1. Data Management
Data Governance
program

2. © 2 0 1 4 A p e r i o M a n a g e A l l r i g h t s r e s e r v e d .
General
The presentation aim to provide introduction of
Data Governance
It include:
• Current data challenges
• How Data Governance solve those challenges
• Suggested proposal for initial data governance initiative
• Tools, methodology and examples

3. © 2 0 1 4 A p e r i o M a n a g e A l l r i g h t s r e s e r v e d .
Current data challenges
• Mandatory information missing
• Duplicated data
• Data not updated
Content
• Difficulties to match data from different sources
• Inconsistence in data elements definition
across the organization
• The need to register new customer quickly
while gathering clean and accurate data about
him/her
• Lack of integration between the marketing,
pricing, service teams and theirs systems
Operational
• Missing marketing opportunities for existing
customers
• Compliance with regulatory reporting in terms of
the level of security and ease of reporting
Organizational

4. © 2 0 1 4 A p e r i o M a n a g e A l l r i g h t s r e s e r v e d .
Data Governance
Proactively manage the organization data while supporting
the organization business and objectives.
Data Governance activities include the implementation of a
structured framework of policies and rules, processes,
roles, responsibilities and ownership.
All promote better data quality that enable the organization
to comply with regulation and standards that are commonly
accepted

5. © 2 0 1 4 A p e r i o M a n a g e A l l r i g h t s r e s e r v e d .
Reasons for starting a
Data Governance program
The trigger for Data Governances programs, may be:
 BI projects
The more BI projects are implemented, the challenge of working with different
data elements across the organization increase, matching and understanding
the data strengthen the need for Data Governance
 MDM projects
Successful MDM implementation require execution of a Data Governance as a
supporting activity
 Data quality problems
Repeated problems of data quality indicate a lack of management in respect to
data elements. Data Governance is exactly the solution for those type of
problems
 BIG DATA
The landscape of data and it’s wealth sharpen the need to manage it wisely to
maximize its potential

6. © 2 0 1 4 A p e r i o M a n a g e A l l r i g h t s r e s e r v e d .
Solution for data challenges
Example
Difficulties to match data
from different sources
Implementation Data Governance can include the following:
 Assign data owners and data stewardship
 Building organization data dictionary
 Improve data entry processes
Benefits:
 Integration between different systems done much more easy and
quickly
 Each data element has an owner, minimize handling of exception
 Long term, no more data duplication

7. © 2 0 1 4 A p e r i o M a n a g e A l l r i g h t s r e s e r v e d .
Solution for data challenges
Example
Mandatory information
missing
Implementation Data Governance can include the following:
 Improve data entry processes
 Perform data “Health” checkups
 Periodic monitoring of data quality and reporting
 Focus on educate the organization about the important of data
quality, mainly for data creators.
Benefits
 Better data quality
 Report and decisions based on validate and accurate data
 Increase level of trust in the data

8. © 2 0 1 4 A p e r i o M a n a g e A l l r i g h t s r e s e r v e d .
Solution for data challenges
Continue
Improving the quality of enterprise information
causes:
Better reporting (Management Information)
and better decisions processes.
Increasing the ability to make the most of
the opportunities and avoid risks that the
organization does not want, denied when it
has a complete picture.
Enhanced capability to comply and
compliance with regulations as well as to
provide evidence for that.

9. © 2 0 1 4 A p e r i o M a n a g e A l l r i g h t s r e s e r v e d .
Action plan
Suggestion
Maturity
evaluation
• Express how Data Governance can helps an organization
accomplish its strategy and objectives
• Use the outcome to support management approval
Pilot
• Identify a specific department or a project for a pilot
• Define small targets while reducing pain points
• Design and implement a basic Data Governance approach,
policies, roles and basic processes
Cross
organization
• Building a plan to implement other departments in the
organization based on the pilot results

10. © 2 0 1 4 A p e r i o M a n a g e A l l r i g h t s r e s e r v e d .
Action plan
Basic solution implementation
The main activities to do:
 Identify data owners for business critical elements
 Formulate and agree on data definitions (glossary of
organizational data)
 Build an implementation plan for the agreed data
definition into legacy/source systems
 Definition and approval of data quality rules, data quality
monitoring and data quality reporting
 Definition and implementation of data quality problem-
solving process.

11. © 2 0 1 4 A p e r i o M a n a g e A l l r i g h t s r e s e r v e d .
Action plan
Tools and methods
Tools
• As part of the pilot, it is enough to use Office tools (Excel)
• Afterwards, based on the needs and the data volume tools like
the following can be evaluated:
• Collibra
• IBM
• Informatica
• And more
Aperio Manage partner with Nicola Askham, the Data Governance coach
bringing Data Governance methodology and proven implementation methods

12. © 2 0 1 4 A p e r i o M a n a g e A l l r i g h t s r e s e r v e d .
Action plan
Example for data dictionary creation
Agreement on
the objectives
and content
• Reach an agreement in the organization for the purpose of the Data Dictionary
• Agreement on the data dictionary content in the first iteration
Involving
stakeholders
• Publish targets and data dictionary content to interested parties
• Identification of potential business owners for the content of the agreed data
• Request and consent of those owners for the business, as well as an
agreement on professional owners
• Consent of all stakeholders in the project for the data content in the first round
Definitions
• Professionals owners defining draft of the data they responsible for
• Draft definitions are published for all stakeholders for testing purposes and comments
• Professionals owners update settings for comments or treat escalation in cases where
there is no consensus
• Business owners checking and confirming the data settings they responsible for
•Data is stored in the organization data dictionary and publish to the whole organization

13. © 2 0 1 4 A p e r i o M a n a g e A l l r i g h t s r e s e r v e d .
Action plan
Example for data dictionary creation – cont.
Ongoing
• Agreeing on a process and
owners for updating and
maintaining the data dictionary
• Review whether to add new
elements to the data dictionary
• Agreement on further initiatives
for new data added to the
dictionary from new sources

14. © 2 0 1 4 A p e r i o M a n a g e A l l r i g h t s r e s e r v e d .
Action plan
Data dictionary - example
Comments:
• Setting with pre-defined values (like “Type”) are adjusted to the organization at
the time of implementation
• Usually “Security level” are defined according to existing security levels already
defined in the organization
Enterprize data dictionary
Element Definition Description Type Business owner Data steward Security level Status
xxxxxxx xxxxxxxxxxx xxxxxxxx
1. Business definition
2. IT definition $John $Roger
1. All
2. Specific Roles
1. In work
2.
Approved
Master Source systemsxxxxxxxx
Source systems yyyyyyyyy
zzzzzzzz
Using systems xxxxxxxx
yyyyyyyyy
zzzzzzzz

15. THANKS
HADAR
HADARG@APERIO-MANAGE.COM
972-54-7693318