The Increasing Criticality of MDM for Personalization for Customers and Employees
Master data management seems to be one of those perennial, evergreen programs that organizations continue to struggle with.
Every couple of years people say, “we're going to get a handle on our master data” and then spend hundreds of thousands to millions and tens of millions of dollars working toward a solution.
The challenge is that many of these solutions are not really getting to the root cause of the problem. They start with technology and begin by looking at specific data elements rather than looking at the business concepts that are important to the organization.
MDM programs are also difficult to anchor on a specific business value proposition such as improving the top line. Many initiatives are so deep in the weeds and so far upstream that executives lose interest and they lose faith in the business value that the project promises. Meanwhile frustrated data analysts, data architects and technology organizations feel cut off at the knees because they can't get the funding, support and attention that they need to be successful.
We've seen this time after time and until senior executives recognize the value and envision where the organization can go with control over its data across domains, this will continue to happen over and over again. Executives all nod their heads and say “Yes! Data is important, really important!” But when they see the price tag they say, “Whoa hold on there, it's not that important”.
Well, actually, it is that important.
We can't forget that under all of the systems, processes and shiny new technologies such as artificial intelligence and machine learning lies data. And that data is more important than the algorithm. If you have bad data your AI is not going to be able to fix it. Yes there are data remediation applications and there are mechanisms to harmonize or normalize certain data elements. But looking at this holistically requires human judgment: understanding business processes, understanding data flows, understanding dependencies and understanding of the entire customer experience ecosystem and the role of upstream tools, technologies and processes that enable that customer experience.
Until we take that holistic approach and connect it to business value these things are not going to get the time, attention and resources that they need.
Seth Earley, Founder & CEO, Earley Information Science
Dan O'Connor, Senior Product Manager at inriver
16. item: color, size resource type
filename
variants
product package alternatives
up-sell
product packages
cross-sell
name
description
(in several
langugages)
brand
market
material
service parts
121 HP
146 HP
Product Data Maturity
17. PIM
• Single Source of Truth for
Product Information
• Product Marketing Focused
to Create a Single Marketing
Message Across All Usage
• Primary Purpose is to
Syndicate Product Data to All
Sales/Marketing Channels
MDM
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Multiple Domains
• Managing Internal Data
Standardization and
Aggregation
• A system of Tools and
Processes as Part of a Data
Governance Process with a
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26. Using Metrics & KPIs to Focus Governance
Measuring here
(business outcomes)
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(process indicators)
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Business Unit Objectives
New Business Opportunities
Average Order Size Total Account Revenue
Business Processes Site Traffic Search Relevance
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taxonomy, search, data
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Content supports
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Content Scorecards
Process Scorecards
Outcome Scorecards
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Digital Team: “How do I know taxonomy / content / search is working?”