a data governance wake-up call - dama ny · enable accountability overcome silos prove value...

A Data Governance Wake-Up CallC O M P L I A N C E Y E S , B U T S O M U C H M O R E

D G I Q J U N E 2 0 1 8

Data is the “New Oil”

2

Improved

Decision-Making

Increased

Customer

Satisfaction

Compliance,

Privacy & Security

Operational

Efficiency

Revenue

Growth

It Requires an Enterprise Data Governance Experience

3

Regulatory

Peace Of MindVisibility

Across Domains

Integrated

Ecosystem

Any Data

Anywhere

Collaboration

& Organizational

Empowerment

Data Governance: The Driving Principle

4

UNDERSTANDDISCOVER

SOCIALIZE

Data

Sets

Data

Issues

Impact &

Lineage

GOVERN

Policies and

Rules

DGOM

Collaboration

Data

Dictionary

Data

Quality

Data

Usage

Reference

Data

Business

Glossary

D ATA V I S I B I L I T Y, C O N T R O L & C O L L A B O R AT I O N U N L O C K B U S I N E S S VA L U E

What do we have?What does it mean?

Where did it come from?

Is it secure?What rules or

restrictions apply?

How accurate is it?

Who is accountable?

Who is using it?

How is it used?How can

I access it?Where is it?

DATA IN CONTEXT

The Facebook

“Data Trauma”

Syndrome

© 2018 erwin, Inc. All rights reserved.5

And it drives

Verizon’s

purchase

price down

$1 billion

© 2018 erwin, Inc. All rights reserved. 6

The New Data Trinity

What data do we have and where is it?

What private data do we store and how is it used?

Who has access and permissions to the data?

Privacy

Security

Governance

Organizations Are Placing Big Bets on Self-Service Analytics, AI and Machine Learning – “Danger Will Robinson”

These disciplines rely on enterprise data to enable the desired outcomes.

All are susceptible to providing bad results if the inputs are bad to begin with.

Organizations need to mitigate the risk that results from:

✓ Poor quality data

✓ Mis-understood data

✓ Incomplete data

✓ Misused data


New Data Governance: Greasing the Wheels!!!Risk Avoidance and Opportunity Enablement

Offense

• Enable Data Driven Agility

• Optimize Data Driven Insights

• Assure Data Driven Transformation

Defense

• Enable Data Compliance

• Optimize Data Operations

• Assure Data Security

© 2016 erwin. All rights reserved. 8

“Analytics is changing how

organizations make decisions

and take actions. Data by itself

has limited value but when

managed as a strategic asset,

data can change how

organizations compete and win.”

Chris Mazzei, Global Chief

Analytics Officer, EY

V I S I B I L I T Y A N D C O N T R O L U N L O C K S

B U S I N E S S VA L U E O F D ATA

9

Key Findings

DG ISN’T JUST FOR

COMPLIANCE ANYMORE

60% say regulatory compliance is the biggest

driver, but it’s not the only one. 49% see it as a

way to improve customer satisfaction, and

45% see it supporting better decision-making.

Reputation management (30%), analytics

(27%) and Big Data (22%) are also key

drivers.

© 2018 erwin, Inc. All rights reserved.

10

Key Findings

ROADBLOCKS

58% of respondents report the biggest

obstacle is the cost of data governance,

followed by understanding the right

approach (42%), executive support

(42%); organizational support (39%),

effective tools (36%) and articulating

business justification (27%).

© 2018 erwin, Inc. All rights reserved.


It’s Here!!

Are You Ready

Compliance isn’t just about GDPR

Sarbanes-Oxley

Dodd-Frank

HIPPA

Basel I, II, III….

Office of Foreign Assets Control (OFAC)

….

In many cases, different regulations mandate conflicting requirements!!


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APPLICATIONS

&

INFRASTRUCTURE

SECURITY

METRICS

GOALS &

STRATEGIES

CAPABILITIES

&

PROCESSES

GROUPS

& ROLES

Framework for Data Context, Control & Collaboration

13

Assess the current state

Plan the way forward

Balance control & agility

Enable accountability

Overcome silos

Prove value

Iterate success

Measure Progress

The DG Team Needs to Expand beyond Owners, Stewards, Custodians

Enterprise Architects Visibility into enterprise goals &

strategy as well as the IT landscape

and roadmap

Data Architects Visibility into the data resources

available, where they reside and

the key use cases

Process Architects Visibility into the business

operations, organizational

structure and mechanisms

14

Data Modeling

Enterprise Data Governance

© 2018 erwin. All rights reserved.

The Role of EA Modeling With Data Governance

• Goals

• Motivations

• Metrics

Align DG with business plans and strategy

• Desired outcomes

• Related tasks / Roadmap / Project Management

• Stakeholders, organizations and communities

Architect DG into the enterprise

• Applications

• Infrastructure

• Business Capabilities

Extend the visibility of your DG program

beyond the data sources


Architecting data governance into the enterprise

The Role of BP Modeling With Data Governance

• Business processes

• Roles

• Value Chains

Align DG with details of the

business operations

• Issues Management

• Detail process flows

• Roles & Responsibilities

Define DG processes and

operating model

• Landscapes

• Processes

• Organizations




Mapping out the business for data governance

The Role of Data Modeling With Data Governance

• Requirements

• Reusable Standards

• Lifecycle

Govern Data Definition and Deployment

• Business Terms

• Data Elements

• Business Rules

Accelerate The Harvesting of DG

Bodies of Knowledge

• Use Cases

• Designs/Schema

• Context




The fabric that stitches data governance together

Identification you want to identify those assets that are critical for

customers to anonymize

Analyze you want to see the impact of identified assets on

processes, systems, locations and technology

Classificationyou want to classify data assets with personable identifiable

information (PII) and show processes and applications

Adherence you want ensure that the data can be removed and put

remediation plans in place for compliance

Reporting you want to create supporting documentation of

compliance and management of GDPR

Discover you want to find out where the data assets are in the

organization

Harvest you want to understand the data assets as models or

structured lists

Governyou want to have a clear understanding of your requirements

for GDPR, resources in place to deliver them and for the

organization to understand their roles in GDPR

High level GDPR operating framework

} Conduct an

audit

Setting up the GDPR operating frameworkA. Identify

business value

1. Identify value drivers

2. Show compliance drivers

3. Define KPIs

B. Setup object types

1. Define overall meta-model for all object types and

relationships

2. Modify Business and IT object types

in EA agile / DG

3. Create views

4. Setup scorecards for

metrics

5. Define operational checklist

C. Define stakeholders

1. Create communities

2. Assign users and roles

3. Assign users to tools

D. Create workflows

1. Define work packages

2. Create status attributes

3. Create work item (task) owners

(stewards)

4. Set up Kanban boards

E. Setup work products

1. Identify outputs for different

stakeholders

2. Identify charts for decision making

3. Verify the best communication

medium for the org

4. Show how to capture GDPR

remediation and adherence processes

F. Setup dashboards |

reports

1. operational progress

2. health of model

3. lawful basis

G. Document operating

model

1. Document operating

procedures

2. Document meta-model, object and

attribute usage

3. Explain Scorecards

4. Map work products to GDPR compliance criteria

H. Educate stakeholders

1. Roles and responsibilities

2. Core views

3. Tool training

Data Governance Is Evolving: The New Normal

© 2016 erwin. All rights reserved. 20

What Data Do We Have?

How Does It Serve the Business? Who, What, Why?

How Is Data Linked to Our Go-Forward Strategy?

What Should We Do First?

How Do We Deploy for Success?

What Does Success Look Like

How Do We Make It Sustainable

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