies & Contact Acquisition Survey
https://www.demandgenreport.com/resources/reports/the-2018-state-of-database-quality-b2b-organizations-look-to-
improve-database-efforts-with-real-time-audience-focused-solutions
Master Data Management (MDM): Help or Hindrance? - Knut Jürgensen
https://www.red-gate.com/simple-talk/sql/database-delivery/master-data-management-mdm-help-or-hindrance/
What is Master Data Management? - StiboSystems
https://www.stibosystems.com/what-is-master-data-management
How to implement MDM? - Stibosystems
https://blog.stibosystems.com/how-to-approach-master-data-management-the-first-three-steps
Key Use Cases of MDM
https://www.ibmbigdatahub.com/podcast/master-data-management-foundation-big-data
Kraft-Heinz Use Case
https://www.stibosystems.com/hubfs/na_site/resources/success-story/success-story_kraft-heinz_en_na.pdf
Die Bayerische Use Case
https://neo4j.com/case-studies/die-bayerische/