1 Managing Information Quality in Organisations Based on a presentation by Dr Mikhaila Burgess School of Computer Science & Informatics Cardiff University.

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Managing Information Quality in Organisations

Based on a presentation by Dr Mikhaila Burgess

School of Computer Science & Informatics

Cardiff University

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Session overview What is quality? What is Data Quality (DQ)? And

why is it important anyway? Potential impact of poor DQ (data quality) Defining Data Quality

Designing for Quality Data Ensuring DQ in databases

So what goes wrong? Potential causes of poor DQ

Managing DQ

… and some exercises

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Data vs Information

Items about things, events, activities, transactions, … Numeric, alphanumeric, figures, sounds, images, … Recorded, stored, but not organised to convey any

specific meaning

“data that have been organised in a manner that gives them meaning for the recipient” (Turban et al, 2005)

known; ‘surprise’ value

One person’s data is another’s information

Data

Information

What is ‘quality’?What does the word actually mean?

Why is DQ important?Impact of Poor Data Quality … some examples

Defining Data QualityHow do we know what we all mean when we talk about DQ?

Designing for Quality DataEnsuring a level of quality is your databases

So what goes wrong?Some causes of poor quality data & information

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Data Entry: Human Aspect Unintentional errors in data entry Lack of understanding Poor Training Intentional incorrect data entry

Malicious / Non-malicious

Poorly defined or out-of-date collection process

Multiple levels of data entry

Garbage in, Garbage out

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Data Entry: Technical Aspect Inaccurate measuring or counting device Errors in the data storage process Missing data fields Data scanner

Poor quality data scanner Inappropriate scanner

Microfiche Microfilm Aperture cards

Incorrect set-up

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Herbarium Catalogue Approx 7 million specimens

Pressed & dried Preserved in spirit

30,000 per year HerbCat

www.kew.org/herbcat/ ePIC – electronic Plant

Information Centre www.kew.org/epic/

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Type Specimen Over 350,000 Original specimen Fixed species name &

description

18th century Reference point for

botanists – applying names correctly (taxonomy & systematics)

http://www.kew.org/collections/herb_types.html

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Random Data

“The snafu started when police used the address as part of what Browne called “random material’’ to test an automated computer system that tracks crime complaints and records of

other internal police information”

Thursday 18th March 2010 – NYPD’s Identity Theft Squad deliver cheesecake to Walter (83) and Rose (82) Martin, Brooklyn, NY

50 raids over 8 years

50 errant visits blamed on computer glitch

Apologise & explain … and to check people “weren’t using that address for identity theft”

Cops Sorry For Coming To Wrong Home 50 Times

(Associated Press & Boston Globe)

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Organisational Issues Scattering of databases throughout different

departments or organisations Lack of awareness of data quality issues Obsession with technology Old (Legacy) databases

Poorly documented data Missing/poor documentation about purpose Obsolete data

Mergers & Acquisitions Non-merging of databases - autonomy Merging of databases Data stored in multiple locations and not correctly linked

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Merging Databases Homonyms & synonyms

Surname, Name, Customer, CustName, … OrderID

ID for order processed for a customer ID for order placed with a supplier

Representational inconsistency Data: eg address Database: eg

Oracle & SQLServer Access & Objectivity

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Merging Databases Designed for different purpose

Database design Data collection

Student database storing module marks, working out number of resits, allowing to

proceed, degree classification storing financial details, whether fees have been paid, ensuring no

awards presented until account is clear

RAF, Navy, Army Codes for individual stock items Merged db’s … Iraq – 3 days out of action!

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Merging Databases Duplicate data

eg customer name: Mikhaila Burgess

Misspellings:

Michaela Burges

Mikalia

Mikkalia

Michael

Michelle

Burge

Burgers

Burgese

Barron

Variations: Dr Mikhaila Burgess

Dr M S E Burgess

Ms M Burgess

M Burgess

Mr M Burgess

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Introducing DQ problems

Creator Custodian Consumer

Data productionSame data collected in different data sets Customer data: Sales, Support, Finance, … Hospital: clinical, diagnosis, specialist treatment, finance, … Different purpose, different data stored

Not necessarily the same values Different entry procedures & constraints Different relevant information Cascading updates?

(Strong et al 1997)

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Introducing DQ problems

Creator Custodian Consumer

Data storagePotentially large volumes of data Accessibility challenges Access codes (eg country: 1-UK, 2-USA, …)Distributed data Heterogeneous storage systems Potentially inconsistent data formats & values

(Strong et al 1997)

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Introducing DQ problems

Creator Custodian Consumer

Data usageInformation needs change Personal requirements Organisational environment Data no longer relevantConflicts between accessibility and security, privacy & confidentialityAccess limitation due to lacking IT resources

(Strong et al 1997)

But who are these people?

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An Issue of Change Organisations change The environment changes

government, competition, market needs, customers, customer requirements …

Requirements & specifications change Different projects have different requirements

Require data for different purposes

Ideal world: stop data entry, clean, ensure fit for purpose, restart with perfect database Tomorrow it will no longer be perfect!

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10 Potholes to IQ#1 Multiple sources of the same information produce different values.

#2 Information is produced using subjective judgments, leading to bias.

#3 Systemic errors in information production lead to lost information.

#4 Large volumes of stored information make it difficult to access information in a reasonable time.

#5 Distributed heterogeneous systems lead to inconsistent definitions, formats, and values.

#6 Nonnumeric information is difficult to index.

#7 Automated content analysis across information collections is not yet available.

#8 As information consumers’ tasks and the organisational environment change, the information that is relevant and useful changes.

#9 Easy access to information may conflict with requirements for security, privacy, and confidentiality.

#10 Lack of sufficient computing resources limits access.

(Strong et al 1997)

Managing InformationManage data/information as a product, not a by-product … TQM for Data!

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The Deloitte CIO club October 2005 50% of CIOs report that data quality

issues have had a negative impact on their business in the last year, and 6% say it affects them on a daily basis. A further 19% are occasionally affected.

50% of CIOs consider data quality to be an IT issue: even though 88% also believe that their non-IT colleagues are aware of the benefits of better quality data.

Data cleansing is reactive, not proactive. Many CIOs stated it only happens “when it’s needed” – for example, when new systems are introduced – with none carrying out regular, programmed data cleansing sweeps.

•

••

Panel admits to lack of strategic approach to managing data quality

http://www.deloitte.com/uk/cio/

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Managing data as a product Data & Information – typically treated as a by-product

Focus on system, not data Treat data/information as a product

An end deliverable that will satisfy customer needs Focus on data & fitness for purpose

Fundamental change in organisations understanding of data Follow four principles …

Understand consumer’s information needs Manage the data production process Manage data as a product with a product life-cycle Data product manager – responsible for managing the

data product

(Lee et al 2006)(Wang et al 1998)

Creator

Consumer

Custodian

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Product & Information ManufacturingISSUE DIFFERENCE (examples)

Intangibility Manufactured products (MP) are tangible; Information Products (IP) are intangible

Inputs Product process requires raw materials, experience, technology;IP process needs 4 inputs – data, experience, technology, time

Consumption IPs can be repeatedly consumed;Raw materials/MPs need to be replaced

Handling MPs – limited/single userIPs potentially used by many simultaneously

… …

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TQM to TDQM TQM – typical foundation for DQ/IQ programmes

Mea

sure

Analys

e

DefineIm

prov

e PLAN

DO

ACT

CHECK

Define the IP Identify characteristics of the IP, determine IQ dimensions Identify IP requirements Identification of IP manufacturing process, and

those involved

Analysis Pinpoints causes of poor IQ; effects on organisation; consider users; Pareto charts, SPC

Measurement Determining extent of IQ problems Looks at results of previous attempts to resolve

issues – learning from experience

Improvement Delivering methods of continuous improvement

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Data Quality Policy For organisation to remain engaged & succeed in

maintaining a viable, sustained DQ effort Proactively support business activities

A DQ policy must reflect the vision of the organisation.

Start DQ management programme … effort not sustained Single DQ Champion or department … others fail to

come on board … not disseminated across business

Organisational policy must involve all functions and activities relating to the maintenance of data products.

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10 Policy GuidelinesThe organisation …1. … adopts the basic principle of treating information as

product, not by-product. 2. … establishes and keeps data quality as a part of the business

agenda. 3. … ensures that the data quality policy and procedures are

aligned with its business strategy, business policy, and business processes.

4. … establishes, clearly defined data quality roles and responsibilities as part of its organisation structure.

5. … ensures that the data architecture is aligned with its enterprise architecture.

(Lee et al 2006)

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10 Policy Guidelines6. … takes a proactive approach in managing changing data

needs. 7. … has practical data standards in place. 8. … plans for and implements pragmatic methods to identify

and solve data quality problems, and has in place a means to periodically review its data quality and data quality environment.

9. … fosters an environment conducive to learning and innovating with respect to data quality activities.

10. … establishes a mechanism to resolve disputes and conflicts among different stakeholders.

(Lee et al 2006)

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Examples …

http://www.lancashirecare.nhs.uk/documents/FOI_12DataQualityPolicy.pdf

http://www.suffolk.gov.uk/CouncilAndDemocracy/OurPerformance/DataQualityPolicy.htm

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Review What is quality?

Defining Quality & DQ Importance of quality data

DQ in databases Database design Database Integrity

Some examples of poor DQ and it’s impact http://www.iqtrainwrecks.com/

Measuring DQ Managing data as product

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ReferencesCROSBY, P.B. (1978) Quality is Free: The Art of Making Quality Certain, McGraw-Hill.DROMEY, R. G. (1996) Concerning the Chimera. IEEE Software, 13(1), pp 33-43.JURAN, J. M. & GODFREY, A. B. (1999) Juran's Quality Handbook (Fifth Edition), McGraw

Hill, USA.LEE, Y.W., PIPINO, L.L., FUNK J.D. and WANG, R. Y. (2006) Journey to Data Quality, MIT

Press, MA, USA.PIRSIG, R. M. (1974) Zen and the Art of Motorcycle Maintenance, Random House.REDMAN, T.C. (1995) “Improve Data Quality for Competitive Advantage,” Sloan Management

Review, 36(2), Winter 1995, pp 99-107.REDMAN, T.C. (1997) Data Quality for the Information Age, Artech House.STRONG, D.W., LEE, Y.W. & WANG, R.Y. (1997) 10 Potholes in the Road to Information

Quality, IEEE Computer, August 1997, pp 38-46.TURBAN, E., ARONSON, J.E., & LIANG, T.P. Decision Support Systems and Intelligent systems

(7th ed), Prentice-Hall.WANG, R., LEE, Y.W., PIPINO, L.L. & STRONG D.M. (1998) “Managing Your Information as a

Product,” Sloan Management Review, 39(4), Summer 1998, pp95-105. WANG, R. & STRONG D. (1996) Beyond Accuracy: What data quality means to data consumers. Journal of Management Information Systems, Spring 1996, 12(4), pp 5-33.

WATSON, R.T. (2003) Data Management: Database and Organizations, Wiley & Sons.