Data-Ed Online Webinar: Data Governance Strategies

Data Governance Strategies• Date: April 14, 2015 • Time: 2:00 PM ET • Presented by: Peter Aiken, PhD • The data governance function exercises authority and control

over the management of your mission critical assets and guides how all other data management functions are performed. When selling data governance to organizational management, it is useful to concentrate on the specifics that motivate the initiative. This means developing a specific vocabulary and set of narratives to facilitate understanding of your organizational business concepts. This webinar provides you with an understanding of what data governance functions are required and how they fit with other data management disciplines. Understanding these aspects is a necessary pre-requisite to eliminate the ambiguity that often surrounds initial discussions and implement effective data governance and stewardship programs that manage data in support of organizational strategy.

• Learning Objectives – Understanding why data governance can be tricky for most organizations – Steps for improving data governance within your organization – Guiding principles & lessons learned – Understanding foundational data governance concepts based on the

DAMA DMBOK

1Copyright 2014 by Data Blueprint

Shannon Kempe

Executive Editor at DATAVERSITY.net

2

Copyright 2015 by Data Blueprint

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Peter Aiken, Ph.D.

4


• 30+ years in data management • Repeated international recognition • Founder, Data Blueprint (datablueprint.com) • Associate Professor of IS (vcu.edu)

• DAMA International (dama.org) • 9 books and dozens of articles • Experienced w/ 500+ data

management practices • Multi-year immersions:

- US DoD - Nokia - Deutsche Bank- Wells Fargo - Walmart

• DAMA International President 2009-2013

• DAMA International Achievement Award 2001 (with Dr. E. F. "Ted" Codd

• DAMA International Community Award 2005

PETER AIKEN WITH JUANITA BILLINGSFOREWORD BY JOHN BOTTEGA

MONETIZINGDATA MANAGEMENT

Unlocking the Value in Your Organization’s

Most Important Asset.

The Case for theChief Data OfficerRecasting the C-Suite to LeverageYour Most Valuable Asset

Peter Aiken andMichael Gorman

We believe ...

Data Assets

Financial Assets

RealEstate Assets

Inventory Assets

Non-depletable

Available for subsequent

use

Can be used up

Can be used up

Non-degrading √ √ Can degrade

over timeCan degrade

over time

Durable Non-taxed √ √

Strategic Asset √ √ √ √

5


• Today, data is the most powerful, yet underutilized and poorly managed organizational asset

• Data is your – Sole – Non-depleteable – Non-degrading – Durable – Strategic

• Asset – Data is the new oil! – Data is the new (s)oil! – Data is the new bacon!

• Our mission is to unlock business value by – Strengthening your data management capabilities – Providing tailored solutions, and – Building lasting partnerships

Asset: A resource controlled by the organization as a result of past events or transactions and from which future economic benefits are expected to flow [Wikipedia]

Presented By Peter Aiken, Ph.D.

Data Governance Strategies

“If you don't know where you are going, any road will get you there.” - Lewis Carroll

Motivation

Beth Jacobs abruptly resigned in March

7


Reported Home Depot data breach could exceed Target hack

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• Strategy – Term of Recent Usage – Context: Organizational -> IT -> Data – Difficult Choices

• Data Governance – What is it? – Why is it important? – Requirements for Effective Data Governance

• Data Governance Components – Frameworks – Building Blocks – Checklists – Worst Practices

• Data Governance (Storytelling) in Action • Take Aways/References/Q&A


Tweeting now: #dataed












What is Strategy?

• Current use derived from military • "a pattern in a stream of decisions" [Henry Mintzberg]

• "a system of finding, formulating, and developing a doctrine that will ensure long-term success if followed faithfully [Vladimir Kvint]

11


Strategy in Action: Napoleon defeats a larger enemy

• Question?

– How to I defeat the competition when their forces are bigger than mine?

• Answer:

– Divide and conquer!

– “a pattern in a stream of decisions”

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– “a pattern in a stream of decisions”

Strategy in Action: Napoleon defeats a larger enemy


13

Wayne Gretzky’s Strategy

He skates to where he thinks the puck will be ...

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Data Strategy in Context• Organizational Strategy

• IT Strategy

• Data Governance Strategy

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Corporate Governance• "Corporate governance - which

can be defined narrowly as the relationship of a company to its shareholders or, more broadly, as its relationship to society….", Financial Times, 1997.

• "Corporate governance is about promoting corporate fairness, transparency and accountability" James Wolfensohn, World

Bank, President Financial Times, June 1999. • “Corporate governance deals

with the ways in which suppliers of finance to corporations assure themselves of getting a return on their investment”,The Journal of Finance, Shleifer and Vishny, 1997.

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Definition of IT GovernanceIT Governance: • "putting structure around how organizations align IT strategy with business strategy,

ensuring that companies stay on track to achieve their strategies and goals, and implementing good ways to measure IT’s performance.

• It makes sure that all stakeholders’ interests are taken into account and that processesprovide measurable results.

• An IT governance framework should answer some key questions, such as how the IT department is functioning overall, what key metrics management needs and what return IT is giving back to the business from the investment it’s making." CIO Magazine (May 2007)

IT Governance Institute, five areas of focus: • Strategic Alignment • Value Delivery • Resource Management • Risk Management • Performance Measures

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No clear connection exists between to business priorities and IT initiatives

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Grow expenses slower than

sales

Grow operating income faster

than sales

Pass on savings

Drive efficiency with technology

Leverage scale globally

Leverage expertise

Deploy new formats

Grow productivity of existing assets

Attract new members

Expand into new channels

Enter new markets

Make acquisitions

Produce significant free

cash flow

Drive ROI performance

Deliver greater shareholder

value

Cus

tom

er

Per

spec

tive Open new

stores

Develop new, innovative formats

Appeal to new demographics

Integrate shopping

experience

Develop new, innovative formats

Remain relevant to all

customers

Increase "Green" Image

Inte

rnal

P

ersp

ectiv

e

Create competitive advantages

Improve use of information

Strengthen supply chain

Improve Associate

productivity

Making acquisitions

Increase benefit from our global expertise

Present consistent view and

experience

Integrate channels Match staffing

to store needs Increase sell through

Fina

ncia

l P

ersp

ectiv

e Reduce expenses

Inventory Management

Human and Intell. Capital investment

Manage new facilities

Improve Sales and margin by facilities

Increased member-base

revenues

Revenue growth Cash flow Return on

Capital

Walmart Strategy Map

See more uniform brand and retail experience

Leverage Growth Return

Gross Margin Improvement

CE

O P

ersp

ectiv

e

Attract more customers & have customer purchasing more

Associate Productivity

Customer Insights

Human Capital Corp. Reputation Acquisition Strategic Planning

Real estate CRM CRM

Analytic and reporting processes

Corporate Reputation - Risk Management, Compliance, Marketing, IT and Data Governance

Corporate Processes

Corporate Data

Inventory Mgmt

Tran

sfor

mat

ion

Port

folio

Supply Chain

Multi ChannelMerchant Tools Supply Chain

Strategic Initiatives

AcctingSales

Transactional Processing

Logistics Associate Locations and Codes

Item

Customer Suppliers

Retail Planning

( Alignment Gap )

Adapted from John Ladley

Strategy is Difficult to Perceive at the IT Project Level

• If they exist ...

• A singular organizational strategy and set of goals/objectives ...

• Are not perceived as such at the project level and ...

• What does exist is confused, inaccurate, and incomplete

• IT projects do not well reflect organizational strategy

Organizational

Strategy

Set of Organizational

Goals/Objectives

Organizational IT

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Division/Group/Project

Q1 Keeping the doors open

(little or no proactive data management)

Q2 Increasing organizational efficiencies/effectiveness

Q3 Using data to create

strategic opportunities

Q4 Both

Improve Operations

Inno

vatio

n

Only 1 is 10 organizations has a board approved data strategy!

Data Governance Strategy Choices

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Supplemental: CMMI Data Strategy ElementsThe data management strategy defines the overall framework of the program. A data management strategy typically includes: • A vision statement, which includes core operating principles; goals

and objectives; priorities, based on a synthesis of factors important to the organization, such as business value, degree of support for strategic initiatives, level of effort, and dependencies

• Program scope – including both key business areas (e.g. Customer Accounts); data management priorities (e.g. Data Quality); and key data sets (e.g. Customer Master Data)

• Business benefits – The selected data management framework and how it will be used – High-level roles and responsibilities – Governance needs – Description of the approach used to develop the data management program – Compliance approach and measures – High-level sequence plan (roadmap).

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7 Data Governance Definitions• The formal orchestration of people, process, and technology to enable an

organization to leverage data as an enterprise asset. - The MDM Institute • A convergence of data quality, data management, business process

management, and risk management surrounding the handling of data in an organization – Wikipedia

• A system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods – Data Governance Institute

• The execution and enforcement of authority over the management of data assets and the performance of data functions – KiK Consulting

• A quality control discipline for assessing, managing, using, improving, monitoring, maintaining, and protecting organizational information – IBM Data Governance Council

• Data governance is the formulation of policy to optimize, secure, and leverage information as an enterprise asset by aligning the objectives of multiple functions – Sunil Soares

• The exercise of authority and control over the management of data assets – DM BoK


DAMA DM BoK & CDMP• Published by DAMA International

– The professional association for Data Managers (40 chapters worldwide)

– DMBoK organized around – Primary data management functions

focused around data delivery to the organization (more at dama.org)

– Organized around several environmental elements

• CDMP – Certified Data Management Professional – DAMA International and ICCP – Membership in a distinct group made up of

your fellow professionals – Recognition for your specialized knowledge

in a choice of 17 specialty areas – Series of 3 exams – For more information, please visit:

• http://www.dama.org/i4a/pages/index.cfm?pageid=3399 • http://iccp.org/certification/designations/cdmp


Data Management Functions

5 Requirements for Effective DGData governance is a set of well-defined policies and practices designed to ensure that data is: 1. Accessible

– Can the people who need it access the data they need? – Does the data match the format the user requires?

2. Secure – Are authorized people the only ones who can access the data? – Are non-authorized users prevented from accessing it?

3. Consistent – When two users seek the "same" piece of data, is it actually

the same data? – Have multiple versions been rationalized?

4. High Quality – Is the data accurate? – Has it been conformed to meet agreed standards

5. Auditable – Where did the data come from? – Is the lineage clear? – Does IT know who is using it and for what purpose?


Source: “5 Steps to Effective Data Governance” by Angela Guess; http://www.dataversity.net/archives/5160

• Integrity • Accountability • Transparency • Strategic alignment • Standardization • Organizational change

management • Data architecture • Stewardship/Quality • Protection

Organizational Data Governance Purpose Statement• What does data governance

mean to my organization? – Managing data with guidance

– Getting some individuals (whose opinions matter)

– To form a body (needs a formal purpose/authority)

– Who will advocate/evangelize for (not dictate, enforce, rule)

– Increasing scope and rigor of

– Data-centric development practices

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• Getting access to data around here is like that Catherine Zeta Jones scene where she is having to get thru all those lasers …

Use Their Language ...

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Practice Articulating How DG Solves Problems


Decision Making Needs

Data Quality/Inventory Management

Organizational Strategy Formulation/Implementation

Operational Data Delivery Performance

Data Security Planning/Implementation

What is the Difference Between DG and DM?• Data Governance

– Policy level guidance – Setting general guidelines

and direction – Example: All information not

marked public should be considered confidential

• Data Management – The business function of

planning for, controlling and delivering data/information assets

– Example: Delivering data to solve business challenges

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DMM℠ Structure

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One concept for process improvement, others include:

• Norton Stage Theory • TQM • TQdM • TDQM • ISO 9000

and focus on understanding current processes and determining where to make improvements.

DMM℠ Capability Maturity Model Levels

Our DM practices are informal and ad hoc, dependent upon "heroes" and heroic efforts

Performed (1)

Managed (2)

Our DM practices are defined and documented processes performed at

the business unit level

Our DM efforts remain aligned with business strategy using standardized and consistently implemented practices

Defined (3)

Measured (4)

We manage our data as a asset using advantageous data governance practices/structures

Optimized

(5)

DM is strategic organizational capability, most importantly we have a process for

improving our DM capabilities

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Assessment Components• \Data Management Practice Areas

Data Management Strategy

DM is practiced as a coherent and coordinated set of activities

Data Quality

Delivery of data is support of organizational objectives – the currency of DM

Data Governance

Designating specific individuals caretakers for certain data

Data Platform/Architecture

Efficient delivery of data via appropriate channels

Data Operations Ensuring reliable access to data

Capability Maturity Model Levels

Examples of practice maturity

1 – PerformedOur DM practices are ad hoc and dependent upon "heroes" and heroic efforts

2 – ManagedWe have DM experience and have the ability to implement disciplined processes

3 – Defined

We have standardized DM practices so that all in the organization can perform it with uniform quality

4 – MeasuredWe manage our DM processes so that the whole organization can follow our standard DM guidance

5 – Optimized We have a process for improving our DM capabilities

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Industry Focused Results

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Data Governance

Platform & Architecture

Data Quality

Data Operations

Optimized (V)

Measured (IV)

Defined (III)

Managed (II)

Initial (I)

• CMU's Software Engineering Institute (SEI) Collaboration

• Results from hundreds organizations in various industries including: ✓ Public Companies ✓ State Government Agencies ✓ Federal Government ✓ International Organizations

• Defined industry standard • Steps toward defining data management "state of the practice"


Data Governance

Data Platform & Architecture

Data Quality

Data Operations

0 1 2 3 4 5Client Industry Competition All Respondents

Comparative Assessment ResultsChallenge

Challenge

Challenge

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1

2

3

4

5

Data

Prog

ram

Coor

dinati

on

Orga

nizati

onal

Data

Integ

ratio

n

Data

Stew

ards

hip

Data

Deve

lopme

nt

Data

Supp

ort O

pera

tions

2007 Maturity Levels 2012 Maturity Levels

Comparison of DM Maturity 2007-2012

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2012 London Summer Games• 60 GB of data/second • 200,000 hours of big

data will be generated testing systems

• 2,000 hours media coverage/daily

• 845 million Facebook users averaging 15 TB/day

• 13,000 tweets/second • 4 billion watching • 8.5 billion devices

connected

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Supplemental: Data Governance Goals and Principles• To define, approve, and

communicate data strategies, policies, standards, architecture, procedures, and metrics.

• To track and enforce regulatory compliance and conformance to data policies, standards, architecture, and procedures.

• To sponsor, track, and oversee the delivery of data management projects and services.

• To manage and resolve data related issues.

• To understand and promote the value of data assets.


from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

Supplemental: Data Governance Activities

• Understand Strategic Enterprise Data Needs

• Develop and Maintain the Data Strategy

• Establish Data Professional Roles and Organizations

• Identify and Appoint Data Stewards

• Establish Data Governance and Stewardship Organizations • Develop and Approve Data Policies, Standards, and

Procedures • Review and Approve Data Architecture • Plan and Sponsor Data Management Projects and Services • Estimate Data Asset Value and Associated Costs



Supplemental: Data Governance Primary Deliverables• Data Policies

• Data Standards

• Resolved Issues

• Data Management Projects and Services

• Quality Data and Information

• Recognized Data Value



Supplemental: Data Governance Roles and Responsibilities

• Participants: – Executive Data Stewards – Coordinating Data Stewards – Business Data Stewards – Data Professionals – DM Executive – CIO

• Suppliers: – Business Executives – IT Executives – Data Stewards – Regulatory Bodies

• Consumers: – Data Producers – Knowledge Workers – Managers and Executives – Data Professionals – Customers



Supplemental: Data Governance Technologies

• Intranet Website • E-Mail • Metadata Tools • Metadata Repository • Issue Management Tools • Data Governance KPI

Dashboard



Supplemental: Data Governance Practices and Techniques

• Data Value

• Data Management Cost

• Achievement of Objectives

• # of Decisions Made

• Steward Representation/Coverage

• Data Professional Headcount

• Data Management Process Maturity



Why is Data Governance Important?Cost organizations millions each year in

• Productivity

• Redundant and siloed efforts

• Poorly thought out hardware and software purchases

• Reactive instead of proactive initiatives

• Delayed decision making using inadequate information

• 20-40% of IT spending can be reduced through better data governance


Largely Ineffective

Investments• Approximately,

10% percent of organizations achieve parity and (potential positive returns) on their investments

• Only 30% of investments achieve tangible returns at all

• Seventy percent of organizations have very small or no tangible return on their investments

45


Application-Centric Development

Original articulation from Doug Bagley @ Walmart

• In support of strategy, organizations develop specific goals/objectives

• The goals/objectives drive the development of specific systems/applications

• Development of systems/applications leads to network/infrastructure requirements

• Data/information are typically considered after the systems/applications and network/infrastructure have been articulated

• Problems with this approach: – Ensures data is formed to the applications and

not around the organizational-wide information requirements

– Process are narrowly formed around applications – Very little data reuse is possible

Data/Information

Network/Infrastructure

Systems/Applications

Goals/Objectives

Strategy

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What does it mean to treat data as an organizational asset?

• An asset is a resource controlled by the organization as a result of past events or transactions and from which future economic benefits are expected to flow to the organization [Wikipedia]

• Assets are economic resources – Must own or control – Must use to produce value – Value can be converted into cash

• As assets: – Formalize the care and feeding of

data – Put data to work in unique and

significant ways

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Evolving Data is Different than Creating New Systems

48


Common Organizational Data (and corresponding data needs requirements)

New Organizational Capabilities

Systems Development

Activities

Create

Evolve

Future State

(Version +1)

Data evolution is separate from, external to, and precedes system development life cycle activities!

Data-Centric Development

Original articulation from Doug Bagley @ Walmart

• In support of strategy, the organization develops specific goals/objectives

• The goals/objectives drive the development of specific data/information assets with an eye to organization-wide usage

• Network/infrastructure components are developed to support organization-wide use of data

• Development of systems/applications is derived from the data/network architecture

• Advantages of this approach: – Data/information assets are developed from an

organization-wide perspective – Systems support organizational data needs

and compliment organizational process flows – Maximum data/information reuse

Systems/Applications

Network/Infrastructure

Data/Information

Goals/Objectives

Strategy

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The special nature of DCD• An architectural focus

• Practice extension

• Personality/organizational challenges unrecognized

• Technical engineering requires different skills

• Extra attention required to communication

• Scarcity of professionals

• Need for a specialist discipline

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When our organizations transform to a data-centric approach, we begin to measure success differently than we did before—same project, same process, but with different measures that include: • asking if our data is correct; • valuing data more than valuing "on time and within budget;" • valuing correct data more than correct process; and • auditing data rather than project documents. - Linda Bevolo















Getting Started


Assess context

Define DG roadmap

Secure executive mandate

Assign Data Stewards

Execute plan

Evaluate results

Revise plan

Apply change management

(Occurs once) (Repeats)

Data Governance Frameworks• A system of ideas for

guiding analyses • A means of organizing

project data • Priorities for data

decision making • A means of assessing

progress – Don’t put up walls until

foundation inspection is passed

– Put the roof on ASAP • Make it all dependent

upon continued funding

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Data Governance from the DMBOK



Data Governance Institute• A system of ideas for guiding analyses • A means of organizing project data • Data integration priorities decision making framework • A means of assessing progress

56


http://www.datagovernance.com/

KiK Consulting• A system of ideas for guiding analyses • A means of organizing project data • Data integration priorities decision making framework • A means of assessing progress

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http://www.kikconsulting.com/

IBM Data Governance Council• A system of ideas for guiding analyses • A means of organizing project data • Data integration priorities decision making framework • A means of assessing progress

58


http://www-01.ibm.com/software/data/system-z/data-governance/workshops.html

Elements of Effective Data Governance

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See IBM Data Governance Council, http://www-01.ibm.com/software/tivoli/ governance/servicemanagement/ data-governance.html.

Baseline Consulting (sas.com)

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American College Personnel Association

61


Supplemental: NASCIO DG Implementation Process

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Supplemental: Data Governance Checklist✓ Decision-Making Authority

✓ Standard Policies and Procedures

✓ Data Inventories

✓ Data Content Management

✓ Data Records Management

✓ Data Quality

✓ Data Access

✓ Data Security and Risk Management


Source: “Data Governance Checklist for Educators” by Angela Guess; http://www.dataversity.net/archives/5198

Supplemental: Data Governance Checklist• The Privacy Technical Assistance Center

has published a new checklist “to assist stakeholder organizations, such as state and local education agencies, with establishing and maintaining a successful data governance program to help ensure the individual privacy and confidentiality of education records.”

• The five page paper offers a number of suggestions for implementing a successful data governance program that can be applied to a variety of business models beyond education.

• For more information, please visit the Privacy Technical Assistance Center: http://ed.gov/ptac



Supplemental: NASCIO Scorecard

65


Supplemental: 10 DG Worst Practices1. Buy-in but not Committing:

Business vs. IT 2. Ready, Fire, Aim 3. Trying to Solve World Hunger or

Boil the Ocean 4. The Goldilocks Syndrome 5. Committee Overload 6. Failure to Implement 7. Not Dealing with Change

Management 8. Assuming that Technology Alone

is the Answer 9. Not Building Sustainable and

Ongoing Processes 10. Ignoring “Data Shadow Systems”

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Simon Sinek: How great leaders inspire action

69


http://www.ted.com/talks/simon_sinek_how_great_leaders_inspire_action.html

What

How

Why

Attaching Stuff to the Engine• Detroit

– 10 different bolts

– 10 different wrenches

– 10 different bolt inventories

• Toyota – Same bolts

used for all assemblies

– 1 bolt inventory – 1 type of

wrench

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71


healthcare.gov• 55 Contractors! • 6 weeks from launch and

requirements not finalized • "Anyone who has written a line of

code or built a system from the ground-up cannot be surprised or even mildly concerned that Healthcare.gov did not work out of the gate," Standish Group International Chairman Jim Johnson said in a recent podcast.

• "The real news would have been if it actually did work. The very fact that most of it did work at all is a success in itself."

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• "It was pretty obvious from the first look that the system hadn't been designed to work right," says Marty Abbott. "Any single thing that slowed down would slow everything down."

• Software programmed to access data using traditional technologies

• Data components incorporated "big data technologies"http://www.slate.com/articles/technology/bitwise/2013/10/problems_with_healthcare_gov_cronyism_bad_management_and_too_many_cooks.html

Formalizing the Role of U.S. Army IT Governance/Compliance


Suicide Mitigation

74


Data Mapping

12

Mental illness

Deployments

Work History

Soldier Legal Issues

Abuse

Suicide Analysis

FAPDMSS G1 DMDC CID

Data objects complete?

All sources identified?

Best source for each object?

How reconcile differences between sources?

MDR

75


Senior Army Official• A very heavy dose of

management support • Any questions as to future

data ownership, "they should make an appointment to speak directly with me!"

• Empower the team – The conversation turned from "can this be

done?" to "how are we going to accomplish this?"

– Mistakes along the way would be tolerated – Implement a workable solution in prototype form

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Communication Patterns

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Source: The Challenge and the Promise: Strengthening the Force, Preventing Suicide and Saving Lives - The Final Report of the Department of Defense Task Force on the Prevention of Suicide by Members of the Armed Forces - August 2010

Vocabulary is Important-Tank, Tanks, Tankers, Tanked

78


How one inventory item proliferates data throughout the chain

79


555 Subassemblies & subcomponents

17,659 Repair parts or Consumables

System 1:18,214 Total items

75 Attributes/ item1,366,050 Total attributes

System 2 47 Total items

15+ Attributes/item720 Total attributes

System 3 16,594 Total items 73 Attributes/item

1,211,362 Total attributes

System 4 8,535 Total items

16 Attributes/item136,560 Total attributes

System 5 15,959 Total items

22 Attributes/item351,098 Total attributes

Total for the five systems show above:59,350 Items

179 Unique attributes3,065,790 values

Business Implications• National Stock Number (NSN)

Discrepancies – If NSNs in LUAF, GABF, and RTLS are

not present in the MHIF, these records cannot be updated in SASSY

– Additional overhead is created to correct data before performing the real maintenance of records

• Serial Number Duplication – If multiple items are assigned the same

serial number in RTLS, the traceability of those items is severely impacted

– Approximately $531 million of SAC 3 items have duplicated serial numbers

• On-Hand Quantity Discrepancies – If the LUAF O/H QTY and number of items serialized in RTLS conflict, there can

be no clear answer as to how many items a unit actually has on-hand – Approximately $5 billion of equipment does not tie out between the LUAF and

RTLS

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Spreadsheet Interpretation

81


Barclays Excel Spreadsheet Horror• Barclays preparing to buy Lehman’s

Brothers assets. • 179 dodgy Lehman’s contracts were

almost accidentally purchased by Barclays because of an Excel spreadsheet reformatting error

• A first-year associate reformatted an Excel contracts spreadsheet – Predictably, this work was done long

after normal business hours, just after 11:30 p.m...

• The Lehman/Barclays sale closed on September 22nd

• the 179 contracts were marked as “hidden” in Excel, and those entries became “un-hidden” when when globally reformatting the document.

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Example of Poor Data GovernanceMizuho Securities

Example • Wanted to sell 1 share for

600,000 yen • Sold 600,000 shares for 1

yen • $347 million loss • In-house system did not

have limit checking • Tokyo stock exchange

system did not have limit checking

• And doesn't allow order cancellations


CLUMSY typing cost a Japanese bank at least £128 million and staff their Christmas bonuses yesterday, after a trader mistakenly sold 600,000 more shares than he should have. The trader at Mizuho Securities, who has not been named, fell foul of what is known in financial circles as “fat finger syndrome” where a dealer types incorrect details into his computer. He wanted to sell one share in a new telecoms company called J Com, for 600,000 yen (about £3,000).

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Seven Sisters (from British Telecom)

http://www.datablueprint.com/thought-leaders/peter-aiken/book-monetizing-data-management/ [Thanks to Dave Evans]

Copyright 2013 by Data Blueprint 85















Maslow's Hierarchy of Needs


You can accomplish Advanced Data Practices without becoming proficient in the Foundational Data Management Practices however this will: • Take longer • Cost more • Deliver less • Present

greaterrisk(with thanks to Tom DeMarco)

Data Management Practices Hierarchy

Advanced Data

Practices • MDM • Mining • Big Data • Analytics • Warehousing • SOA

Foundational Data Management Practices

89

Copyright

2015by Data Blueprint

Data Platform/Architecture

Data Governance Data Quality

Data Operations


Technologies

Capabilities

Take Aways• Need for DG is increasing

– Increase in data volume – Lack of practice improvement

• DG is a new discipline – Must conform to constraints – No one best way

• DG must be driven by a data strategy complimenting organizational strategy

• Comparing DG frameworks can be useful

• DG directs data management efforts

• The language of DG is metadata

• Process improvement can improve DG practices


The File Naming Convention Committee's Output

91


Data Governance Council Hotel

92


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Supplemental: Data Governance Checklist• Decision-Making Authority

– Assign appropriate levels of authority to data stewards – Proactively define scope and limitations of that authority

• Standard Policies and Procedures – Adopt and enforce clear policies and procedures in a written data

stewardship plan to ensure that everyone understands the importance of data quality and security

– Helps to motivate and empower staff to implement DG

• Data Inventories – Conduct inventory of all data that require protection – Maintain up-to-date inventory of all sensitive records and data systems – Classify data by sensitivity to identify focus areas for security efforts

• Data Content Management – Closely manage data content to justify the collection of sensitive data,

optimize data management processes and ensure compliance with federal, state, and local regulations



Supplemental: Data Governance Checklist, cont’d• Data Records Management

– Specify appropriate managerial and user activities related to handling data to provide data stewards and users with appropriate tools for complying with an organization’s security policies

• Data Quality – Ensure that data are accurate, relevant, timely, and complete for their intended

purposes – Key to maintaining high quality data is a proactive approach to DG that requires

establishing and regularly updating strategies for preventing, detecting, and correcting errors and misuses of data

• Data Access – Define and assign differentiated levels of data access to individuals based on

their roles and responsibilities – This is critical to prevent unauthorized access and minimize risk of data breaches

• Data Security and Risk Management – Ensure the security of sensitive and personally identifiable data and mitigate the

risks of unauthorized disclosure of these data – Top priority for effective data governance plan



Supplemental: 10 DG Worst Practices in Detail

1. Buy-in but not Committing: Business vs. IT

– Business needs to do more – Data governance tasks need

to recognized as priority – Without a real business-resource commitment, data governance

takes a backseat and will never be implemented effectively 2. Ready, Fire, Aim

– Good: Create governance steering committee (business representatives from across enterprise) and separate governance working group (data stewards)

– Problem: Often get the timing wrong: Panels are formed and people are assigned BEFORE they really understand the scope of the data governance and participants’ roles and responsibilities

– Prematurely organize management framework and realize you need a do-over = Guaranteed way to stall DG initiative


Source: “Data Governance Worst Practices” by Angela Guess; http://www.dataversity.net/archives/4895

Supplemental: 10 DG Worst Practices in Detail3. Trying to Solve World Hunger or Boil the Ocean

• Trap 1: Trying to solve all organizational data problems in initial project phase

• Trap 2: Starting with biggest data problems (highly political issues) • Almost impossible to establish a DG program while tacking data problems

that have taken years to build up • Instead: “Think globally and act locally”: break data problems down into

incremental deliverables • “Too big too fast” = Recipe for disaster

4. The Goldilocks Syndrome • Encountering things that are either one

extreme or another • Either the program is too high-level and

substantive issues are never dealt with or it attempts to create definitions and rules for every field and table

• Need to find happy compromise that enables DG initiatives to create real business value




Supplemental: 10 DG Worst Practices in Detail5. Committee Overload

• Good: People of various business units and departments get involved in the governance process

• Bad: more people -> more politics -> more watered down governance responsibilities

• To be successful, limit committee sizes to 6-12 people and ensure that members have decision-making authority

6. Failure to Implement • DG efforts won’t produce any business value if

data definitions, business rules and KPIs are created but not used in any processes

• Governance process needs to be a complete feedback loop in which data is defined, monitored, acted upon, and changed when appropriate

• Also important: Establish ongoing communication about governance to prevent business users going back to old habits


Source: “Data Governance Worst Practices” by Angela Guess; http://www.dataversity.net/archives/4895Source: “Data Governance Worst Practices” by Angela Guess; http://www.dataversity.net/archives/4895


7.Not Dealing with Change Management • Business and IT processes need to be

changed for enterprise DG to be successful • Need for change management is seldom addressed • Challenges: people/process issues and internal politics

8.Assuming that Technology Alone is the Answer • Purchasing MDM, data integration or data quality

software to support DG programs is not the solution • Combination of vendor hype and high

price tags set high expectations • Internal interactions are what make

or break data governance efforts




9.Not Building Sustainable and Ongoing Processes • Initial investment in time, money

and people may be accurate • Many organizations don’t establish a budget, resource

commitments or design DG processes with an eye toward sustaining the governance effort for the long term

10.Ignoring “Data Shadow Systems” • Common mistake: focus on “systems

of record” and BI systems, assuming that all important data can be found there

• Often, key information is located in “data shadow systems” scattered through organization

• Don’t ignore such additional deposits of information



ReferencesWebsites

• Data Governance Book

Data Governance Book

Compliance Book


IT Governance Books


Interdependencies

103

Data Governance

Master Data Data Quality

makes the case and is

responsible for

is a necessary but insufficient prerequisite

to success

MD capabilities constrain governance

effectiveness

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