Enabling an Analytics-Driven Organization

The First Step in Information Management

www.firstsanfranciscopartners.com

Enabling an Analytics-Driven Organization

Kelle O’Neal

[email protected]

415-425-9661

@1stsanfrancisco

Samra Sulaiman

[email protected]

202-320-9764

mailto:[email protected]

pg 2

Why We’re Here

Purpose:

Understand the People, Process and Technology needed to support an Analytics-Driven Organization

Outcome: Understanding how Data Management and Data Governance Support Analytics Knowing the organizational constructs needed to trust Analytics Data An ability to manage change Practical knowledge that can be immediately implemented

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

Table of Contents

1. Introduction: Clarification of terms and Level setting 2. Enabling Analytics through EDM:

• Master and Reference Data Management • Meta Data Management • Data Quality • Architecture • Security and Privacy

3. Creating “line of sight” from Data to Analytics 4. Building the Organization 5. Addressing Change 6. Findings from Research: The relationship between Descriptive and Predictive

Analytics 7. Summary and Wrap up

pg 3 © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential

What is Analytics?

Data Insight Action

© 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential pg 4

Our Focus Today

pg 5

The Big Picture: EIM Framework


Provides a holistic view of information in order to manage information as a corporate asset

Enterprise Information Management

Information Strategy

Architecture and Technology Enablement

Content Delivery

BI, Performance Management , and Analytics

Data Management Information Asset Management

GOVERNANCE

ORGANIZATIONAL ALIGNMENT

Content Management

Why is EIM Important?

Internal pressures:

Desire to understand customer at any time 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/opportunities

External pressures:

Greater amounts of new regulations

Increasing Customer Demands – my information anywhere at any time

Technology and market changes outpacing ability to respond

EIM ensures the right people are involved in

determining standards, usage and integration of data across projects, subject areas and lines of

business


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

Retention/Archiving

Master Data Management

Big Data Management

Metadata Management Reference Data Management

Privacy/Security

DATA GOVERNANCE



Symbiotic Relationship

An EIM initiative is an important component of a Data Governance

Strategy

Must Have “Tools”…

• Documented and enforced governance policies and processes

• Clear accountability, ownership and escalation mechanisms

• Continuous measurement and monitoring of data quality & adoption

• Executive support to create a culture of accountability around the quality of the data…it’s everyone’s concern

• Solid alignment between business & IT

Technology alone will not solve the problem

You can’t “do” EIM without Data Governance


Data Governance Definition

Data Governance is the organizing framework for establishing the strategy, objectives and policy for effectively managing corporate data.

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

Communication 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 communication


pg 10

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

How Data Management / Governance facilitates Analytics

Provides a focus on data as a foundational asset of the company so that it can be used in multiple ways effectively

Defines data standards to ensure data consistency

Maps data from source to target and identifies transformations

Creates rules, standards, policies and processes for data cleansing and validation

Articulates most trusted and timely data sources to facilitate data sharing

Identifies potential data irregularities and creates a process to resolve them

“Between 25 percent and 30 percent of a BI initiative typically goes toward initial data cleansing.”

Competing on Analytics, Davenport and Harris



Enabling Analytics Through EDM

• Master and Reference Data Management • Meta Data Management • Data Quality • Architecture • Security and Privacy






Retention/Archiving


Big Data Management


Privacy/Security

DATA GOVERNANCE








Retention/Archiving


Big Data Management

Metadata Management

Privacy/Security

DATA GOVERNANCE



Reference Data Management

pg 15

Master Data Management (MDM)


What is MDM?

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

pg 16

MDM Key Considerations


Category Decision

Entity Types

• What type of data will be managed in the MDM Hub • What are the agreed upon definitions of each type • What is the required cardinality between the entity types • What constitutes a unique instance of an entity

Key Data Elements • Purpose, definition and usage of each data element

Hierarchies and Relationships • Purpose, definition and usage of each hierarchy / relationship structure

Audit Trails and History • How long do we have to keep track of changes

Data Contributors

• What type of data do they supply • Why is this needed • At what frequency should they supply it • What should be taken for Initial load versus ongoing

pg 17

MDM Key Considerations (continued)


Category Decision

Data Quality Targets • How good does the data have to be • Root cause analysis

Data Consumers • Who needs the data and for what purpose • What do they need and at what frequency

Survivorship • What should happen when…

Lookups • Which attributes are lookup attributes • What are the allowable list of values per attribute • How different are the values across the applications and how do we deal with

inconsistencies

Types of Users and Security • What types of users have to be catered for • Can they create, update, delete, search • Can they merge, unmerge

Delete • How should deletes be managed

Privacy and Regulatory • Privacy and regulatory issues






Retention/Archiving


Big Data Management


Privacy/Security

DATA GOVERNANCE



pg 19

Reference Data Management


What is Reference Data?

Reference Data enables an enterprise to make sense of its data and to turn it into real business information. Reference Data are those codes that categorize data and enable an organization to compare that data with internal and external sources.

What is the Total sales for all Males who are Silver Customers that live in states on the Eastern Seaboard and are 35-44 years old?






Retention/Archiving


Big Data Management


Privacy/Security

DATA GOVERNANCE



pg 21

Meta Data Management


Ente

rpri

se

Goal: A common glossary governed at the enterprise level

Lin

e o

f B

usi

nes

s

Application A

Business Glossary includes:

• Common terms, definitions, business rules, etc.

Conceptual Data Model or Enterprise Logical Data Model includes:

• Key business concepts/subject areas

• Key business relationships

Application B

Data Model/Dictionary Data Model/Dictionary

Model to Model






Retention/Archiving


Big Data Management


Privacy/Security

DATA GOVERNANCE



pg 23

Data Quality


What is Data Quality?

The planning, implementation and control activities that apply quality management techniques to measure, assess, improve and ensure the “fitness of data” for use.

pg 24

Data Quality Dimensions


Dimension Key Questions Impact

Completeness Is all appropriate information readily available? Are data values missing or in an unusable state?

Incomplete data can cause major gaps in data analysis which results in increased manual manipulation and reconciliation

Conformity Are there expectations that data values need to reside in specified formats?

If so, do all values conform to those formats?

By not maintaining conformance to specific data formats, there is an increased chance for data misrepresentation, conflicting presentation results, discrepancies when creating aggregated reporting, as well as difficulty in establishing key relationships

Consistency Is there conflicting information about the same underlying data object in multiple data environments?

Are values consistent across all data sources?

Data inconsistencies represent the number one root cause in data reconciliation between different systems and applications. A significant amount of time by business groups is being consumed with manual manipulation and reconciliation efforts

Accuracy Do data objects accurately represent the “real-world” business values they are expected to model?

Incorrect or stale data, such as customer address, product information, or policy information, can impact downstream operational and analytical processes

Uniqueness Are there multiple, unnecessary representations of the same data objects within a given data set?

The inability to maintain a single representation for each entity, such as agent name or contact information (across all component business systems), leads to data redundancy and inconsistency, as well as increased complexity in terms of reconciliation

Integrity Which data elements are missing important relationship linkages that would result in a disconnect between two data sources?

The inability to link related records together can increase both the complexity and accuracy of any corresponding business intelligence derived from those sources. It directly correlates to the level of trust the business has in the data

Timeliness Is data available for use as specified and in the time frame in which it was expected?

The timeliness of data is extremely important. Data delayed in data denied. Could lead to reporting delays, providing slate information to customers and making decisions based stale data






Retention/Archiving


Big Data Management


Privacy/Security

DATA GOVERNANCE



pg 26

Reference Architecture: Conventional EDW


Data Quality Business Rules Engine Meta Data Management Security/Privacy

Acquisition Management Aggregation/ Persistence

Access/Delivery

Staging (structured

data)

• Cleanse • Enrich • Transform • Create

golden record (MDM)

Sources

ODS

Data Mart(s)

Analytics

Data Services

Other Data Retrieval Systems

Archival services

EDW • Internal: HR, Finance

• External: Market data, Credit scores

pg 27

Reference Architecture: How Big Data Fits In


Data Quality Business Rules Engine Meta Data Management Security/Privacy

DATA GOVERNANCE

Acquisition Management Aggregation/ Persistence

Access/Delivery

Staging (structured

data)

• Cleanse • Enrich • Transform • Create

golden record (MDM)

Sources

ODS

Data Mart(s)

Analytics

Data Services

Other Data Retrieval Systems

Archival services

EDW

Structured: • Internal: HR,

Finance • External:

Market data, Credit scores

Unstructured: • Sentiment • Clickstream • PDF

Semi-structured: • XML, JSON

Staging (Semi-structured & unstructured data)

Hadoop






Retention/Archiving


Big Data Management


Privacy/Security

DATA GOVERNANCE



Security


Confidentiality

Integrity Availability

According to NIST, “The security management practices domain is the foundation for security professionals' work and identifies key security concepts, controls, and definitions. NIST defines computer security as the "protection afforded to an automated information system in order to attain the applicable objectives of preserving the integrity, availability, and confidentiality of information system resources (this includes hardware, software, firmware, information/data, and telecommunications).”

Your Analytics infrastructure and data should comply with the normal InfoSec and Privacy practices of your organization!

pg 30

Key Security Domains


1. Security and Risk Management: Security, Risk, Compliance, Law, Regulations, and Business Continuity

2. Asset Security: Protecting Security of Assets

3. Security Engineering: Engineering and Management of Security

4. Communication and Network Security: Designing and Protecting Network Security

5. Identity and Access Management: Controlling Access and Managing Identity

6. Security Assessment and Testing: Designing, Performing, and Analyzing Security Testing

7. Security Operations: Foundational Concepts, Investigations, Incident Management, and Disaster

8. Software Development Security: Understanding, Applying, and Enforcing Software Security

Reference: (ISC)2

pg 31

Big Data Analytics Privacy


According to U.S. President’s Council of Advisors on Science and Technology, “Big data drives big benefits, from innovative businesses to new ways to treat diseases. The challenges to privacy arise because technologies collect so much data (e.g., from sensors in everything from phones to parking lots) and analyze them so efficiently (e.g., through data mining and other kinds of analytics) that it is possible to learn far more than most people had anticipated or can anticipate given continuing progress.”*

Some Key Challenges include:

Difficulty in data anonymization and masking due to sheer volume, number of sources and variety of data

Collecting information without explicit consent

Lack of or insufficient data governance practices – according to Rand Secure Archives Data Governance Survey in 2013, “44% of the respondent have no formal data governance policy”*

Infrastructure – both on-premise and cloud-based

Reference: MIT Technology Review Custom + Oracle Courtesy of Samra Sulaiman, ConsultData, LLC


Analytics


What happened?

Why did it happen?

What will happen?

How can we make it happen?

Diagnostic

Prescriptive

Descriptive

Predictive

Reference: Gartner

Val

ue

Difficulty

Courtesy Samra Sulaiman, ConsultData, LLC

Key Components of an Effective Analytics Strategy

People Process

Technology Data


pg 35

People


Roles Key Responsibilities

Business SME/Manager (decision maker) • Defines the business problem, business objectives

Analyst (explorer) • Specific domain area expert • Works with raw data • Creates reports • Leverages data visualization tools

IT • Sets up infrastructure • Pre-processes data • Tests and deploys models

Data Scientist • Develops models • Performs statistical analysis • Explores data trends, anomalies

Process

Descriptive

Prescriptive

Prescriptive


Courtesy of Samra Sulaiman, ConsultData, LLC

Typical Analytics Life-cycle:

• Define the business problem—e.g., forecasting future sales based on past performance

• Design data requirements—e.g., aside from internal data sources, can data be enriched using external data sources such as credit scores, social media data feeds?

• Pre-process data—rationalize and cleanse data; apply the appropriate level of data quality dimensions

• Perform data analytics—data analytics can be performed using various algorithms or machine learning techniques to gain insight

• Visualize the results—various tools can be leveraged to visualize the insight, show anomalies, etc.

Define Problem

Design

Pre-process

Analyze

Visualize

pg 37

Technology


Define your use cases before selecting your tools!


Creating “Line of Sight”

• Select use cases: How EDM Impact Analytics

pg 38

From Data to Analytics

Data Insight Action


pg 40

Select Use Cases


How EDM Impacts Analytics:

1. How Data Quality impacts Analytics:

Demand forecasting

Sentiment analysis

2. How Meta Data impacts Analytics:

Glossary

Lineage

3. How Master Data Management impacts Analytics:

Hierarchy management

pg 41

How Data Quality Impacts Analytics


Demand forecasting: a retail company wants to forecast future product sales based on historical data to better manage inventory

Data Quality Considerations: Accuracy Completeness Consistency Timeliness Uniqueness Integrity Conformity

Focus: highest quality data Courtesy of Samra Sulaiman, ConsultData, LLC

pg 42

How Data Quality Impacts Analytics (continued)


Sentiment analysis: a software product company wants to analyze consumer feedback quickly after product launch

Focus: relevant data - eliminate ‘noise’ quickly Courtesy of Samra Sulaiman, ConsultData, LLC

Data Quality Considerations • Accuracy • Completeness Consistency Timeliness • Uniqueness • Integrity • Conformity Relevance (new)

pg 43

How Meta Data Impacts Analytics


• How data flows across the infrastructure/company?

• How the data is derived? • How is data transformed?

• Data type changes • Calculations • Business/DQ rules

What? Why? How?

• Ability to track upstream/data producers and

downstream/data consumers • Which system transformed the data? • How data was transformed (which rules and calculations

were applied)? • Ability to perform root-cause-analysis – tracing data errors

from a report back to the source

• What data exists today? • Who owns the data? • Which system is the ‘System of

Record’ or ‘Trusted Source’? • Are there standard business rules

for that data?

• Common understanding of available data • Ability to locate needed data more quickly • Ability to know who can answer questions about the data • Ability to trust the data due to the governance process • Audit trail of who touched/changed a term • Data quality rules, metrics, etc.

pg 44

How MDM Impacts Analytics


Global Company

US

Subsidiary A

Branch A Branch B Branch C

Subsidiary B

Europe

Subsidiary C

Branch D Branch E

Business Challenges:

Regulatory Compliance - e.g., inability to uniquely identify all counterparties to a transaction

Sales & Marketing - e.g., inability to roll-up sales by subsidiaries or by region

Product – e.g., poor inventory management due to lack of product hierarchy and inconsistent product data

pg 45

How MDM Impacts Analytics (continued)


Executive sponsorship is critical!

Business-driven with close collaboration with IT

Holistic strategy to avoid re-work later; leverage existing (funded) projects, if possible

Strong Data Governance is key to success

Iterative process – rapid and continuous delivery of key capabilities that business needs

pg 46

A Balancing Act!


Rank Number Topic As a …. I want to… So that I can … Acceptance Criteria

1 16 “Project” Field

identifier

Data

Governance

Lead

identify from which project the Work In Progress

business terms has originated and from which

project the Work In Progress business term groups

has originated. Work In Progress is part of

Ungoverned Business Terms.

see which project teams are accountable

for specific business terms and be able to

manage them accordingly during the work

in progress business term section

A “free-form” text field titled “Project ” that can be completed on

the same page as the business term and business term group is

being developed in the InfoMap tool.

Target Release Date: 05/01/15 (IT)

Note:-Managing this via business term group can be complicated

since some of the business terms are shared by multiple projects.

This approach has been rejected.

2 1 Data Concept

Management in

InfoMap

CG Data

Governance

Lead

- be able to see all DG defined core, non-core (i.e. BL

governed) & ungoverned business concept with their

associated data subject and data concept

association (per the Core Concepts spreadsheet)

mange the core and non-core business

concepts

Flags - CG core / non core, BL ownership (IM, DIST, SERV, GBS and

other entities e.g. PCS, ITG, GBS HR, GBS FIN, etc.),

Standard report configuration that can be shared between the CG

DGL & B/L DGLs.

3 2 Data Concept

Management in

InfoMap

CG Data

Governance

Lead

- For all core & non-core assign the accountable,

consulted and informed data governance business

l ine & named owner

manage the ongoing definition, assignment

of accountability of Data Subjects and

Concepts within InfoMap

Report status and progress

ACI assignment at the BL level.


DGL & B/L DGLs.

4 3 Data Concept

Management in

InfoMap

CG Data

Governance

Lead

- see core & non core (business governed) data

concepts that have no business definition by

business l ine and owner.

assign and manage the development of

core concept definitions to their

appropriate owners.

Add Inflight (WIP) Group within Governed Business Terms


DGL & B/L DGLs.

Data Sources DQ Solutions Your Data Management

solutions should be proportionate to your Data Analytics needs and focused on business value

Courtesy Samra Sulaiman, ConsultData, LLC


Data Governance Operating Models

pg 47

Data Governance is critical for Analytics

pg 48

You can’t “do” Analytics without Data Governance

An Analytics initiative is an important use case for a Data

Governance Office

Must Have “Tools”…

• Documented & enforced data quality policies and processes to ensure data consistency and standards

• Understood business logic that maps data from source to target

• Clear data accountability, ownership and escalation mechanisms

• Continuous measurement & monitoring of data quality, adoption & value

• Clearly defined data elements, attributes and computation/derivation of shared data

• Really know your data quality before diving into an Analytics “Project”

Data Governance is the program that ensures that the Analytics

content is trusted


Operating Model

Outlines how Data Governance will operate

Forms basis for the Data Governance organizational structure – but isn’t an org chart

Ensures proper oversight, escalation and decision making

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

Creates the infrastructure for accountability and ownership

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


Process to create an Operating Model

• How are decisions made?

• Who makes them?

• How are Committee’s used?

Culture

• Centralized

• Decentralized

• Hybrid

Operating Model • Data Governance

Owner

• SME’s

• Leadership

People


Pros: Formal Data Governance

executive position

Data Governance Steering Committee reports directly to executive

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 Organizational Impact

New roles will most likely require Human Resources approval

Formal separation of business and technical architectural roles Bus / LOBs

Operating Model - Centralized

DG Executive Sponsor

DG Steering

Committee

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 Committee

LOB/BU Data Governance Working Group

pg 52

Operating Model - Decentralized

Data

Stewards

Application

Architects

Business

Analysts Data Analysts

Pros: Relatively flat organization

Informal Data Governance bodies

Relatively quick to establish and implement

Cons: Consensus discussions tend to take

longer than centralized edicts

Many participants compromise governance bodies

May be difficult to sustain over time

Provides least value

Difficult coordination

Business as usual

Issues around co-owners of data and accountability


pg 53

Operating Model - Networked

Pros: Flat structure Data management is aligned to lines of business and/or

IT ensuring there is a clear understanding of data requirements for that organizational unit

Relatively quick to establish and implement Known documented connections and RACI charting

creates accountability without impacting an organization chart

Cons: Collaborative decisions tend to take longer to

implement than centralized edicts

Many participants compromise governance bodies (making it potentially unruly)

RACI’s and the Network itself needs to be maintained

Little enforceable consistency around managing data across the enterprise

Difficult coordination Autonomy at the LOB level can be challenging to coordinate

Data Governance Office


pg 54

Operating Model - Hybrid

Pros: Centralized structure for establishing appropriate direction

and tone at the top

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

Data Governance Lead position is a full time, dedicated role – DG gets the attention it deserves

Working groups with broad membership for facilitating collaboration and consensus building

Potentially an easier model to implement initially and sustain over time

Pushes down decision making

Ability to focus on specific data entities

Issues resolution without pulling in the whole team

Cons: Data Governance Lead position is a full time, dedicated role

Working groups dynamics may require prioritization of conflicting business requirements

Too many layers

Data Governance Steering Committee


Data Governance Working Group

Business Stakeholders IT Enablement

Data Governance Organization


pg 55

Operating Model - Federated

Pros: Centralized Enterprise strategy with decentralized

execution and implementation

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

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

Potentially an easier model to implement initially and sustain over time

Pushes down decision making

Ability to focus on specific data entities, divisional challenges or regional priorities

Issues resolution 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 priorities and Enterprise priorities

Enterprise Data Governance Steering Committee

Enterprise Data Governance Office

Data Governance Groups

Data Governance Organization

Business Stakeholders

IT Enablement

Divisional DG Office


IT Enablement



IT Enablement


IT Enablement



Operating Model Roles and Responsibilities

Data Governance Steering Committee

− Provides overall strategic vision

− Approves funding, budget and resource allocation for strategic data projects

− Establishes annual discretionary spend allocation for data projects

− Adjudicates intractable issues that are escalated

− Ensures strategic alignment with corporate objectives and other business unit initiatives


− Chairs the Data Governance Steering Committee and Data Governance Working Group

− Acts as the glue between the Data Governance Steering Group and the Working Committee

− Defines the standards, metrics and processes for data quality checks, investigations, and resolution

− Advises business and technical resources on data standards and ensures technical designs adhere to data architectural best practices to ensure data quality

− Adjudicates where necessary, creates training plans, communication plans etc.


− Governing body comprised of data owners across Business and IT functions that own data definitions and provide guidance & enforcement to drive change in use and maintenance of data by the business

− Validates data quality rules and prioritize data quality issue resolution across the functional areas

− Trains, educates, and creates awareness for members in their respective functional areas

− Implements data business processes and are accountable to decisions that are made

pg 56



Aligning Governance and Analytics

pg 57

Example: Data & BI Governance Structure

Accountable for Governance and Change Leadership for Data & BI across Company • Executive Data & BI Owner

• Forum Chair

• Membership – Executive Process Owners

• Meeting Cadence - Monthly

Data & BI Governance Leadership Forum

Accountable for Master Data Quality across (Customer, Product, etc) • MDM Practice Lead • Membership – Chief Data Stewards

• Meeting Cadence – Bi-Weekly

Data Stewardship Forum

Accountable for BI Standardization & Adoption • BI Practice Lead

• Membership – Functional Reporting Leads • Meeting Cadence – Bi-Weekly

Business Intelligence Forum

Strategy & Guidance Agreed Decisions

Strategic Initiative Alignment

Initiative Requests Project / Initiative Progress Intractable Issues


Example: Data & BI Governance Forum Roles

Forum to strategize, plan and review Master Data initiatives led by team members Forum to drive Company’s Performance Measurement Architecture development Forum to discuss & define strategic direction impacting policy, process & technology Data & BI decision forum for Project X as well as other Corporate initiatives

Market to Sell

Idea to Offer

Finance World Wide Operation

Hire to Retire Issue to

Resolution to Prevention

FP&A IT

Data & BI Governance Leadership Forum

Forum Chair – Data Governance Sr. Director

Process Owner VP SALES SVP Product Strategy CFO SVP Services VP HR SVP Finance VP Info Tech

Data Ownership Customer Product Chart of Acct Vendor Employee

Executive Data & BI Owner – EVP XXX

Executive Process Owner’s represent the Functions within their Process Domains • Active participation is critical to our success • When necessary, delegation to peer Functional Owners is acceptable

VP WWOPS


Example: Analytics Operating Model – Immediate

IT Advisor

Enterprise Infrastructure Committee

Executive Office (CEO)

Strategy & Risk

(CRSO)

IT (CIO)

Accounting (CAO)

Global Services (COO)

PMO

Head of Business Analytics

CEO

Credit Analytics Client Analytics Market Analytics

LOB … LOB … LOB …

Data Stewards

Executive Sponsor Analytics CFO


Example: Analytics Operating Model – Long Term

IT Advisor



Strategy & Risk

(CRSO)

IT (CIO)

Accounting (CAO)


PMO


Head of Business Analytics Analytics Working Group

LOB Reporting LOB … LOB … LOB … LOB … LOB …

CEO

Credit Analytics Client Analytics Market Analytics


Analytic Directors

Data Stewards


Example: Data Governance and Analytics

Sponsor - Data Governance

Business Steward Leads

Data Governance Office DGO Chair

IT Lead DG Coordinator

Data Management IT Support Group


Data Stewards

Marketing Fin. Accting

Fin. Treasury Risk ECM Ops.

HR

Fin. FP&A

Credit Admin Fin. Ext. Reporting

Legal/ Compliance

SVB Analytics Privacy/CSO

IT Advisor



Strategy & Risk (CRSO)

IT (CIO)

Finance (CAO/CFO)


PMO



Analytics Working Group

Analytic Directors

Credit Analytics

Client Analytics

Market Analytics


CEO This is a role / relationship chart and NOT an organization chart

Data Stewards


Example: Data Governance and Analytics

Sponsor - Data Governance

Business Steward Leads

Data Governance Office DGO Chair

IT Lead DG Coordinator

Data Management IT Support Group


Data Stewards

Marketing Fin. Accting

Fin. Treasury Risk ECM Ops.

HR

Fin. FP&A

Credit Admin Fin. Ext. Reporting

Legal/ Compliance

SVB Analytics Privacy/CSO

IT Advisor



Strategy & Risk (CRSO)

IT (CIO)

Finance (CAO/CFO)


PMO



Analytics Working Group

Analytic Directors

Credit Analytics

Client Analytics

Market Analytics


CEO This is a role / relationship chart and NOT an organization chart

Data Stewards



OCM Basics

pg 64

EIM Means Change

Successful EIM means a change to the information 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 systematic way to manage and sustain those changes – or there is marginal likelihood of success


Two Sides to Change Management

WHO? WHAT? WHEN? WHERE? WHY?

Something old stops, and something new starts

Relatively easy to plan for and anticipate

SITUATIONAL REORIENTATION PEOPLE GO THROUGH AS THEY COME TO TERMS WITH THEIR NEW

SITUATION It’s important to help affected individuals let go

of the old situation and get comfortable with the new way

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

PSYCHOLOGICAL

For change to be successful, BOTH sides need to be addressed © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential

pg 66

Why Do People Resist Change?

Loss of identity and their familiar world

− Loss Analysis

Disorienting experience of the transition between the old and the new

Weak/no sponsorship by executive leaders and managers

− Lack of alignment

− No involvement

Overloaded with current responsibilities

No answer to WIIFM

No involvement in the crafting the solution

Each individual’s capacity to handle change

Other work and personal issues

How well an organization has handled changes in the past

67 © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential

pg 67

68

What Might Resistance Look Like?

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

Reduction in productivity and missed deadlines

Going back to the old way of doing things

Lack of attendance in project status meetings and events, or training

Higher absenteeism

Open expression of negative emotion

From executives, resistance could be:

− No visible sponsorship of data governance; no open endorsements

− Refusal or reluctance to provide needed resources and/or information

− Repeatedly canceling or refusing to attend critical meetings


69

Getting People through Change Successfully Requires….

Clear definition of what is changing

− Make sure the new behaviors, skills and attitudes are clearly defined and communicated

− Provide examples, training and allow time for practice

Attention to feedback:

− What are people saying and how are you addressing it?

− You must respond to feedback; be sure and attach the actions you take to the feedback you received so that associates know you are listening

Some reward or recognition structure to encourage new behaviors

Measurement and performance management


Communication Framework to Drive Change


State the aspiration, the BHAG (Big, Hairy, Audacious Goal) What is the desired

future? What is the value of

the future state to the company? How does that

future state move forward the overall business? High-level, strategic

statement of a Goal

Vision Picture Plan Participation Paint the picture of

how the future will look and feel once Data Management is implemented. How are people

going to get their work done and interact with each other? How will a day be

organized? Future State Principles

Lay out the plan for achieving the future state; the steps and timeline in which people will receive information, training, and the support they need to transition to the future

Orient managers to tell employees how and when their worlds are going to change

Start with where people are and work forward

What does this mean to me?

Overall Roadmap Group specific

roadmaps

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

Show associates their roles and relationships to each other in the future

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

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

What is my role? Who does What Across Enterprise Group Specific

Adapted from William Bridges, Managing Transitions

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? Why you are

executing your Vision

Purpose

pg 70

Change Management Phases

Organizational alignment implemented Structure Jobs/people Policies/procedures Incentives Performance management

Change integration/adoption assessment

Communication plan execution Training development and delivery Feedback and analysis of results Leadership alignment checkpoint Measurement approach & metrics Organizational impact analysis Resistance management Implementation checklist development

Information gathering and analysis Stakeholder Analysis Sponsorship development Change plan development Leadership alignment checkpoint Communications planning 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 EIM Phases

****Communication Launch

Information Gathering and Analysis

Stakeholder Analysis/Loss Analysis

Change Readiness Assessment

Leadership Alignment

Sponsorship Development: Assessment and Road Map

Detailed Change Planning

Communication Plan

Operationalize Implement Strategize & Plan Assess & Align Project Initiation

Planning for Change

****Collect, Analyze and Report on Feedback

Implementation Checklist

More Leadership Alignment

Metrics and measurement

Org Impact Analysis: structure, jobs, training, policies

Managing Change

****Lesson Learned Assessment

Organizational Alignment Action Plan

Change Integration Checklist

Transitioning to the Business

Implementing/ Sustaining Change


The Potential Value of OCM to Your Business

Can result in real monetary value to the business

− Acceleration of planned changes

− Faster realization of planned benefits

− Minimizes business disruption: loss of staff, lower productivity, etc.

Greater likelihood that the IM/DG changes implemented will be sustained


The Correlation is There

There is data that shows a strong correlation between effectively managing change and meeting objectives

− “Show me the numbers”

Analysis from:

− Prosci’s Best Practices studies

−McKinsey studies

− Your own organizational experience?


2002 McKinsey Study

Examined 40 projects

Evaluated

− ROI expected

− ROI delivered

− Level of change management effectiveness

Results Direct correlation between change

management effectiveness and gap between ROI expected and ROI delivered

Those that were above average on those

factors realized 143% of expected value

Those that were below on all three factors

realized 35% of expected value From the article “Helping Employees Embrace Change”, McKinsey Quarterly 2002 Number 4, Jennifer A. LaClair and Ravi P. Rao



Using reorganization as an opportunity to change

mind-sets and behaviors of the workforce

Focusing as much on how the new organizational

model would work as on what it looks like

Accelerating pace of implementation to make the new

model deliver value as soon as possible

Addressing all risks and bottlenecks as early as

possible, before and during implementation

Developing a clear communication plan for all

internal and external stakeholders

Ensuring that IT, financial, human resources, and

other systems were updated to support new

organizational model

Defining detailed metrics for reorganization’s

effect on short and long-term performance

and assessing progress against them

KEY STRATEGIES KEY PROCEDURES

2010 McKinsey Reorganization Study

Taking Organizational Redesign from Plan to Practice, McKinsey Global Survey Results, 2010 Courtesy of McKinsey & Company

Contributes to: Achieving Project Objectives…


Consider:

There is a direct and profound effect that a strong change management program bears on an organization’s ability to meet or exceed its project objectives

95% of those who rated their change management program as excellent met or exceeded their project objectives as opposed to only 17% of those who rated their change management program as poor or non-existent


pg 79

Contributes to: Staying on Schedule…


pg 80

Consider:


There is also significant correlation between the quality of the change management program and the project’s ability to stay on or ahead of schedule.

75% of those respondents with excellent change management programs had projects that were on or ahead of schedule

Poorly Managed Change Results in:

Lower productivity

Resistance

Turnover of valued employees

Apathy for the future state

Arguing about the need for change

More people taking sick days or not showing up

Changes not fully implemented; benefits not achieved

People finding work-arounds or reverting to the old way of doing things

The change being totally scrapped

Divides are created between ‘us’ and ‘them’


pg 82

Bottom Line…


There is significant correlation between the effectiveness of a change management program

and achieving Data Governance results

Five Key Factors for DG Success

Executive Sponsorship

Aligned leadership

Clear communication (early and often)

Stakeholder Engagement

Measurement


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 essential to structuring your initiative to deal with it.



Survey Results

pg 85

Descriptive Analytics Program


• Descriptive Analytics/BI is still going strong

Predictive Analytics Program


• Predictive Analytics is still emerging

Relationship of the Programs


• Majority are under the same umbrella

• Very little outsourcing of overall Program

Predictive Analytics Investment


• Big Market Opportunity

Descriptive Analytics Investment


• It’s not “either/or”, both are receiving investment

Organizational Construct


• All Analytics will be managed together

Measuring Effectiveness of Descriptive Analytics


• Improved data is a measurement of effectiveness

• Usual suspects of better decision making, better understanding of results, improved processes

Measuring Effectiveness of Predictive Analytics


• Very similar to Descriptive Analytics

Relative Measures of Effectiveness


• No comparison between the programs, although they appear to be seeking a similar outcome

Take-Aways

• Organizations still struggle for skilled resources Resources

• Descriptive BI isn’t going away Investment

• Optimization needs to occur across Descriptive and Predictive BI Outcome



Wrap-Up

pg 96

Approach to practical, affordable analytics

Identify efficiency and operational metrics for BI Analytics environments

Confirm scope and seed Analytics &

Metrics model

Define cost of ownership and operating standards

Synthesize and map to

benchmarks

Develop and present efficiency

sustaining plan

Rationalize metrics and predictive models

Align metrics to business

Develop transition plan to unified

metrics

Data Efficiency Corporate Metrics

Analytics / BI Architecture Sustaining

Plan


98

Kelle O’Neal

[email protected]

415-425-9661

@1stsanfrancisco


Thank you!

Samra Sulaiman

[email protected]

202-320-9764

mailto:[email protected]


Who Took the Survey?

0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0%

Executive Management

Finance Management and / or Reporting

Content and / or Digital Asset Management

Application Development

Data and / or Information Architecture

Business Intelligence and / or Analytics

Information / Data Governance

Corporate Research and / or Library

Marketing and / or Market Research

IT Management

Software or System Vendor

Other

Job Function

Demographics

Demographics

Demographics

Demographics

Business Intelligence v Data Science



Data & Analytics

Enabling an Analytics-Driven Organization