Now that your organization has decided to move forward with Master Data Management (MDM), how do you make sure that you get the most value from your investment? In this webinar, we will cover the critical success factors of MDM that ensure your master data is used across the enterprise to drive business value. The key processes involved in mastering data�· Data Governance’s role in mastering data�· Leveraging data stewards to make your MDM program efficient�· How to extend MDM from one domain to multiple domains�· Ensuring MDM aligns to business goals and priorities
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Agenda
• Why is MDM important?• Why is MDM challenging?• How do we ensure it’s successful?
Presenter
Presentation Notes
We will take a moment to validate why Master Data Management is important and also why it can be challenging. Then we’ll just directly into how to make MDM successful. For the purposes of this webinar, I will refer to MDM in it’s broadest sense covering all data types.
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[ WHY IS MDM IMPORTANT? ]
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Business and IT Drivers
Reduce operational costs Increase sales force effectiveness Improve sales and profits Strengthen customer relationships
“A manufacturer can expect to save from $800,000 to $1.2 million for every $1 billion in sales by achieving data sync.”
“Businesses that use a formal, enterprise-wide strategy for Global Data Synchronization will realize 30% lower IT costs in integration and data reconciliation at the departmental level through the rationalization of traditionally separate and distinct IT projects.”
Analysts Agree… MDM is Important for Addressing Key Business Requirements
Presenter
Presentation Notes
Although there are a variety of reasons to start an MDM initiative, the main reason is to drive business value. Because the term MDM also applies to a toolset, there’s the possibility of mistaking MDM for the implementation of a tool, not the process of mastering your data. Implementing a tool doesn’t necessarily determine success. So the first step in ensuring value is delivered is recognizing that Master Data Management is a process that delivers business value.
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MDM is the Foundation to EIM Vision
MDM provides foundational capabilities to achieve broader information management vision
Process Automation
Architectural Improvements
Flexible Data Architecture
IT Transformation
and Adaptability
PAST PRESENT FUTURE
Transaction Management
Data Warehousing
Master Data Management
Integrated Information Management and Delivery
Process automation and management of transactions with application specific data within isolated business applications including ERP, CRM, SCM, eCommerce and other systems over the past decade
Data extraction and normalization for operational as well as management reporting and functional analytics. Data integrity and lack of standards have constrained the maturity of data analytics in the past.
MDM and PIM comprises a set of processes, governance, policies, and tools that consistently define and manage the master data or foundational data that supports core business process and is required for accurate data analytics and decision-making
EIM and adaptive architecture to deliver business capabilities and flexibility to future changes
Big Data Management
Integration and management of big data and its relationship across the enterprise through people, processes and technology. Find insights in new types of data, makes an organization more agile, and answer questions that were previously considered beyond reach
Presenter
Presentation Notes
Now having said that, both the business process of mastering data and the technical capabilities that are provided by a Master Data Management solution can create a foundation for Integrated Information Management and Delivery. They do this by providing a better way to ensure accuracy of enterprise data, share consistent data and integrate internal and external data so that it is fit for use.
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Challenges of MDM Success
According to a recent TDWI survey, many of the MDM challenges are organizational and collaborative issues—not technical ones.
Half of users surveyed (56%) realize that MDM can be hamstrung without data governance.
Presenter
Presentation Notes
Although this study is a couple of years old, I still love this slide because it is still true. Statistics show that the primary reason MDM projects fail is that they don’t have the executive sponsorship, the data ownership, or the cross-functional collaboration to agree upon things like data definitions that comes with a Data Governance Program. So although many companies start with the concept of Master Data Management, it’s important to recognize that Data Governance should be initiated as early as possible to avoid many of the common challenges to MDM success. So our next tip for ensuring value is delivered is to leverage data governance as part of your MDM strategy.
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[ LEVERAGE GOVERNANCE ]
Presenter
Presentation Notes
So our next tip for ensuring value is delivered is to leverage data governance as part of your MDM strategy. I’m sure many of you have heard that before, so I’m going to talk about how.
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Data Governance Definition
Data Governance is the organizing framework for establishing strategy, objectives and policy for effectively managing corporate data.
It consists of the organization, processes, policies, standards and technologies required to manage and ensure the availability, usability, integrity, consistency, auditability and security of data.
Communication & Metrics
Data Strategy
Data Policies, Processes & standards
Data Modeling
& Standards
A Data Governance Program consists of the inter-workings of strategy, standards, policies, measurements and communication.
Presenter
Presentation Notes
Go through the definition Inherently, Data Governance is the point where the business needs of data intersect with the IT activities of storing, maintaining and managing it. If a company decides to go down the path of investing in a MDM solution, it will be important to leverage a Data Governance group to provide a collaborative decision making body to decide on data standards such as how to represent country of origin, what are the correct names to be used for Manufacturer or Manufacturing site. This mandate that DG provides will help with the change management that will occur for those areas who will need to change how they represent entities, and inevitably someone will have to make a change based on the standards driven through mastering data. Let’s look at how this works.
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Governance provides business context
Master Data Management
MDM Strategy
Technology Infrastructure
MDM Organization
Com
pone
nts Data Architecture
& SecurityData
MasteringData
Quality
Data Sharing
Measurements & Monitoring
Metadata Management
GOVERNANCE
ORGANIZATIONAL ALIGNMENT
Presenter
Presentation Notes
MDM’s sole purpose is to manage the complexity of integrating and managing master data from multiple internal and external sources MDM moves the data through well defined processes to ensure cleanliness, accuracy, uniqueness and then makes the data available in a variety of ways In order for it to be successful, there needs to be some thought about the strategy and the organization, as well as the supporting technology and how that fits into the existing infrastructure. There are multiple components to MDM in addition to the primary data mastering component. For example, there’s the requirement to leverage metadata to ensure you understand the master data. Data Quality is an important component to ensure efficiency by not attempting to match and merge duplicate data. This just results in a less efficient match and merge process, more exception handling and more manual resolution. As well, it’s not just how you get data into an MDM hub and what happens to it in the hub, but also how do you share that master data with the rest of the organization. All of this activity requires decisions to be made about the data.
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Governance Decisions for MDM
Category DecisionEntity Types • What type of data will be managed in the MDM Hub
• What are the agreed upon definitions of each type• What is the required cardinality between the entity types• What constitutes a unique instance of an entity
Key Data Elements • Purpose, definition and usage of each data element
Hierarchies and Relationships
• Purpose, definition and usage of each hierarchy / relationship structure
Audit Trails and History • How long do we have to keep track of changes
Data Contributors • What type of data do they supply• Why is this needed• At what frequency should they supply it• What should be taken for Initial load versus ongoing
Presenter
Presentation Notes
The Data Governance forum is a great place to make some of the key MDM decisions. The Governance group is already making decisions on data standards and definitions of shared data, so why not leverage this activity to ensure that the MDM decisions meet the business requirements as expressed through data Governance?
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Governance Decisions for MDM (cont.)
Category DecisionData Quality Targets • How good does the data have to be
• Root cause analysis
Data Consumers • Who needs the data and for what purpose• What do they need and at what frequency
Survivorship • What should happen when…
Lookups • Which attributes are lookup attributes• What are the allowable list of values per attribute• How different are the values across the applications
and how do we deal with inconsistencies
Types of Users and Security • What types of users have to be catered for• Can they create, update, delete, search• Can they merge, unmerge
Delete • How should deletes be managed
Privacy and Regulatory • Privacy and regulatory issues
Recommendations Meeting – Master Data Management (MDM) Assessment 071411
Presenter
Presentation Notes
If we think about the content of this slide and the previous slide, you can see how these decisions could be hampered without cross-functional collaboration between the impacted lines of business and IT. Imagine if these decisions were made solely in the context of a technology implementation, how would you know that the data quality targets support the business use of the data? Or how would you know the impact of deleting data across multiple users?
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[ MDM POLICIES & PROCESSES ]
Presenter
Presentation Notes
It’s also possible to leverage the governance program in the creation and execution of the MDM Policies and Process. Let’s look specifically at that overlap.
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Creating Policies
Charter Principles Policies Processes Procedures
Presenter
Presentation Notes
One of the key things that a governance group does is create a Charter, Principles, Policies, and Processes. If MDM is a priority for an organization, the data governance group can focus their initial efforts around the MDM requirements so the policies and processes support the MDM solution and make it more effective and more sustainable. Charter: Vision, Mission, Objectives, Goals, Alignment with Corporate Strategy, Scope Principles: High level guidelines that frame the way that data is intended to be governed, i.e. Policies: Business rules to effectively manage and govern data Processes: The key processes involved with adhering to the policies Procedures: The unique LOB implementation of processes, articulating the use of technology, etc.
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MDM Policies
• Security, Privacy, Access, Visibility• Party – Rules supporting:
— Party relationships— Hierarchies— Data lifecycle - CRUD— Data classification— Data integrity
• Product – Rules supporting:— Product relationships— Product definition— Product components (Items) and their relationship to Product
Presenter
Presentation Notes
Here is a sample of typical policies that support an MDM program. The policies are the codification of the data principles into rules or standards that are then implemented in the hub. Those policies can be specific to a data domain, or in some cases can be domain-specific.
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Standard MDM Processes
• Exception Handling• Duplicate Handling• Consensus Delete• Company / Customer On-boarding• Company Merger• Hierarchy Management• Match / Merge• Data Quality
Presenter
Presentation Notes
Processes articulate the How of the Policies from a high level. The difference between a process and a procedure is that the process is general enough to be implemented in multiple ways, and the procedure is the step by step activity of a process in a specific system. The Stewardship team is a great resource to Execute MDM Processes and Procedures One of the biggest delays that I’ve seen occur in an MDM implementation is not having data stewards, or business data experts available. to address the exception report that inevitably come with the initial loads in the MDM hub. These exception reports can be quite long and they need to be addressed in order to adjust the MDM business rules, and ensure all the data gets into the system. Business Request is the business request creation process that enables new requests through a standard approval and creation process. The business request process ensures that all relevant parties participate and are involved in the analysis, resolution and approval process, Hierarchy management is the creation and management of relationships within and between aggregate and individual entities (parent/child, groups, etc.). This also includes and enables maintaining attribute values unique to the data. Model Hierarchies: creation, viewing, updating and maintaining of hierarchies Maintain access at the data level Integrate external data sources and resolve conflicts
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Issue Management Process
Decision Meeting
Data Governance
Working Group Chair
Data Governance
Working Group
Coordinator
Impacted Business Lead/Data Steward
Identifying Business/IT
Formalize recommendation
Identify options/ evaluate
implications
Issue identified by Business/IT
Identify issue type and severity
Stewards consults other
Stewards regarding issue
(weekly stewards meeting)
Identify options/ evaluate
implications (impact
assessment)
Issue and supporting documentation
brought to Coordinator
Issue and impact logged in issues
log
Chair reviews issues log
Issue is evaluated in
monthly meeting
Update all documentation
Review issue and impact
assessment
Update issue and impact
assessment, if necessary
Formalize recommendation
Voting membership
votes
Coordinator closes issue
Publish communication
Issue and impact assessment brought to
Business Lead/Data Steward
Communicate analysis and
recommendations back to DQS
Presenter
Presentation Notes
This is a typical Issue Management Process. The issue could be a request for a new data element or attribute, maybe it’s a change request in the form of requesting a modification to a hierarchy. It could also be a request to change the way look ups and search is performed. Whatever the issue, the idea is that there is a known process that involves input and analysis from both the business and IT, That you leverage the knowledge and activity of the Data Steward, and that you incorporate the cross-functional participation of a Data Governance group to facilitate agreement from all impacted parties.
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[ ENSURE ALIGNMENT ]
Presenter
Presentation Notes
Understanding a process from the perspective of others Working individually towards a common goal
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Alignment Process
• Why is this important?
• Why should we care?
Value
• Who cares?• Why should
they care?
Stakeholders• How does the
value benefit the stakeholders?
Linkage
pCopyright (c) 2014 First San Francisco Partners
Presenter
Presentation Notes
Identify and assess the importance of key people and/or groups Executive sponsorship is critical Determine and communicate the “What’s in it for me” Leverage Data Governance Align MDM to other broader, strategic initiatives and goals Identify Value Identify Stakeholders Create Linkage Reference TDWI study findings Identify Stakeholders: Stakeholder Analysis and engagement Why Executive sponsorship and participation is important: Conflict Resolution Resource Allocation Long-term Funding Alignment with Strategic Direction The What’s in it for me needs to be articulated per key stakeholder and functional area that is impacted, it also need to include business value statements and IT value statements, as well as There are two additional steps in creating Alignment, and that is 1. Determine success criteria and metrics and 2. Communicate the message We will talk about those aspects later in the tutorial.
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Example: Stakeholder Map
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MDM Program
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Sales/MarketingImprove Segmentation
Understand RiskOptimize Relationships for
Revenue
ITImproved Productivity
Proactively support businessContain Costs
Single View of Customer
Improved Data Quality
Common Service Platform
Example: Articulate Linkage
The Single Repository of Customer data will improve my understanding of customers by providing me a trusted source of timely, accurate and pertinent data from which to execute analytics, segmentation and risk assessment.
The common service platform for data access and sharing will increase IT productivity by providing a more unified integration infrastructure. This will enable IT to better support the business in a timely manner because there will be repeatable processes and less rework.
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[ MEASUREABLE SUCCESS CRITERIA ]
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Metrics and Measurement
• Metrics and Measurement The right metrics help maintain alignment Metrics define the requirements for the information you
need to answer the questions Measurement is the data reviewed, tracked and reported on
an on-going basis
• Key Performance Indicator (KPI) A Key Performance Indicator (KPI) is a quantifiable metric
that the MDM Program has chosen that will give an indication of MDM program performance. A KPI can be used as a driver for improvement and reflects
the critical success factors for the MDM Program
• A metric is not necessarily a KPI
Presenter
Presentation Notes
The right metrics help maintain alignment Metrics have no value if they aren’t aligned to the interests of a stakeholder Ensure there is some way of measuring how the Company Data Standardization Program is helping stakeholders progress toward their goals What information do you need to track and measure to those goals?
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Example: Metrics and KPI’s
Measurement Target Frequency
Increased confidence in the quality of information
Reduce time spent in data reconciliation activities
Number of requests coming into the DG Group
Data owner assigned for each entity type
Length of time from account opening to availability online
Number of target systems using master data
Reduce time spent on creating a common customer list after an acquisition
Improved ability to find the right data quickly and easily
Data quality becomes a part of performance objectives across LOB’s
Presence/Usage of a unique identifier
Survey – yes
50%
Increasing
100%
24 hours
10
20% reduction from previous
Survey – yes
Increasing
100%
Every 6 months
Monthly
Monthly
Quarterly
Monthly
Quarterly
After every acquisition
Quarterly
Quarterly
Quarterly
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Impact Determines Success
Issues• Report Quality
and Accuracy• Low
Productivity• Regulatory
Compliance / Audit Response
Goals• Improve data’s
usability• Improve
efficiency and productivity
• Reduce compliance / audit cost
Metrics/KPI’s• Data Quality• Data
remediation time
• Effort to comply
• Use of identifiers
Impact• Improve client
relationships• Address new
markets• Reduce/avoid
fines• Improve
analysis & decision making
Presenter
Presentation Notes
How do you identify which metrics to use and measure? In order to determine the value you want to provide, you need to start by dissecting the issues to create your metrics. Start with the business challenge and then create the measurement and metrics that address the business need. The point is to clarify the issue, what is meant by the issue, why that issue is important and what is the change you’d like to see, i.e. the goal. Many times just by clarifying “what you mean” and “why you care”, you can come up with a way to track a change over time or measure the result. Measurement is also iterative. You will find that the more you know, you can adjust your metrics and measurements to become more precise, or focus on different things to drive value Instead of asking “How do I measure Data Lineage”, ask “What is the issue I’m trying to address”. Then you’ll be able to outline the variable and inputs and ultimately come to a way of identifying what can be measured and what the metrics are you’d like to use. Linking progress metrics and impact metrics Aligning metrics to key business goals and objectives
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[ EXTENDING MDM ]
Presenter
Presentation Notes
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Extending MDM to the Enterprise
• Create a Roadmap: Steps to implement and operationalize a MDM program in a
phased approach given known IT and Business initiatives Presentation / high level work plan detailing the phased
implementation steps necessary, resource requirements and potential costs involved to deliver the intended future state
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Roadmap Overview
6 Months 12 Months 18 Months 24 Months
Data Quality
Client & ProspectContact
& Partner Extend DQ Product & Account
Goals:• Establish DG program• Business Case Approval• Establish DQ Foundation
• Profiling• Reporting• Scorecards
• Define Client, Prospect & other Entity types and attributes
Goals:• Establish the MDM
Foundation • Matching• Profiling• Reporting
• Single Source for Client & Prospect
• Single Source for Credit Lines
Goals:• Single Source for Contact
& Partner• DQ at point of entry• Enable reporting and
analysis groups• Enable Address
synchronization across operational systems
Goals:• Measure, Refine &
Monitor• Single Source for
Product and Account• 360 degree view of
client• Improve monitoring of
master data across operational systems
Operationalize Data Governance DG Management and Maintenance
27
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Keys to Success
Successful MDM Implementation
Technology
Process People
Failed MDM Implementation!
Technology
Process People
Presenter
Presentation Notes
Having been involved with MDM for almost a decade, I’ve learned a few things. The most important is that MDM is not just about technology implementation. There needs to be equal weight on the people and process side so that data is mastered, not just a tool implemented. The following factors are usually evident in a successful program: First create a strategy and then follow it (agreed on starting point and steps necessary) Ensure solid alignment between Business and IT Identify and assess the importance of key people and/or groups Clearly defined and measureable success criteria Small iterations versus all or nothing Executive sponsorship is critical Really know your data Leverage prior experience/work…don’t re-invent the wheel Plan for time and resources required for manual reconciliation Communicate, Communicate, Communicate
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