How Johnson Controls Mobilized Their Data Governance Program for Big Data & MDG on HANA

Matthew Vandevere – DATUM

Organizations struggle to balance ERP roll-outs with Data Governance initiatives, but there is a way to integrate deployment activities to achieve maximum value by:

establishing a vision for MDG on HANA with proven strategies and tactics for deployment

taking advantage of MDG on HANA to drive value in advance of ERP deployment

Utilizing platform capabilities to accelerate ERP Implementations

LEARNING POINTS

About Johnson Controls

Today, there are nearly 170,000 employees

and many business partners in the

Johnson Controls’ family delivering products and services

wherever their customers

live, work and travel.

JCI’s Evolving Strategies

Deliberate and explicit choices

Company choices v. business unit-only choices

Play to win in the markets they choose

Data-driven v. supported anecdotes

What does JCI want to be?

Quick Facts about DATUM

• Fast-Growth Solutions Company Recognized by

• Recognized by SmartCEO Magazine as a Future 50 rising star• Named Leader in Data Governance 2.0 by Forrester -

February 2015• 70% of ASUG Data Governance 2014 SIG Annual Meeting

“Success Stories” are users of our SolutionsDATUM Customers

The ERP Program Challenge for Data GovernanceIn

form

atio

n Tr

ustw

orth

ines

s

Time

ERP Data Readiness

ERP

Trig

ger

Dip in Data Trust

Mobilization Point

Organization

Data Gov. Policy

Steward

ship

Tools

Recognition of Lost ROI

The Scramble How Could That Be? We Own It Let’s Start with Data!

Migration Degradation Reliability Team Work

RISK REDUCTION

VALUE CREATION

Mobilize Earlier!

Typical ERP program activities are inherently designed to focus on the critical data governance requirements

until AFTER Go-Live

Prioritizing what is Governed

Strategic Insights

Information

Data

KPI’s / Measures

The foundation of the governance program is set based on the ERP program, but will expand based on the data and information that is most important to

the business (processes and analytics)

Prioritized Data for Governance

Governance Model Evolution

Governance Model

Standards and Business Rules drive design and build activities and are iteratively identified and evaluated as part of Unity Program

Governance Model will be formalized as part of the Unity Program during DD (e.g. BPD’s, Build, Testing, ..) focused on SAP (Unity); reviewing and approving standards & rules

Governance Roster largely represented by Unity Program Team (business representatives)

Data Standard and Business Rule Ownership (business ownership) still transitional

Targeted external communication of proposed standards and business rules

Project Mode(Design, Build, Test, Deploy)

Standards and Business Rules approved and managed as part of the deployed system

Governance Model implemented to support deployed sites, pending deployments (and targeted legacy sites) – SAP and non-SAP focus

Standards and Business Rules driving Unity benefits (operations, analytics, compliance, …) Governance Roster represented by key Business Owners taking ownership of the system(s)

Data Standard and Business Rule Ownership transitioned to end-state Business Owners

Broad communication of approved standards and business rules

Steady-State(Sustain / Optimize)

Process

People

Data Governance Model needs to be established during the ERP Program

BluePrint (Design) – Think Governance

Data Object List Customer Master Credit Master

Customer Master Info

RecordBilling

Document

Design Activities

Business Data Dictionary

(Customer Example)

Customer Name(KNA1-KUNNR)

Account Group(KNA1-KTOKD)

Industry Code 1(KNA1-BRAN1)

Ref Acct Group(KNA1-KTOCD)

Data Design

Required, Optional, Not Used

List of Allowed Value Settings

Security

Data Standards

Business Usage Business Owner System of

Record Allowed Values

Business Rules (DQ)

Scenario Specific Rules (e.g. Acount Group rules for Sold To, Ship To, Payer, and Hierarchy)

Data Conversion

Baseline for Wave System Mappings

Conversion Rules

Data Construction Rules

Process Definitions – What about Data ?

Does not clearly define the data process within a business process context

Business stage gates are not clear and/or not defined

Documentation is driven from IT or founded on technical specifications

Insufficient documentation providing required data to support the process (and impact)

Lack of consistent process documentation existed or adherence in the current state

MDG-enabled, Optimized Data Processes

Roles/Tasks now clearly align and roll-up to support business processes

Business process driven stage gates defined and managed via workflow

Optimized process enables a Just In Time (JIT) data collection process that aligns to enable business processes and support critical milestones

Leveraging our process documentation and complementary tools provides scalable, standardized, and governed processes

Establishing a Repeatable Data Governance Framework

Is there Compliance, Financial or

Operational Impact?

Where should

we govern?

How should we govern?

(Point of Entry,

Passively, None?)

Frequency of change?Impact?

Risk/Benefits

EXAMPLE: We need to add a commodity code for Lead/Cores supporting compliance and analytical requirements.

Who has decision rights?

Governance decisions for every data element are evaluated and determined independently, but

always follow the same methodology and approach

Importance of a Data Governance Framework

Linking the Data Governance Framework

Business Data Glossary

(BDG)

Business Data Dictionary

(BDD)

Governance Rules(Scenario Based)

Process Decomposition (L1 – L5)

Level 1Process

Level 2Sub-Process

Level 3Activities

Level 4Process Steps

Level 5Data Elements

Data Glossary and Dictionary

Link toGovernance Model

Governance Rules and Standards on Process Steps and Data Elements

Data Standards Repository

Measures & MetricsDefinitions

Data Governance Framework

Synchronizing Process, Data and Rules

Overview of Planning Item and Finished Good Global WorkflowsVersion 1.1 – September 27, 2013

QuEST Activity Brief Approved

Marketing

Request Planning Item

0

days3

Material Master LDA

Create New Planning Item/

ZREP

1

days2

Pricing LDA

Populate Planning Price

4

days0

This Database

Waiting for IR Approval

5

days0

Marketing

Update New Finished Good

Request

0

days2

Sales & IT Services

Update Classifications

1.1

days2

Demand Planning

Update Classifications

1.2days2

LDAs

Update FG Setup and MOE & Classifications

2

days2

R&D QA

Populate Shelf Life Data

4.2

days2

R&D SST

Populate Dimensions and

Weights

4.1

days0

Workflow

Awaiting Packaging

Specs to be moved to Pending

3

days2

LDAs

Create PKI and PKI Det Rules and

Activate the FG

5

End of FG Global WF

days2

R&D SST

Populate Declared Weight

2.2

days2

Demand Planning

Populate Classifications

2.3

Workflow triggers Pricing Workflow to

circulate.

days2

Material Master LDA

Populate MOEs and

Classifications

3

QuEST Investment Reco

Approved

Approve Waiting for IR Step

Marketing

End of Planning WF

Planning Item Workflow

Finished Good Global Workflow

days2

Sales & IT Services

Populate Classifications

2.4

days2

Ops Planners

Assign Production Plant

2.1

Workflow sends email notification to Sales Planning, Demand

Planners, Ops Planners & Replen

(only for non-seasonal)

Workflow sends email notification to Sales Planning, Demand

Planners, Ops Planners & Replen

______________________________________________________________________________________

Business Process Flows

Process Overview List

Governance Rule Composer

Governance Rule #

Field Level Details List

Process Stage Gates

Rule Reference #

Enabling MetaData

Process Flows

Standards, Rules and Metrics Business Process Flows import and remain “synced” with the data and business rules required to operationalize and govern them

1 to many

1 to manymany to many

Rules before Tools !

“Rule Readiness” dictates “Tool

Readiness” … how well the organization is

positioned to develop governance rules will dictate solution and initiative success.

Rules will support both ERP and MDG Activities

Leveraging Governance Rules…

Assess Data in order to… Validate Governance Rules Determine if site data is ready for

Deployment Define timing & staffing required

for cleansing & enrichment Cleansing & Enrichment

Goals Only cleanse & enrich data

required for migration Ensure process is intuitive &

driven by business (not IT) Cutover …

Pre-Mock Load Testing against target configuration

Multiple Mock Loads (SIT, UAT, SIM)

Reduce timing of outage window

ExampleIngest data - 111k

records

De-Dupe

1200 records

Execute Relevancy Rules – 17k records*Narrow down the data to what’s relevant to the business.

Auto Cleanse – 1300 records

*Address Standardization, etc.

Manual Cleansing*Data Construction, Data

Enrichment, Duplicate selection, Data Corrections

Operationalizing Business Knowledge for MDG

JCI Data Governance FrameworkInformation Value Management (IVM)

SAP MDG

SAP Information Steward

Existing docs

Business knowledge

System Config

Business Metrics

Rule Extract(Func. Spec)Load

Template1

2

3

Step 1: Establish Data Governance Framework

• Interview Data Stewards, BPO, IT and SME roles to determine critical governance and MDG configuration inputs

• Configure Rule Composer to reflect JCI’s Data Governance Framework

• Produce standardized templates to capture existing business rules

Rule Load Template

Required MDG Inputs1

Step 2: Define Data Stds & Business Rules

Existing docs& processes Tacit knowledge System Config

GovernanceTemplate

• Capture existing rules and transform into JCI Data Governance Framework

• Complete Rule Definitions for specific intended Governance Strategy

JCI Data Governance FrameworkInformation Value Management (IVM)

2

Measure Rule Readiness for MDG

105 Rules 75.4%

MDG based governance requires robust rule definitions

Targeting Rule Definition Gaps

By establishing and following a structured governance framework, we can easily define GAPS within each business rule that are required functional inputs for MDG

Ensuring a ‘Complete’ Functional Design

Ready for Execution

Step 3: Functional Extract Ready for Build

Functional Spec. Extract for MDG

Example Rows from Extract

3

Competing with ERP objectives, timelines and deliverables can often impede Data Governance objectives

Ensuring that Data Governance is actively part of (not informed by) the ERP program is critical

Establishing and following a structured, repeatable Data Governance Framework ensures relevancy and success

Early identification, capture and measurement of critical MDG-specific inputs is the only way to avoid delays and successful deliver

KEY LEARNINGS

Questions

Matthew VandevereStrategy PrincipalDATUM LLCmatthew.vandevere@datumstrategy.com610.659.3114