‘reliable business data – ‘implementing a...

©2007-2016 Axis Software Designs, Inc. All Rights Reserved

‘Reliable Business Data –

‘Implementing A Successful

Data Governance Strategy

with Enterprise Modeling

Standards’

Welcome! Let Me Introduce Myself…

Marcie Barkin Goodwin

President & CEO

Axis Software Designs

[email protected]

www.axisboulder.com

©2007-2016 Axis Software Designs, Inc. All Rights Reserved page 2

A Brief Bio of Marcie

Axis Software Designs – a consulting and product development company specializing in enterprise modeling standards, procedures and best practices.

Senior Enterprise Architect, providing modeling infrastructure consulting services and data modeling training to private industry and government agencies for over 25 years.

Designer of ‘EM-SOS!™’ – Enterprise Modeling Set of Standards

Partner – Sandhill Consultants


Agenda

The Big Picture – Data Governance What Is It & Why Do We Need It? The Cost of Unreliable Business Data Data Governance in Modeling Best Practices

DAMA DMBOK/DMM Perspective

A Strategy for Reliability – Modeling Best Practices Properties of Reliable Data Standardized Data Design Practices & Oversight

Communication & Metadata Integrity (Who Can You Trust?) Reusability (Saving Time & Money) Reduce Redundancy (Say Again?)

Reliable Business Data in Action Best Practices - The Essentials

Strategy, Standards and Procedures Implementing a Standards & Best Practices Framework

We Know It’s Important, But Who Has Time?


The Big Picture – Data Governance In Modeling

As in life, as in our IT & Modeling environments…

Enter Governance.

IT Governance, including Data Governance, is a

philosophy of accountability, substantiated by a

structured plan to guide IT processes towards

accurate and accessible information.

“It’s astonishing, in this world, how things don’t turn out at all the way you expect them to.”

Agatha Christie


Data Governance – What Is It?

“Data Governance is the formal orchestration of people, processes, and technology to enable an organization to leverage data as an enterprise asset.” – MDM Institute

“Information Governance is the specification of decision rights and an accountability framework to encourage desirable behavior in the valuation, creation, storage, use, archival and deletion of information. It includes the processes, roles, standards and metrics that ensure the effective and efficient use of information in enabling an organization to achieve its goals.” – Gartner


Data Governance – Why Do We Need It?

…Because generally speaking, the management and

quality of our data is sorely lacking.

The costs of managing disparate IT assets is expensive and the work efforts are cumbersome

Data processes aren’t consistent and standardized, thus creating different report results

Regulatory compliance - Sarbanes-Oxley, etc.:

“Where management has outsourced…functions to third party service provider(s), management maintains a responsibility to assess the controls over the outsourced operations.”

AND – ‘On average, American companies believe that 25% of

their data is inaccurate.’ Survey: ‘Experian Quality Data’


Unreliable Business Data

Rarely do organizations design from an enterprise

perspective, and as a result they ‘sub optimize’ solutions

by allowing diverse standards, naming conventions,

codes, etc.

This ‘silo’ approach to data and modeling efforts results

in data inconsistencies which have a deleterious effect

on the overall understanding, accessibility, integration

and reliability, or integrity of the data.

And other problems exist as well… ©2007-2016 Axis Software Designs, Inc. All Rights Reserved page 9

Unreliable Business Data (Cont.)

Tendency to clone multiple, non-standardized versions of data structures resulting in: Unreliable answers from data analysis Variable data structures that are non-reusable, inconsistent,

inflexible and expensive to fix

Lack of enforcement exists for: Naming standards and class words Business dictionaries & abbreviations

Overruns of Application Development schedules and budgets

Lack of Business to Data Traceability

Difficulties with Data Integration & Interoperability


Regulatory

Economic Disaster

Sept 9, 2010 San

Bruno, CA

Public Health & Safety

The absence of well documented standards, common business term definitions, and an agreed-to format for exchange makes it almost impossible for parties to understand each other.

The Cost of Unreliable Business Data


Do These Issues Look Familiar?

Same object name has different definitions and usage Expensive conflicts in processes Inaccurate and risky decision making

Siloed metadata (turf wars) Unnecessary duplication and cost due to ambiguity in meaning and

usage

Same object definition applied to different names Higher rates of redundancy and data errors

Inconsistent meaning across lines of business Reduced value of BI

Reduced opportunity for re-use Increased cost and time to market, decreased quality and value

Change and growth management difficult to impossible Lost opportunity, increased cost of doing business


Data Governance in Modeling Best Practices Data modeling Best Practices support the objectives

of data governance and produce reliable business data.

Develop Roles and Responsibilities including data custodians

Develop standards for model and model object naming

Develop standardized procedures regarding how models are to be created, organized, stored, changed, archived, backed up, versioned and migrated through a model development life cycle

Develop audit procedures that ensure ongoing compliance with standards, business rules and/or government regulations

A Best Practices strategy prevents the risk of creating incorrect data which may result in critical harm in the real world.


Blueprints - DAMA DMBOK & CMMI DMM

The DAMA-DMBOK2 Guide Knowledge Area Wheel


Data Management Maturity Model

Properties of Reliable Data

Consistency of: Meaning Usage Form (Datatype, etc.) Interface Results


For data to be reliable, it must be consistent and usable across all time, space and function of the owning organization. It can be characterized by some or all of the following properties:

Predictable Measureable Enforceable Manageable Portable Reusable

Standardized Data Design Practices & Oversight A modeling strategy which incorporates an enterprise perspective is key to ensuring the reliability of business data.

Strategies are determined by clarifying where you are, and determining where you want to be

Strategies are not ‘one size fits all’, but they do share the critical goals of: Communication – the all-important foundation Metadata Integrity “The naked truth is always better than the best-dressed lie.” Ann Landers Reusability - The reuse of model objects, brings

consistency to model structures Non-Redundancy - Redundancy is one of the most

common causes of inefficiency & data inaccuracy. ©2007-2016 Axis Software Designs, Inc.

All Rights Reserved page 16

Reliable Business Data in Action

When an organization manages its data as a corporate asset, and makes modeling best practices a part of its data governance efforts, the focus turns towards the business value of the data, not just the IT requirements.

The business drivers for managing data as a corporate asset are:

Competitiveness

Regulatory compliance

Realizing the benefit of investments in BI and other information-based initiatives

Risk mitigation ©2007-2016 Axis Software Designs, Inc. All Rights Reserved page 17

Reliable Business Data in Action (Cont.)

The keys to successful best practices modeling implementation are:

Management support of a data governance program,

including an integral best practices approach to modeling

Development of a modeling strategy that builds governance

and quality into the process

Involvement by both the business side as well as IT

Resistance to creating or accepting silos of standards and

procedures

Development of a ‘Model Management Framework’ to

support the modeling strategy


A Modeling Framework

Must support:

Strategic goals & policies of the organization

Standards – which are extensible across the enterprise

Processes & Data Modeling Life Cycle – to ensure repeatable, efficient data design and outcomes

Data Governance Roles & Responsibilities

Must include:

Audit documentation – for changes in processes & content

Metrics Reports – to measure improvement in the data design process (reuse, decreased redundancy)

Must be:

Easily accessible, maintained & enforced

Multi-user environment with associated permissions ©2007-2016 Axis Software Designs, Inc. All Rights Reserved page 19

The Challenges

Organizations generally understand the need for modeling

standards and procedures, but face several obstacles in

getting the job done:

There isn’t a plan for developing a Framework

In-house resources are already working full time

Resources may not have infrastructure knowledge or writing skills

Internal politics or ‘silo groups’ can’t agree on what should be implemented

“We know we need it, but who has the time?”


A Modeling Framework Buy vs Build? DMBOK/DMM

A great blueprint of what is needed

The details need to be defined

Buy vs Build

Time

Money

Expertise (quality of the result)

Consistency across the organization

Acceptance and buy-in

Business

IT


Critical Framework Content List


Procedures Administrative Change Control Data Object Promotion Issue Resolution Logical DM Submission &

Deliverables Model Backup Model Change Control Model Creation & Population New User – Model Repository Notification & Update Physical DM Submission &

Deliverables Stewardship Synchronization Versioning

Standards Classword – Domain – Datatype List Color Themes Data Object Definitions Guidelines Library Structure & Naming Model Repository Profiles Physical Model Object Naming User Defined Properties

Critical Framework Content List


Blueprints for Enterprise Model Development

Building an Enterprise Data Model

Data Model Life Cycle DMLC Top Level

DMLC Detailed View

Shared Data Object Creation

Agile (Iterative)

Data Architecture -Development Integration

DMLC Definitions

Model Quality Reports Basic Model Metrics & Reuse

Glossaries Glossary

Templates Template Usage

Forms Administrative Change Control Form Logical Data Model Check List Model Change Control Form New User Repository Profile Form Physical Data Model Check List

Implementing a Standards & Best Practices Framework

Key Steps

Understanding ‘Where you are’ – Gap Analysis Assessment

Using Assessment to define a strategy – Compare to known ‘Best Practices’ guidance & solutions

The Decision: Buy vs Build

Collaborate on a Tactical Plan – Engage the stakeholders

Integrate with overarching Data Governance & Management initiatives

Develop a Proof of Concept – Prototype/Pilot

Deploy, Check & Adjust (If necessary)

Communicate, Educate & Align throughout the Implementation (especially between Business & IT)


Benefits of A ‘Buy’ Solution

Supports Data Governance principles & requirements

Supports any Data Model Life Cycle methodology

(Waterfall, Agile, etc.)

Solves the problem of requiring in-house resources to

analyze & write necessary standards documentation

Compatible with the company’s data modeling solution

Implemented on the company’s intranet for accessibility


Standard Classwords implemented using ERwin Domains with inheritance


DMM – Framework Mapping

1 Performed

Ad Hoc localized Standards

2 Managed Top Level Data Management Life Cycle (DMLC) Naming Standards Automated Templates Standards Training

3 Defined Detailed cross functional DMLC Standard processes and milestones Standardized health checks

Maturity

Business Value

4 Measured

Product and process metrics Change management and process improvement based on metrics

5 Optimized Adaptive DMLC (Agile) Global deployment of EM-SOS! Data Object Collections and Repository Global Shared Data Objects and Reuse


Modeling Environment

Assessment

Axis offers a comprehensive assessment incorporating a strategy which centers on the reuse and non-redundancy of data structures within an organization.

This short term consulting engagement analyzes the customer’s current modeling environment and delivers a ‘Findings and Recommendations’ document delivering analysis and specific recommendations for streamlining and improving the modeling infrastructure.

You can start by conducting a ‘Standards Self Check’ at: http://www.sandhillconsultants.com/emsos.asp


Contact Information


[email protected] 303-981-6525

For information, please contact:

Robert Lutton

[email protected]

416-219-4504

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