Building & Sustaining the Operating Model for Data Governance

Building & Sustaining the Operating Model for Data Governance

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Your Business Intelligence & Analytics are Only as Good as

Your Data

(Uh oh)

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So, Why is Data Governance so Important?


…Because Master Data Challenges Materialize Across the Organization

Ineffec&ve Business Decisions Lack of consistent informa,on needed to make cri,cal decisions suppor,ng innova,on and growth Business decisions are corrupted by data integrity challenges Opera&onal Planning

Customer Insight Lack of consolidated 360 degree view of customer data across all channels Territory alignment and analy,cs Customer hierarchy and grouping visibility/profitability analysis

IT Opera&onal Efficiencies Reusable frameworks to leverage best prac,ces across all data domains Mi4ga4ng Risk for Applica,on roll out Applying enabling technology

Gaining Procurement and Supply Chain Efficiencies Reduce procurement spend Enable Strategic Sourcing Op4mize inventory levels Increase visibility to historical and in-‐flight spend Extend sourcing reach and impact through compliance Enforcement of nego,ated contracts


Evolution of Information Value in ERP Landscape Inform

a,on

Trustworthiness

Time

ERP Data Readiness

Mobiliza,on Point

Organiza&on Data Gov. Policy

Recogni&on of Lost ROI

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

Focus is on migra,ng data to the ERP and making it “fit” or business-‐ready for ERP processes.

Data regardless of type, degrades at a pace of between 2 and 7% per month because of external factors, poorly defined processes and user issues.

Business and IT begin to recognize a need for a working rela,onship to resolve informa,on reliability beyond “break-‐fix” and address ownership, organiza,on, processes and value

Data and Informa,on Management is embedded in all process improvement and transforma,onal programs as a key success factor with commiYed ownership and par,cipa,on.

RISK REDUCTION

VALUE CREATION

Mobilize Earlier!


Data & Analytical Insight Opportunities are Dependent on EIM Success

Execu&ve Insight

Opera&onal Challenges

We Want: •  Trusted Informa,on for BeYer Decisions •  Predic,ve Analy,cs to Drive Performance

•  Holis,c Enterprise View •  Real-‐Time Trusted Informa,on

•  “Sexy BI/Analy,cs” •  Opera,onal Integrity

We Need: •  Quality Controls Aligned to Process Integrity

•  Process & Policy Defini,ons •  Organiza,onal Alignment •  Organiza,onal Ownership •  Individual Data Discipline

•  Stewardship Tools & Applica,ons •  Sustain & Enforce the Basics


BUILDING A BUSINESS CASE

13


Business Case Challenge #1 Generic 'value’ statements are simply not enough to iniHate a Data Governance “Program” in an environment where every dollar spent is heavily scruHnized


The Data Steward Says…

“Poor data quality is a primary reason 40% of all business iniHaHves fail to achieve their desired benefits”

“Data quality affects overall producHvity by as much as 20%”

“Customer data is esHmated to be 25% inaccurate on average”

”Master data quality degrades between 2%-‐7% per month”

The Execu&ve Response…

Pro Tip: Avoid Generic Value Statements

“I guess that makes sense, but how does that specifically relate to us and what our challenges are here ?”

“OK, but how exactly does spending more money on master data deliver this value ? I just don’t see the connecHon.”

”We’ve tried this before and it never seems to help … why is this different ?”


Business Case Challenge #2

Models based purely on tacHcal savings generally don’t provide enough value or benefit to jusHfy any tool or headcount investments


The Data Steward Says…

“Almost 40% of my staff’s Hme is spent chasing down and fixing data quality issues …”

“Our current processes and tools simply can’t address the complexity of effecHvely managing our data – I know that we can eliminate over 10 touch points alone with new workflow applicaHon”

“If we take on this acquisiHon, I probably need to add 4-‐6 more people just to handle the increased data volumes.”

The Execu&ve Response…

Pro Tip: Avoid Focusing Solely on Tactical Savings

“Isn’t ensuring data quality their job?”

“I never heard about this as a significant issue before, using our current tools, why is this suddenly an issue I need to address now?”

“So if we implement this workflow tool, how many people can we eliminate the data management team?”


Prioritizing Against Strategic Value Drivers

Strategic Insights

Informa&on

Data

KPI’s / Measures

When building your business case, it’s important to priori,ze and focus on the data, informa,on and metrics that actually provide key business insights

Cri,cal Data


Determining the Business Value of Data

Strategic -‐ “Transform the Business” Focused on the ‘top-‐line’ value drivers associated

with growth, innova,on and scale.

Tac&cal – “Run the Business” Focused on the execu,on-‐based

savings that can be realized based on op,mizing the data model and data

maintenance processes.

Opera&onal -‐ ”Grow the Business” Focused on savings driven based on improving

data quality, deploying a sustainable governance model and op,mizing our data

(and related) processes.

MOQ Defined for MM Cost to Maintain

Time to Complete Number of Systems / Interfaces

AYributes & Hierarchy (and related) Data Quality

Compliance to Rules & Standards Single Version of Truth

Supply Chain Op,miza,on and Planning

New Product Introduc,on Reducing Days Sales Outstanding Customer Profitabli,y Analysis

Mergers and Acquisi,ons

Value Drivers


Governance Rules

Linking Value Drivers to Data

The Why, How, Where and Who of managing and governing our data

KPI’s and Metrics

KPI PorYolio “The informa,on we need to drive business growth”

Business Rules “The steps to capture the required data”

Governance Strategy “How we govern the underlying data to

drive trust”

Shareholder Value

Commercial Profit

Revenue Growth Net Price

Commercial Margin

Std. Base Margin Order Fill Rate

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

______________________________________________________________________________________

Process & Workflow

1

2

3

CalculaHon formulas and required data to drive & support the metric

Linked

together

Solu,on Deployment Scorecards, Tools,

Dashboards, etc.


The Critical Components to Connect Data to Business Value

Clarity of Sight

C-‐Suite Grow Revenue

Shareholders Revenue Growth of 10% Year on Year

Sales Increase Customer Share of Wallet

Sales & Supply Chain Improve Availability

of Product

Business Process Improve Supply Chain And Order Fulfillment

Integra,on

Process Metrics Improve Order Fill

Rate by 8%

A

B

% Order Fill Rate

NA | SAP ECC

EMEA | SAP ECC

LATAM | SAP ECC

NA | SAP ECC

EMEA | SAP ECC

LATAM | SAP ECC

VBUP | LFSTA

VBUP | LFSTA

VBUP | LFSTA

VBEP | BMENG

VBEP | BMENG

VBEP | BMENG

(A/B)*100

# Items delivered as ordered

# Items delivered

Informa&on Trust

Everyone knows execuHve support (line of sight) is required for sustaining successful data management programs – the real trick is “connecHng to dots” between key performance measures and data quality (this creates clarity of sight)


A Real DATUM Customer…

0 5 10 15 20 25 30 35 40

Combined APV-‐ECC 5 PEP-‐ECC 6

% Dup

licate Re

cords

System

Duplicate Name

Duplicate Address

•  Duplicate vendor records cause mul,ple problems: •  Spend Analy,cs, Category Spend – cannot be realized •  Strategic Sourcing cannot be realized (typically 1-‐3% savings on total spend) •  Significant tax overpayment, or paying the same vendor 2X •  Fraud, Risk, Money Laundering •  Insight and decision making are compromised


Details Matter, Governance is Crucial

•  Accurate Tax Reporting is not achievable given the data integrity •  Risk of non-compliance with tax regulations •  Risk can be mitigated through proper Tax ID validation

93% of ac&ve Customer and Vendor records have NO Tax ID

77,453

6618

Vendor Records

Without Tax ID

With Tax ID 178,830

7114

Customer Records

Without Tax ID

With Tax ID


Improve Value and Compe&&veness Innova,on and Loyalty

Capital Op,miza,on

Manage Risk

Employee Sat and Development

Rewards & Recogni,on

Strategy Plan Maturity

Program Status & Execu,on

Organiza&on Readiness Opera&onal Efficiency

Value Delivery

Opera,ng Margin

Asset Efficiency

Business Process Op,miza,on

Shareholder Value

“Moving the needle” on Shareholder value requires decomposing business objec,ves into a poruolio of cross-‐func,onal business ini,a,ves and measures. The success of

these strategic ini,a,ves requires a clearly defined target value, organiza&on readiness, process excellence and informa&on trust of which metric and data

standards, governance rules and data quality provide the founda,on.

Revenue Growth

Metric Approach — Target Areas for Results


Key Business Case Action Items

Looking beyond a simple ‘Business JusHficaHon’ and delivering a true ‘Business Case’ requires a structured and disciplined approach

Developing an Effec4ve Business Case

§  Use tac,cal saving as a baseline, but also focus on iden,fying ‘achievable’ value against the strategic drivers that really maYer to the business

§  Involve key stakeholders throughout the en,re process -‐ ul,mately they will stand behind the recommenda,ons and ‘own’ the numbers

§  Use real data and real examples and clearly ar,culate the ‘as-‐is’ versus ‘to-‐be’ visually

§  Tie your program recommenda,ons (‘what you’re going to do’) to specific benefits (‘what they will get’) … Remember: generic benefit statements won’t be sufficient


ESTABLISHING THE OPERATING

MODEL


50+

Pro Hp: Find out how complex your material process is and use it in the business case!

How Many SAP Screens does it Typically Take to Create a Manufactured Product?


13+ Cost Accoun,ng Quality Sales

Requestor Material Master Coordinator

Engineering Marke,ng Purchasing

Finance Plant Logis,cs

Produc,on Control

Manufacturing Warehouse

How many funcHonal groups typically parHcipate and who is the single, true owner?

How Many Functional Groups Participate in the Process?


Requestor

MDM Coordinator

Engineering

Marke,ng

Finance

Plant Logis,cs

Prod. Control

Manufacturing

Cost Accoun,ng

Sales

Prod. Control

Manufacturing

Cost Accoun,ng

Who is Responsible for What, and When, ��Creates COMPLEXITY… You Need an Operating Model


Key Considerations for a Successful Operating Model

Delivering the Opera,ng Model (Priori,za,on, Designing Ownership, Process Improvement, Solu,on Readiness, Accelerated

Deployment)

People & Policies Process Design & Methodology

Business Impact of Data & Informa,on

Informa,on Value Management® Informa,on Design & Knowledge Management

Sustaining Governance based on Business

Outcomes Roles and Responsibili,es

Design for Rules, Standards, Metrics, Maturity Readiness

Content and Accelerators DATUM’s SaaS Platform


Best in Class OrganizaHons UHlize 3 Levels of Ownership

Data Management Support

IT, Data Center of Excellence, Business Support

Opera,onal Data Governance

Data SME -‐ Business Data Governance Program Leadership Data SME -‐ IT

Business Ownership

Business Data Owner (Mgmt-‐Level Business Owner)

Opera,onal Data Owner (Sub-‐Process Leadership)

Strategic support fo

r data

management iniHaHves

ensuring alignment with

vision, goals and

objecHves

Level of organizaHonal

ownership that acHvely

‘owns’ and ‘manages’ the

data management

program and people

Manages the tacHcal acHviHes and support requirements for data and related processes

“Next Level’ leadership with the ability to make organizaHonal, poroolio and funding decisions while also influencing projects

Who Should Own Data Governance?


Operating Model Construction

We typically see three (3) methods of forming a Data Governance OrganizaHons – each one has benefits and risks, which need to be carefully considered

§  Net-‐New: Forma,on of an en,rely new organiza,onal en,ty focused on data and informa,on governance.

§  Li` & Shi`: Iden,fy exis,ng, data-‐centric organiza,onal constructs and repurpose them into a formal governance opera,ng model.

§  Leverage Exis&ng: Augment an exis,ng organzia,onal governance model to include data and informa,on governance.

Data Governance

Opera&ng Model


DATUM’s Methodology for Creation of Operating Models

Business accountability and a well defined Opera4ng Model for master data governance – with ‘appropriate’ models for

tacHcal data maintenance and effecHve governance

Data Standards and Business Rules that are “operaHonalized” -‐

accessible by those providing it and consistently applied and governed across relevant business processes

OpHmized and integrated processes to ensure the right data is captured at the right Hme, by the right person, with the proper data quality checks

Integrated tools to capture, monitor and enforce data standards and business rules for master data and ‘orchestrate’ the end to end business processes and enable analyHcs and decision-‐making

Successful Programs

To be successful, companies must address gaps across all of the core components of successful programs.


Compliance – use and leverage the standards, processes and plauorms we’re introducing together

What DATUM Expects from the Organization …

Par4cipa4on – asisst us in iden,fying and defining data standards that will drive the benefits we desire

Insight – what do you need from us to ensure your business needs will be met ?

Informa4on -‐ who in your organiza,on needs to be informed and what data elements included ?

Communica4on -‐ what are you hearing? are there concerns that need to be addressed by EDM?

Par4cipa4on in the Data Management efforts is cri4cal to long term success

Ownership – regional through local governance


ENABLING TECHNOLOGIES

“Rule Readiness” dictates “Tool Readiness” … how well the organiza,on is posi,oned to develop governance rules will dictate solu,on and ini,a,ve success.


Exis&ng docs

Tacit knowledge

System Config

Business Metrics

Representa&on of Current Policy

29 Rules 75.9%

Rule Readiness

Rule Readiness to Support Objectives…�� Or “RULES BEFORE TOOLS”


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Mobilizing the Enterprise for Information and Data Success

Solu&ons Consul&ng SAP GTM Data Advisory and EIM Partner Focused on Master Data through Big Data Recognized by Gartner as a Thought Leading Consultancy People, Process…. then Technology ü  Developing Enterprise Data Management roadmaps &

strategy mapped to business value ü  Defining op,mized processes that create, manage and

govern data ü  Op,mizing enterprise business processes via data integrity ü  Designing the Opera,ng Model for Governance ü  Provide a Management Solu,on to foster Organiza,on and

Produc,vity within your Informa,on & Data Program

About DATUM


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Building & Sustaining the Operating Model for Data Governance

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