Most Common Data Governance Challenges in the Digital Economy

Most Common Data Governance Challenges in the Digital EconomyJustin McCullough (Janssen Pharmaceuticals) & David Woods (DATUM)Session ID #5095

WHO WE ARE

• The world’s sixth-largest consumer health company• The world’s most comprehensive medical devices business• The world’s sixth-largest biologics company• The world’s fifth-largest pharmaceuticals company• 128,300 Employees Worldwide

We Proudly Serve

• We help large enterprises achieve competitive advantage & profitability faster by leveraging data as an asset

• Recognized thought-leader in Data and Analytics• Information Value Management® is the leading software

platform for information management

Johnson & Johnson DATUM LLC

THE DIGITAL ECONOMY

SAP’s DIGITAL BUSINESS FRAMEWORK

1.Outcome-based customer experience

2.Re-platforming core business processes and bringing together business process and analytics in real-time

3.Smarter, engaged workforce

4.Supplier collaboration accelerating growth innovation

5.Harnessing the IoT and Big Data to drive real-time insights and new business models

SAP Defines Digitization Across Five Key Pillars

DIGITAL DICTATES A NEW ‘REALITY’ FOR DATA

Data validated decisionsBatch processing of dataHistorical data drives decisionsGovernance improves data qualityProcess dictates data design Data based decisionsReal-Time, instant accessReal-Time data drives decisionsGovernance ensures data qualityData insights drive process changes

BEFORE AFTER

THE DATA LANDSCAPE IS MORE COMPLEX …

Enterprise Data

Structural Data

Org Data

Reference Data

Core Master Data

Functional Data

Transactiional Data

Documents

Metadata

Configuration Data

Market Research

Customer Feedback

Mobile Data

POS Data

Demographics

Experimental Data

User Generated Data

Social Media

Search History

Transaction ‘click’ Data

Data

Effective data management no longer just involves managing large data volumes, but now must handle data variety and velocity.

Data governance is becoming an integral part of our ability to fully capitalize on our investments in ERP platforms and analytics tools.

While our program started with a focus on the data we manage internally, our model is designed to scale beyond that as we prioritize the governance of ‘other’ data sources that are critical to our growth in the digital economy.

Data Created Within Our Organization

Data Created Outside Our Organization

… AND WE ALL THINK ABOUT DATA DIFFERENTLY

Operations

Compliance

Analytics

Our Governance FocusHow We Define It

Functional Usage

(Business Teams)

Type of Data(Data Scientists &

Architects)

System Representation

(IT Teams)

THE DIGITAL CALL-TO-ACTION FOR DATA GOVERNANCE

• Standardization and simplification of business processes and analytics (in real-time)

• Ability to quickly scale and adapt through organic growth, acquisition or divestiture

• Informed collaboration across fragmented systems, processes and disparate data sets

• Leveraging data to become more efficient - “how can we do more with less”

Strategic Data Value Drivers

1. Misdirected focus (prioritizing what matters)

2. Project-Focused mindset (lack of scalable model)

3. Misaligned tool strategy (theory to execution)

4. Inability to transition from Project to Steady-State

5. Failure to Measure Readiness

FIVE MOST COMMON POINTS OF FAILURE

Challenge # 1:Prioritizing What Matters

PRIORITIZATION DRIVES A DATA SIMPLIFICATION PROCESS

Why does this Data matter?

Context Matters!

Metrics Processes People

Systems

Success for this Data is…

• Better Performance• Reduced Risk• Improved Security• Better Use of Capital• Increased Agility• Speed to Market• Improved Use of Tech

What Level of

Governance?

“Data governance value to many organizations is non-financial and embedded in the intrinsic value, business value and performance value

of information*”*Referencing Doug Laney’s published work on “Infonomics”

• Prioritization starts with your Business Capability Roadmaps• Focus on areas that provide the most ‘bang for the buck’• Develop a repeatable Decision Tree for Governance Decisions

Challenge # 2:Developing a Scalable Model

ESTABLISHING A UNIFIED GOVERNANCE PLATFORM IS CRITICAL

• Ditch the “Project-Focused” Mindset• Utilize a standards & rules repository• Design from all 3 lenses: Compliance, Analytics & Operations

Challenge # 3:Misaligned Tool Strategy

FOCUSING ON THE ‘VALUE’ OF DATA GOVERNANCE

Maintenance

Execution Processes

“Data is for Critical to enable

Digital Growth”

• Standardized Tools• Low Cost

Execution• “Lift & Shift” to

Automate Current Process

Governance

Scenario Business

RulesGovernance Strategy

Critical Data

Standards

• Clear Decision Rights• Ownership & Accountability• Data Standards for Analytics• Business Rules for Execution

• Regulatory Compliance• Data Quality• Data Process Efficiency• Prioritized Focus

Over the past few years, the proliferation of exciting new technology solutions have led companies to quickly implement tools expecting to find a silver bullet for data…

…however, they have struggled to implement effective solutions that only provide automation capabilities, but don’t actually realize any business value

• Context matters as much as control in the digital economy• Current Data Mgmt tools and governance methods cannot always

be applied to digital initiatives• Focus on tools that match business needs

Challenge # 4:Inability to Transition from Project to Steady-State

GOVERNANCE OPERATING MODEL EVOLUTION

Governance Model

• Standards and Business Rules drive design and build activities

• Governance Model should be formalized during BluePrint/Design

• Governance Roster largely represented by Project Team (IT and Business)

• Data Standard and Business Rule Ownership is in a transitional stage

Project Mode(Design, Build, Test, Deploy)

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

• Standards and Business Rules driving program benefits

• Governance Roster represented by key Business Owners taking ownership

• Broad communication of approved standards and business rules

Steady-State(Sustain / Optimize)

Process

People

• The Governance Model must start during the project – not after• Establish end-state ownership even during transitional project

stages• Communicate standards & rules in the context of business relevance & value

Challenge # 5:Failure to Measure Readiness

MEASURING ‘TRUE’ READINESS

Capabilities

Organizational

Process

Tools

Focus on capabilities and ensure that ALL the of enabling components are lined-up at each stage of program evolution ….

… and providing meaningful metrics that identify specific gaps and facilitate closer to ensure information trust• There is no technology ‘silver bullet’

• One enabling component cannot out pace the others & achieve real value

• Metrics are critical and must be tied to business objectives (not just DQ)

Summary

APPROACH TO DATA IN THE DIGITAL ECONOMY

Strategic Program Alignment

Establish Digital Core

Operationalize Instant Insights

Optimize & Differentiate

“RUN” “GROW” “INNOVATE”

Data Governance Framework(Information Value Management®)

Data Applications(SAP EIM Tools)

In-Memory Analytics(BW on HANA & HANA EE)

SAP S/4HANA

Challenge #1Prioritize the data that matters and build the

Digital Core

Challenge #3Select tools that are ‘fit for purpose” and

drive digital value

Challenge #4Plan for and manage

the transition of information ownership

Challenge #5Measure readiness for

business transformation into a Digital Enterprise?

Challenge #2Build a scalable model

to Support trusted data in the Enterprise

THANK YOU & QUESTIONS

David WoodsPrincipal PartnerDATUM [email protected] (m)

Justin McCulloughDirector, Enterprise Business Architecture Janssen Pharmaceuticals, Inc.Johnson & Johnson Family of [email protected]

mailto:[email protected]



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