Data-Ed: Unlock Business Value through Data Quality Engineering

Unlock Business Value through Data Quality Engineering

Presented by Peter Aiken, Ph.D.10124 W. Broad Street, Suite C

Glen Allen, Virginia 23060804.521.4056

Copyright 2013 by Data Blueprint 2

Unlock Business Value through Data Quality EngineeringOrganizations must realize what it means to utilize data quality management in support of business strategy. This webinar focuses on obtaining business value from data quality initiatives. I will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor data quality. Showing how data quality should be engineered provides a useful framework in which to develop an effective approach. This in turn allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.

Date: June 11, 2013Time: 2:00 PM ET/11:00 AM PTPresenter: Peter Aiken, Ph.D.

Time:• timeliness• currency• frequency• time period

Form:• clarity• detail• order• presentation• media

Content:• accuracy• relevance• completeness• conciseness• scope• performance

Time:• timeliness• currency• frequency• time period

Form:• clarity• detail• order• presentation• media

Content:• accuracy• relevance• completeness• conciseness• scope• performance

Copyright 2013 by Data Blueprint

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Meet Your Presenter: Peter Aiken, Ph.D.• 25+ years of experience in data management• Multiple international awards &

recognition• Founder, Data Blueprint (datablueprint.com)

• Associate Professor of IS, VCU (vcu.edu)

• President, DAMA International (dama.org)

• 8 books and dozens of articles• Experienced w/ 500+ data management

practices in 20 countries• Multi-year immersions with organizations as

diverse as the US DoD, Nokia, Deutsche Bank, Wells Fargo, and the Commonwealth of Virginia

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1. Data Management Overview

2. DQE Definitions (w/ example)

3. DQE Cycle & Contextual Complications

4. DQ Causes and Dimensions

5. Quality and the Data Life Cycle

6. DDE Tools

7. Takeaways and Q&A

Outline

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Data Program Coordination

Feedback

DataDevelopment


StandardData

Five Integrated DM Practice AreasOrganizational Strategies

Goals

BusinessData

Business Value

Application Models & Designs

Implementation

Direction

Guidance

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OrganizationalData Integration

DataStewardship

Data SupportOperations

Data Asset Use

IntegratedModels

Leverage data in organizational activities

Data management processes andinfrastructure

Combining multipleassets to produceextra value

Organizational-entity subject area data

integration

Provide reliable data access

Achieve sharing of data within a business area


Five Integrated DM Practice Areas

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Manage data coherently.

Share data across boundaries.

Assign responsibilities for data.Engineer data delivery systems.

Maintain data availability.

Data Program Coordination

Organizational Data Integration

Data Stewardship

Data Development

Data Support Operations


• 5 Data Management Practices Areas / Data Management Basics

• Are necessary but insufficient prerequisites to organizational data leveraging applications (that is Self Actualizing Data or AdvancedData Practices)

Basic Data Management Practices– Data Program Management– Organizational Data Integration– Data Stewardship– Data Development– Data Support Operations

http://3.bp.blogspot.com/-ptl-9mAieuQ/T-idBt1YFmI/AAAAAAAABgw/Ib-nVkMmMEQ/s1600/

maslows_hierarchy_of_needs.png

Advanced Data Practices• Cloud• MDM• Mining• Analytics• Warehousing• Big

Data Management Practices Hierarchy (after Maslow)


Data Management Body of Knowledge

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Data Management

Functions

• Published by DAMA International– The professional association for

Data Managers (40 chapters worldwide)– DMBoK organized around

• Primary data management functions focused around data delivery to the organization (dama.org)

• Organized around several environmental elements

• CDMP– Certified Data Management Professional– DAMA International and ICCP– Membership in a distinct group made up of your

fellow professionals– Recognition for your specialized knowledge in a

choice of 17 specialty areas– Series of 3 exams– For more information, please visit:

• http://www.dama.org/i4a/pages/index.cfm?pageid=3399 • http://iccp.org/certification/designations/cdmp


DAMA DM BoK & CDMP

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Overview: Data Quality Engineering

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6. DDE Tools


Outline

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Data Data

Data

Information

Fact Meaning

Request

A Model Specifying Relationships Among Important Terms

[Built on definition by Dan Appleton 1983]

Intelligence

Use

1. Each FACT combines with one or more MEANINGS. 2. Each specific FACT and MEANING combination is referred to as a DATUM. 3. An INFORMATION is one or more DATA that are returned in response to a specific REQUEST 4. INFORMATION REUSE is enabled when one FACT is combined with more than one

MEANING.5. INTELLIGENCE is INFORMATION associated with its USES.

Wisdom & knowledge are often used synonymously

Data

Data

Data Data

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Definitions• Quality Data

– Fit for use meets the requirements of its authors, users, and administrators (adapted from Martin Eppler)

– Synonymous with information quality, since poor data quality results in inaccurate information and poor business performance

• Data Quality Management– Planning, implementation and control activities that apply quality

management techniques to measure, assess, improve, and ensure data quality

– Entails the "establishment and deployment of roles, responsibilities concerning the acquisition, maintenance, dissemination, and disposition of data" http://www2.sas.com/proceedings/sugi29/098-29.pdf

✓ Critical supporting process from change management✓ Continuous process for defining acceptable levels of data quality to meet business

needs and for ensuring that data quality meets these levels• Data Quality Engineering

– Recognition that data quality solutions cannot not managed but must be engineered– Engineering is the application of scientific, economic, social, and practical knowledge in

order to design, build, and maintain solutions to data quality challenges– Engineering concepts are generally not known and understood within IT or business!

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Spinach/Popeye story from http://it.toolbox.com/blogs/infosphere/spinach-how-a-data-quality-mistake-created-a-myth-and-a-cartoon-character-10166


Improving Data Quality during System Migration

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• Challenge– Millions of NSN/SKUs

maintained in a catalog– Key and other data stored in

clear text/comment fields– Original suggestion was manual

approach to text extraction– Left the data structuring problem unsolved

• Solution– Proprietary, improvable text extraction process– Converted non-tabular data into tabular data– Saved a minimum of $5 million– Literally person centuries of work

Unmatched Items

Ignorable Items

Items Matched

Week # (% Total) (% Total) (% Total)1 31.47% 1.34% N/A2 21.22% 6.97% N/A3 20.66% 7.49% N/A4 32.48% 11.99% 55.53%… … … …14 9.02% 22.62% 68.36%15 9.06% 22.62% 68.33%16 9.53% 22.62% 67.85%17 9.50% 22.62% 67.88%18 7.46% 22.62% 69.92%


Determining Diminishing Returns

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Time needed to review all NSNs once over the life of the project:Time needed to review all NSNs once over the life of the project:NSNs 2,000,000Average time to review & cleanse (in minutes) 5Total Time (in minutes) 10,000,000

Time available per resource over a one year period of time:Time available per resource over a one year period of time:Work weeks in a year 48Work days in a week 5Work hours in a day 7.5Work minutes in a day 450Total Work minutes/year 108,000

Person years required to cleanse each NSN once prior to migration:Person years required to cleanse each NSN once prior to migration:Minutes needed 10,000,000Minutes available person/year 108,000Total Person-Years 92.6

Resource Cost to cleanse NSN's prior to migration:Resource Cost to cleanse NSN's prior to migration:Avg Salary for SME year (not including overhead) $60,000.00Projected Years Required to Cleanse/Total DLA Person Year Saved

93Total Cost to Cleanse/Total DLA Savings to Cleanse NSN's: $5.5 million

Copyright 2013 by Data Blueprint 17

Quantitative Benefits


Six misconceptions about data quality

1. You can fix the data

2. Data quality is an IT problem

3. The problem is in the data sources or data entry

4. The data warehouse will provide a single version of the truth

5. The new system will provide a single version of the truth

6. Standardization will eliminate the problem of different "truths" represented in the reports or analysis

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The Blind Men and the Elephant

• It was six men of Indostan, To learning much inclined,Who went to see the Elephant(Though all of them were blind),That each by observationMight satisfy his mind.

• The First approached the Elephant,And happening to fallAgainst his broad and sturdy side,At once began to bawl:"God bless me! but the ElephantIs very like a wall!"

• The Second, feeling of the tuskCried, "Ho! what have we here,So very round and smooth and sharp? To me `tis mighty clearThis wonder of an ElephantIs very like a spear!"

• The Third approached the animal,And happening to takeThe squirming trunk within his hands, Thus boldly up he spake:"I see," quoth he, "the ElephantIs very like a snake!"

• The Fourth reached out an eager hand, And felt about the knee:"What most this wondrous beast is like Is mighty plain," quoth he;"'Tis clear enough the Elephant Is very like a tree!"

• The Fifth, who chanced to touch the ear, Said: "E'en the blindest manCan tell what this resembles most;Deny the fact who can,This marvel of an ElephantIs very like a fan!"

• The Sixth no sooner had begunAbout the beast to grope,Than, seizing on the swinging tailThat fell within his scope."I see," quoth he, "the ElephantIs very like a rope!"

• And so these men of IndostanDisputed loud and long,Each in his own opinionExceeding stiff and strong,Though each was partly in the right,And all were in the wrong!

(Source: John Godfrey Saxe's ( 1816-1887) version of the famous Indian legend ) 19Copyright 2013 by Data Blueprint


No universal conception of data quality exists, instead many differing perspective compete.• Problem:

–Most organizations approach data quality problems in the same way that the blind men approached the elephant - people tend to see only the data that is in front of them

–Little cooperation across boundaries, just as the blind men were unable to convey their impressions about the elephant to recognize the entire entity.

–Leads to confusion, disputes and narrow views• Solution:

–Data quality engineering can help achieve a more complete picture and facilitate cross boundary communications

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Structured Data Quality Engineering1. Allow the form of the

Problem to guide the form of the solution

2. Provide a means of decomposing the problem

3. Feature a variety of tools simplifying system understanding

4. Offer a set of strategies for evolving a design solution5. Provide criteria for evaluating the quality of the

various solutions6. Facilitate development of a framework for developing

organizational knowledge.

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Polling Question #1

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• Does your organization address or plan to address data/information quality issues

• Responses– A. We did last year (2012)– B. We are this year (2013)– C. We will next year (2014)– D. We hope to next year (2014)







6. DDE Tools


Outline

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Tweetingnow: #dataed


Mizuho Securities• Wanted to sell 1 share for

600,000 yen• Sold 600,000 shares for 1

yen• $347 million loss• In-house system did not

have limit checking• Tokyo stock exchange

system did not have limit checking ...

• … and doesn't allow order cancellations

CLUMSY typing cost a Japanese bank at least £128 million and staff their Christmas bonuses yesterday, after a trader mistakenly sold 600,000 more shares than he should have. The trader at Mizuho Securities, who has not been named, fell foul of what is known in financial circles as “fat finger syndrome” where a dealer types incorrect details into his computer. He wanted to sell one share in a new telecoms company called J Com, for 600,000 yen (about £3,000).

Infamous Data Quality Example

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Four ways to make your data sparkle!1.Prioritize the task

– Cleaning data is costly and time consuming

– Identify mission critical/non-mission critical data

2.Involve the data owners – Seek input of business units on what constitutes "dirty"

data3.Keep future data clean

– Incorporate processes and technologies that check every zip code and area code

4.Align your staff with business– Align IT staff with business units

(Source: CIO JULY 1 2004)

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• Deming cycle• "Plan-do-study-act" or

"plan-do-check-act"1. Identifying data issues that are

critical to the achievement of business objectives

2. Defining business requirements for data quality

3. Identifying key data quality dimensions

4. Defining business rules critical to ensuring high quality data

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The DQE Cycle


The DQE Cycle: (1) Plan

• Plan for the assessment of the current state and identification of key metrics for measuring quality

• The data quality engineering team assesses the scope of known issues– Determining cost and impact– Evaluating alternatives for

addressing them

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The DQE Cycle: (2) Deploy

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• Deploy processes for measuring and improving the quality of data:

• Data profiling– Institute inspections and

monitors to identify data issues when they occur

– Fix flawed processes that are the root cause of data errors or correct errors downstream

– When it is not possible to correct errors at their source, correct them at their earliest point in the data flow


The DQE Cycle: (3) Monitor• Monitor the quality of data

as measured against the defined business rules

• If data quality meets defined thresholds for acceptability, the processes are in control and the level of data quality meets the business requirements

• If data quality falls below acceptability thresholds, notify data stewards so they can take action during the next stage

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The DQE Cycle: (4) Act• Act to resolve any

identified issues to improve data quality and better meet business expectations

• New cycles begin as new data sets come under investigation or as new data quality requirements are identified for existing data sets

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DQE Context & Engineering Concepts • Can rules be implemented stating that no data can be

corrected unless the source of the error has been discovered and addressed?

• All data must be 100% perfect?

• Pareto – 80/20 rule– Not all data

is of equal Importance

• Scientific, economic, social, and practical knowledge

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Data quality is now acknowledged as a major source of organizational risk by certified risk professionals!

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6. DDE Tools


Outline

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Two Distinct Activities Support Quality Data

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• Data quality best practices depend on both– Practice-oriented activities– Structure-oriented activities

Practice-oriented activities focus on the capture and manipulation of data

Structure-oriented activities focus on the data implementation

Quality Data


Practice-Oriented Activities

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• Stem from a failure to rigor when capturing/manipulating data such as:– Edit masking– Range checking of input data– CRC-checking of transmitted data

• Affect the Data Value Quality and Data Representation Quality • Examples of improper practice-oriented activities:

– Allowing imprecise or incorrect data to be collected when requirements specify otherwise

– Presenting data out of sequence

• Typically diagnosed in bottom-up manner: find and fix the resulting problem

• Addressed by imposing more rigorous data-handling governance

Quality of Data Representation

Quality of Data Values

Practice-oriented activities


Structure-Oriented Activities

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• Occur because of data and metadata that has been arranged imperfectly. For example: – When the data is in the system but we just can't access it; – When a correct data value is provided as the wrong response to a query; or – When data is not provided because it is unavailable or inaccessible to the customer

• Developer focus within system boundaries instead of within organization boundaries • Affect the Data Model Quality and Data Architecture Quality• Examples of improper structure-oriented activities:

– Providing a correct response but incomplete data to a query because the user did not comprehend the system data structure

– Costly maintenance of inconsistent data used by redundant systems

• Typically diagnosed in top-down manner: root cause fixes• Addressed through fundamental data structure governance

Quality of Data Architecture

Quality of Data Models

Structure-oriented activities


Quality Dimensions

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A congratulations letter from another bankProblems

• Bank did not know it made an error

• Tools alone could not have prevented this error

• Lost confidence in the ability of the bank to manage customer funds

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4 Dimensions of Data Quality

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An organization’s overall data quality is a function of four distinct components, each with its own attributes:

• Data Value: the quality of data as stored & maintained in the system

• Data Representation – the quality of representation for stored values; perfect data values stored in a system that are inappropriately represented can be harmful

• Data Model – the quality of data logically representing user requirements related to data entities, associated attributes, and their relationships; essential for effective communication among data suppliers and consumers

• Data Architecture – the coordination of data management activities in cross-functional system development and operations

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Effective Data Quality Engineering

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Data Representation

Quality

As presented to the user

Data Value Quality

As maintained in the system

Data Model Quality

As understood by developers

Data Architecture Quality

As an organizational

asset

(closer to the architect)(closer to the user)

• Data quality engineering has been focused on operational problem correction– Directing attention to practice-oriented data imperfections

• Data quality engineering is more effective when also focused on structure-oriented causes– Ensuring the quality of shared data across system boundaries


Full Set of Data Quality Attributes

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Difficult to obtain leverage at the bottom of the falls

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Frozen Falls 43


New York Turns to Big Data to Solve Big Tree Problem• NYC

– 2,500,000 trees• 11-months from 2009 to 2010

– 4 people were killed or seriously injured by falling tree limbs in Central Park alone

• Belief– Arborists believe that pruning and otherwise maintaining trees

can keep them healthier and make them more likely to withstand a storm, decreasing the likelihood of property damage, injuries and deaths

• Until recently– No research or data to back it up

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http://www.computerworld.com/s/article/9239793/New_York_Turns_to_Big_Data_to_Solve_Big_Tree_Problem?source=CTWNLE_nlt_datamgmt_2013-06-05


NYC's Big Tree Problem• Question

– Does pruning trees in one year reduce the number of hazardous tree conditions in the following year?

• Lots of data but granularity challenges– Pruning data recorded block by block– Cleanup data recorded at the address level– Trees have no unique identifiers

• After downloading, cleaning, merging, analyzing and intensive modeling– Pruning trees for certain types of hazards caused a 22 percent reduction in the

number of times the department had to send a crew for emergency cleanups• The best data analysis

– Generates further questions• NYC cannot prune each block every year

– Building block risk profiles: number of trees, types of trees, whether the block is in a flood zone or storm zone

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http://www.computerworld.com/s/article/9239793/New_York_Turns_to_Big_Data_to_Solve_Big_Tree_Problem?source=CTWNLE_nlt_datamgmt_2013-06-05







6. DDE Tools


Outline

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Letter from the Bank… so please continue to open your mail from either Chase or Bank One

P.S. Please be on the lookout for any upcoming communications from either Chase or Bank One regarding your Bank One credit card and any other Bank One product you may have.

Problems• I initially discarded the letter!• I became upset after reading it• It proclaimed that Chase has data

quality challenges

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Polling Question #2

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• Does your organization utilize a structured or formal approach to information quality?

• A. Yes • B. They say they are but they aren't • C. No







6. DDE Tools


Outline

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Data acquisition activities Data usage activitiesData storage

Traditional Quality Life Cycle

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restored data

Metadata Creation

Metadata Refinement

Metadata Structuring

Data Utilization


Data Manipulation

Data Creation

Data Storage

Data Assessment

Data Refinement

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data architecture & models

populated data models and

storage locations

data values

datavalues

datavalues

valuedefects

structuredefects

architecturerefinements

modelrefinements

Data Life Cycle ModelProducts

data

restored data

Metadata Refinement

Metadata Structuring

Data Utilization


Data Manipulation

Data Creation

Data Storage

Data Assessment

Data Refinement

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populated data models and

storage locations

datavalues

Data Life Cycle Model:QualityFocus

data

architecture & model quality

model quality

value quality

value quality

value quality

representation quality

Metadata Creation

architecture quality


Startingpointfor newsystemdevelopment

data performance metadata

data architecture

dataarchitecture and

data models

shared data updated data

correcteddata

architecturerefinements

facts &meanings

Metadata &Data Storage

Starting pointfor existingsystems

Metadata Refinement• Correct Structural Defects• Update Implementation

Metadata Creation• Define Data Architecture• Define Data Model Structures

Metadata Structuring• Implement Data Model Views• Populate Data Model Views

Data Refinement• Correct Data Value Defects• Re-store Data Values

Data Manipulation• Manipulate Data• Updata Data

Data Utilization• Inspect Data• Present Data

Data Creation• Create Data• Verify Data Values

Data Assessment• Assess Data Values• Assess Metadata

Extended data life cycle model with metadata sources and uses

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Polling Question #3

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• Do you use metadata models, modeling tools, or profiling to support your information quality efforts?

• A. Yes• B. No







6. DDE Tools


Outline

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Profile, Analyze and Assess DQ• Data assessment using 2 different approaches:

– Bottom-up– Top-down

• Bottom-up assessment:– Inspection and evaluation of the data sets– Highlight potential issues based on the

results of automated processes• Top-down assessment:

– Engage business users to document their business processes and the corresponding critical data dependencies

– Understand how their processes consume data and which data elements are critical to the success of the business applications

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Define DQ Measures• Measures development occurs as part of the strategy/

design/plan step • Process for defining data quality measures:

1. Select one of the identified critical business impacts

2. Evaluate the dependent data elements, create and update processes associate with that business impact

3. List any associated data requirements

4. Specify the associated dimension of data quality and one or more business rules to use to determine conformance of the data to expectations

5. Describe the process for measuring conformance

6. Specify an acceptability threshold

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Set and Evaluate DQ Service Levels• Data quality inspection and

monitoring are used to measure and monitor compliance with defined data quality rules

• Data quality SLAs specify the organization’s expectations for response and remediation

• Operational data quality control defined in data quality SLAs includes:– Data elements covered by the agreement– Business impacts associated with data flaws– Data quality dimensions associated with each data element– Quality expectations for each data element of the identified dimensions in

each application for system in the value chain– Methods for measuring against those expectations– (…)

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Measure, Monitor & Manage DQ


• DQM procedures depend on available data quality measuring and monitoring services

• 2 contexts for control/measurement of conformance to data quality business rules exist:– In-stream: collect in-stream measurements while creating data– In batch: perform batch activities on collections of data

instances assembled in a data set

• Apply measurements at 3 levels of granularity:– Data element value– Data instance or record– Data set

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Overview: Data Quality Tools4 categories of activities:

1) Analysis2) Cleansing3) Enhancement4) Monitoring

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Principal tools:1) Data Profiling2) Parsing and Standardization3) Data Transformation4) Identity Resolution and

Matching5) Enhancement6) Reporting


DQ Tool #1: Data Profiling• Data profiling is the assessment of

value distribution and clustering of values into domains

• Need to be able to distinguish between good and bad data before making any improvements

• Data profiling is a set of algorithms for 2 purposes:– Statistical analysis and assessment of the data quality values within a

data set– Exploring relationships that exist between value collections within and

across data sets

• At its most advanced, data profiling takes a series of prescribed rules from data quality engines. It then assesses the data, annotates and tracks violations to determine if they comprise new or inferred data quality rules

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DQ Tool #1: Data Profiling, cont’d• Data profiling vs. data quality-business context and

semantic/logical layers– Data quality is concerned with proscriptive rules– Data profiling looks for patterns when rules are adhered to and when

rules are violated; able to provide input into the business context layer

• Incumbent that data profiling services notify all concerned parties of whatever is discovered

• Profiling can be used to…– …notify the help desk that valid

changes in the data are about to case an avalanche of “skeptical user” calls

– …notify business analysts of precisely where they should be working today in terms of shifts in the data

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Courtesy GlobalID.com

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DQ Tool #2: Parsing & Standardization • Data parsing tools enable the definition

of patterns that feed into a rules engine used to distinguish between valid and invalid data values

• Actions are triggered upon matching a specific pattern

• When an invalid pattern is recognized, the application may attempt to transform the invalid value into one that meets expectations

• Data standardization is the process of conforming to a set of business rules and formats that are set up by data stewards and administrators

• Data standardization example:– Brining all the different formats of “street” into a single format, e.g.

“STR”, “ST.”, “STRT”, “STREET”, etc.

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DQ Tool #3: Data Transformation• Upon identification of data errors, trigger data rules to

transform the flawed data• Perform standardization and guide rule-based

transformations by mapping data values in their original formats and patterns into a target representation

• Parsed components of a pattern are subjected to rearrangement, corrections, or any changes as directed by the rules in the knowledge base

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DQ Tool #4: Identify Resolution & Matching• Data matching enables analysts to identify relationships between records for

de-duplication or group-based processing• Matching is central to maintaining data consistency and integrity throughout

the enterprise• The matching process should be used in

the initial data migration of data into a single repository

• 2 basic approaches to matching:• Deterministic

– Relies on defined patterns/rules for assigning weights and scores to determine similarity

– Predictable– Dependent on rules developers anticipations

• Probabilistic – Relies on statistical techniques for assessing the probability that any pair of record

represents the same entity– Not reliant on rules– Probabilities can be refined based on experience -> matchers can improve precision as

more data is analyzed

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DQ Tool #5: Enhancement• Definition:

– A method for adding value to information by accumulating additional information about a base set of entities and then merging all the sets of information to provide a focused view. Improves master data.

• Benefits:– Enables use of third party data sources– Allows you to take advantage of the information and research carried

out by external data vendors to make data more meaningful and useful

• Examples of data enhancements:– Time/date stamps– Auditing information– Contextual information– Geographic information– Demographic information– Psychographic information

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DQ Tool #6: Reporting• Good reporting supports:

– Inspection and monitoring of conformance to data quality expectations– Monitoring performance of data stewards conforming to data quality

SLAs– Workflow processing for data quality incidents– Manual oversight of data cleansing and correction

• Data quality tools provide dynamic reporting and monitoring capabilities

• Enables analyst and data stewards to support and drive the methodology for ongoing DQM and improvement with a single, easy-to-use solution

• Associate report results with:– Data quality measurement– Metrics– Activity

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6. DDE Tools


Outline

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• Develop and promote data quality awareness• Define data quality requirements• Profile, analyze and asses data quality• Define data quality metrics• Define data quality business

rules• Test and validate data quality

requirements• Set and evaluate data quality

service levels• Measure and monitor data quality• Manage data quality issues• Clean and correct data quality defects• Design and implement operational DQM procedures• Monitor operational DQM procedures and performance


Overview: DQE Concepts and Activities

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Concepts and Activities• Data quality expectations provide the inputs necessary

to define the data quality framework:– Requirements– Inspection policies– Measures, and monitors

that reflect changes in data quality and performance

• The data quality framework requirements reflect 3 aspects of business data expectations1. A manner to record the expectation in business rules2. A way to measure the quality of data within that dimension 3. An acceptability threshold

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from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International


Questions?

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your questions to Peter now.

Data Systems Integration & Business Value Pt. 1: MetadataJuly 9, 2013 @ 2:00 PM ET/11:00 AM PT

Data Systems Integration & Business Value Pt. 2: CloudAugust 13, 2013 @ 2:00 PM ET/11:00 AM PT

Sign up here: www.datablueprint.com/webinar-schedule or www.dataversity.net


Upcoming Events

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References & Recommended Reading

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• The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International• http://www2.sas.com/proceedings/sugi29/098-29.pdf


Data Quality Dimensions

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Data Value Quality

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Data Representation Quality

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Data Model Quality

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Data Architecture Quality

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10124 W. Broad Street, Suite CGlen Allen, Virginia 23060804.521.4056

Data-Ed: Unlock Business Value through Data Quality Engineering

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Transcript of Data-Ed: Unlock Business Value through Data Quality Engineering