Monitor the Quality of your Master Data - PowerPoint Presentation

Slide1

Monitor the Quality of your Master Data

THOMAS RAVNTRA@PLATON.NET

March 16th

th

2010, San FranciscoSlide2

Platon

A leading Information Management consulting firm

Independent of software vendors

Headquarter in Copenhagen, Denmark

220+ employees in 9 offices

300+ customers and 800+ projectsFounded in 1999Employee 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

MDM

“Information Management is the discipline of managing and l

everaging

information in a company as a strategic asset”

“Master Data Management (MDM) is the structured management of Master Data in terms of definitions, governance, architecture, technology and processes”

Data

Governance

“Data Governance is the cross-functional discipline of managing, improving, monitoring, maintaining, and protecting data”

Information Management

3

“Data Quality Management is the discipline of ensuring high quality data in enterprise systems”

DQMSlide4

Components of an effective MDM approach

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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.

Control in which systems Master Data is entered and how it is synchronized across systems.

Manage Master Data Repository.

To be able to share data you need to share definitions and business rules. Definitions require management, rigor and documentation.

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.

Measure and monitor the quality of dataSlide5

Typical Data Problems - 1

5

No

Name

Address

Purchase90328574

IBM

187 N.Pk. Str. Salem NH 01456

8,494.0090328575

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?Slide6

Typical Data Problems - 2

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Name

Street

Zip

CodeCityCAFÉ SPORTSCLUB15 3rd

Street10001

New YorkCAFÉ SPORT KLUB15 Third St.

.NYC

Is this the same customer?

Are these the same products?

Description, System 2

1 L Cappucino - Mathilde Cafe

FETA W/OLIVES & GARLIC 60G, 45+

1000 ML YOG. PEACH/BANANA

Description, System 1

1/1L Mathilde Cafe Ice Cappucino

45+ FETA M/OLI+HVIDL 60G, 45+

YOGHURT PÆRE/BANAN, 1000MLSlide7

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 applicationSometimes a field might even have been used for different purposes by different parts of the organization

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Customer No

Name

EmailFax

1234

Johnjohn@mail.comVip Customer

3368Petepete@mail.comTel: 112233442345Bob

bob@mail.comSlide8

Where Does the Bad

Data Come From?

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State is a required field – regardless of countrySlide9

Where Does the Bad

Data Come From?

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Top 5 Sources of Bad Data

Lack of ownership and clearly defined responsinility

Lack of common definitions for data

Lack of control of field usage

Lack of process control

Lack of synchronization between systems 10Slide11

What is Good Data Quality?

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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 standardsSlide12

“Information producers will create information only to the quality level for which they are

trained,

measured

and held

accountable

.” Larry English“The Law of Information Creation”

12Slide13

Data Standards & Data Quality

It’s all about the Meta Data…

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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!Slide14

Defining Good Data standards

14

Business description

Data entry format and conventions

Definition owner

Stakeholders

Definition and keys

Life cycle

Classification(s)

Hierarchies

For every entity define:

For every

field

define:

Consider what a user needs to know to produce high quality data

Business Owner(s)Slide15

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Data Standards – An Example

Challenges

Relating the data definitions to the process documentation

Keeping

the definitions up to date

The same piece of information may be entered in multiple different systemsSlide16

Defining Good Data standards

There are two basic approaches to defining your data standardsDefine a system independent Enterprise Information Model and then map attributes to system fields, or

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

16Slide17

Generating Garbage

Garbage In = Garbage Out

Quality

Standard1

In + Quality

Standard2

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 processesAreas where process improvements are neededSlide19

Data Quality Monitoring

For this to work, clearly-defined standards, targets for data quality and follow-up mechanisms are requiredThere 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 definedThus a data quality monitoring concept should go hand in hand with a data governance model

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The Dimensions of Data Quality

Validity

Accuracy

Consistency

Integrity

Data

Quality

Timeliness

Completeness

Does data reflect the real world objects or a

trusted source?

Are business rules on field and table relationships met?

Are

shared data

elements

synchronized correct across the system landscape?

Do we have all required data?

Are all data values within the valid

domain for the field?

Are data available at the time needed?

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KPI Examples in the different dimensions

Dimension

KPI Example

Completeness

Pct

of active customer records with an email addressValidityPct of active US customers with a phone number of 10 digitsAccuracyPct of active customers with an mailing address that is verified as correct against Dun & Bradstreet

ConsistencyPct. of customer records shared

between our CRM system and our ERP system that has identical values for name, address and telephone number. IntegrityPct. of active product records with [type] =

“Service” where [weight] = 0, or Pct. of open sales orders that refer to an active customer.TimelinessPct. 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

Difficulty of Measurement

Completeness

Validity

Integrity

Timeliness

Consistency

AccuracySlide23

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The steps in building a monitoring concept

Building a data quality monitoring concept involves the

following five

basic steps:

Identify stakeholdersConduct interviews with stakeholders and selected business usersIdentify data quality candidate KPI’sSelect KPI’s for data quality monitoringFor each KPI, define detailsSlide24

Finding Good Data Quality KPI’s

Perform a thorough data assessment (profiling) exercise searching for common data quality problems and look for abnormalities

Collect

business input

Business process requirements

Data

quality pain points

Business Intelligence

Business

KPIs

XXX

XXX

XXX

XXX

XXX

XXX

XXX

XXX

XXX

DEFINED KPIs

KPIFrq

TargetUoM

ABC

KPI Candidates

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

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Tying Data Quality KPIs to Business Processes

It is essential that KPIs are not just made up, so your organization has something to measureDon’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. 25Slide26

Tying Data Quality KPIs to Business Processes

Procurement

No duplicate vendors

Correct industry code for vendors

Correct placement in hierarchy (parent vendor)

Correct email address for vendors

Business Meta Data

DEFINED KPIs

KPI

Frq

Target

UoM

A

B

C

Data quality requirements

Business Meta Data is required to define the actual KPIs.

Ex: A vendor record is uniquely defined as an address of a vendor where we place orders, receive shipments from or…..

Define the data entities used within the process

Material Master

Data

Data Entity Scope

Macro process

Process

Is the required data quality aspect meaningful to monitor?

It may be better to improve data validation or perhaps problems are not experienced

Spend analysis

Vendor Selection

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Vendor Master

DataSlide27

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 stakeholdersThis 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’sData 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 difficultPay 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 formattedUS Customers

CA Customers

MX Customers

5%

43%

77%

Value

Avg. Value

25%

Recs

85,000

38,000

19,000

Data Insight

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 valueWithout target values, you don’t know when the quality is ok. Remember fit-for-purposeSpecifying a low and target threshold allows for traffic light reporting that provides an easy overviewDefining 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 %

30Slide31

31Indirect 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 organizationSlide32

Cross field KPIs and Process KPIs

Common KPIs that are not related to a single field

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

hereSlide35

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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 dataSlide36

Monitoring Process

A simple example

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Publish KPI

Analyze KPIs

Evaluate root cause

Implement Improvements

Plan corrective actions

Low value in KPI?

Y

NSlide37

37

Monitor the Quality of your Master Data

Thomas Ravn

Practice Director, MDM

E: tra@platon.net

M: +1 646-400-2862

PLATON US INC.

5 PENN PLAZA, 23

rd

Floor

NEW YORK NY 10001 www.platon.net