Sustaining Data Governance and Adding Value for the Long Term

The First Step in Information Management

www.firstsanfranciscopartners.com

Sustainable Data Governance: Adding Value for the Long Term

Kelle O’Neal [email protected]

415-‐425-‐9661 @1stsanfrancisco

Why We’re Here

pg 2 Copyright (c) 2015 - First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential

Purpose:

Understand criQcal success factors for sustainability of a Data Governance Discipline

Outcome: §  Understanding Data Governance FoundaQon

§  Understanding how to make governance a core competency §  PracQcal knowledge that can be immediately implemented

Agenda

§  Level SeTng -‐ FSFP’s perspecQve on Data Governance

§  Obstacles & Challenges to Sustainability §  CreaQng Sustainable Data Governance −  OrganizaQon −  Alignment −  Metrics & Measurements −  CommunicaQon −  Embedding Governance

§  Ensuring success



Level SeTng

Data Governance DefiniQon

§  Data Governance is the organizing framework for establishing the strategy, objecQves and policy for effecQvely managing corporate data.

§  It consists of the processes, policies, organizaQon and technologies required to manage and ensure the availability, usability, integrity, consistency, auditability and security of your data.

CommunicaQon and Metrics

Data Strategy

Data Policies and Processes

Data Standards

and Modeling

A Data Governance

Program consists of the inter-‐workings

of strategy, standards, policies and communicaQon

pg 5 © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential

pg 6

Data Governance Framework

© 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential

•  Vision & Mission •  Objectives & Goals •  Alignment with Corporate

Objectives •  Alignment with Business

Strategy •  Guiding Principles

•  Statistics and Analysis •  Tracking of progress •  Monitoring of issues •  Continuous Improvement •  Score-carding

•  Policies & Rules •  Processes •  Controls •  Data Standards & Definitions •  Metadata, Taxonomy,

Cataloging, and Classification

•  Operating Model •  Arbiters & Escalation points •  Data Governance Organization

Members •  Roles and Responsibilities •  Data Ownership & Accountability

•  Collaboration & Information Life Cycle Tools

•  Data Mastering & Sharing •  Data Architecture & Security •  Data Quality & Stewardship

Workflow •  Metadata Repository

•  Communication Plan •  Mass Communication •  Individual Updates •  Mechanisms •  Training Strategy

•  Business Impact & Readiness •  IT Operations & Readiness •  Training & Awareness •  Stakeholder Management & Communication •  Defining Ownership & Accountability

Change Management

Develop and execute architectures, policies and procedures to manage the full data lifecycle

Enterprise Data Management

Enterprise Data Management Ensure data is available, accurate, complete and secure

Data Quality Management Data Architecture Data

RetenQon/Archiving

Master Data Management

Big Data Management Metadata Management Reference Data

Management

Privacy/Security

DATA GOVERNANCE


pg 8

The Big Picture: EIM Framework


Provides a holisQc view of data in order to manage data as a corporate asset

Enterprise InformaQon Management

InformaQon Strategy

Architecture and Technology Enablement

Content Delivery

Business Intelligence and Performance Management Data Management InformaQon Asset

Management

GOVERNANCE

ORGANIZATIONAL ALIGNMENT

Content Management


Obstacles & Challenges

The landscape is changing …

pg 10 Copyright (c) 2014 - First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential pg 10 Copyright (c) 2015 - First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential

Obstacles

§  CompeQng prioriQes and lack of resources §  Data Ownership and other territorial issues §  Lack of cross-‐business unit coordinaQon §  Lack of data governance understanding §  Resistance to change or transformaQon §  Lack of execuQve sponsorship and buy-‐in §  Resistance to accountability §  Lack of business jusQficaQon §  Inexperience with cross-‐funcQonal iniQaQves §  Change of personnel


Obstacles


Why is Data Governance Important? Internal pressures: §  Desire to understand customer at any Qme

from any channel

§  Data Quality issues are persistent §  Balance of old mainframe systems with new

technologies

§  Movement to the cloud and losing control of data

§  Data Volumes are increasing

§  Mobile apps enabling data to be created and accessed anywhere

§  Project oriented approach to addressing issues/opportuniQes

External pressures: §  Greater amounts of new regulaQons

§  Increasing Customer Demands – my informaQon anywhere at any Qme

§  Technology and market changes outpacing ability to respond


Ensures the right people are involved in determining standards, usage and

integra4on of data across projects, subject areas and lines of business


Establishing the OrganizaQon

Don’t base your program on specific individuals


Process

• How are decisions made?

• Who makes them? • How are Commihee’s used?

Culture

• Centralized • Decentralized • Hybrid

OperaQng Model • Data Governance

Owner •  SME’s •  Leadership

People


OperaQng Model

§  Outlines how Data Governance will operate §  Forms basis for the Data Governance organizaQonal structure – but isn’t an org chart

§  Ensures proper oversight, escalaQon and decision making

§  Ensures the right people are involved in determining standards, usage and integraQon of data across projects, subject areas and lines of business

§  Creates the infrastructure for accountability and ownership pg 17 Copyright (c) 2014 - First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential

Wikipedia: An OperaQng Model describes the necessary level of business process integraQon and data standardizaQon in the business and among trading partners and guides the underlying Business and Technical Architecture to effecQvely and efficiently realize its Business Model. The process of OperaQng Model design is also part of business strategy.

Types of OperaQng Models

§  Centralized −  Similar to a top down project model

§  Decentralized −  Flat structure, more virtual/grassroots in nature

§  Hybrid / Federated


Pros: •  Formal Data Governance execuQve posiQon •  Data Governance Steering Commihee reports directly to execuQve

•  Data Czar/Lead – one person at the top; easier decision making

•  One place to stop and shop •  Easier to manage by data type

Cons: •  Large OrganizaQonal Impact •  New roles will most likely require Human Resources approval

•  Formal separaQon of business and technical architectural roles

Bus / LOBs

pg 19

OperaQng Model -‐ Centralized

Copyright (c) 2014 - First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential

DG Execu4ve Sponsor

DG Steering Commi<ee

Center of Excellence (COE)

Data Governance Lead

Technical Support Data

Architecture Group

Technical Data Analysis Group

Business Support Business Analysis Group

Data Management

Group

LOB/BU Data Governance Steering Commi<ee

LOB/BU Data Governance Working Group

pg 20

OperaQng Model -‐ Decentralized


Data Stewards Application Architects

Business Analysts Data Analysts

Pros: •  RelaQvely flat organizaQon •  Informal Data Governance bodies •  RelaQvely quick to establish and implement

Cons: •  Consensus discussions tend to take longer than centralized edicts

•  Many parQcipants compromise governance bodies

•  May be difficult to sustain over Qme •  Provides least value •  Difficult coordinaQon •  Business as usual •  Issues around co-‐owners of data and accountability

pg 21

OperaQng Model -‐ Hybrid


Pros: •  Centralized structure for establishing appropriate direcQon and tone at the top

•  Formal Data Governance Lead role serving as a single point of contact and accountability

•  Data Governance Lead posiQon is a full Qme, dedicated role – DG gets the ahenQon it deserves

•  Working groups with broad membership for facilitaQng collaboraQon and consensus building

•  PotenQally an easier model to implement iniQally and sustain over Qme

•  Pushes down decision making •  Ability to focus on specific data enQQes •  Issues resoluQon without pulling in the whole team

Cons: •  Data Governance Lead posiQon is a full Qme, dedicated role •  Working groups dynamics may require prioriQzaQon of conflicQng business requirements

•  Too many layers

Data Governance Steering Commihee

Data Governance Office

Data Governance Working Group

Business Stakeholders IT Enablement

Data Governance Organization

OperaQng Model -‐ Federated


Pros: •  Centralized Enterprise strategy with decentralized execuQon and implementaQon

•  Enterprise Data Governance Lead role serving as a single point of contact and accountability

•  “Federated” Data Governance pracQces per Line of Business (LOB) to empower divisions with differing requirements

•  PotenQally an easier model to implement iniQally and sustain over Qme

•  Pushes down decision making •  Ability to focus on specific data enQQes, divisional challenges or regional prioriQes

•  Issues resoluQon without pulling in the whole team

Cons: •  Too many layers •  Autonomy at the LOB level can be challenging to coordinate •  Difficult to find balance between LOB prioriQes and Enterprise prioriQes

Enterprise Data Governance Steering Commihee

Enterprise Data Governance Office

Data Governance Groups

Data Governance OrganizaQon

Business Stakeholders

IT Enablement

Divisional DG Office


IT Enablement



IT Enablement


IT Enablement


OperaQng Model Roles and ResponsibiliQes

§  Data Governance Steering Commihee −  Provides overall strategic vision −  Approves funding, budget and resource allocaQon for strategic data projects −  Establishes annual discreQonary spend allocaQon for data projects −  Adjudicates intractable issues that are escalated −  Ensures strategic alignment with corporate objecQves and other business unit iniQaQves

§  Data Governance Office −  Chairs the Data Governance Steering Commihee and Data Governance Working Group −  Acts as the glue between the Data Governance Steering Group and the Working Commihee −  Defines the standards, metrics and processes for data quality checks, invesQgaQons, and resoluQon −  Advises business and technical resources on data standards and ensures technical designs adhere to data architectural best

pracQces to ensure data quality −  Adjudicates where necessary, creates training plans, communicaQon plans etc

§  Data Governance Working Group −  Governing body comprised of data owners across Business and IT funcQons that own data definiQons and provide guidance &

enforcement to drive change in use and maintenance of data by the business −  Validates data quality rules and prioriQze data quality issue resoluQon across the funcQonal areas −  Trains, educates, and creates awareness for members in their respecQve funcQonal areas −  Implements data business processes and are accountable to decisions that are made


Typical DG Office Deliverables

§  Some Typical Deliverables: §  Documented DG Strategy, Vision, Mission, ObjecQves §  Documented DG Guiding Principles §  Documented roles & responsibiliQes of the various members §  Up to date OperaQng Model §  RACI matrices §  Templates for Policies and Processes §  Templates for capturing metrics and measurement requirements §  Templates for steering commihee meeQngs §  Training Plans §  CommunicaQon Plans §  Template for regular DG communicaQon §  Templates for logging issues needing escalaQon and eventual resoluQon §  Templates for new DG service requests §  Checklists for new projects to ensure adherence to DG standards


Typical Roles

§  Business Steward §  Data Owner §  Data Steward §  Data Quality Analyst §  Business Analyst §  Data Architect §  Technical Leads (MDM, Metadata, Reference Data, App)


Sample Data Governance OperaQng Model


Direc4on TBD

Execu4ve Sponsor Business and IT

Business Steward Leads Service Order Management

Finance FP&A Sales

Market Strategy Analy4cs

Data Governance Steering Commi<ee

Finance (CFO)

InternaQonal (President)

Global Services (COO)

IT (CIO)

MarkeQng (CMO)

Data Governance Office Data Governance Leads

Business and IT Data Governance Coordinator

Management

Provides budget and resource approvals. Forum for issue escalaQon

Craps the enterprise data strategy, including polices, processes and standards to ensure that data is managed as an asset

Execu4ve Level

Management Level

Stewards data within their BU to ensure that the enterprise policies are applied

Tac4cal Level

Strategic Level Provides overall strategic direcQon, budget and resource approvals forum for issue escalaQon

Execu4on

Data Management IT Support Group

Data Quality Lead Metadata Lead

Data Architect

BI Delivery

Opera4ons External Repor4ng

DGWG

Enterprise Architect

BA

Data Analyst IT Security

Privacy Legal

Data Stewards

Risk

Centralized Data Steward Pool

Accoun4ng

Data Governance Leadership Team

Sample MulQ-‐Domain OperaQng Model


Program Oversight & DirecQon

ExecuQve Sponsor

Program Management DG Working Group Data Governance Program Management Team

DG Program Manager

DG Coordinator

Program ExecuQon

IT Manager

Data Domain Owners

Business Data Leads Data AcquisiQon Data Stewardship IT Enablement

Supply Chain InternaQonal Sales HR Finance IT MarkeQng

Customer Product Employee Vendor Supplier

DG Data Quality Manager

Direc4on

TBD

Enterprise Data Sub-‐Commi<ee

Business Data Stewards


Business Unit Officers Data Owners IT Partner(s)

Data Governance Office (DGO)

Management

Program Oversight. Allocates budget & resource. Empower Business Data Stewards. Forum for issue escalaQon.

Craps the Enterprise Data Strategy, processes and standards to ensure that data is managed as an asset.

Execu4ve Level

Management Level

Stewards data within their BU to ensure that the enterprise policies, standards & processes are applied.

Tac4cal Level

Strategic Level Provides overall strategic direcQon, budget & resource approvals. Forum for issue escalaQon. Approval of data domains under governance control.

Execu4on

Technical Data Stewards

Local Data Governance Working Groups

Chair: Enterprise Data Officer

Chair: Data Governance Office Lead

IT Partner(s) Sr. Execu4ves Business Units

Sample Enterprise OperaQng Model

Business & Technical Data SMEs

Scalability at the Data Domain

Security, Balance, PosiQon & TransacQons Accountable ExecuQve

Company/Account Accountable ExecuQve

Enterprise Data Sub-‐Commihee Member

Security, Balance, PosiQon & TransacQons Business Data Owner

Company Business

Data Owner

Security Business

Data Steward

Balance, PosiQon & TransacQon

Business Data Steward

Company Business

Data Steward

Account Business Data Steward

Security DG Working Group

BP&T DG Working Group

Company DG Working Group

Account DG Working Group

Layers scale: §  OrganizaQon §  Maturity §  Complexity of Domain

Leadership can be responsible for mulQple domains

Data Stewardship = focused

Account Business

Data Owner

Members of DG Steering Commi<ee

Members of Business Data Steward Prac4ce Group

Members of Enterprise Data Sub-‐Commi<ee

Copyright (c) 2015 -‐ First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and ConfidenQal

pg 30

Use Case – Account Local Data Governance Alignment


Accountable ExecuQve

Business Data Steward

Local Data Governance Working Group

Business Steward Lead

Account Domain Enterprise Opera4ng Model

Keys to a Successful DG OrganizaQon


§  Governance team must contain members from mulQple lines of business §  Ensures cross funcQonal buy-‐in and ownership §  Key lines of business must be represented

§  Team members must represent both business and IT §  IT needs to be able to implement per the governance policies and the business needs to be aware of IT limitaQons…

§  Team needs to meet on a regular basis §  Business is constantly changing §  Discuss new and emerging programs §  Current IT acQviQes and their effect on the data §  Review policies and study measurement output

§  Agreed upon fundamentals that serve as the Guiding Principles §  If this doesn’t exist, the first mandate is to create this §  Standards are mechanisms for Qe-‐breaking

§  Clear lines of communicaQon §  Regular interacQon with execuQve management §  Ensure communicaQon methods to enforce policies at the steward and stakeholder level §  Invite stewards, project managers, stakeholders etc to provide status updates on criQcal iniQaQves that affect the data

§  Ensure the Opera4ng Model fits the culture of the company


Alignment


Random House DicQonary: a state of agreement or cooperaQon among persons, groups, naQons, etc., with a common cause or viewpoint.

Wikipedia: Alignment is the adjustment of an object in relaQon

with other objects, or a staQc orientaQon of some object or set of objects in relaQon to others.

Understanding a process from the perspec4ve of others Working individually towards a common goal

DefiniQon of Alignment


Impact on Governance Programs

Sources of mis-‐alignment §  Lack of understanding −  Of how an individual’s role fits into Corporate ObjecQves

−  Of other jobs, roles, experiences, objecQves

§  ConflicQng/ compeQng objecQves §  PoliQcs §  CommunicaQon styles §  Personality conflicts

Importance of Alignment §  Creates a conQnual “buy-‐in” process with all Stakeholders

§  Helps organizaQons “think globally and act locally”

§  OpQmizes resources to manage costs

§ Work towards a common goal

§ Minimizes risk


Alignment Process

• Why is this important?

• Why should we care?

Value

• Who cares? • Why should they care?

Stakeholders • How does the value benefit the stakeholders?

Linkage


IdenQfy and Align Values


Value of DG to Business Value of DG to IT

IdenQfy Stakeholders

§ Who are the Stakeholders? §  IT §  OperaQons §  Compliance §  Line of Business

§ What are their drivers? §  What are their key goals? §  What are their concerns? §  What are they trying to avoid?

§ What are their prioriQes? §  Which goals are criQcal? §  What happens if those goals aren’t achieved?


pg 39Proprietary & Confidential

Stakeholder Map


Value of DG to Business

Value of DG to IT

Linkage is the tacQcal process of mapping your delivery to the issues important to the stakeholder.

•  Per Stakeholder, idenQfy what is important to them and why. §  What happens if they don’t achieve their goal?

•  List elements of DG soluQon •  Choose Top 3

•  Choose up to 3 elements of the DG soluQon and arQculate how those deliverables can help that person achieve their goals

§  ConQnually ask yourself, So What?

Linkage delivers Alignment

Create Linkage


PotenQal Deliverables

§  Consistency of customer/product/employee data

§  Improve data quality

§  Improve data consumpQon and appropriate usage

§  Create and understand data lineage §  Create a data platorm to support a single face to the Customer

§  Facilitate the concept of “Single Sourcing” of data to the Data Warehouse and Business ApplicaQons

§  Create and implement common enterprise systems/tools and processes for selected data


DG Program

Sales/MarkeQng Improve Understanding

of Customers Improve SegmentaQon

Understand Risk

IT Improved ProducQvity ProacQvely support

business Lower TCO

Improved Data Quality

Single Repository of Customer Data Create Data Lineage

ArQculate Linkage


The Single Repository of Customer data will improve my understanding of customers by providing me a trusted source of Qmely, accurate and perQnent data from which to execute analyQcs, segmentaQon and risk assessment.

CreaQng and understanding Data Lineage will improve IT producQvity by reducing the Qme spent searching for data, ensure the appropriate data is used and validaQng the data. Data Lineage that is created and understood by both IT and business will facilitate a common language and enable IT to beher support the business growth and expansion.

Linkage creates Alignment



Measurement & Metrics

Why are Metrics Important?

Alignment

Rele-‐vance

Value


Aligning Benefit to Value


Benefits of Data Governance •  Data lineage and auditability •  Improved data transparency and quality •  Repeatable processes and reusable arQfacts •  Consistent definiQons •  Appropriate use of informaQon •  CollaboraQon among teams, business units, etc.. •  Accountability for informaQon use •  Quality of all data types •  Easier sharing of informaQon •  Visibility into the enterprise via data •  InformaQon security

Content property of IMCue and FSFP, Copyright 2013 ReproducQon prohibited

Impact Determines Success


Issues • Report Quality and Accuracy

• Low ProducQvity • Regulatory Compliance / Audit Response

Goals •  Improve data’s usability

•  Improve efficiency and producQvity

• Reduce compliance / audit cost

Metrics/KPI’s • Data Quality • Data remediaQon Qme

• Effort to comply

Impact •  Improve client relaQonships

• Address new markets

•  Improve producQvity

•  Improve analysis & decision making

Content property of IMCue and FSFP, Copyright 2013 ReproducQon prohibited

DefiniQon

§ Metric −  A metric is any standard of measurement

§  Number of business requests logged §  Number of data owners idenQfied §  Percentage business requests resolved within agreed SLA, etc.

§  Key Performance Indicator (KPI) −  A Key Performance Indicator (KPI) is a quanQfiable metric that the DG Program has chosen that will give an indicaQon of DG program performance. −  A KPI can be used as a driver for improvement and reflects the criQcal success factors for the DG Program

§  A metric is not necessarily a KPI


Metrics/KPIs examples


People

§  # of DGWG decisions backed up by the steering commihee §  # of approved projects from the DGWG §  # of issues escalated to DGP and resolved §  # of data owners idenQfied §  # of data managers idenQfied §  DG adop4on rate by company personnel (Survey)

Process

§  # of data consolidated processes §  # of approved and implemented standards, policies, and processes §  # of consistent data definiQons §  Existence of and adherence to a business request escalaQon process to manage disputes regarding data §  Integra4on into the project lifecycle process to ensure DG oversight of key ini4a4ves

Technology

§  # of consolidated data sources consolidated §  # of data targets using mastered data §  Address accuracy for mailing/shipping §  Data integrity across systems §  Records/data aged past target §  Presence and usage of a unique identifier(s)


CreaQng Metrics

Process to Establish Metrics


Issues • What are the issues in your group?

• What do you mean by that?

• Why is it important?

• What are your objecQves?

Goals • What is the change you would like to see? What acQon?

• How will that change impact you?

• What is the impact if those objecQves aren’t met?

Metrics/KPI’s • What processes are involved in that change?

•  How is informaQon used in that process?

• What informaQon is used? What data?

• What data improvements are needed?

Impact • PosiQve change created by addressing issues

• Benefit of improving data to impact objecQve

GeTng to Data Change Metrics

Issues/Objec4ves

Goals Informa4on Data Data Change Addi4onal Ac4on

Report Quality and Accuracy

Improve Data Understanding

Accounts Client InformaQon Reduce duplicaQon of client data

Improve Data Transparency

Increase completeness of record

Reduce Manual RemediaQon

Track data lineage Ensure thoroughness of data sources

Products owned

Increase Completeness of record

Ensure thoroughness of data sources

Households RelaQonship

Groups


Sample Data Metrics

Data Change Measurement Target Frequency Reduce DuplicaQon of Client Data

% DuplicaQon 1% Daily

Increase Completeness of Client Record

% Completeness of key fields 99% Daily

Track Data Lineage Completeness of lineage in metadata

99% Monthly

Ensure Thoroughness of Client Data Sources

Review of data acquisiQon and ETL process

Business consensus

Quarterly

Increase Completeness of Products Owned

% Completeness of key fields 99% Weekly

Ensure Thoroughness of Product Data Sources

Review of data acquisiQon and ETL process

Business consensus

Quarterly


Data Understanding

Data Transparency

Reduce Manual RemediaQon

GeTng to Business Change / Impact Metrics


Goal Measurement Target Frequency

Improve Data Understanding Completeness of Business Glossary % of Business Users Trained

100% 100%

Monthly Monthly

Improve Data Transparency Completeness of Lineage 80% Monthly

Reduce Manual RemediaQon Time to complete report process (baseline is 6 days) 1 Day Monthly

Increase Report Quality and Accuracy

Improved Business Stakeholder SaQsfacQon Survey Reduced Issue Requests

Business Approval 10% drop

Quarterly Monthly

This is your KPI

BU 2 SCORECARD

BU 4 SCORECARD

BU 1 SCORECARD

BU 3 SCORECARD

DATA GOVERNANCE SCORECARD

(FUTURE STATE) STRATEGIC VIEW

OPERATIONAL SCORECARDS

CONSOLIDATED BY BUSINES UNIT

SETU

P

RULES THRESHOLDS DATA QUALITY DIMENSIONS FFREQUENCY WEIGHTING ALL SCORECARDS

START WITH A BASELINE

Scorecard Approach: Show some vision forward


ATTRIBUTE SCORECARD

ATTRIBUTE SCORECARD

ATTRIBUTE SCORECARD

ATTRIBUTE SCORECARD

ATTRIBUTE SCORECARD

ATTRIBUTE SCORECARD

ATTRIBUTE SCORECARD

ATTRIBUTE SCORECARD

ATTRIBUTE SCORECARD

ATTRIBUTE SCORECARD

ATTRIBUTE SCORECARD

ATTRIBUTE SCORECARD

ATTRIBUTE SCORECARD

ATTRIBUTE SCORECARD

ATTRIBUTE SCORECARD

ATTRIBUTE SCORECARD

ATTRIBUTE SCORECARD

ATTRIBUTE SCORECARD

ATTRIBUTE SCORECARD

ATTRIBUTE SCORECARD

ATTRIBUTE SCORECARD

ATTRIBUTE SCORECARD

ATTRIBUTE SCORECARD

ATTRIBUTE SCORECARD

ATTRIBUTE SCORECARD

ATTRIBUTE SCORECARD

ATTRIBUTE SCORECARD

ATTRIBUTE SCORECARD

ATTRIBUTE SCORECARD

ATTRIBUTE SCORECARD

Ahribute level Supports OperaQonal Use Case

EnQty Level Supports Company Data Governance

(Strategic Value)


CommunicaQon & Stakeholder Management

Why is CommunicaQon Important?


Ø Creates Awareness Ø Aligns expectaQons

Ø Creates an opportunity for feedback / engagement

Ø ProacQvely addresses Change

Ø Publishes Success

Ø Answers the quesQons “Why?” and “What’s in it for me?”

Ø Aligns acQviQes

TranslaQng Data Value into Business Value

§  CommunicaQon is key to maintaining commitment

§  The right metrics help maintain alignment −  Metrics have no value if they aren’t aligned to the interests of a stakeholder −  Ensure there is some way of measuring how the improvement in data is helping stakeholders progress toward their goals −  What informaQon do you need to track and measure to those goals?

§  Translate the value statement into the language of the recipient


Purpose: Increase Stakeholder Engagement


Using this framework enables clear gaps in stakeholder engagement to be idenQfied and subsequent change strategies to be put in place to enable the gaps to be closed

T I M E Status Quo Vision

COM

MIT

MEN

T /

ENTH

USI

ASM

High

Contact I’ve heard about this program/project

Low

I know the concepts

Awareness

I understand how Program/project positively impacts

and benefits me and the organization

Positive Perception

This is how we do business Institutionalization

Understanding I understand what this means to me and the organization as a whole

Adoption I am willing to work hard to make this a success

Internalization I’ve made this my own and will constantly create innovative ways to use it

•  Engagement Strategy: •  Focused effort must be given

to high priority groups •  Provide sufficient level of

informaQon to less influenQal groups to ensure buy-‐in

•  Move people and or groups to the right by trying to increase their level of interest

•  Forms the foundaQon of your engagement / communicaQon strategy

Stakeholder Engagement Strategy


Meet Their Needs

Key Player

Lower Priority

Show Considera4on

Stakeholder Influence

Stakeh

olde

r Infl

uence

Stakeholder Interest

What is a CommunicaQon Plan?

§  CommunicaQon Plan DefiniQon −  A wrihen document that helps an organizaQon achieve its goals using wrihen and spoken words. −  Describes the What, Why, When, Where, and How

§  Importance of a CommunicaQon Plan −  Gives the working team a day-‐to-‐day work focus −  Helps stakeholders and the working team set prioriQes −  Provides stakeholders with a sense of order and controls −  Provides a demonstraQon of value to the stakeholders and the business in general −  Helps stakeholders to support the DG Program −  Protects the DG Program against last-‐minute demands from stakeholders


CommunicaQon Plan

§  Brings it all together: −  Who do we need to communicate to? −  What informaQon will be important to them? −  Metrics that map to their professional and personal goals −  How frequently should they be updated? −  What is the method of communicaQon? −  Who should be communicaQng to them?


Components of a CommunicaQon Plan


Communica4on Plan Stakeholder: XXX

QualitaQve InformaQon Any general qualitaQve informaQon that I would like to receive related to this deliverable

QuanQtaQve InformaQon Of the quanQtaQve metrics that have been defined, which are the ones I would like to be informed about AND how do I want the metric communicated to me to make the message perQnent

Frequency How open do I want to be informed about progress

Method What is my preferred mechanism of receiving the informaQon

Item Frequency Description Purpose Audience Documentation From Date Owner Status

Meetings

First BSL Meeting One-Time Introduction

Get explicit buy-in from the participants and resource ask

DGWG BSLs PowerPoint Presentation John 8/25/11 John Complete

DGWG Core Team Kickoff Meeting One-Time DGO kickoff and vision from IT Sponsor Kickoff DGWG-Core, IT Sponsor PowerPoint presentation John 9/15/11 John Complete

DGO Launch Logistics One-Time Communication announcing the DGO Plan on the best way to communicate the DGO launch and PR effort

DGO, SVB Corporate Communication

Email John TBD John Complete

DGO-DGWG-Core Status Meeting Weekly DGWG accomplishments, progress towards goals and issues

Status DGWG-Core members SharePoint Agenda & Content John Ongoing Flo In progress

Meeting with DGO IT Lead Weekly Planning and strategy Status/Planning DGO Chair, DGO IT Lead and DGC

John Ongoing John

DGO & MDM alignment meetings Weekly MDM Implementation update Status MDM team, DGO Chair & DGC Agenda Rebecca Ongoing Rebecca

Mentoring program (Data Stewardship Program)

Weekly Opportunity to learn from Business Steward Leads. Best practices, polices, processes, standards, definitions

Enrichment DGWG Data Stewards Data Stewardship Best practices. DGO Polices, processes, standards, definitions

TBD TBD TBD Not Started

Meeting with Program Sponsors Bi-Weekly? Provide DGWG accomplishments, progress towards goals and issues

Status DGO Chair, Biz and IT Sponsor PowerPoint presentation John TBD John Not Started

DGO-DGWG Decision (Core & Advisory) Meeting

Monthly DGWG voting meeting Vote and approve DGWG materials DGWG members SharePoint Agenda & Content John Ongoing Flo In progress

DGO-DGWG - DM IT Support Group Meeting Monthly DGWG DM IT Support Group team monthly update Bring the advisory team up to speed on status before the decision meeting

DGWG Advisory members SharePoint Agenda & Content John TBD Flo Not Started

EIC Meeting Monthly DGWG accomplishments, progress towards goals, issues, documents for informational purposes only

Status, Informational EIC members PowerPoint presentation John Ongoing John In progress

Meeting with SAM - Fund Business stakeholders As needed Relationship building/Expectations/Impact DGO resource engagement Business Stakeholders Informal/deck, Email John TBD Flo Not Started

Meeting with Purchasing stakeholders As needed Relationship building/Expectations/Impact DGO resource engagement Business Stakeholders Informal/deck, Email John TBD Flo Not Started

Meeting with Product Implementation stakeholders As needed Relationship building/Expectations/Impact DGO resource engagement Business Stakeholders Informal/deck, Email John TBD Flo Not Started

Meeting with Global Product stakeholders As needed Relationship building/Expectations/Impact DGO resource engagement Business Stakeholders Informal/deck, Email John TBD Flo Not Started

DGO Town Halls One/Year DGWG accomplishments and progress towards goals Forum for open discussion

Team Building All DGWG members PowerPoint presentation John TBD Flo Not Started

Sample CommunicaQon Plan


And these are just the meeQngs! Also: •  Awareness & Training •  CommunicaQon Vehicles •  Knowledge Sharing • ….


Embedding Data Governance

Ensuring DG is Sustainable

•  Incorporate DG goals into other goals, objecQves and incenQves Incorporate

•  Align DG with strategic objecQves, programs and projects Align

•  Embed DG into standard project, change control, new iniQaQve and operaQonal processes

Embed

•  Focus on delivering business value Focus pg 66 Copyright (c) 2015 - First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential

Incorporate IncenQves

Carrots SQcks

Oversight AllocaQon


Align with ObjecQves, Programs and Projects

§  Examples:

§  Alignment with Stakeholder goals (already discussed)

§  Alignment with Corporate ObjecQves

§  Alignment with strategic Programs/Projects


Example: Alignment with Corporate ObjecQves


Example: Tie Principles to Corporate Strategic ObjecQves

Corporate Objec4ve

Principle

Client Data is a key asset to our company. We will enhance and manage this asset by emphasizing clear strategies, decisive acQon, innovaQon and results.

Capabili4es Business stakeholders will get informaQon delivered at the right Qme, locaQon and amount as efficiently as possible.

Execu4on Data Governance will introduce, support and drive standardizaQon of enterprise data.

Brand Best in class customer data quality will significantly improve both the internal as well as external customer experience.

People Data Governance should increase producQvity through centralized, streamlined processes and eliminate non-‐value added acQviQes. Maximizing automaQon is a key way to improve human resource efficiencies and is preferable over manual processes.

Principles drive crea.on and execu.on of policies, standards, processes, etc….



Program / Project Alignment

Project IniQaQon

Project ExecuQon

Change Control OperaQonal


Sample: Embed in Project IniQaQon Process


IdenQfy informaQon/ infrastructure

needs Profile to Iden4fy

data issues

Analyze to Iden4fy root causes/ gaps

Design solu4ons to root cause

problems / gaps

Implement process & Tech

soluQons

Sustain

Proac.vely iden.fy problems and solve root causes

Sample: Embed Data Governance Into Your Project Methodology


Engage DG, DQ, DA, MDM, Metadata Leads

Assess adherence to Guiding Principles

Alignment Workshop

Assess adherence to Guiding Principles



AddiQonal DG, DQ, DA, MDM and Metadata related deliverables added to ‘typical’ list: Data Profiling Reports, New/modified Score-‐cards, AddiQonal Metadata, New/modified Processes, Data Model Reviews, etc


Engage DG Lead

Sample: Embed Data Governance with Change IniQators/Control


A process flow will help ensure consistent change requests related to data

Sample: OperaQonal Process (Client On-‐Boarding)

New Client Request

DocumentaQon & Due Diligence

Terms confirmed

Agreement / Contract Created

Create Client


• ExisQng or Previous Client (Master Data Check) • Data Standards and ValidaQon • Data Quality Check

• Regulatory Checks • RACI / Data Ownership • Data Enrichment • Data ClassificaQon • Data RemediaQon • Decision Making / EscalaQon Processes

• Hierarchy / RelaQonship Check • Client SegmentaQon

• Contract Management • Document Management

• Update Master Data • Create Hierarchies • Data Standards and ValidaQon • Data Quality Check • Data Sharing, Access & Use Policy • …

Sample: OperaQonal Process (Unique Device IdenQficaQon Management)


IdenQfy Products for Submission

IdenQfy Data Sources

Profile Product Data

IdenQfy and Address DQ

Issues

Aggregate Data Cleanse / Enrich Data

Review / Approve Data for

Submission

Submit Data and resolve errors

Publish data for internal /external consumpQon

ArQfacts needed for IdenQfying Product Data & Sources






Issues



Submission



Data DicQonary / Business Glossary Data Inventory Data Flow Diagrams Product Data Hierarchies Data Standards Data Ownership and RACI matrices

ArQfacts needed for Profiling and Addressing DQ






Issues



Submission



• Data Quality Standards • Data Quality Rules • Data Profiling SoluQons • Data RemediaQon Processes • Decision Making & EscalaQon Processes

ArQfacts needed for AggregaQng, Cleansing, Enriching






Issues



Submission



• Product Hierarchies and RelaQonships • Match / Merge Rules • Data ValidaQon and Cleansing Rules • Data AcquisiQon Policies (Purchasing and IntegraQng)

• ExcepQon and Error Handling Processes

ArQfacts needed for Review, Approve, & Submit






Issues



Submission



• Data Profiling • Data Management Workflows • RACI Matrices • Decision Making and EscalaQon Processes • Approval Process • ExcepQon and Error Handling Process • Measurement and Monitoring of the process

ArQfacts needed to Publish & Manage






Issues



Submission



• Data Sharing Policies (Usage, Access Rights) • Data RetenQon • Training and CommunicaQon


Ensuring Success

Principle Descrip4on Be clear on purpose Build governance to guide and oversee the strategic and enterprise mission

Enterprise thinking Provide consistency and coordinaQon for cross funcQonal iniQaQves. Maintain an enterprise perspecQve on data

Be flexible If you make it too difficult, and people will circumvent it. Make it customizable (within guidelines), and people will get a sense of ownership

Simplicity and usability are the keys to acceptance

Adopt a simple governance model people can use. A complicated and inefficient governance structure will result in the business circumvenQng the process

Be deliberate on par4cipa4on and process Select sponsors and parQcipants. Do not apply governance bureaucracy solely to build consensus or to saQsfy momentary poliQcal interest

Enterprise wide alignment and goal congruence Maintain alignment with both enterprise and local business needs. Guide prioriQzaQon and alignment of iniQaQves to enterprise goals

Establish policies with proper mandate and ensure compliance

Clearly define and publicize policies, processes and standards. Ensure compliance through tracking and audit

Communicate, Communicate, Communicate! Frequent, directed communicaQon will provide a mechanism for gauging when to “course correct”, manage stakeholder and effecQveness of the program

Governance Design Principles


Ensuring Success

§  The following factors are usually evident in a successful program: −  First create a strategy and then follow it (agreed on starQng point & steps necessary) −  Ensure solid alignment between Business & IT −  Clearly defined and measureable success criteria −  Small iteraQons vs. all or nothing −  ExecuQve sponsorship is criQcal −  IdenQfy and assess the importance of key people and or groups −  Really know your data −  Leverage prior experience/work…don’t re-‐invent the wheel −  Embed governance into the operaQons of your company −  Communicate, Communicate, Communicate!



Thank you!

Kelle O’Neal [email protected]

415-‐425-‐9661 @1stsanfrancisco


Appendix 1 Roles & ResponsibiliQes


Direc4on

TBD Execu4ve Sponsor

Business & IT

Business Data Stewards


Business Unit Officers Data Owners IT Partner(s)

Data Governance Office (DGO)

Management

Program Oversight. Allocates budget & resource. Empower Business Data Stewards. Forum for issue escalaQon.

Craps the Enterprise Data Strategy, processes and standards to ensure that data is managed as an asset.

Execu4ve Level

Management Level

Stewards data within their BU to ensure that the enterprise policies, standards & processes are applied.

Tac4cal Level

Strategic Level Provides overall strategic direcQon, budget & resource approvals. Forum for issue escalaQon. Approval of data domains under governance control.

Execu4on

Technical Data Stewards

Local Data Governance Working Groups

Reference OperaQng Model

Business & Technical Data SMEs


Core Data Governance Roles Role Common Aliases Insights

Execu4ve Sponsor (Business) In an ideal state there is execuQve sponsorship in both Business & IT. If there is a single sponsor, look to the Business.

Data Owner (Business) Business Data Owner, Accountable ExecuQve, Business Steward Lead

Probabili4es: -‐“Owner” may not be accepted by culture -‐May not be able to idenQfy “Owners”

Large/Complex Organiza4ons: May need both Data Owner and Business Steward Lead

Data Steward (Business) Business Data Steward, Data Custodian, Chief Data Steward

It’s all about the details. Never assume the R&R’s based on the Qtle.

Technical Data Steward (IT) Technical Lead, IT Support Partner

Data Architect (IT) Open part of Enterprise Architecture Member of Architecture Review Board (ARB) May not exist, however responsibiliQes should be assigned

Business Analyst (Business) BA’s with Data Governance experience are extremely valuable and provide criQcal support to the Data Stewards.

Data Governance Office Lead DGO Lead


SupporQng Roles Role Common Aliases Insights

Data Analyst Business Data Analyst, Technical Data Analyst

Data Architect InformaQon Architect (IA) Different from an Enterprise Architect, open part of Enterprise Architecture Member of ARB If ARB/EA funcQons don’t exist: Assign responsibiliQes.

Data Quality Analyst

Librarian Knowledge Worker Common in MDM Programs, more so when MDM technology is in place. Role is dedicated to Data Maintenance acQviQes associated with Data Governance.

Data SME Subject Maher Expert, Knowledge Worker, User, Data Entry Clerk

SME’s can be found in the Business & IT, and at all levels in an organizaQon. In some organizaQons a “SME” is considered highly skilled, respected role.



Leadership Roles & Decision Making Bodies

pg 91

ExecuQve Sponsor (Business and IT)

§  Chairs the Data Governance Steering Commihee §  UlQmate authority and responsible for overall program direcQon §  Provides overall strategic vision §  Sets strategy and direcQon for Data Governance & Management §  Works with the Data Governance Office to formulate the data

governance strategy §  Sets direcQon for the Data Governance Working Group (DGWG)

and ensures that the implementaQon is in-‐line with the strategy §  Conveys the data management and governance strategy to the

other Exec Commihees §  Clarifies business strategies to the DGWG §  Provides reinforcement to enable the success of data governance

through communicaQon §  Gathers funding and resource availability for the governance

program §  Approves changes to the data governance strategy Resources: Virtual

Primary ResponsibiliQes

Set Strategy and Steer

Skills/CapabiliQes

§  Generally a Corporate ExecuQve/Officer of Company §  Recognized cross-‐funcQonal leadership and influencing skills §  PoliQcally astute


!Roles may be different in

large or complex organizaQons , i.e. the DGO

Lead can run the DGSC


Resources: Virtual


Membership §  Chaired by the ExecuQve Sponsor (Business) §  IT Sponsor §  Cross LOB execuQves §  Data Owners §  Data Governance Office Lead (usually non-‐voQng) §  IT Partner

Oversight


§  Brings corporate and cross LOB perspecQve §  Approves budget and allocates funding §  Approves funding for enhancements §  Appoints and approves data governance resources §  Nominates, selects and empowers and mandates the DGWG §  Ensures strategic alignment between DG program and other

business unit iniQaQves §  Ensures strategic alignment with corporate objecQves §  Adjudicates intractable issues that are escalated by the Data

Governance Working Group (DGWG) §  Approves funding for enhancements §  Enforces the data governance polices, processes and standards for

the organizaQon §  Approves changes to the data governance strategy §  Has the final say in all data governance decisions §  Owns key data assets across enterprise

!

May have addiQonal decision making bodies in

large or complex organizaQons


Resources: Virtual


§  Data Governance Office Lead §  Data Stewards §  Technical Data Stewards §  Business & Technical Data SMEs §  Key Stakeholders

Management & ExecuQon

Membership


§  Governing body across Business and IT funcQons that own data definiQons and provide guidance & enforcement to drive change in use and maintenance of data

§  Defines data polices, processes and standards (PPS) §  PrioriQzes opportuniQes to develop data polices, processes and

standards, and iniQates data quality iniQaQves §  Advises data stewards on the development and maintenance of

the data PPS §  Assists in the approval and enforcement of data data PPS §  Assess compliance and manages risk §  Resolves issues that have been escalated to the DGWG §  Approves data polices, processes and standards §  Reviews and approves appeals and excepQons; escalates rare

excepQons

!

Local DGWG for large or complex organizaQons Led by the Data Owner,

Business Data Lead or Data Steward


Data Governance Roles

Data Owner

§  Member of Data Governance Steering Commihee §  Accountable for represenQng the Business Unit and corporate

interests from an Enterprise perspecQve §  Accountable for the Business Unit at the Data Governance Steering

Commihee §  IdenQfies and prioriQzes issues and suggested enhancements from

end users §  Helps to promote the data governance program across the

Enterprise §  Serves as an escalaQon point for all data governance issues for the

Data Steward and Data Governance Working Group §  Works with other Data Owners to idenQfy and resolve specific data

quality issues §  Responsible for ensuring compliance with data governance policies

and standards across the Enterprise and within the Business Unit §  Seeks and manages funding for iniQaQves to improve data quality §  Trains, educates, and creates awareness for members in their

respecQve funcQonal areas

Resources: Virtual


§  Business RepresentaQve §  Ability to syndicate and achieve organizaQonal change in a decentralized environment

§  Demonstrated program management and enterprise-‐wide coordinaQon experience

§  Expert communicaQon skills (verbal and wrihen) with the ability to communicate complex issues / requirements to technical and non-‐technical audiences as well as educate the business about data management

Manage

CapabiliQes/Skillsets


!

“Data Owner” may not be embraced

Complexity/Scope may require addiQonal layers/roles to execute at the enQty level

Business Steward Lead

§  Responsible for represenQng the LOB and corporate interests from an enterprise perspecQve

§  Represents the LOB at the Data Governance Working Group (DGWG) §  IdenQfies and prioriQzes issues and suggested enhancements from end users §  Helps to promote the data governance program across the enterprise

(primarily within their LOB) §  Defines polices and standards to ensure data quality within the LOB §  Sets goals on how to manage business informaQon beher §  Serves as an escalaQon point for all data governance issues within the LOB §  IdenQfies and resolves LOB-‐specific data quality issues; works with

appropriate §  Responsibility for ensuring compliance with data governance policies and

standards within the LOB §  Seeks and manages funding for iniQaQves to improve data quality §  Trains, educates, and creates awareness for members in their respecQve

funcQonal areas

Resources: Virtual


•  Solid knowledge and understanding of the business, organizaQon, and funcQonal area

•  Excellent communicaQon skills (wrihen and oral) •  FacilitaQon and consensus building skills •  Ability and willingness to work as part of a team •  Ability to funcQon independently •  ObjecQvity, CreaQvity and Diplomacy

Execute

CapabiliQes/Skillsets


!

Business Steward lead takes the place

of the “Data Owner”

Data Steward (Business)

§  Develop policies and standards to ensure data quality; has overall accountability for data quality

§  Ensures compliance with data governance policies and standards §  UlQmately accountable for the execuQon of the data governance

strategy §  Ensures that all policies, standards, escalaQons, and decisions follow

the predefined processes §  Performs root cause and impact analysis §  Responsible and accountable to Business Steward Lead for the subject

maher knowledge within a parQcular LOB §  Works on Data Governance Working Group (DGWG) when assigned to

specific requests and projects §  Works with the Business Steward leads to help define metrics to

measure and monitor data quality §  Ensures consistency of data quality processes within an LOB §  Resolves daily data quality operaQonal issues and performs root

cause analysis to idenQfy point of failure §  ParQcipates in the wriQng of data definiQons and genealogy Resources: Virtual


CapabiliQes/Skillsets §  Experience developing standards, processes and policies §  Exposure to mulQple business units in relevant industry in order to

understand linkages and dependencies §  Ability to understand upstream and downstream needs §  Can represent a broader view (beyond LOB) §  Knowledge of data / content management §  Experience with technical wriQng §  Excellent oral / wrihen communicaQon

Execute


Data SME (Business and IT)

§  Member of Data Governance Working Groups for specific data domain(s) §  Recognized experts within the organizaQon §  May not be officially responsible for managing an Domain but may be

consulted on topics related to Domain §  Considered a data “go-‐to” person within their Business Unit §  Deep understanding of use and impact of data within and across Business

Unit §  Ability to parQcipate in development of standards, processes and policies §  Ensures compliance with data governance policies and standards §  Ensures that all policies, standards, escalaQons, and decisions follow the

predefined processes §  Performs root cause and impact analysis §  Responsible and accountable to Data Steward for the subject maher

knowledge within Business Unit. §  Resolves daily data quality operaQonal issues and performs root cause

analysis to idenQfy point of failure §  ParQcipates in the wriQng of data definiQons and genealogy §  Works with other SMEs and the data steward to idenQfy and address data

interdependencies across businesses and funcQons §  Work with other SMEs and the data steward to resolve issues §  Drive awareness and adopQon of policies, standards and business rules Resources: Virtual


CapabiliQes/Skillsets §  Business RepresentaQve §  Considered a data “go-‐to” person within their business unit §  Deep understanding of use and impact of data within and across business unit §  Ability to parQcipate in development of standards, processes and policies §  Exposure to mulQple business units in relevant industry in order to understand

linkages and dependencies §  Knowledge of data / content management §  Excellent oral / wrihen communicaQon

Execute


Business Data Analyst

§  Supports the Business Data Steward

§  Work with Technical Data Stewards and IT Data Governance resources to support data modeling, metadata and data quality acQviQes.

•  Leverage well thought out methodology applying specific data enQty and business process experQse.

•  Provide metrics and reporQng support (both adhoc and repeQQve) to data management programs and Data Governance

•  Make recommendaQons for correcQng and prevenQng errors and defects that include process changes, data cleansing and integrity rule updates.

•  DocumenQng the types and structure of the business data (conceptual & logical modeling)

•  Analyze and mine business data to idenQfy paherns and correlaQons among the various data points

•  Design and create data reports and reporQng tools to help business execuQves in their decision making

Resources: Dedicatedl


CapabiliQes/Skillsets §  Strong relaQonship with technical staff §  Ability to map and tracing data from system to system in order to solve a given business or system problem

§  Ability to perform staQsQcal analysis of business data §  Able to translate business quesQons into data requirements to IT §  Able to analyse large sets of complex datasets, examining for both standard and anomalies of data

Execute


Support Roles

Resources: Dedicated

Execute -‐ TacQcal

Role Primary ResponsibiliQes

Data Librarian

§  Uses well documented "playbooks", execute manual data remediaQon/data cleansing acQviQes.

§  Execute manual processes to close the gap on key data that cannot be fixed by automaQon tools and technology.

§  Apply the established data quality playbook of policies and processes to the data i.e. IdenQfy and remediate duplicate records, improve completeness for criQcal data ahributes.

Data Users

§  Defines business requirements §  Understands the data’s term of use §  Complies with data governance policies §  Involved in accessing and invesQgaQng integrated

datasets for staQsQcal and research purposes



The Data Governance Office


§  Documented DG Strategy, Vision, Mission, ObjecQves §  Documented DQ, MDM/RDM and Metadata Management Strategies

§  Documented DG Guiding Principles

§  Documented roles & responsibiliQes of the various members

§  Up to date OperaQng Model

§  RACI matrices

§  Templates for Policies and Processes

§  Templates for capturing metrics and measurement requirements §  Templates for steering commihee meeQngs

§  Training Plans

§  CommunicaQon Plans

§  Template for regular DG communicaQon

§  Templates for logging issues needing escalaQon and eventual resoluQon

§  Templates for new DG service requests

§  Checklists for new projects to ensure adherence to DG standards Resources: Dedicated


Lead, Advise & Support


Data Quality Management MDM Management

Metadata Management

Coordinator/ Program Manager

Data Governance

Lead


!

Strong Partnership between the DGO

and IT DG OrganizaQons

Data Governance Office Lead

§  Co-‐chairs Data Governance Working Group (DGWG) meeQngs with the IT lead

§  Establishes policies and guidelines through DG best pracQces

§  Oversee the integrity, usability, and security of all data assets under DGO purview

§  Responsible for enterprise-‐wide change management and communicaQons for DG efforts

§  Defines the polices, processes and standards, metrics and processes for data quality checks, invesQgaQons, and resoluQon across organizaQon

§  Advises business on data polices, processes and standards

§  Develops the DG strategy based on direcQon set by the execuQve sponsors

§  Resolves conflicts on data across business and technical areas

§  Works closely with IT lead to develop and evolve data governance communicaQons and branding

§  Trains, educates, and creates awareness for members in their respecQve funcQonal areas

Resources: Dedicated FTE


CapabiliQes/Skillsets •  Extensive leadership experience with experQse in Data Governance •  PoliQcally astute and ability to communicate effecQvely both up and down •  Ability to effecQvely monitor industry and regulatory acQviQes and assess impact on Data

Governance pracQce •  Excellent listening, wrihen, and oral communicaQon skills •  Strong organizaQonal, analyQcal, and decision-‐making skills •  Ability to anQcipate and solve problems •  Proven business leader with technology delivery capabiliQes •  EnergeQc, innovaQve, and flexible

Lead and Advise



Metadata Management


Data Governance

Lead


Data Governance Office Coordinator/Program Manager

§  Provides administraQve support to the DGO §  Assists with the administraQve funcQon of the Data Governance Office §  Solicits meeQng agenda items and drap agenda §  Prepares and distributes the agenda and other §  Publishes meeQng minutes §  Updates issue log with resolved agenda items §  Coordinates follow-‐up acQviQes regarding acQon items, issues, and risks §  Records key decisions §  Tracks resoluQon of program issues and follow up §  Documents issues details, and updates issue log with resolved agenda items §  Coordinates follow-‐up acQviQes regarding acQon items, issues, data

documentaQon in projects and risks §  MeeQng management: prepares and distributes the agenda and other

related material to the members. Produces minutes and ensures they, and related documents are published to a common collaboraQon area accessible by all stakeholders

§  Gathers and reports DG metrics §  Manages data definiQon reviews, approvals for important data concepts

Resources: Dedicated


CapabiliQes/Skillsets •  Advanced knowledge of project/program management tools and disciplines •  Proven problem solving skills •  High-‐degree of credibility and influence within the business and technology •  Strong coordinaQon, interpersonal, and facilitaQon skills •  Analysis, evaluaQon, and ability to arQculate value of projects/programs and make

informed and objecQve recommendaQons •  CommunicaQon across project members/parQcipants to: address issues, track and

convey status, provide guidance, assign, manage issues, manage scope •  Capacity to work across projects/releases simultaneously

Administra4ve/Suppor4ng



Metadata Management


Data Governance

Lead



IT Data Governance Roles

Data Governance Office -‐ IT Lead

§  Co-‐chairs Data Governance Working Group (DGWG) meeQngs with the Business lead

§  Responsible for represenQng technology interests from an enterprise perspecQve

§  Responsible for ensuring the technical implementaQon and execuQon of DGSC and DGWG decisions unless otherwise specified

§  IdenQfies issues and suggested enhancements for all users §  Responsible for the Data Management IT support Group §  Custodian of informaQon across the enterprise §  Member of Architecture Review Commihee (ARC) §  Advises technical resources on data standards and ensures technical designs

adhere to data architectural best pracQces to ensure data quality §  Resolves conflicts on data across business and technical areas §  Advises on implementaQon of data polices, processes and standards §  Trains, educates, and creates awareness for members in their respecQve

funcQonal areas

Resources: Virtual or Dedicated


CapabiliQes/Skillsets •  Extensive leadership experience with experQse in Data Governance

technologies and implementaQon •  Understanding of implicaQons of policies and standards on data structures •  Excellent listening, wrihen, and oral communicaQon skills •  Ability to anQcipate and solve problems •  Proven technology delivery capabiliQes •  EnergeQc, innovaQve, and flexible

Lead and Advise

IT Data Governance

Data Quality Support MDM Support

Metadata Support

IT Governance Lead


Data Management IT Support Group

§  Collaborate with the DGWG to provide the day-‐to-‐day custodianship of data assets

§  Partners with the DGO to support and evolve the Data Governance Program §  IdenQfy business data requirements by working with all data consQtuents §  Create metrics and measures to help analyze and report on the quality of the

data §  Communicates with DGO regarding data issues §  Validates data quality rules and prioriQze data quality issue resoluQon §  Stores, retains and disposes of data as per requirements §  Designs technical infrastructure to meet requirements (i.e. data architecture) §  Trained and fully knowledgeable about data governance processes and

standards and confirms adherence §  Performs impact analysis for changes to exisQng data sources, informaQon

architecture, security, etc. §  Ensures security of informaQon §  Maintains enterprise data model §  Support ongoing data cleansing and data migraQon

Resources: Virtual


Execute

IT Data Governance

Data Quality Support MDM Support

Metadata Support

IT Governance Lead


!

Strong Partnership between IT DG and DGO OrganizaQons

Technical Data Steward (IT Support Partner)

§  Supports the Data Steward for specific data domain(s) §  Member of Data Governance Working Group (DGWG), Collaborates with the

DGWG to provide the day-‐to-‐day custodianship of data assets §  Responsible for designing technical infrastructure to meet requirements (i.e.

data architecture) §  Responsible for managing technical aspects of Data Governance, e.g., technical

metadata, data modeling §  Assists in compliance with data standards, policies §  IdenQfy business data requirements by working with all data consQtuents §  Create metrics & measures to help analyze and report on the quality of the

data §  Communicates with DGO regarding data issues §  Validates data quality rules and prioriQze data quality issue resoluQon §  Stores, retains and disposes of data as per requirements §  Performs impact analysis for changes to exisQng data sources, informaQon

architecture, security, etc. §  Ensures security of informaQon according to exisQng InformaQon Security

Policy §  Support ongoing data cleansing and data migraQon Resources: Virtual


CapabiliQes/Skillsets §  Able to translate business quesQons into technical requirements §  Able to analyze large sets of complex datasets, examining for both

standard and anomalies of data §  Have specific data enQty knowledge, understands the business

process that uses the data and is generally responsible for technical development/enhancement/support in one or more applicaQons or data sources that house the data.

§  Has subject maher experQse in specific data enQty

Execute



Best PracQces

Ensuring Success

§  Don’t need to re-‐invent the wheel: Leverage exisQng execuQve leaderships and decision making forums with expanded decision making responsibiliQes

§  Prevent “delegated responsibiliQes” in decision making bodies – require “peer proxy” at the execuQve level

§  Titles need to fit the culture: Focus on the Roles & ResponsibiliQes, not the name of the role §  One individual may fill mulQple roles: Role-‐based not job-‐based §  Ensure robust support for Data Stewards with key support roles: Business Analyst, Data SMEs. §  Know the program focus and fill roles accordingly: Example: A focus on Data Quality will require

experienced Data Analysts, BA’s, IT Roles §  Establish PracQce Groups/Centers of Excellence to support and evolve the program, provide

educaQon and training: §  Business Data Steward PracQce Group (chaired by DGO Lead) §  Technical Data Steward PracQce Group (chaired by IT DG Lead)

§  Where possible, make roles “official”: §  Job DescripQons Updated; Goal Alignment



Appendix 2 Metrics

Sample StaQsQcs

•  Improve efficiencies by 10% in 1 year, building to 40% by year 3 •  Reduce New Project iniQaQon cost by 50%

•  Project delays due to bad data resulted in cost and Qme increase 1.5x iniQal esQmate

•  Reduce number of duplicate records by 30% in 1 year •  Improve ability to respond to regulatory concerns thereby saving up to $5M in external fees per

incident §  ConsulQng fees are $9m for audit; 40% of audit cost spent on gathering data

§  Employee burn out was consistently cited as major risk of maintaining the status quo §  Credit Risk RaQng remediaQon project resulted in over 2,500 hours of effort, largely redundant data

processing §  Global expansion (addiQonal markets and 24x7 processing ) will conQnue to exert pressure on

manual processes, further increasing the likelihood of an oversight leading to reporQng mistakes or noncompliance

Copyright (c) 2014 - First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential pg 113


Improved ProducQvity


Efficiency

§  Total cost of IT / Party (Customer, Member, etc.) §  End User Labor / Number Users §  Total BI/DW Budget / Total Users, and / or Support §  Number of interfaces, File feeds §  Cost per Interface §  DG / Compliance cost divided by Total Income §  DG / Compliance cost vs. risk reserves / premiums §  Budget / TB (GB) §  Benchmarks −  Number IT tools −  Maintenance budgets −  License costs −  Training costs


Courtesy of John Ladley, IMCue SoluQons

pg 115

Data Impact on ProducQvity


Manual Data RemediaQon Data Access and Retrieval Project and project iniQaQon costs

CalculaQng ProducQvity Impact


Produc4vity Loss Inputs Quan4fica4on of Impact

Manual Data ReconciliaQon

•  Employee Time •  Opportunity Costs

$ Impact = E1(Hours x FTE cost) + E2(Hours x FTE cost) + ... +En(Hours x FTE) + [opportunity cost of E1, 2, 3, ... n]

Data Access and Retrieval

•  Above •  Incremental costs of hardware sopware and process

$ Impact = [E1(Hours x FTE cost) + E2(Hours x FTE cost) + ... +En(Hours x FTE)] + [opportunity cost of E1, 2, 3, ... n] + (incremental cost of data sopware, hardware, and support)

Project Delays •  Above •  Increased consulQng fees •  Opportunity cost of delay

$ Impact = [E1(Hours x FTE cost) + E2(Hours x FTE cost) +En(Hours x FTE)] + ΔconsulQng cost + Δsupport cost + opportunity cost of delay

Actual Value IdenQfied

§  TargeQng a 10% producQvity improvement in Year 1 with the ulQmate goal of reducing those costs by 40% over 3 years −  EsQmate 10 FTEs allocated to reconciling data −  EsQmated 5-‐7 addiQonal FTEs dedicated to manual data collecQon/update processes −  The acceleraQng adopQon of regulaQon will conQnue to increase the demands for more transparent data and flexible reporQng

§  Project delays due to bad data resulted in cost and Qme increase 1.5x iniQal esQmate: −  2 year delay −  $3.1m of $5 million spent −  50%-‐60% of the funcQonality delivered



Decreased Costs

Impacted Costs


Technology IT Resources MarkeQng Sales …

pg 120


§  Reduce call center agent Qme searching for client informaQon from 5 min to 2 min −  Improved producQvity could enable 1-‐2 people to be reassigned to other high value acQviQes −  Improved Customer SaQsfacQon with improved call handling Qme

§  Improved markeQng effecQveness −  ProducQvity improvement of 5–10% would enable markeQng team to increase number of executed campaigns

§  Reduce sales rep commission reconciliaQon from 3 days/month to 3 hours/month


Customer Experience


Direct Mail Cost Savings = Cost per mail piece * Number of returned mail pieces Cost Savings = Time to manually remediate mailing list * Cost for Employee

Email Cost Savings = Cost per outbound email * Number of bounced emails Cost Savings = Time to manually remediate mailing list * Cost for Employee


Reduced Risk

Risk Categories


• Liquidity Risk • Credit Risk • Market Risk • Cash Flow

• Regulatory PenalQes

• Cost to adhere • Opportunity Cost

• CompeQQon • M&A

• Supplier exposure • Employee turnover • Loss of Info

OperaQonal Strategic

Financial Regulatory

Risk Measurement

§  Threat metrics −  Cost per downQme event −  Loss of customer confidence

§  Financial Risk −  Liquidity −  OperaQonal costs −  Equity / market value reducQon

§  Data Governance Compliance −  “Hits” on web-‐based tools −  Access counts on repositories

§  Legal Compliance −  PotenQal penalQes per subject area −  LiQgaQon fees over Qme


Courtesy of John Ladley, IMCue SoluQons



Value Iden4fied Metric Risk

ConsulQng fees are $9m for audit; 40% of audit cost spent on gathering data

Cost to comply, future costs to respond to an audit

Regulatory

Insurance premiums for error and omissions increased, unable to argue against the premium change

Insurance costs Financial

Credit Risk RaQng remediaQon project resulted in over 2,500 hours of effort, largely on data issues

Future Project costs Financial, Regulatory

More accurate counterparty data improved risk assessment and enabled reducQon in capital allocaQon

Counterparty data accuracy, Change in Capital allocaQon amount

Financial, Regulatory

Improved Client data protecQon, auditability, and integrity Decrease in number of privacy incidents or complaints

Regulatory

Global expansion (addiQonal markets and 24x7 processing ) will conQnue to exert pressure on manual processes, further increasing the likelihood of an oversight leading to reporQng mistakes or noncompliance

ReporQng accuracy, reporQng mistakes Strategic, OperaQonal

Employee burn out was consistently cited as major risk of maintaining the status quo.

Frequency of employee ciQng “data remediaQon” as an issue

OperaQonal


Increased Revenue

Categories of Value to Revenue

§  Increased “walletshare” §  Improved "household" penetraQon

§  Increased upsell/cross-‐sell §  Improved markeQng -‐ response rate, purchase value per response, campaign effecQveness and opQmized segmentaQon

§  Increased sales effecQveness and face Qme

§  Reduced churn §  Improved lifeQme value of Customers


Focus on CreaQng Business Value


Issues Goals Impact

Common 3rd Party Customer Data AcquisiQon & IntegraQon processes

Improved reporQng and analyQcs

More accurate payment reconciliaQon

Common Customer DefiniQon

Report inaccuracy

Difficulty accessing Data

Difficulty targeQng / profiling

Lack of Data

Poor Data Quality & Lack of

Governance

Improved Access to accurate centralized Customer Data

360° View of Customer -‐ “single source of truth”

Improved Customer Data Maintenance Processes

Enable Sales Force AutomaQon and other tools

Improved customer experience

Increased business effecQveness and agility

Improved ability to measure success

Improved ProducQvity

Reduced Risk

Support ERP roll out and other system changes Automated customer engagement

Faster Qme to market

Facilitate Customer-‐centricity

Increased up-‐selling and cross-‐selling


Appendix 3 OrganizaQonal Change Management


Agenda

§ Quick terminology review § Obstacles to successful data governance change

§ OrganizaQonal Change Management (OCM)

§ 5 Key factors for successful DG change

131 © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential


Terminology

132

Data Governance DefiniQon

§  Data Governance is the organizing framework for establishing the strategy, objecQves and policy for effecQvely managing corporate data.

§  It consists of the processes, policies, organizaQon and technologies required to manage and ensure the availability, usability, integrity, consistency, auditability and security of your data.

CommunicaQon and Metrics

Data Strategy

Data Policies and Processes

Data Standards

and Modeling

A Data Governance

Program consists of the inter-‐workings

of strategy, standards, policies and communicaQon


Data Governance Framework

§  Vision & Mission §  Objectives & Goals §  Alignment with Corporate

Objectives §  Alignment with Business

Strategy §  Guiding Principles

§  Statistics and Analysis §  Tracking of progress §  Monitoring of issues §  Continuous Improvement §  Score-carding

§  Policies & Rules §  Processes §  Controls §  Data Standards & Definitions §  Metadata, Taxonomy,

Cataloging, and Classification

§  Operating Model §  Arbiters & Escalation points §  Data Governance Organization

Members §  Roles and Responsibilities §  Data Ownership & Accountability

§  Collaboration & Information Life Cycle Tools

§  Data Mastering & Sharing §  Data Architecture & Security §  Data Quality & Stewardship

Workflow §  Metadata Repository

§  Communication Plan §  Mass Communication §  Individual Updates §  Mechanisms §  Training Strategy

•  Business Impact & Readiness •  IT Operations & Readiness •  Training & Awareness •  Stakeholder Management & Communication •  Defining Ownership & Accountability

Change Management


OrganizaQonal Change Management

135

§  Successful OCM is −  A structured, organized means to

address the people side of change −  Helping people through change

quickly and successfully so that business value is achieved

§  ProacQvely idenQfying and dealing with change issues will allow the data governance team to −  Address risk areas −  Increase the speed of change

adopQon



Data Governance Impact

136

Obstacles to Data Governance Change

§  CompeQng prioriQes and lack of resources §  Data Ownership and other territorial issues §  Lack of cross-‐business unit coordinaQon §  Lack of data governance understanding §  Resistance to change or transformaQon §  Lack of execuQve sponsorship and buy-‐in §  Resistance to accountability §  Lack of business jusQficaQon §  Inexperience with cross-‐funcQonal iniQaQves §  Change of personnel


Obstacles


Why is Data Governance Important?

Internal pressures: §  Desire to understand customer at any

Qme from any channel

§  Data Quality issues are persistent §  Balance of old mainframe systems with

new technologies §  Movement to the cloud and losing control

of data §  Data Volumes are increasing §  Mobile apps enabling data to be created

and accessed anywhere

§  Project oriented approach to addressing issues/opportuniQes

External pressures: §  Greater amounts of new regulaQons §  Increasing Customer Demands – my

informaQon anywhere at any Qme §  Technology and market changes outpacing

ability to respond

Ensures the right people are involved in determining standards, usage and integraQon of data across projects, subject areas and lines of business.


140

Success Factors for Sustainable Governance

§  Clear understanding of the business drivers −  MoQvaQon −  Metrics

§  Clear business case for DG AND DG change management §  ProacQve, realisQc change management §  EffecQve organizaQon −  Clear definiQon of ownership and accountability −  Strong leadership −  Sponsorship from key execuQves

§  Project management −  Development and implementaQon of InformaQon Management and Data Governance


141

Data Governance Means Change

§  Successful DG means a change to the informaQon management culture, processes and policies

§  Changing that culture means that you are asking people to think and behave differently about how data is accessed and used

§  You need an organized and systemaQc way to manage and sustain those changes – or there is marginal likelihood of success



OCM Basics

142

143

Two Sides to Change Management

WHO? WHAT? WHEN? WHERE? WHY?

ü  Something old stops, and something new starts

ü  RelaQvely easy to plan for and anQcipate

SITUATIONAL REORIENTATION PEOPLE GO THROUGH AS

THEY COME TO TERMS WITH THEIR NEW SITUATION

ü  It’s important to help affected individuals let go of the old situaQon and get comfortable with the new way

ü  Everyone processes at a different rate and are rarely aligned with the milestones of the implementaQon plan

PSYCHOLOGICAL

For change to be successful, BOTH sides need to be addressed For change to be successful, BOTH sides need to be addressed


144

Two Sides to Change Management

For change to be successful, BOTH sides need to be addressed For change to be successful, BOTH sides need to be addressed

For change to be successful, BOTH sides need to be addressed


Why Do People Resist Change?

§  Loss of idenQty and their familiar world −  Loss Analysis

§  DisorienQng experience of the transiQon between the old and the new §  Weak/no sponsorship by execuQve leaders and managers −  Lack of alignment −  No involvement

§  Overloaded with current responsibiliQes §  No answer to WIIFM §  No involvement in the craping the soluQon

§  Each individual’s capacity to handle change §  Other work and personal issues §  How well an organizaQon has handled changes in the past

145 145 © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential

146

What Might Resistance Look Like?

§  Trying to outlast the changes: bargaining for exempQon from new policies or processes

§  ReducQon in producQvity and missed deadlines

§  Going back to the old way of doing things §  Lack of ahendance in project status meeQngs and events, or training

§  Higher absenteeism

§  Open expression of negaQve emoQon

§  From execuQves, resistance could be: −  No visible sponsorship of data governance; no open endorsements −  Refusal or reluctance to provide needed resources and/or informaQon −  Repeatedly canceling or refusing to ahend criQcal meeQngs


147

GeTng People through Change Successfully Requires….

§  Clear definiQon of what is changing −  Make sure the new behaviors, skills and aTtudes are clearly defined and communicated −  Provide examples, training and allow Qme for pracQce

§  AhenQon to feedback: −  What are people saying and how are you addressing it? −  You must respond to feedback; be sure and ahach the acQons you take to the feedback you received so that associates know you are listening

§  Some reward or recogniQon structure to encourage new behaviors §  Measurement and performance management


CommunicaQon Framework for DG Leaders

148

§ Explain why we’re doing what we’re doing -‐ the purpose behind the outcome § What is/was the problem? § Who said so and on what evidence? § What could occur if no one acts to solve it? § What could happen if that occurs?

Purpose Picture Plan Par4cipa4on §  Paint the picture

of how the future will look and feel once DG is implemented.

§  How are people going to get their work done and interact with each other?

§  How will a day be organized?

§  Lay out the plan for achieving the future state -‐ the steps and Qmeline in which people will receive informaQon, training, and the support they need to transiQon to the future

§  Get managers oriented to tell employees how and when their worlds are going to change

§  Start with where people are and work forward

§  Establish each person’s part in both the future state and the plan to get there

§  Show associates their roles and relaQonships to each other in the future

§  Show associates what part they play in achieving the future and the transiQon process to get there

§  All this helps them let go of the past and focus on the future

Courtesy of William Bridges, Managing Transi.ons


149

Change Management Phases

Implemen4ng/ Sustaining Change

Managing Change

Planning for

Change

ü  OrganizaQonal alignment implemented §  Structure §  Jobs/people §  Policies/procedures §  IncenQves §  Performance management

ü  Change integraQon/adopQon assessment

ü  CommunicaQon plan execuQon ü  Training development and delivery ü  Feedback and analysis of results ü  Leadership alignment checkpoint ü Measurement approach & metrics ü  OrganizaQonal impact analysis ü  Resistance management ü  ImplementaQon checklist development

ü  InformaQon gathering and analysis ü  Stakeholder Analysis ü  Sponsorship development ü  Change plan development ü  Leadership alignment checkpoint ü  CommunicaQons planning ü  Training needs assessment and planning


Meet with Program/Project Manager, and lay out CM Approach for the Program/Project

Monitor & Refine Extend

Change Management Alignment to DG Phases


****Communica4on Launch

Informa4on Gathering and Analysis Stakeholder Analysis/Loss Analysis Change Readiness Assessment Leadership Alignment

Sponsorship Development: Assessment and Road Map Detailed Change Planning Communica4on Plan

Operationalize Implement Strategize & Plan Assess & AlignProject

Initiation

Planning for Change

****Collect, Analyze and Report on Feedback

Implementa4on Checklist More Leadership Alignment Metrics and measurement Org Impact Analysis: structure, jobs, training, policies

Managing Change

****Lesson Learned Assessment

Organiza4onal Alignment Ac4on Plan Change Integra4on Checklist Transi4oning to the Business

Implemen4ng/ Sustaining Change


Key Success Factors

151

Five Key Factors for DG Success

§  ExecuQve Sponsorship §  Aligned leadership §  Clear communicaQon (early and open)

§  Stakeholder Engagement

§ Measurement


ExecuQve Sponsorship

§  Has been the #1 success factor for successful change in the Prosci studies since the late 1990s

§  Determine the sponsor’s level of comfort with leading change −  Provide coaching as needed

§  Develop a “roadmap” of events and communicaQons to keep the sponsor engaged in the data governance effort


Sponsorship “Roadmap”

§  The roadmap shows the key acQviQes and responsibiliQes of the project sponsor in support of DG changes.

§  One of the key roles is to engage other leaders in support of the changes § Why: Clarifies the roles of the project sponsor throughout the project life cycle −  Helps idenQfy the acQons needed to build successful sponsorship of the project changes across all leadership levels −  IdenQfies the acQons needed to demonstrate acQve and visible parQcipaQon throughout the enQre project

§  Review the roadmap open: in light of DG progress, what needs to be added or changed?


Sponsorship Roadmap Template


Leadership Alignment

§ Why do it? −  Alignment a criQcal success factor validated by the Prosci best pracQces studies −  EssenQal to take the Qme to assess alignment and address any disconnects −  When leaders are not aligned, change is derailed −  Provides the opportunity for sponsoring senior execuQve to reinforce the expectaQon for support and commitment

§  Repeat at reasonable intervals throughout the iniQaQve


Leadership Alignment Example


CommunicaQon

§  Start your communicaQon process early §  Create an idenQty for your iniQaQve that the organizaQon can grab onto

§  Build repeatable key messages around it −  Be prepared to repeat those messages open

§  Remember to customize your messages by the stakeholder groups impacted; different groups may require different messages

§  Test to make sure that your messages are geTng out there and that awareness and understanding are building


CommunicaQon Plan Example


Stakeholder Analysis

§  Early on, idenQfy who will be impacted by the DG program

§  Determine what is needed from each of them, how they are likely to react to data governance changes, and what sorts of issues/ concerns they are likely to have


Stakeholder Analysis Guide


Engagement Strategy: •  Focused effort must be given to high priority groups

• Provide sufficient level of informaQon to less influenQal groups to ensure buy-‐in

• Move people and or groups to the right by trying to increase their level of interest

•  Forms the foundaQon of your engagement / communicaQon strategy

Stakeholder Engagement Strategy


Meet Their Needs

Key Player

Least Important

Show Considera4on

Stakeholder Influence

Stakeh

olde

r Influ

ence

Stakeholder Interest

Engage your Stakeholders





Business Stakeholders IT Enablement

Data Governance Organization

Key Players

Key Players, Interested Stakeholders

Key Players

Influence, A

ccountability, Enforcem

ent

Measuring Progress/AdopQon

§  Governance is not sustainable without monitoring −  The “nuisance” of change will overwhelm perceived subjecQve benefits

164

– There will be no defense if there are complaints of longer cycle times or interference


What Do You Measure?

§  AdopQon −  How well is the data governance rollout going? −  How are people adapQng to the necessary changes that DG has brought?

§  Amount of improvement / delta from a “state” −  Measure “enabling” aspect of DG – how well does DG influence soluQons with

measurable results? §  Improved processes, projects §  Improved idenQficaQon and reacQon to risk

−  Measure “innovaQon” aspect of DG – how well does DG fundamentally change how business is conducted? §  Trusted AnalyQcs


Pre and Post Change Feedback

§  Series of quesQonnaires or surveys −  Collect data on how well prepared your target audiences are for data governance implementaQon

§ Why ? EssenQal to know how well changes are progressing. −  Without this insight, problems (and resentment) can fester and issues are compounded

§  Can be conducted as interviews, surveys or focus groups § When −  The pre-‐change survey should be administered within 3-‐4 weeks of a change −  The post-‐change survey should be administered at regular intervals (30-‐60-‐90 days)


Pre-‐change Survey


Post-‐change Survey


Final Thoughts

§ Be absolutely clear and specific about what is changing and what that will mean in terms of required behavior changes: people can’t change behavior if they don’t know what they’re supposed do differently.

§ Appreciate that there is a psychology to change: understanding how people react is essenQal to structuring your iniQaQve to deal with it.


Data & Analytics

Sustaining Data Governance and Adding Value for the Long Term