4th knowhow master data management …...1 understanding the business value of mdm a lean approach...
TRANSCRIPT
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Understanding the business value of MDM
a lean approach
Nicolas Fournier (TIBCO Software)
4th KnowHow Meeting … Hotel Hafen Hamburg … 28 Aug 2019 Master Data Management … Governance … Quality … Integration … Security www.DataCampus.eu
2 https://www.gartner.com/smarterwithgartner/how-to-create-a-business-case-for-data-quality-improvement/
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DAMA’s six primary dimensions of data quality
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DAMA UK, “The Six Primary Dimensions of Data Quality,” October 2013, http://www.damauk.org/RWFilePub.php?&cat=403&dx=2&ob=3&rpn=catviewleafpublic403&id=106193
Completeness The proportion of stored data against the potential of "100% complete"
Validity Data are valid if it conforms to the syntax (format, type, range) of its definition.
Consistency The absence of difference, when comparing two or more representations of a thing against a definition.
Uniqueness Nothing will be recorded more than once based upon how that thing is identified.
Timeliness The degree to which data represent reality from the required point in time.
Accuracy The degree to which data correctly describes the "real world" object or event being described.
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How Boston is not Boston
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It’s worth keeping in mind that data quality software solutions generally assess the validity of data.
For example, in order for the address table to be complete it must have a city attribute. In order for the city attribute to be valid it must be a string that is no longer than 50 characters.
Accuracy is frequently the domain of the business and subject matter experts. This is one reason why TIBCO Software recommends participation by the business and subject matter experts in governance workflows.
For example, in our figure to the left each geographic grouping is a valid definition for Boston. Which of these definitions of “Boston” is accurate depends on the use case.
Boston City
Boston MSA
Boston CSA
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Can classic techniques from Lean be used to understand the value master data management adds to the organization?
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Applying the eight kinds of waste to assessing data management (DOWNTIME)
Defects Over-
production Waiting
Not Utilizing Talent
Trans- portation
Inventory Motion Extra-
Processing
Poor quality data, which leads to: Incorrect reports, Failed business
processes, etc.
Multiple groups create the own analyses (from their own inventory of data) because
they don’t trust/understand the findings of other groups
Waiting for IT to provide access to data. Waiting for IT prepare data. Waiting for IT to load data
into the EDW.
Is wrangling data in Excel to address alleviate data quality problems the best use of your
employees’ time? How does this impact morale and employee retention?
Poor process design (or lack of) leads to endless email threads with attached
spreadsheets
Maintaining multiple repositories of the “same” data everywhere. (usually entropy
sets in and the data is “mostly” the same).
“Swivel chair” integration”. Re-keying the same data into multiple systems.
All that work that’s done by other teams to QA/QC the prior team’s work
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● Global 500 Services Company ● Global Engineering and R&D Services Company ● NGO focused on delivering humanitarian medical care ● Global Distribution System ● Dutch cooperative bank
Examples
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Supporting operations with customer-driven data management
CONTEXT: Global 500 services company with multiple lines of business
Multiple lines of business which frequently provides services to the same customers (e.g. same customer contracts for pest control, equipment care, employee & guest safety, water treatment, and sanitation services.)
PROBLEM: Customer data was separately managed
Each LoB managed customer information independently. As customer information changed each service line needed to independently source the updates. This often led to service delivery problems across the lines of business and customer dissatisfaction for the organization.
SOLUTION: Customer-driven data management
The self-service data management tools are used by customers to update their organizational hierarchies (e.g. store locations, org structures, etc) and entitlements for the portal, documentation, and reporting.
RESULT: Improved service quality and cost reduction
By having customers manage their data the organization ensured they were alway using the most current and accurate data. By centralizing data management for all LoBs, they ensured that the information was consistent across the organization.
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Using DOWNTIME to assess issues at the Global 500 Services Company
Defects Over-
production Waiting
Not Utilizing Talent
Trans- portation
Inventory Motion Extra-
Processing
Poor data quality and inconsistency of customer data created service delivery issues and harmed the overall brand
Is having account management chase customers the best use of their time? Customers know their information letting them perform data entry reduces errors.
Customer data was separately managed by each service line
Each service line needed to independently source the updates
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Global Engineering and R&D Services Company
Massari, F. “Business Involvement is key to success in MDM,” Gartner Data & Analytics, Frankfurt, 2018.
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Using DOWNTIME to assess issues at the Global Engineering and R&D Services Company
Defects Over-
production Waiting
Not Utilizing Talent
Trans- portation
Inventory Motion Extra-
Processing
Data quality problems in onboarding processes
IT originally, not the business, tried to address DQ issues where accuracy (fit for purpose) was the main issue. Perhaps right people, but wrong job.
Multiple sources of the same information
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Gobin, P. “Global Product MDM in a Distributed Organization,” Master Data Management & Data Governance Summit UK, 2015
NGO focused on providing humanitarian medical care
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Using DOWNTIME to assess issues at the NGO focused on providing humanitarian medical care
Defects Over-
production Waiting
Not Utilizing Talent
Trans- portation
Inventory Motion Extra-
Processing
Inaccurate measures of inventory levels across the three distribution centers Misclassified materials
Delays in catalog production
Multiple sources of inventory & reference data codes
A lack of common classifications requires all distribution centers to perform their own translations.
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Global distribution system
Stevens, J. “Mastering a Strong foundation for operational excellence and enhanced analytics,” Gartner Data & Analytics Summit US, 2015
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Using DOWNTIME to assess issues at Global Distribution System
Defects Over-
production Waiting
Not Utilizing Talent
Trans- portation
Inventory Motion Extra-
Processing
Operational issues and analytical failures caused by inconsistency and inaccuracy
Could the lack of a centralized source have led to the profusion of tables (see inventory)
Data scientists are spending valuable time looking for authoritative sources of common reference data
20,763 variations on a theme
Every application and division owner is maintaining the reference tables, maintenance cost estimate ~$7M/annum
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Dutch cooperative bank
Van Daatselaar, P. “RDM Projectaanpak,” TIBCO Reference Data Day -Utrecht, 2019.
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Using DOWNTIME to assess issues at the Dutch Cooperative Bank
Defects Over-
production Waiting
Not Utilizing Talent
Trans- portation
Inventory Motion Extra-
Processing
Operational: customer service issues Analytical: inconsistency in data leading to reporting issues across systems Governance: Regulators need more transparency in data sourcing
Multiple sources of reference data throughout the organization require maintenance
Teams emailing spreadsheets of information to each other (sometimes propagating data quality issues)
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● Lean can be applied to MDM and DQ. It’s a well known framework that can facilitate communication with others.
● The eight kinds of waste can be used as a framework for categorizing the kinds of waste we see (and beginning cost estimation).
● Costs are just one part of the equation (they’re more measurable). However it’s worth focusing on the value (benefits) that an MDM creates because it provides a forward looking vision for the program.
Final Thoughts:
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• Contact: [email protected]
• TIBCO EBX website: https://www.tibco.com/de/products/tibco-ebx-software
• TIBCO EBX whitepaper: https://www.tibco.com/de/node/65161
Thank you - any questions?
