Monitor the Quality of your
Master Data
THOMAS RAVN
[email protected]
March 16thth 2010, San Francisco
WWW.PLATON.NET
Platon
●
A leading Information Management consulting firm
●
Independent of software vendors
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Headquarter in Copenhagen, Denmark
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220+ employees in 9 offices
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300+ customers and 800+ projects
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Founded in 1999
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Employee owned company
“Platon received good feedback in our satisfaction survey. Clients cited the following strengths:
experience and skill of consultants, business focus and the ability to remain focused on the needs of the
client, and a strong methodological approach”
Gartner July 2008
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Key Concepts and Definitions
“Master Data Management (MDM) is
the structured management of Master
Data in terms of definitions,
governance, architecture, technology
and processes”
“Information Management is the
discipline of managing and leveraging
information in a company as a strategic
asset”
Information
Management
Data
Governance
MDM
DQM
“Data Quality Management is the
discipline of ensuring high quality data
in enterprise systems”
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“Data Governance is the crossfunctional discipline of managing,
improving, monitoring, maintaining, and
protecting data”
Components of an effective
MDM approach
Formalize business ownership and stewardship
around data.
Ensure that Master Data is taken
into account each and every time a
business process or an IT system is
changed.
Business
ownership,
responsibility,
accountability
IT Change
control
Control in which systems Master
Data is entered and how it is
synchronized across systems.
Manage Master Data Repository.
Common
definitions
MDM
Protect,
validate and
integrate data
in IT
applications
Effective
Master Data
processes
Data Quality
Management
Measure and monitor the
quality of data
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To be able to share data you need to
share definitions and business rules.
Definitions require management,
rigor and documentation.
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Capturing Master data efficiently
needs to be built into the business
processes. Equally consistent
usage of Master Data needs to be
ensured across business processes
and business functions.
Typical Data Problems - 1
No
Name
Address
90328574
IBM
187 N.Pk. Str. Salem NH 01456
8,494.00
90328575
I.B.M. Inc.
187 N.Pk. St. Sarem NH 01456
3,432.00
90328575
International Bus. M.
187 No. Park St Salem NH 04156
2,243.00
09243242
Int. Bus. Machines
187 Park Ave Salem NH 04156
5,900.00
12398732
Inter-Nation Consults
15 Main St. Andover MA 02341
6,800.00
99643413
Int. Bus. Consultants
PO Box 9 Boston MA 02210
10,243.00
43098436
I.B. Manufacturing
Park Blvd. Boston MA 04106
15,999.00
How much did we spend
with IBM last year?
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Purchase
Typical Data Problems - 2
Is this the same
customer?
Name
Street
Zip Code
City
CAFÉ SPORTSCLUB
15 3rd Street
10001
New York
CAFÉ SPORT KLUB
15 Third St.
.
NYC
Are these the same
products?
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Description, System 2
Description, System 1
1 L Cappucino - Mathilde Cafe
1/1L Mathilde Cafe Ice Cappucino
FETA W/OLIVES & GARLIC 60G, 45+
45+ FETA M/OLI+HVIDL 60G, 45+
1000 ML YOG. PEACH/BANANA
YOGHURT PÆRE/BANAN, 1000ML
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Typical Problems - 3
● A common problem is overloading of fields, which is the misuse
of a field compared to the intended use. Often because the
field the user wanted to use wasn’t available in the application
● Sometimes a field might even have been used for different
purposes by different parts of the organization
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Customer No
Name
Email
Fax
1234
John
[email protected]
Vip Customer
3368
Pete
[email protected]
Tel: 11223344
2345
Bob
[email protected]
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Where Does the Bad Data Come
From?
State is a required field
– regardless of country
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Where Does the Bad Data Come
From?
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Top 5 Sources of Bad Data
1. Lack of ownership and clearly defined responsinility
2. Lack of common definitions for data
3. Lack of control of field usage
4. Lack of process control
5. Lack of synchronization between systems
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What is Good Data Quality?
Larry English:
●
Quality exists solely in the eye of a customer of a product or service based on the value they
perceive
●
Information quality is consistently meeting ‘end customers’ expectations through information
and information services, enabling them to perform their jobs effectively
●
To define information quality, one must identify the "customer" of the data - the knowledge
worker who requires data to perform his or her job
Platon definition:
Data Quality is the degree to which data meets the defined standards
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“The Law of Information Creation”
“Information producers will create information only to
the quality level for which they are trained, measured
and held accountable.”
Larry English
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Data Standards & Data Quality
It’s all about the Meta Data…
● Good Meta Data is prequisite to achieve great
data quality (inferred from the trained part of
the ”Law of Information Creation”)
You can only achieve high quality data if you have
standards to measure against!
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Defining Good Data standards
For every entity define:
For every field define:
● Definition and keys
● Business description
● Hierarchies
● Data entry format and conventions
● Classification(s)
● Definition owner
● Life cycle
● Stakeholders
● Business Owner(s)
Consider what a user needs to know to produce high quality data
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Data Standards – An Example
Challenges
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Relating the data definitions to the process documentation
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Keeping the definitions up to date
●
The same piece of information may be entered in multiple different systems
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Defining Good Data standards
● There are two basic approaches to defining your data standards
1. Define a system independent Enterprise Information Model and then
map attributes to system fields, or
2. Define data definitions for a system (screen/table) specific view of
data
● If you have one primary system where a data entity is used, option
2 is preferable
● If you have many different systems where the same data entity is
used, option 1 is preferable
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Generating Garbage
Garbage In = Garbage Out
QualityStandard1 In + QualityStandard2 In
= Garbage Out
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Data Quality Monitoring
● Like most other things, data quality can only be managed properly
if it is measured and monitored
● A data quality monitoring concept is necessary to ensure that you
identify
● Trends in data quality
● Data quality issues before they impact critical business processes
● Areas where process improvements are needed
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Data Quality Monitoring
● For this to work, clearly-defined standards, targets for data quality
and follow-up mechanisms are required
● There is little point in monitoring the quality of your data if no one in
the business feels responsible and if clear business rules data
have not yet been defined
● Thus a data quality monitoring concept should go hand in hand
with a data governance model
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The Dimensions of Data Quality
Are all data values within
the valid domain for the
field?
Do we have all
required data?
Are data available at the
time needed?
Timeliness
Data
Quality
Are business rules on field and
table relationships met?
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Accuracy
Does data reflect the real world
objects or a trusted source?
Are shared data elements
synchronized correct across
the system landscape?
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KPI Examples in the different
dimensions
Dimension
KPI Example
Completeness
Pct of active customer records with an email address
Validity
Pct of active US customers with a phone number of 10 digits
Accuracy
Pct of active customers with an mailing address that is
verified as correct against Dun & Bradstreet
Consistency
Pct. of customer records shared between our CRM system
and our ERP system that has identical values for name,
address and telephone number.
Integrity
Pct. of active product records with [type] = “Service” where
[weight] = 0, or Pct. of open sales orders that refer to an
active customer.
Timeliness
Pct. of supplier records where the time from request of a
new record to completion and release of the record is less
then 24 hours
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The Dimensions of Data Quality
Business Impact
Accuracy
Timeliness
Consistency
Integrity
Validity
Completeness
Difficulty of
Measurement
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The steps in building a
monitoring concept
● Building a data quality monitoring concept involves the following
five basic steps:
1. Identify stakeholders
2. Conduct interviews with stakeholders and selected business users
3. Identify data quality candidate KPI’s
4. Select KPI’s for data quality monitoring
5. For each KPI, define details
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Finding Good Data Quality KPI’s
● To find good data quality KPIs collect business input through
interviews with stakeholders (use Interviewing Technique) and a
data assessment. The technique Data Profiling contains more
details on how to analyze data
Collect business input
• Business process requirements
• Data quality pain points
• Business Intelligence
• Business KPIs
KPI Candidates
DEFINED KPIs
XXX
XXX
XXX
XXX
XXX
XXX
XXX
A
XXX
B
C
XXX
Perform a thorough data assessment
(profiling) exercise searching for
common data quality problems and
look for abnormalities
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KPI
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Frq
Target
UoM
Tying Data Quality KPIs to
Business Processes
● It is essential that KPIs are not just made up, so your organization
has something to measure
● Don’t measure data quality because it’s great to have high quality
data. Measure it because your business processes depend on it
● Derive data quality KPIs from business process requirements
● Start with a high level business process like procurement (also
known as a macro process) and then break it down.
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Tying Data Quality KPIs to
Business Processes
Define the data
entities used within
the process
Procurement
Macro process
Material Master
Data
Vendor Master
Data
Process
Vendor
Selection
Spend analysis
Data Entity Scope
Data quality
requirements
No duplicate
vendors
Correct industry
code for
vendors
Correct placement
in hierarchy
(parent vendor)
Correct email
address for
vendors
Business Meta Data
Ex: A vendor record is uniquely defined as an address of a
vendor where we place orders, receive shipments from or…..
Is the
required data
quality aspect
meaningful to
monitor?
KPI
Frq
Target
A
B
C
It may be better to improve
data validation or perhaps
problems are not
experienced
Business Meta Data is required to
define the actual KPIs.
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DEFINED KPIs
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UoM
Tying Data Quality KPIs to
Business Processes
● Using a simple model like the one illustrated on the previous slide
allows you to tie data quality KPIs to business processes and to
business stakeholders
● This relationship is critical for the success of the data quality
monitoring initiative. Clearly illustrating how poor data quality
impacts specific business processes is instrumental in getting the
executive support and the business buy in
● When conducting data quality KPI interviews you may encounter
KPI suggestions like “measure if there is a valid relationship
between gross weight and product type”. Ask why this is important
and which process this is important for
● A particular data quality KPI may be important for multiple different
processes. Document the relationship to all relevant processes
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Defining Data Quality KPI’s
● Data quality KPIs should express the important characteristics of quality
of a particular data element
● Typically units of measures are percentages, ratios, or number of
occurrences
● For consistency reasons, try to harmonize the measures. If for instance one
measure is “number of customers without a postal code” while another is
“percentage of customers with a valid VAT-no” a list of measures will look
strange, since one measure should be as high as possible, and the other as
low as possible
● A good simple approach is to define all data quality KPI’s as percentages,
with a 100% meaning all records meet the criteria behind this KPI
● Be careful not to define too many measures, as this will just make the
organizational implementation more difficult
● Pay attention to controlling fields (like material type) that may determine
rules like whether a specific attribute is required
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Defining Hierarchies
● Use hierarchical measures where possible, so that measures can be
rolled up in regions and countries for instance
● In the below example a KPI related to customer data is broken down in
individual countries to allow detailed follow up
● A concern here is that fields may be used differently in different
countries. Given the below data insight, it might make sense to define a
separate KPI’s for CA and perhaps ignore MX and US
KPI: Customer
Fax number
correctly
formatted
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Avg. Value
25%
Data Insight
Value
Recs
US Customers
5%
85,000
CA Customers
77%
19,000
MX Customers
43%
38,000
● Fax numbers are not required for US
customers since all communication is
done via email.
● Fax is the primary communication
channel with Canadian customers.
● Only some customers in Mexico have a
fax machine.
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Defining KPI Thresholds
● Along with each KPI two thresholds should be defined:
● Lowest acceptable value
● Without specifying the lowest acceptable value (or worst value), it’s difficult
to know when to react
● If the measure falls below this threshold action is required
● Target value
● Without target values, you don’t know when the quality is ok. Remember fitfor-purpose
● Specifying a low and target threshold allows for traffic light
reporting that provides an easy overview
● Defining appropriate thresholds can be difficult as even a single
product record with wrong dimensions may cause serious process
impact. But without any indication of when to be alerted any form
of automated monitoring is difficult
Target Value: 95 %
Lowest acceptable value: 80 %
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Indirect Measures
● Consider critical fields (e.g. weight of a product or customer type)
where the correct value is of utmost importance, but it’s close to
impossible to define the rules to check if a new value entered is
correct….
● One approach is to measure indirectly by for instance reporting
what users have changed these values for which products over the
last 24 hours, week or whatever is appropriate in your organization
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Cross field KPIs and Process
KPIs
● Common KPIs that are not related to a single field
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Number of new customer records created this week
●
Average time from request to completion of a new material record
●
Number of materials with a non-unique description (or pct. of materials
with a unique description)
●
Number of vendors, where a different payment is defined in different
purchasing organizations
●
Number of open sales orders referring to an inactive customer
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Think Prevention!
● Every possible business rule related to completeness, integrity,
consistency and validity should be enforced by the system at the
time of data entry.
● If it isn’t, consider implementing a data input validation rule rather
than allowing bad data to be entered and then measure it!
● However, there are cases, where the business logic of a field is too
ambiguous to be enforced by a simple input validation rule.
● Process (workflow) adjustments may also be the answer.
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Documentation of KPIs
KPI Name:
A meaningful name of the KPI that expresses what is being measured
Objective:
Why do you measure this? What business processes are impacted if there data is not ok?
Dimensions:
What data quality dimensions (integrity, validity, etc.) are this KPI related to?
Frequency of measure:
How often do you wish to report on this KPI? Daily, daily, weekly or monthly?
Unit of measure:
What is the unit of the KPI? Number of records, pct of records, number of bad values, etc.?
Lowest acceptable
measure:
Threshold that indicates if the data quality aspect the KPI represents is at a minimal
acceptable level. The value here must be in the unit of measure of the KPI.
Target value:
At what value is the KPI considered to represent data quality at a high level?
Responsible:
The person responsible for the particular KPI.
Formula:
The tables and fields that are used to analyze and calculate the KPI. This is the functional
design formula that forms the basis for the technical implementation.
Hierarchies:
When reporting on a KPI it is very useful to be able to slice and dice the measure according to
different dimensions or hierarchies. For a customer data KPI for instance, good hierarchies
would be regions, country, company code and account group.
Being able to view the KPI through a hierarchy also makes it easier to follow up with specific
groups of business users.
Notes and assumptions:
If certain assumptions are made about the KPI make sure to document it here
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Remember!
● Quality is in the Eye of the beholder!
● Data quality is defined by our Information Customers
● Data is not always clean or dirty in itself – it may depend on the
viewpoint and a defined standard
● Focus on what’s important to those that use the data
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Monitoring Process
● A simple example
Publish KPI
Analyze
KPIs
Low
value in
KPI?
Y
Evaluate
root cause
N
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Plan
corrective
actions
Implement
Improvements
Monitor the Quality of your Master
Data
Thomas Ravn
Practice Director, MDM
E: [email protected]
M: +1 646-400-2862
PLATON US INC.
5 PENN PLAZA, 23rd Floor
NEW YORK NY 10001
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