Master Data Management

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2 Master Data Management How the quality of Master Data can improve your Planning & Forecast An approach Jurgen Maas, Jan Veerman

3 What is MDM? MDM comprises the processes, governance, policies, standards and tools that consistently define and manage the critical data of an organisation to provide a single point of reference Source: Wikipedia Types of data (for the sake of simplicity) Master Data Represents the business objects which are agreed on and shared across the enterprise Analytical Data Represent the information recorded from transactions

4 Known & Costly Issues Insurance company found customer records with 99/99/99 in creation date field of policy Healthcare company found 9 different values in gender field Food/ Beverage retail chain found the same product was their No.1 and No.2 best sellers across their business Gartner estimates that more than 25% of critical data within large businesses is somehow inaccurate or incomplete (source: InformationWeek, 2006) Bad data costs grocery retailers $675 million in lost sales every year (source: GS1, 2013)

5 Known & Costly Issues

6 Our Experience What the customer promises What we encounter Structured, clean data (Un)structured, data quality challenges 70% of data (on average) originates from S&OP processes Data does play a large role in our projects Build extended knowledge on Data Management over time Importance of data increased over time (Big Data, IoT, Analytics)

7 Current Status Future Status Processes Mainly internal focus Few (structured) sources Governance by IT Technology Platforms to support IT Structured and unstructured sources Intelligence in coding People Programming skills needed Language barrier IT/ Business Data source driven Processes True data democratisation Combination of in- & external sources Structured & unstructured Technology Heavy use of ML, AI, algorithms Workflow enabled Cloud based People Business in the lead Support self-service 80% analysis, 20% data

8 IT controlled versus Self-Service IT needs to maintain control over by whom, how and where data is used DATA VISUALIZATION DATA ANALYTICS Business users increasingly want more advanced data preparation functionalities SELF-SERVICE DATA PREPARATION DATA INTEGRATION DATA

9 Value Add for Planning & Forecasting Self- Service Data preparation Analytics Visualization Business enablement Minimize most used self-service tool Less discussion on difference in data Shadow Data Speed Improved Planning No IT dependency Ability to connect new sources Enabler for prototyping Speed Ability to run different scenarios Better prepared for events Scenarios Pareto -Shift 80% data gathering, 20% analytics è20:80 More time for analytics Faster process Higher quality data Added value activities (analytics/ scenarios) Improved Planning

10 Value Add for Planning & Forecasting Negative Impacts Positive Impacts RESULTS Undermining confidence Missed opportunities Lost revenue Reputational damage Decision making Productivity Compliance Marketing Forecast Accuracy Sales Margin Inventory

11 MDM Maturity Framework (Gartner) OPTIMIZED PERFORMING INITIAL Ad-hoc projects Little to no policies, standards or ownership DEVELOPING Value of data recognized Team formed and prioritized at executive level Strategy created DEFINED Strategy enacted Stewardship established and standards/ policies documented Prepared technical environment Workflows created to address quality issues at the root cause/ source Mature, holistic and proactive approach to data management Information silos fully integrated Root-cause analysis driving improvement of data quality with continuous improvement feedback loops Fully automated Entire company culture aligned to the data strategy and standards Focus on finetuning continuous improvement Level 1 Level 2 Level 3 Level 4 Level 5

12 MDM Phased Approach Assessment Example 1) Initial 2) Developing 3) Defined 4) Performing 5) Optimized Data Model - Master Data Definition - Master Data Model - Data Landscape Data Quality - Data Quality Assessment - Impact on Business - Reasons/ Sources for Poor Performance - Improvement Usage & Ownership - Data Usage - Data Ownership - Data Access Data Protection - Data Protection Maintenance - Storage - Data Lifecycle

13 MDM Phased Approach Assessment Example 1) Initial 2) Developing 3) Defined 4) Performing 5) Optimized Data Model - Master Data Definition - Master Data Model - Data Landscape Data Quality - Data Quality Assessment - Impact on Business - Reasons/ Sources for Poor Performance - Improvement Usage & Ownership - Data Usage - Data Ownership - Data Access Data Protection - Data Protection Maintenance - Storage - Data Lifecycle

14 MDM Phased Approach Design Example 1) Initial 2) Developing 3) Defined 4) Performing 5) Optimized Data Model - Master Data Definition - Master Data Model - Data Landscape Data Quality - Data Quality Assessment - Roles defined (function house) - Impact on Business - Responsibilities clear - Reasons/ Sources for Poor Performance - Delegation/ succession in place - RACI transparent - Improvement Usage & Ownership - Data Usage - Data Ownership - Data Access Data Protection - Data Protection Maintenance - Storage - Data Lifecycle

15 Quick Wins 1 2 Perform initial assessment Define MDM Vision & Strategy 3 Decide on level to achieve 4 Define actions to cross the bridge You can t eat the elephant in one piece so balance your act 6 5 Define transparent and achievable KPI s MDM on most important data elements 7 8 Get clean, stay clean Select/ use state of the art MDM tooling

16 Conclusions Data Quality remains a challenging topic Data Management still very labour intense Data Owner = Business Data Standards = Business & IT New technologies & methods can overcome this Don t try to implement it all at once Decide per topic the required maturity level Decide on the Data Objects to apply MDM Follow the steps as a guide from the previous slide Hire expertise to get started è embed in organisation

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