© ISO 2016 Data quality — Part 61 Data quality management Process reference model Qualité des données — Partie 61 Gestion de la qualité des données Modèle de référence des procédés INTERNATIONAL STAND[.]
INTERNATIONAL STANDARD ISO 8000-61 First edition 2016-11-15 Data quality — Part 61: Data quality management: Process reference model Qualité des données — Partie 61: Gestion de la qualité des données: Modèle de référence des procédés Reference number ISO 8000-61:2016(E) © ISO 2016 ISO 8000-61:2016(E) COPYRIGHT PROTECTED DOCUMENT © ISO 2016, Published in Switzerland All rights reserved Unless otherwise specified, no part o f this publication may be reproduced or utilized otherwise in any form or by any means, electronic or mechanical, including photocopying, or posting on the internet or an intranet, without prior written permission Permission can be requested from either ISO at the address below or ISO’s member body in the country o f the requester ISO copyright o ffice Ch de Blandonnet • CP 401 CH-1214 Vernier, Geneva, Switzerland Tel +41 22 749 01 11 Fax +41 22 749 09 47 copyright@iso.org www.iso.org ii ISO 8000-61:2016(E) Contents Page Foreword v Introduction vi Scope Normative references Terms, definitions and abbreviated terms 3.1 Terms and definitions 3.2 Abbreviated terms Fundamental principles of data quality management The data quality management process 5.1 The basic structure o f the data quality management process 5.2 The detailed structure o f the data quality management process 5.3 The elements of a process description The Implementation process 6.1 Overview of Implementation 6.2 Data Quality Planning 6.2.1 Overview o f Data Quality Planning 6.2.2 Requirements Management 6.2.3 Data Quality Strategy Management 6.2.4 Data Quality Policy/Standards/Procedures Management 6.2.5 Data Quality Implementation Planning 6.3 Data Quality Control 6.3.1 Overview o f Data Quality Control 6.3.2 Provision o f Data Specifications and Work Instructions 6.3.3 Data Processing 6.3.4 Data Quality Monitoring and Control 10 6.4 Data Quality Assurance 11 6.4.1 Overview o f Data Quality Assurance 11 6.4.2 Review o f Data Quality Issues 11 6.4.3 Provision of Measurement Criteria 12 6.4.4 Measurement o f Data Quality and Process Performance 12 6.4.5 Evaluation of Measurement Results 12 6.5 Data Quality Improvement 13 6.5.1 Overview o f Data Quality Improvement 13 6.5.2 Root Cause Analysis and Solution Development 13 6.5.3 Data Cleansing 14 6.5.4 Process Improvement for Data Noncon formity Prevention 14 The Data-Related Support process 15 7.1 Overview of Data-Related Support 15 7.2 Data Architecture Management 15 7.3 Data Transfer Management 15 7.4 Data Operations Management 16 7.5 Data Security Management 17 The Resource Provision process 17 8.1 Overview of Resource Provision 17 8.2 Data Quality Organization Management 17 8.3 Human Resource Management 18 Relationship between data quality management and data governance 19 10 Implementation requirements 19 Annex A (normative) Document identification 20 © ISO 2016 – All rights reserved iii I SO 0 - : (E ) Bibliography iv ISO 8000-61:2016(E) Foreword ISO (the International Organization for Standardization) is a worldwide federation of national standards bodies (ISO member bodies) The work o f preparing International Standards is normally carried out through ISO technical committees Each member body interested in a subject for which a technical committee has been established has the right to be represented on that committee International organizations, governmental and non-governmental, in liaison with ISO, also take part in the work ISO collaborates closely with the International Electrotechnical Commission (IEC) on all matters o f electrotechnical standardization The procedures used to develop this document and those intended for its further maintenance are described in the ISO/IEC Directives, Part In particular the di fferent approval criteria needed for the di fferent types o f ISO documents should be noted This document was dra fted in accordance with the editorial rules o f the ISO/IEC Directives, Part (see www.iso org/directives) Attention is drawn to the possibility that some o f the elements o f this document may be the subject o f patent rights ISO shall not be held responsible for identi fying any or all such patent rights Details o f any patent rights identified during the development o f the document will be in the Introduction and/or on the ISO list of patent declarations received (see www.iso org/patents) Any trade name used in this document is in formation given for the convenience o f users and does not constitute an endorsement For an explanation on the meaning o f ISO specific terms and expressions related to formity assessment, as well as in formation about ISO’s adherence to the World Trade Organization (WTO) principles in the Technical Barriers to Trade (TBT) see the following URL: www.iso org/iso/foreword.html The committee responsible for this document is Technical Committee ISO/TC 184, Automation systems and integration , Subcommittee SC 4, Industrial data ISO 8000 is organized as a series o f parts, each published separately The structure o f ISO 8000 is described in ISO/TS 8000-1 Each part o f ISO 8000 is a member o f one o f the following series: general data quality, master data quality, transactional data quality, and product data quality This part o f ISO 8000 is a member o f the general data quality series but is also applicable to the other series A list of all parts in the ISO 8000 series can be found on the ISO website © ISO 2016 – All rights reserved v ISO 8000-61:2 016(E) Introduction T he abi l ity to cre ate, col le c t, s tore, ma i nta i n, tra n s fer, pro ce s s a nd pre s ent i n formation and data to s upp or t bu s i ne s s pro ce s s e s i n a ti mely and co s t e ffe c tive ma n ner re qui re s b o th an unders ta nd i ng o f the cha rac teri s tic s o f the i n formation a nd data that de term i ne its qua l ity, and an abi l ity to me a s u re, ma nage a nd rep or t on i n formation a nd data qua l ity ISO 8000 defi ne s charac teri s tics o f i n formation and data that de term i ne its qua l ity, and provide s me tho d s to manage, me a s u re a nd i mprove the qua l ity o f i n formation a nd data When a s s e s s i ng the qua l ity o f i n formation and d ata, it i s u s e fu l to p er form the as s e s s ment i n accordance with documented metho ds I t is al so imp or tant to cument the tai loring of s tandardi zed methods with res p ec t to the exp ec tation and requirements p er tinent to the bus ines s case at hand I S O 0 i nclude s p ar ts appl ic able to a l l typ e s o f data and p ar ts appl ic able to s p e ci fic typ e s o f data I S O 0 c a n b e u s e d i ndep endently or i n conj u nc tion with qua l ity ma nagement s ys tem s T here i s a l i m it to data qua l ity i mprovement when on ly the noncon form ity o f data i s corre c te d, s i nce the noncon form ity c an re c u r H owever, when the ro o t c au s e s o f the data noncon form ity a nd the relate d data are trace d and corre c te d th rough data qua l ity pro ce s s e s , re c u rrence o f the s ame typ e o f data noncon form ity c an b e prevente d T here fore, a framework for pro ce s s- centric d ata qua l ity ma nagement i s re qu i re d to i mprove data qua l ity more e ffe c tively and e fficiently Fur thermore, data qua l ity c a n b e i mprove d th rough as s e s s i ng pro ce s s e s and i mprovi ng under-p er form i ng pro ce s s e s identi fie d b y the as ses s ment T h i s p ar t o f I S O 0 s p e c i fie s the pro ce s s e s re qu i re d is used as a re ference for as s e s s i ng and i mprovi ng for data qua l ity management T h i s s p e c i fic ation the c ap abi l ity o f the pro ce s s e s or i ncre as i ng organ i z ationa l m atu rity with re s p e c t to data qua l ity ma nagement T h i s p ar t o f I S O 0 c a n b e u s e d on its own or i n conj u nc tion with o ther p ar ts o f I S O 0 T h i s p a r t o f I S O 0 i s i ntende d data qua l ity, with a fo c us for u s e b y tho s e ac tors that have a ve s te d i ntere s t i n i n formation or on one or more i n formation s ys tem s b o th i nter- and i ntra- organ i z ation views , th roughout a l l ph as e s o f the data l i fe c ycle Annex A contai n s an i n formation s ys tem vi identi fier that u nambiguou s ly identi fie s th i s p ar t of I SO 8000 in an op en INTERNATIONAL STANDARD ISO 8000-61:2016(E) Data quality — Part 61: Data quality management: Process reference model Scope T h i s p ar t o f I S O 0 s p e c i fie s the pro ce s s e s re qu i re d for d ata qua l ity management E ach pro ce s s i s defi ne d b y a pu rp o s e , outcome s and ac tivitie s that a re to b e appl ie d for the a s s u rance o f data qua l ity The following are within the scope of this part of ISO 8000: — fu ndamenta l pri nc iple s o f data qua l ity management; — the s tr uc tu re o f the data qua l ity management pro ce s s; — defi n ition s o f the lower level pro ce s s e s — the rel ation sh ip b e twe en data qua l ity ma nagement and data governance; for data qua l ity management; — implementation requirements The following is outside the scope of this part of ISO 8000: — de ta i le d me tho d s or pro ce dure s b y wh ich to ach ieve the outcome s o f the defi ne d pro ce s s e s This part of ISO 8000 f structured data stored in databases but also less structured data such as images, audio, video and electronic documents This part of ISO 8000 organization level because, for instance, multiple software applications are sharing and exchanging data i s applicable to managing the qua lity o d igital data s ets that i nclude no t on ly can b e us e d by an organi z ation managi ng data qua lity at the T h i s p a r t o f I S O 0 i s u s e d a s a pro ce s s re ference mo del b y i nterna l and ex terna l p a r tie s , i nclud i ng cer ti fic ation b o d ie s , to assess pro ce s s c ap abi l ity or organ i z ationa l matu rity for d ata qua l ity management and to en nce data qua l ity th rough pro ce s s i mprovement T h i s p ar t o f I S O 0 c an b e u s e d i n conj u nc tion with, or i ndep endently o f, qua l ity ma nagement s ys tem s standards (e.g ISO 9001) Normative references The following referenced documents are indispensable for the application of this document For dated re ference s , on ly the e d ition cite d appl ie s For undate d re ference s , the late s t e d ition o f the re ference d c u ment (i nclud i ng any a mend ments) appl ie s ISO 8000-2, Data quality — Part 2: Vocabulary Terms, definitions and abbreviated terms 3.1 Terms and definitions For the pu r p o s e s o f th i s c u ment, the term s and defi nition s given i n I S O 0 -2 apply © ISO 2016 – All rights reserved ISO 8000-61:2016(E) 3.2 Abbreviated terms DBMS database management system Fundamental principles of data quality management The following fundamental principles apply to managing the quality o f data — Process approach: the processes that use, create and update data are defined and operated These processes become repeatable and reliable by also defining and operating processes for managing data quality — Continuous improvement: data are improved through effective measurement and correction of data nonconformities that arise from data processing Such improvements, however, not prevent the same noncon formities occurring repeatedly Sustained improvement arises from analysing, tracing and removing the root causes o f poor data quality, usually requiring the improvement o f processes — Involvement o f people: specific responsibilities for data quality management exist at di fferent levels o f the organization End users have the greatest direct e ffect on data quality through data processing activities In addition, data quality specialists per form the necessary intervention and control to implement and embed processes for improvement o f data quality across the organization Finally, oversight by top management ensures the necessary resources are made available and directs the organization towards achieving the vision, goals and objectives for data quality The data quality management process 5.1 The basic structure o f the data quality management process The basic structure o f the data quality management process is as follows — The data quality management process consists o f Implementation, Data-Related Support, and Resource Provision This is depicted in Figure — To achieve continuous improvement o f data quality, the Implementation process is per formed following the Plan-Do-Check-Act pattern — The Data-Related Support process enables the Implementation process by providing in formation and technology related to data management — The Resource Provision process improves the e ffectiveness and e fficiency o f the Implementation and the Data-Related Support processes by providing resources and training services at the organizational level ISO 8000-61:2016(E) Figure — Basic structure of data quality management NOTE The structure of Implementation, Data-Related Support, and Resource Provision processes is adapted rom the concept o f Primary, Support and Organizational processes in ISO 12207:1995 and from the Plan-DoCheck-Act cycle from ISO 9001 f The Plan-Do-Check-Act cycle is also applicable to improving the per formance o f any o f the lower level processes o f data quality management These improvements will contribute to more e ffective and e fficient data quality The Plan-Do-Check-Act cycle consists o f: — plan: establish the strategy and implementation plans as necessary to deliver results in accordance with data requirements; — do: implement the processes; — check: monitor and measure data quality and process per formance against the strategy and data requirements and report the results; — act: take actions to continually improve process per formance 5.2 The detailed structure of the data quality management process As shown in Figure , the data quality management process is a hierarchy of lower level processes, as follows a) The Implementation process consists o f four sub-processes based on the “Plan-Do-Check-Act” pattern: 1) Data Quality Planning, corresponding to “Plan”: ― Requirements Management; ― Data Quality Strategy Management; © ISO 2016 – All rights reserved I SO 0 - : (E ) ― Data Quality Policy/Standards/Procedures Management; ― Data Quality Implementation Planning; 2) Data Quality Control, corresponding to “Do”: ― Provision o f Data Specifications and Work Instructions; ― Data Processing; ― Data Quality Monitoring and Control; 3) Data Quality Assurance, corresponding to “Check”: ― Review o f Data Quality Issues; ― Provision o f Measurement Criteria; ― Measurement o f Data Quality and Process Per formance; ― Evaluation o f Measurement Results; 4) Data Quality Improvement, corresponding to “Act”: ― Root Cause Analysis and Solution Development; ― Data Cleansing; ― Process Improvement for Data Noncon formity Prevention b) T he D ata-Related Supp or t proces s provides I mplementation with in formation, cons traints and technology This process consists o f: 1) Data Architecture Management; 2) Data Trans fer Management; 3) Data Operations Management; 4) Data Security Management c) T he Resource P rovis ion pro ces s en hances the p erformance of I mplementation and D ata-Related Support by providing resources at the organizational level This process consists o f: 1) Data Quality Organization Management; 2) Human Resource M anagement The sub-processes o f Implementation take place in sequential order, while those o f Data-Related Support and Resource Provision take place as and when necessary ISO 8000-61:2016(E) — c) Policies , s tandards and pro cedures are communicated throughout the organiz ation, covering the consistent application to data quality management Ac tivities — Management o f quality policies for data quality management: Speci fy fundamental intentions and rules for data quality management in the organization Ensure the data quality policies are appropriate for the data quality strategy, comply with data requirements and establish the foundation for continual improvement o f the e ffectiveness and e fficiency o f data quality management — Management o f standards for data quality management: Speci fy the standards related to data quality for the consistent communication and appropriate use o f data across the organization — Management o f procedures related to data quality management: Speci fy rules and procedures related to data quality for consistent data quality management across the organization 6.2.5 Data Quality Implementation Planning a) P urp ose The purpose o f Data Quality Implementation Planning is to identi fy the resources and sequencing by which to per form Data Quality Control, Data Quality Assurance, Data Quality Improvement, D ata-Related Supp or t and Resource P rovis ion acros s the organi zation b) O utcomes — A scope and target are defined for data quality in accordance with the data quality objectives — I mplementation plans are es tabl ished in detai l — Manpower, financial and technology resources are allocated and managed to ensure success ful execution of the implementation plans — Roles , res p ons ibi lities and authorities are al lo cated and control led to cover al l as p ec ts of data quality management NOTE ISO/TS 8000-150 provides detail on roles and responsibilities that contribute to e ffective and e fficient data quality management — Progress is monitored against implementation plans to achieve improved data quality — c) Performance res u lts are evaluated to rep or t to top management on the effec tivenes s of the implementation plans, with those plans being updated as necessary based on the results Ac tivities — Establishment o f the data quality implementation plan: Define the scope and target o f data quality and prepare detailed implementation plans — Resource al lo cation: D etermine and provide the resources needed to implement the plans and achieve data quality objectives — Data stewardship allocation: Assign formal accountability to individuals who have appropriate expertise in and authority for business processes These individuals approve proposals to change data when noncon formities have been identified and proposals to use data for new purposes This accountability ensures the e ffective control and use o f data sets — I mplementation of the s tewardship al lo cated plan: I mplement detai led plans b ased on the resources and data ISO 8000-61:2016(E) — Performance evaluation: Monitor the status of the implementation of the plans, evaluate p er formance re s u lts , rep or t tho s e re s u lts to top management and, when ne ce s s a r y, up date the pla n s th rough s u ltation with s ta keholders 6.3 Data Quality Control 6.3.1 Overview of Data Quality Control D ata Qua l ity C ontrol i s c a rrie d out b a s e d on the i mplementation pla n e s tabl i she d i n D ata Qua l ity Planning (see 6.2) The process, when successful, delivers data that meet requirements The process i nvolve s cre ati ng , u s i ng a nd up dati ng data accord i ng to s p e c i fie d work i n s truc tion s and mon itori ng qua l ity by che cki ng whe ther the data form to pre - de term i ne d s p e c i fication s D ata Qua l ity C ontrol s i s ts o f D efi n ition o f D ata Sp e ci fic ation s and Work I n s tr uc tion s (see 6.3.2), Data Processing (see 6.3.3 6.3.2 ) and D ata Qua l ity M on itori ng and C ontrol (s e e for D ata Qua l ity 6.3.4) Provision o f Data Specifications and Work Instructions a) Purpose T he pu rp o s e o f P rovi s ion o f D ata Sp e ci fic ation s and Work I n s truc tion s i s to e s tabl i sh the b as i s on wh ich to p er form D ata P ro ce s s i ng and D ata Qua l ity M on itori ng a nd C ontrol , ta ki ng accou nt o f the outcome s o f the D ata Qua l ity Pla n ni ng , the D ata-Relate d Supp or t and the Re s ou rce P rovi s ion processes b) Outcomes — D ata s p e ci fic ation s are defi ne d to de s crib e the re qui re d cha rac teri s tic s o f data for D ata P ro ce s s i ng a nd D ata Qua l ity M on itori ng and C ontrol — Work i n s truc tion s a re defi ne d to s p e c i fy the appro ach to D ata P ro ce s s i ng — Work i n s truc tion s a re defi ne d to s p e c i fy the appro ach to D ata Qua l ity M on itori ng and C ontrol NO TE Wo rk i n s tr uc tion s fo r D ata Qu a l ity M on itor i ng a nd C ontrol i nclude me tho d s to me a s u re data nonconformities and process performance c) Activities — P rovi s ion o f data s p e ci fic ation s: D evelop s p e ci fic ation s that de s crib e cha rac teri s tic s o f data a nd a re u s e d — P rovi s ion for o f work P ro ce s s i ng or 6.3.3 b o th D ata P ro ce s s i ng a nd D ata Qua l ity M on itori ng and C ontrol for i n s truc tion s: D evelop work i n s truc tion s that a re used either for D ata D ata Qua l ity M onitori ng and C ontrol Data Processing a) Purpose T he pu rp o s e o f D ata P ro ce s s i ng i s , b y fol lowi ng appl ic able work i n s tr uc tion s , to del iver data that me e t re qu i rements i n the corre s p ond i ng data s p e c i fic ation b) Outcomes — D ata P ro ce s s i ng s forme d to the appl ic able work i n s truc tion s NO TE — D ata P ro ce s s i ng i s a n i nte gra l p a r t o f m a ny d i fferent typ e s o f p ro ce s s ac ro s s the o rga n i z ation D ata me e ts the appl ic able data s p e ci fic ation © ISO 2016 – All rights reserved ISO 8000-61:2016(E) — Re cord s are kep t o f a l l D ata P ro ce s s i ng ac tivity, whe ther p er forme d by p e ople or b y s o ftwa re applications NO TE D ata lo ggi ng ta ke s p l ace to a de gre e th at i s ap pro pr i ate to the b ene fit ach ie ve d processing cost for the a s s o c iate d c) Activities — E xe c ution o f work i n s truc tion s: C re ate, u s e, up date a nd dele te data i n accorda nce with data s p e ci fic ation s a nd work i n s truc tion s E xe c ution i s i mprove d b y e duc ati ng end u s ers i n the appl ic ation o f the s p e c i fic ation s a nd work i n s truc tion s When data i s pro ce s s e d b y s o ftwa re appl ic ation s , emb e d the s p e c i fic ation s and work i n s tr uc tion s with i n th i s s o ftwa re NO TE D ata P ro ce s s i n g i s a n i nte gra l p a r t o f b u s i ne s s p ro ce s s e s , b ei n g p er for me d b y end u s ers ac ro s s the orga n i z ation T here fore , when ap p l yi n g D ata P ro ce s s i n g with i n a ny p a r t o f a n o rga n i z atio n , the ap pro ach i s s p e c i fic to the b u s i ne s s pro ce s s e s o f th at p a r t o f the o rga n i z atio n — Data logging: Create and store records of people or software applications, data processing time s tamp and a h i s tor y o f data mo d i fic ation s a nd tran s fers to help i n traci ng the ro o t cau s e s o f data nonconformities NO TE L e ga l re s tr ic tion s c a n ap p l y to d ata lo ggi n g ac tivity 6.3.4 Data Quality Monitoring and Control a) Purpose T he pu rp o s e o f D ata Qua l ity M on itori ng and C ontrol i s , by to identi fy a nd re s p ond when D ata P ro ce s s i ng fai l s fol lowi ng appl ic able work i n s tr uc tion s , to del iver data that me e t the re qu i rements i n the corre s p ond i ng data s p e c i fic ation b) Outcomes — Ri s ks a re identi fie d and quanti fie d agai n s t the appl ic ab le data s p e c i fic ation s , coveri ng the corre s p ond i ng i mp ac ts on the orga ni z ation or o ther s ta keholders — — P rioritie s are identi fie d with re s p e c t to mon itori ng a nd control l i ng o f ri s ks Re cord s are kep t for comp ari ng p er formance with plan ne d re s u lts for pro ce s s e s mon itore d with re s p e c t to identi fie d ri s ks NO TE — T he co mp a r i s on o f p er fo r m a nce c a n ta ke p l ace at i nter va l s or co nti nuo u s l y E nd u s ers a re no ti fie d when p lan ne d re s u lts are no t ach ieve d u s ers to fol low data for pro ce s s e s , s e eki ng tho s e s p e ci fic ation s a nd work i n s truc tion s more e ffe c tively i n i mplementi ng and maintaining the processes — D ata noncon form itie s a re identi fie d, cla s s i fie d a nd corre c te d — Re cord s a re kep t o f ac tion s ta ken to add re s s data noncon form itie s — Sta keholders a re no ti fie d o f ac tion s ta ken to add re s s data noncon form itie s — Gu idel i ne s , r u le s nonconformities and pro ce dure s are refi ne d a nd appl ie d to prevent re c u rrence o f data c) Activities — D ata qua l ity ri s k as s e s s ment: I denti fy ri sks th roughout the data l i fe c ycle, ana lys e the i mp ac t i f e ach ri sk wa s to o cc u r and de term i ne ri s k prioritie s to e s tabl i sh the b a s i s control of processes and data 10 for mon itori ng and ISO 8000-61:2016(E) — Monitoring and control o f processes: According to the identified risk priorities, monitor and measure process per formance Monitoring and measuring takes place either at intervals or continuously and in accordance with applicable work instructions I f planned results have not been achieved during Data Processing then, to ensure future formity o f the data, end users respond by updating and maintaining processes — Monitoring and control o f data: According to the identified risk priorities, monitor and measure formity o f data to the applicable specification Monitoring and measuring takes place either at intervals or continuously and in accordance with applicable work instructions I f data noncon formities are found then correct the data when viable and distribute to stakeholders a record o f the viability and degree o f success for each corrective action — Prevention o f data noncon formity recurrence: Act to prevent recurrence o f similar data noncon formities by refining and applying guidelines, rules and procedures 6.4 Data Quality Assurance 6.4.1 Overview of Data Quality Assurance Data Quality Assurance measures data quality levels and the process per formance related to data noncon formities or other issues that have arisen as a result o f Data Quality Planning (see 6.2) or Data Quality Control (see 6.3 ) This measurement provides evidence by which to evaluate the impact o f any identified poor levels o f data quality on the e ffectiveness and e fficiency o f business processes Data Quality Assurance consists o f Review o f Data Quality Issues (see 6.4.2), Provision of Measurement Criteria (see 6.4.3 ), Measurement of Data Quality and Process Performance (see 6.4.4) and Evaluation of Measurement Results (see 6.4.5) 6.4.2 Review of Data Quality Issues a) Purpose The purpose o f Review o f Data Quality Issues is to identi fy the starting point for deciding to measure data quality levels and process per formance with the potential to generate opportunities to improve data quality b) Outcomes — Data quality assurance is initiated in response to issues arising as a result o f Data Quality Planning or Data Quality Control NOTE Various types o f issue are possible, including: unresolved data noncon formities; indications o f the recurrence o f particular types o f noncon formity; stakeholders indicating their expectations have not been met; and reports o f possible problems with data requirements or the methods for formance testing of data — A set o f related data noncon formities is identified as triggering the need for appropriate measurement o f data quality levels and process per formance as part o f Data Quality Assurance c) Activities — Data quality assurance initiation: Respond to the reporting o f unresolved data noncon formities from within Data Quality Control, indications o f the recurrence o f particular types o f noncon formity or other issues raised against the results o f Data Quality Planning or Data Quality Control — Issue analysis: Review noncon formities arising from Data Processing to identi fy those that are possibly connected to the reported issue that has triggered the need for Data Quality Assurance This review creates a set of related nonconformities This set is the basis for further investigation through the measurement o f data quality levels and process per formance © ISO 2016 – All rights reserved 11 ISO 8000-61:2016(E) NOTE Further investigation addresses aspects o f data quality management, including: trends and patterns in the occurrence o f data noncon formities; the cause o f stakeholder needs not being met; and the ways in which an individual noncon formity can propagate to cause other noncon formities 6.4.3 Provision of Measurement Criteria a) Purpose The purpose of Provision of Measurement Criteria is to establish the basis on which to per form Measurement o f Data Quality and Process Per formance with respect to the set o f data noncon formities output by the Review o f Data Quality Issues process b) Outcomes — A scope is defined for the data and processes to be the subject o f measuring — Metrics are defined relating to the characteristics o f data and the per formance o f the processes — Measurement methods are defined by which to determine values for the identified metrics c) Activities — Determination of the data and processes to measure: On the basis of the set of data noncon formities output by the Review o f Data Quality Issues process, determine the scope o f target data and processes to measure — Development of metrics: Develop or select the measurement indicators and corresponding metrics used to measure the quality levels o f data and the per formance level o f processes — Development of measurement methods: Develop or select measurement methods related to measuring the data characteristics and process performance 6.4.4 Measurement of Data Quality and Process Performance a) Purpose The purpose o f Measurement o f Data Quality and Process Per formance is, in accordance with the outputs of the Provision of Measurement Criteria process, to generate input for the Evaluation of Measurement Results process b) Outcomes — A plan is established by which to conduct measurement o f data quality and process per formance — Appropriate resources are deployed for the measurement — Values are measured for data quality and process per formance c) Activities — Establishment of measurement resources: Establish appropriate resources to measure data quality and process per formance without disrupting the execution o f business processes — Measurement o f data quality levels: Measure the data quality levels by implementing the measurement plans and determining the measurement results — Measurement o f process per formance levels: Measure the process per formance levels by implementing the measurement plans and determining the measurement results 6.4.5 Evaluation of Measurement Results 12 ISO 8000-61:2016(E) a) Purpose The purpose of Evaluation of Measurement Results is to establish the priorities for performing Data Quality Improvement b) Outcomes — Measurement results are analysed to provide a quantitative perspective on identified data nonconformities — An impact is evaluated, indicating the e ffect o f poor levels o f data quality or poor process per formance on the organization or other stakeholders c) Activities — Analysis o f measurement results: Quantitatively analyse measurement results o f data quality and process per formance These results are generated by Measurement o f Data Quality and Process Performance — Evaluation o f the impact: Identi fy the consequences o f any identified poor levels o f data quality or poor process performance on the organization 6.5 Data Quality Improvement 6.5.1 Overview of Data Quality Improvement Data Quality Improvement involves analysing the root causes o f data quality issues based on the assessment results derived from Data Quality Assurance (see 6.4) In order to prevent future data noncon formities, Data Quality Improvement corrects existing noncon formities and also trans forms processes as appropriate Data Quality Improvement consists o f Root Cause Analysis and Solution Development (see 6.5.2), Data Cleansing (see 6.5.3 ) and Process Improvement for Data Noncon formity Prevention (see 6.5.4) 6.5.2 Root Cause Analysis and Solution Development a) Purpose The purpose o f Root Cause Analysis and Solution Development is to establish, in accordance with the data quality strategy and with the priorities identified by Data Quality Assurance, the basis on which to per form Data Cleansing and/or Process Improvement for Data Noncon formity Prevention b) Outcomes — Root causes and associated impacts are analysed for each identified data quality issue, based on the results from the Data Quality Assurance process and taking account o f the data quality strategy — Solutions are proposed involving data cleansing and process improvements to prevent recurrence o f identified root causes — The cost-e ffectiveness is analysed for each identified solution — The priority is determined for each identified solution — A plan is established to implement the identified solutions c) Activities — Analysis o f root causes o f data noncon formities: Analyse the root causes o f each data quality issue and assess the effect of the issue on business processes in the organization © ISO 2016 – All rights reserved 13 ISO 8000-61:2016(E) — Development of improvement solutions to eliminate the root causes: Propose solutions to eliminate the root causes and prevent recurrence o f noncon formities Evaluate the feasibility o f the proposed improvements through cost-benefit analysis 6.5.3 a) Data Cleansing Purpose The purpose o f Data Cleansing is to ensure, in response to the results o f Root Cause Analysis and Solution Development, the organization is able to access data sets that contain no nonconformities capable o f causing unacceptable disruption to the e ffectiveness and e fficiency o f decision making using those data b) Outcomes — A detailed specification is developed for data cleansing to correct each identified data noncon formity NOTE Cleansing can involve both human intervention to correct data values and also the use of automated tools to per form systematic actions on data sets — A schedule is developed and implemented in consultation with stakeholders to execute the required data cleansing — A record is kept o f all corrections made to the data — Actions are developed to prevent the recurrence of actual or the occurrence of potential data nonconformities c) Activities — Correction of data nonconformities and related data: Correct data nonconformities and related data, implementing developed solutions and make a record o f the corrections — Prevention o f data noncon formity recurrence: Act to prevent the recurrence o f each actual or the occurrence o f each potential data noncon formity 6.5.4 Process Improvement for Data Nonconformity Prevention a) Purpose The purpose o f Process Improvement for Data Noncon formity Prevention is to trans form processes, taking account o f the results o f Root Cause Analysis and Solution Development, and to increase the extent to which the organization achieves a systematic and systemic approach to achieving data quality b) Outcomes — Proposals are produced in detail for process improvements NOTE The process improvements can be either improvements of existing processes or suggestions of NOTE Improvements o f organization, people, architecture, hardware and so ftware can be specified in planned future processes The process that needs an improvement can be a constituent o f the data quality management process, a data management process or any business process per formed in the organization the detailed proposals for process improvements — A schedule is agreed with stakeholders for implementation o f the process improvements — The agreed schedule is carried out — The effectiveness is evaluated for the process improvements that are implemented 14