1. Data Quality Management, Data Governance and Master Data Management: Why You Need All Three
Bryn Davies, Director, InfoBluePrint (Pty) Ltd
Cape Town, August 2017

2. InfoBluePrint
DAMA DMBOK

3. Applications Come & Go, But Data is Forever
(Or: “Data: Always the Bridesmaid”)

4. A World Driven by Data…
© InfoBlueprint (Pty) Ltd 2007

5. Data Categorised DATA Master Data Transactional Data
Provides context to transactional data
Transactional Data
Characteristic Data
Defines individual characteristics of the master entity being defined
Reference Data
Defines fixed value domain data for classification
Metadata
Example:
Name
Address
Town
Region ID
Status
Level
Amount
B.T. Davies
25 Short St CT
West
Active
Gold
R251.87

6. Typical Master Data Domains
Party (natural & juristic)
Customers
Suppliers
Employees
Intermediaries
Location
Address
Place
Region
Geocode
Item
Product
Service
Asset
Part
Materials

7. Data Quality
2007
2017

8. Example Business Areas Requiring a Data Quality Focus
Business Intelligence, Analytics
Customer Relationship Management
Customer Experience
Customer Centricity
Governance, Risk and Compliance
Eg POPI, BCBS239, Sanctions/PEP
Supply Chain Management
Marketing & Sales
Human Resources
Digital & Online Channel Programmes
New Application Implementations

9. Common Data Problems – Party Data eg Customer
Typically key data for individuals and businesses such as:
Name, title
ID related information (ID no, company registration no, VAT no)
Address/location information (physical, postal, delivery, billing)
Contact details/types (address, phone, cell, , website)
Status (eg. active vs inactive)
Product/Service type/class
Marketing attributes eg LSM, demographics, psychographics
Inconsistent spelling, formatting and structure
Incorrect
Out of date
Missing
Duplication of instances (individual and business) within and across different systems
“Account Centric” vs “Customer Centric” - Same person to a human but different to the computer system!
Lack cross-correlation and hierarchies: inability to achieve single view, householding, reliable segmentation

10. Consequences of Unreliable Customer Information
Impossible to create a Single View of Customer
Unable to communicate
Wrong/inappropriate communication
Ineffective marketing
Unreliable reports
Poor decisions
Wrong decisions!
Inefficiency – wasted time and effort doing “scrap and rework”
Frustrated employees
Issue resolution takes longer – costs more
Frustrated customers
Complex, difficult new system roll-outs (over time/budget due to data issues)
Never ending “data clean-ups”
Legal and compliance problems (eg. PoPI, Solvency II, FATCA…)

11. Quality Data leads to Quality Business
Costs can be as much as between 15% and 20% of operating expenditure (study by Larry English et al)
© 2012 InfoBluePrint

12. Quality Data leads to Quality Business

13. POPI Expected within next year
All “responsible parties” i.e. businesses, will be obliged to “implement measures to safeguard and ensure the integrity of personal information”
Every organisation must appoint a registered “Information Protection Officer” (IPO) who must demonstrate compliance
A Fundamental Point:
Poor data quality is a symptom of data that is not in control
It is impossible to safeguard and ensure the
integrity of something that is not in control

14. InfoBluePrint’s 10 Key Points for Data Quality
1. (Quality) Data is an Asset 2. Data is a Product 3. Quality is Defined by the Data Customer 4. Data Quality is not just about Dirty Data 5. Data Quality Measurement is Mandatory 6. Don’t do DQ without DQ Software 7. Data Quality is the Responsibility of Business 8. Get the First DQ Project Right First Time 9. Understand the Human Element 10. Doing Nothing will Cost you the Most

15. Key Point: Data is an Asset
We indeed live in the “Information Age”
Data must be treated like other valuable assets such as people, money, property, plant etc – inventory, govern and manage it!
Put another way: focus on the “I” in “IT”
Introduce Information Resource Management

16. A Database is like a Lake*
The water is the data
The streams represent business processes feeding the database
Factories upstream are sources of pollutant
Information users drink the water
*Analogy courtesy Tom Redman

17. The InfoBluePrint Approach

18. Data Governance

19. Data Governance – Key Points
Data Governance is not about governing data – it is about governing the people and processes that touch the data
Data Governance is not a product, a service or a project – it is a formal organisational lifestyle
Management is the decisions you make
Governance is the structure for making them
CIO Magazine

20. Definition
The strategic and coherent approach to the management of an organisation's most valued assets
Those decisions and actions which concern the management of data in all parts of the business and which are related to the implementation of strategies directed towards creating and sustaining competitive advantage
Data Governance is the act of leading the data function and managing related investments to:
optimize performance of the organization’s information assets;
fulfil fiduciary and financial responsibilities;
mitigate enterprise information risk;
align the function’s priorities with those of the business; and
enable data executive decision making.

21. HR Governance Definition
The strategic and coherent approach to the management of an organisation's most valued assets
Those decisions and actions which concern the management of HR in all parts of the business and which are related to the implementation of strategies directed towards creating and sustaining competitive advantage
HR governance is the act of leading the human resource function and managing related investments to:
optimize performance of the organization’s human capital assets;
fulfil fiduciary and financial responsibilities;
mitigate enterprise HR risk;
align the function’s priorities with those of the business; and
enable HR executive decision making.

22. The system of rules and procedures for governing data.
Law and Order
ORDER
Automation for monitoring & enforcing the rules and procedures to use and protect the data.
LAW
The system of rules and procedures for governing data.
PEOPLE
PROCESS
TECHNOLOGY
© 2012 InfoBluePrint 22

24. and enforcement) over the management of data assets” (DAMA DMBoK)
Organisational Model
“Data Governance is the exercise of authority and control (planning, monitoring
and enforcement) over the management of data assets” (DAMA DMBoK)
DG Office
DG Steerco
DG Council
Data Steward Teams
Data Governance Steering Committee
Approves strategy and direction
Resolves escalated issues
Co-exists with other strategic Steerco’s
Data Governance Council
Approve enterprise data definitions
Formulate data governance program decisions
Ratify principles, standards, policies & processes
Strategic issue resolution
Encourage and facilitate change
Data Governance Office
The face of data governance across the enterprise
Implements strategic data governance transformation
Incorporated within the Data Governance Council
Data Steward Teams
Point of contact for daily data issues
Subject matter experts
Supplies data stewards
Day to day consumers of data

25. Metadata Management & Business Glossaries
“You can’t manage what you can’t define”
A Business Glossary is a repository that contains items associated with the informational content of a Business Term. 
Is central to a Data Governance Programme
Minimum set of attributes to be captured:
Term Name, Definition and Example
Term Acronym or Abbreviation (optional)
Term Security, Privacy and Compliance (usage) rules
Data Steward Name and Contact information
Business Rules

26. Master (& Reference) Data Management
Master data management (MDM) is a technology-enabled discipline in which business and IT work together to ensure the uniformity, accuracy, stewardship, semantic consistency and accountability of the enterprise’s official shared master data assets. Master data is the consistent and uniform set of identifiers and extended attributes that describes the core entities of the enterprise including customers, prospects, citizens, suppliers, sites, hierarchies and chart of accounts.
Gartner

27. An MDM Strategy is a Pre-Requisite to Architecture & Technology
Business Goals
Data Types
Processing Requirements
MDM Scope
MDM Roadmap
Functional Component Dependencies
Business Benefit Realisation
MDM Business Solution
Solution Functional Components
Solution Patterns
Integration Requirements
MDM Technical Solution
Technology
Implemented Solution
Every company needs to determine what MDM means in their Company .

28. Specialist Technology Required
Name: Dr Ellen Van Der Heijde
Title: Dr
First: Ellen
Last: Van Der Heijde
Gender: Female
Title: Mr
First: R
Middle: J
Last: MacDonald
Gender: Male
Name: Mr RJ & Mrs FB MacDonald
Title: Mrs
First: F
Middle: B
Gender: Female
Name: Jalila Abdul-Alim (Do Not Call)
First: Jalila
Last: Abdul-Alim
Gender: Female
Note: Do Not Call
Standardize and Parse
Split names and name elements
Identify individuals and businesses
Derive additional attributes

29. Specialist Technology Required
First: Bob
Last: Fulmar
Gender: Male
Title: Mr
First: Robert
Last: Fulmar
Gender: Male
DoB: 12/05/1978
Phone:
Address:
9405 Main St
Fairfax
Virginia
22030
Title: Dr
First: Robert
Last: Fulmar
Gender: Male
DoB: 12/05/1978
Phone:
Address:
9407 Main St
Fairfax VA
Title: Dr
First: R
Last: Fulmer
DoB: 01/01/1978
Address:
9407 Main Street
Fairfax VA
Match, merge, dedupe, link data from disparate sources
Enrich data from external sources
Create ‘best’ record based on survivorship rules

30. High Level Definitions of MDM Styles
Registry - the system contains pointers to where master data lives in operational systems
Transaction - the system becomes the one and only source of master data
Co-existence - the system contains master data where practical, with links to other master data sources where impractical
Consolidation - the system contains master data as copy. This style is suitable for analytical purposes

31. MDM vs Data Warehouse MDM Data Warehouse Objective
To ensure consistent sharing of reliable, cross enterprise domain data.
Analytics and BI
Use
Usually for operational
Supports BI and analytics, but does not usually support transactional activities
Database design
Typically normalised
Typically de-normalised
Volumes
Comparatively low
Comparatively high
Content
Master and reference data
Master, reference and transactional data
Based on a presentation by Monica Opris, State Street Global Services

32. Critical interdependencies
DQ because:
DG because:
MDM because:
DQ needs:
DG provides the structures for the preventive part of DQ, eg. people/process
DG drives out metadata issues
DG provides direction and policies required to manage the data
MDM provides the technology platform for persisted quality data
MDM forces enterprise view of Data Quality
MDM drives common data models and hierarchies (eg. party)
DG needs:
DQ provides the framework, processes and artefacts for measuring and managing data improvement
DQ provides supporting artefacts and processes
DQ monitoring is a dashboard for Data Governance effectiveness
MDM provides a physical data DMZ
MDM drives new roles and responsibilities for data
MDM provides a technical platform to support Data Governance
MDM needs:
Initial migration must take on quality master data (and external data)
Consistency in format/value/rules is required
DQ of hub data must be controlled and known!
DG resolves people & process issues for MDM
DG drives ownership and stewardship
DG forces preventive measures to be in place
DG council govern the data in the hub
© 2015 InfoBlueprint

33. Even Dilbert has Data Problems

34. Thank You, Questions & Contact Details