Business Process-DrivenMDM & BI

Strategy & Vision

by: Mark D SchoeppelMarch 2016

MDM BI – Vision

Transaction Systems

Business Process Management

Capture Consume

Master Data Management

Instantiate Provide

Monitor

Business Intelligence

Data WarehouseBig Data

Visualization

Goals:• Enable Speed of Innovation for MDM BI activities across the

Enterprise, the Operational Units, and the Business Users• Define Governance and Data Management Practices

throughout the Enterprise• Improve the accuracy of Master Data for Unity and Legacy

Systems• Build Enterprise Class Metrics and KPIs based on Business

Processes

Enterprise• Data Stewards• Data Governance• BI Team• Technology Team

Operational Unit• MDM BI COEs• BRM Teams• Data Scientists• Data Analysts

Business Users• Data Stewards• Reporting Stewards

Operational Unit

Business UsersEnterprise

Ideation

Transactionand KPI

Consumption

SelfService

Requirements

Data ProfilingGovernance

Visualization Testing

Publishing

Architecture

Standards

Methods

Technologies

Training

Solution Design

Data Ingestion

AnalyticAlgorithms

DataExploration

ApplicationDevelopment

Master Data

Business Intelligence

Big Data

Data Cleansing

MDM BI – Roles and Functions

Business Data Governance – Overview

Master Data ManagementDW & BI ManagementData Quality ManagementMetadata ManagementData Security ManagementData Architecture ManagementData Development

Misconception Reality

It's an IT responsibility Data Governance requires a partnership between Business, Technology, and Operations

One size fits allThe organization, processes, and technology must be tailored to fit the culture and leverage existing governance structures and technology

It can succeed through a grass roots bottoms up effort Success requires executive advocacy and sponsorship

It's about having the right tools Data governance requires the integration of organization, processes, and technology tools

It can be an add on responsibility that doesn't need to be measured or rewarded

Data stewardship may require full time staff commitment. If the role is not measured or rewarded, the result will be ineffective governance action

It’s a big bang implementation Standing up data governance structures is an evolutionary process that requires effective change management

Data governance is the orchestration of people, process, and technology to enable the leveraging of data as an enterprise

asset through a well-defined organizational structure, policies, rules, decision rights and accountabilities for

decision making and management of Master Data.

Data Governance – 180 Day Plan

• Create the appropriate review and escalation methods for managing data quality and integrity• Enable the linkage between business process/data owners who “champion” data and metrics with the data

architects and data stewards who manage the transaction level detail

Governance &

Stewardship

• Integrate roles across functions (e.g., data cleansing, data architects, data stewards, process/data owners)• Understand the needs of the consumer of the data and connect appropriately

People/ Organizatio

n

• Define end to end, consistent processes across all data types, linking transaction level data with Management Information

• Define proper controls to manage data quality and integrity on a sustainable basisProcess

• Identify Tools for cleansing, mapping, identifying anomalies, etc.• Leverage data management tools and infrastructure that have rapid scalability and functionality• Define consistent architecture that enables “one version of the truth”

Technology

MDM BIData Cleansing

MDM BIProduct

MDM BICustomer

MDM BIVendor

MDM BIOperational

MDM BIFinancial

MDM BIStat/Mgmt Rptg

EBPM - PTP

EBPM - PTD

EBPM - OTC

EBPM - RTR

EBPM - FTPAs requirements for MDM and KPIs are

assembled, PRIORITIZATION and

sequence can be further refined

Outcomes

MDM BI – Discovery Approach

Enterprise Data Model

Master Data Models

Required KPIs

MDM BI – Deployment ApproachDiscovery Analyze / Define Design

Deliverable

• Conduct Discovery Sessions

• Define Solution Scope • Define Solution Concept • Define General System

Concept • Describe Potential

Impact• Plan Project

• Analyze Guidance Architecture

• Analyze Data Architecture

• Create Data Schema Map

• Assess Data Quality• Analyze System

Architecture• Analyze System

Requirements

• Design Guidance Architecture

• Design Data Architecture

• Design System ArchitectureDesign Application Specification

• Design Data Migration • Design Human

Transition SupportActivity

• Discovery Summary• Solution Scope• Solution Concept• System Concept • Impact Summary• Project Plan

• Organizational Model • Guidance Model • Data Model • Data Schema Map• Data Quality

Assessment • System Interaction

Diagram • System Requirements

Summary

• Organizational Model • Guidance Model Data

Model • System Interaction

Diagram • Application Specification

Data Migration Plan• Organization Change

Management Plan• Training Plan

• Discovery & Analyze initiated via common stakeholder interviews

• Design & Build executed on Global Template

• SAP Deployments will review efforts from previous phases but largely be testing and refining exercise

Build Test Deploy

Sustain

MDM – Implementation ApproachesConsolidation Registry Coexistence CentralizedFor Reporting, analysis, and central reference

Mainly for real-time central reference

For harmonization across databases and for central reference

Acts as system of record to support transactional activity

Matches and physically stores a consolidated view of master data

Matches and links to create a “skeleton” system of record

Matches and physically stores a consolidated view of master data

Matches and physically stores the up-to-date consolidated view of master data

Updated after the event and not guaranteed up-to-date; authoring remains distributed

Physically stores the Global ID, links to data in source systems and transformations

Updated after the event and not guaranteed up-to-date; authoring remains distributed

Supports transactional applications directly – both new and legacy – typically through SOA interfaces

No publish and subscribe; not used for transactions but could be used for reference

Virtual consolidated view is assembled dynamically and is often read-only; authoring remains distributed

Publishes the consolidated view; not usually used for transactions but could be used for reference

Central authoring of master data

Analytical Focus Operational Focus Operational Focus Operational FocusSystem of Reference System of Reference System of Reference System of Record

EnterpriseMaster Data

Model

LegacyERP

MDM PeopleMDM Processes

MDM Tools

Tran

sfor

mati

on

Certified MasterData

2

3

4

1. Non-Certified Data from the Legacy Systems is ingested and transformed into the Enterprise Master Data Model built during the Discovery Phase

2. The Enterprise Data Model is adjusted as necessary and cleansed data is pushed back to the Legacy ERP System

3. Certified Master Data is produced and the required refinements to processes and data architecture are made to enable downstream consumption

4. All of this is enabled by dedicated MDM personnel, utilizing MDM tools and processes

MDM – Pre-Cleanse & Deployment Process

1

Non-CertifiedData

BI – Ownership Structure

Sandbox (50%)[user created content]

Shared (30%)[user created and shared

content]Production

(20%)

Gather Data

Visualize

PublishConsume

IdeateBusiness

Users

Require-ments

Profile Data

DesignDevelop

Test

IT

Sandbox Environment• Business users author and use BI content with no

constraints or limitations. This is where data exploration, discovery, and what-if analyses happen.• Tools and technologies: Microsoft Office• IT involvement is strictly limited to infrastructure

and tools support plus monitoring to identify usage patterns, commonalities, and opportunities (using BI on BI) for potential production hardening.• Content produced here is used in individual tasks

and low-risk applications.

Shared Environment• Business users share and collaborate on BI content

with their colleagues.• Tools and technologies: SharePoint BI, Office 365• IT steps up monitoring and now watches for red

flags (too much data, too many users, too critical or risky applications) and opportunities (using BI on BI) for production hardened BI Content.• Content produced here is shared within

departments and workgroups. Low-risk, low-criticality decisions can be made based on this content.

Production Environment• Business uses and authors BI content within the

limitations and constraints of the enterprise data model, standards, policies, rules, guidelines, etc.• Tools and technologies: EDW, Visual Studio,

SharePoint BI, Office 365• Owned, run, and managed by IT.

IT Benefits• Backlog Reduction – only heavily-used,

complex, or critical applications come to IT for production-hardening.• Requirements already defined; Project Lifecycle

is greatly reduced; Enhancements during testing cycle minimized.• Shadow IT is embraced as a competitive

advantage; however, using the strategic technology stack defined by IT.

Business Benefits• Business users are empowered to

create BI content on their own schedule without any constraints or limitations – at the speed of business innovation.

• They modify the model and visualizations through iteration until the requirements are identified and met.

Hadoop = Data Lake• Land all data in Hadoop as-is from any source• Enables Analytics Sandbox• Enables MDM Pre-Processing• Enables EDW Population with Relevant Data• Enables Application Access via API Layer (including 3rd party

developers)Actively Archive from EDW to Hadoop• Little-Used Historic EDW Data resides in Hadoop (lower cost

storage)• Define an archive strategy for various data types

Enable business analytics• Identify tools, methods, and security requirements for

interaction with the distributed file system• Introduce exploratory analytics without jeopardizing SLAs• Introduce new machine learning, or data mining techniques

on years worth of dataEnable BU Innovation• BU Teams continue to innovate with their business users

within the Enterprise Framework – ingestions driven by BU OR Enterprise requirements

• Data Scientists and Analysts can access all Data for Analytics• BU IT & Business Teams can access all Data for Visualizations

with proper security• Enterprise Data Model, HDFS Standards, and Access Methods

extensible to manage localizations at the BU level and below

BI – Data Flows

Data Sources/Transports

Transaction Data

Customer Data

External Data

Industry Data

Sensor Data

DB

Files

REST

JMS

HTTP

SOAP

Hadoop

Compute + storage … … …

… … … …

… … … …

… … … Compute + storage

supporting technologies & packages

EDW

BI Tools & Applications

Query & Visualization Tools

JDBC/ODBC Compliant Tools & Applications

Analytic & Reporting Tools

RMahout

Excel

ExcelPowerPointPower View

MDM

API Layer

BU1 BU2 BUn

BU1 BU2 BUn

BU1 BU2 BUn

Establish, Maintain, and Periodically Review and Recommend Changes to Data Governance Policies, Standards, Guidelines, and Procedures

The Team responsible to develop the strategy, govern the tools selected to acquire and transform relevant data into knowledge to drive business decisions and actions to achieve desired results. In addition, the resulting information has to be tailored to – and distributed to – the appropriate levels of management and operations in a timely manner to be most effective. In some cases BI Execution of Reporting and Analytics is performed as well.

Provide Quality Assurance – Oversight, Monitor, Report Results to Data Governance Council

VP MDM & BI

Business Governance

Leader

Data Stewards by Domain

Data Stewards by Business Unit

Data Governance

Leader

Data Quality

Data Architecture

Data Conversions

Big Data Architect

BI Governance Leader

BI Leads

BI Visualization Developers -

Enterprise

BI ETL Developers -

Enterprise

BI Developers – Business Units

Technology & Tools Leader

Technology SMEs

DBAs

System SMEs

• Develop and Deliver Data Governance Program Educational, Awareness & Mentoring Materials

• Assist in Defining Data Quality Metrics for Periodic Release

• Support Data Quality Issue Analysis and Remediation for “Strategic” Data

• Oversee Enterprise Data Governance Program Development / Architect Solution & Framework

• Administer the Program including facilitate the Data Governance Council meetings

• Provide the Agenda for the Data Governance Council Meetings to the Approved by Council Owner Pre-Meeting

• Facilitate Data Governance Organization, Tactical & Operational Stewards, the Data Governance Council Involvement

• Conduct Audits to Ensure that Policies, Procedures and Metrics are in Place for Maintaining/Improving the Program

Functionally Aligned Roles

Organizationally Aligned Roles

Sample Organization