The document introduces a Statistical Training Framework aligned to the Generic Statistical Business Process Model (GSBPM). The Framework consists of 13 high-level statistical headings covering key skills for working in statistical organizations. It allows individuals to self-assess their skills and target training needs. Each heading is broken down into basic, intermediate, and advanced levels. The Framework is designed to improve statistical expertise within organizations and across the statistical system by facilitating measurement and enhancement of skills according to the GSBPM. When used with a Skills Register, it can help identify strengths, gaps, and target training more effectively.
Using Business Intelligence: The Strategic Use of Analytics in GovernmentIBM Government
IBM Center for the Business of Government addresses the value of analytics for measurably improving each of four government sector: health care, logistics, revenue management, and intelligence. Using the business strategy of leveraging analytics to promote promote change, these sectors can run as efficiently as any successful business.
Performance Assessment of Agricultural Research Organisation Priority Setting...iosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Using Data Mining Techniques in Customer SegmentationIJERA Editor
Data mining plays important role in marketing and is quite new. Although this field expands rapidly, data mining is still foreign issue for many marketers who trust only their experiences. Data mining techniques cannot substitute the significant role of domain experts and their business knowledge. In the other words, data mining algorithms are powerful but cannot effectively work without the active support of business experts. We can gain useful results by combining these techniques and business expertise. For instance ability of a data mining technique can be substantially increased by combining person experience in the field or information of business can be integrated into a data mining model to build a more successful result. Moreover, these results should always be evaluated by business experts. Thus, business knowledge can help and enrich the data mining results. On the other hand, data mining techniques can extract patterns that even the most experienced business people may have missed. In conclusion, the combination of business domain expertise with the power of data mining techniques can help organizations gain a competitive advantage in their efforts to optimize customer management. Clustering algorithms, a group of data mining technique, is one of most common used way to segment data set according to their similarities. This paper focuses on the topic of customer segmentation using data mining techniques. In the other words, we theoretically discuss about customer relationship management and then utilize couple of data mining algorithm specially clustering techniques for customer segmentation. We concentrated on behavioral segmentation.
Applying systemic methodologies to bridge the gap between a process-oriented ...Panagiotis Papaioannou
This work is an application of the Soft Systems Methodology (SSM) to improve an information system to fully support the related process-based management system and help its internal improvement. Design and Control Systemic Methodology (DCSYM) is used as a modelling tool to facilitate conceptual models comparison within the SSM context.
Using Business Intelligence: The Strategic Use of Analytics in GovernmentIBM Government
IBM Center for the Business of Government addresses the value of analytics for measurably improving each of four government sector: health care, logistics, revenue management, and intelligence. Using the business strategy of leveraging analytics to promote promote change, these sectors can run as efficiently as any successful business.
Performance Assessment of Agricultural Research Organisation Priority Setting...iosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Using Data Mining Techniques in Customer SegmentationIJERA Editor
Data mining plays important role in marketing and is quite new. Although this field expands rapidly, data mining is still foreign issue for many marketers who trust only their experiences. Data mining techniques cannot substitute the significant role of domain experts and their business knowledge. In the other words, data mining algorithms are powerful but cannot effectively work without the active support of business experts. We can gain useful results by combining these techniques and business expertise. For instance ability of a data mining technique can be substantially increased by combining person experience in the field or information of business can be integrated into a data mining model to build a more successful result. Moreover, these results should always be evaluated by business experts. Thus, business knowledge can help and enrich the data mining results. On the other hand, data mining techniques can extract patterns that even the most experienced business people may have missed. In conclusion, the combination of business domain expertise with the power of data mining techniques can help organizations gain a competitive advantage in their efforts to optimize customer management. Clustering algorithms, a group of data mining technique, is one of most common used way to segment data set according to their similarities. This paper focuses on the topic of customer segmentation using data mining techniques. In the other words, we theoretically discuss about customer relationship management and then utilize couple of data mining algorithm specially clustering techniques for customer segmentation. We concentrated on behavioral segmentation.
Applying systemic methodologies to bridge the gap between a process-oriented ...Panagiotis Papaioannou
This work is an application of the Soft Systems Methodology (SSM) to improve an information system to fully support the related process-based management system and help its internal improvement. Design and Control Systemic Methodology (DCSYM) is used as a modelling tool to facilitate conceptual models comparison within the SSM context.
504 PART 5 Meeting Other HR GoalsL05 Discuss the roleof .docxevonnehoggarth79783
504 PART 5 Meeting Other HR Goals
L05 Discuss the role
of HRM technology
in high-performance
work systems.
Compensation :
Organizations can reinforce the impact of this kind of performance managernent by
tinf,ing compensation in part to performance measures. Chapter 12 depcribed a num'
ber oimethtd, fot doing this, including merit pay, gainsharing, and ,profit sharing'
Lincoin Electric has for decades paid its production workers a piecework rate. Not
only does this motivare individual employees to look for the most efficient ways lo
do ih.it jobs, but because the company is known for this compensation method, it
atrracts workers who value rvorking hard in order to earn more. In addition, Lincoln
has been paying all of irs employees a profit-sharil^g bonus "every year since 193+,"
in che words of Lincoin's CEO John M. Stropkl Jr.29 Compensation systems also can
help ro creare the conditions that contribute to high per{ormance, including team'
*ork, .-po*efment, and job satisfaction. For example, as discussed in Chapter 12,
compensation can be linked to achievement of team objectives'
Organizations can increase empowerment and job satisfactiorrl by including
"*plo"y"",
in decisions about compensation and by communicating the basis for deci-
sio.rs abo.,t pay. When the organization designs a pay structure, it can set up a task
force that inciudes employees with direct experience in various types of jobs' Some
organizations share financial information with their employees and invite them to
,"Jo*rr"rrd pay increases for themselves, based on th.it contributkins. Employees
also may pu.li.ipur. in serting individual or group goals for which thiey can receive
bo.r,rr.r. R"r.u..h has found th"t .mploy"e participation in decisions about pay poli'
cies is linked to greater satisfaction with the pay and rhe job.rO And as pve discussed in
Chaprer 11, whln organizations expiain their pay structures to emplgyees,.the.cotn-
munication can er1hance employees' satisfaction and belief that the system is fair'
HRl Technology
Human resource departments can improve their own and their organization's perfor'
*"'r.. by appropriately using r-r..' t"ch.tology (see the "HR Oops!" box). New tech-
.roiogy r-,r"utiy i.r,rol,r", autJ-atian and. collaboranon-that is, using equipment and
information processing to per{orm activities that had been performed, by people and
faciiitating.l".tror-,i. Jommunication between people. Over thelast feq decades, auto-
,rrurio., hu", irrrprou"d HRM efficielcy by reducing the number of peopie needed to
per-
form routine tasks. Using automation can free HRM experts to concentrate on ways
to
determine how human resoufce managemenl can help the-organizatiorl meet its
goals,
,o t.ch.,ology also can make this function more valuable'rl For example, information
technoiogy irovicles ways ro build and improve systems for knowledge;generation
and
,t *i.rg, ir iurr of a iearning organization. Among the appiications .
Learning Analytics in Enterprise Performance ManagementTatainteractive1
http://www.tatainteractive.com/ : TIS is clear in its roadmap that learning analytics is gaining momentum and is here to stay while maturing over the next decade. TIS’ ability to harness the right set of data, transform data and use open source platforms to analyze data brings to our customers a high impact solution at reasonable investments. Visit http://www.tatainteractive.com/ for more.
This paper illustrates the similarities between the problems of customer churn and employee turnover. An example of employee turnover prediction model leveraging classical machine learning techniques is developed. Model outputs are then discussed to design \& test employee retention policies. This type of retention discussion is, to our knowledge, innovative and constitutes the main value of this paper.
Total Workforce Management 2013: The State of Time and AttendanceADP Marketing
The paper looks at addressing critical workforce management issues, it looks at the trends influencing time and attendance today, and how time and attendance interacts with other critical workforce management systems, including payroll, rostering, and absence management.
Total workforce management 2013 the state of time and attendanceADP Marketing
Find out how organisations are responding to today’s challenges in workforce management. This report looks at how time and attendance factors in employee engagement, what savings businesses are making with integrated systems, and how time and attendance plays a role in business improvement.
This presentation provides a high-level overview of BPM and where it is today.
It also touches on some of the core technologies and standards.
Its focus is on the four specific “Challenges” facing BPM and they are aligned to the four phases of the typical application development life cycle.
1. Discovery
2. Design
3. Development
4. Deployment
Comparative Analysis
Xxxxxxxx Yyyyyyyyy
ITM 619
xx/xx/xxxx
Dr. Webb
Microsoft Dynamics & SAP ECC6.0 Comparative Analysis
1
Introduction .................................................................................................................................................. 2
Software Platforms ................................................................................................................................... 2
Microsoft ............................................................................................................................................... 2
SAP ........................................................................................................................................................ 2
Human Resources ..................................................................................................................................... 3
SAP, Human Resources.......................................................................................................................... 3
Microsoft, Human Resources ................................................................................................................ 4
Comparison ................................................................................................................................................... 5
Table 1: Comparison of Systems ............................................................................................................... 5
Interpretation ............................................................................................................................................ 7
Considerations .............................................................................................................................................. 8
Culture of the firm/Nature of the business ............................................................................................... 8
Interconnectivity ....................................................................................................................................... 8
Costs (short term and long term) .............................................................................................................. 8
Conclusion ..................................................................................................................................................... 9
References .................................................................................................................................................. 10
Appendix A .................................................................................................................................................. 11
Microsoft Dynamics & SAP ECC6.0 Comparative Analysis
2
Introduction
This project is the analysis of two different ERP systems, Microsoft Dynamics GP10 and SAP ERP Release
6.0.
Soft.
The CorPeuM mission is to improve the execution of strategy!
The basis of all performance management is in administering an organization’s business activities (sales, marketing, production, product development, etc.) in an environment that is increasingly uncertain. As outlined in ‘What is Strategy Execution?’, a strategy execution system should support the way in which these business processes are planned and monitored. This will require a number of integrated application capabilities, including:
Business Modelling: The system should be able to model an organization’s current and proposed business processes that show how they are connected to achieve the organization’s purpose.
Metric Categories: It should be possible to view that business model in terms of a number of metric categories such as the resources it consumes, the risks being run, the workload being performed, and the outcomes that are generated. Measures from these different categories will need to be displayed in combinations. For example, to show whether an activity is worthwhile requires its costs to be shown, along with the work performed and any outcomes. In addition, these metric views should be tailored to those people responsible for particular areas of the business.
Methodology support: It should adapt to an organisation’s chosen management methodology. i.e. it should conform with the terminology used and the way in which planning activities are prescribed.
Initiative management: It should allow the creation, selection, approval and monitoring of projects/strategic initiatives that improve organizational performance and how they link to corporate goals.
Scenario planning: It should allow combinations of initiatives to be assessed and the side-by-side analysis of alternate business models, through which senior management can set future plans.
Dynamic reports and analyses: It should communicate plans and results through personalised reports, analyses, dashboards, scorecards and strategy maps but in the context of how well the plan is being executed, so that the future can be better managed.
Dynamic workflow management: The system should be able to cope with continuous planning and monitoring of execution, which intelligently involves the right people at the right time, from across the enterprise.
Most people would agree that these capabilities are essential for managing strategy and its execution. Similarly, most CPM software vendors would claim to have these, but as they say, the devil is in the detail.
„Барометър на нагласите“ е първото национално проучване, посветено на ангажираността на служителите в държавната администрация и на факторите, от които тя зависи. То е инструмент за определяне на субективни, общо споделяни мнения на служителите, относно различни аспекти на тяхната работа в държавната администрация като цяло.
В първото национално проучване „Барометър на нагласите“ 2019 г. участваха общо 6246 служители от държавната администрация. Проучването е представително по отношение на всички демографски и организационно-административни променливи.
In some organizations, there is a blending of responsibilities for internal audit with aspects of risk, quality, control, and compliance. This occurs, for example, when the CAE is given responsibility for enterprise risk management, or where the head of risk or compliance reports to the CAE. The importance of effective safeguards under such circumstances is at its greatest. The governing body’s added oversight of the CAE’s nonassurance responsibilities can be an effective safeguard.
More Related Content
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504 PART 5 Meeting Other HR GoalsL05 Discuss the roleof .docxevonnehoggarth79783
504 PART 5 Meeting Other HR Goals
L05 Discuss the role
of HRM technology
in high-performance
work systems.
Compensation :
Organizations can reinforce the impact of this kind of performance managernent by
tinf,ing compensation in part to performance measures. Chapter 12 depcribed a num'
ber oimethtd, fot doing this, including merit pay, gainsharing, and ,profit sharing'
Lincoin Electric has for decades paid its production workers a piecework rate. Not
only does this motivare individual employees to look for the most efficient ways lo
do ih.it jobs, but because the company is known for this compensation method, it
atrracts workers who value rvorking hard in order to earn more. In addition, Lincoln
has been paying all of irs employees a profit-sharil^g bonus "every year since 193+,"
in che words of Lincoin's CEO John M. Stropkl Jr.29 Compensation systems also can
help ro creare the conditions that contribute to high per{ormance, including team'
*ork, .-po*efment, and job satisfaction. For example, as discussed in Chapter 12,
compensation can be linked to achievement of team objectives'
Organizations can increase empowerment and job satisfactiorrl by including
"*plo"y"",
in decisions about compensation and by communicating the basis for deci-
sio.rs abo.,t pay. When the organization designs a pay structure, it can set up a task
force that inciudes employees with direct experience in various types of jobs' Some
organizations share financial information with their employees and invite them to
,"Jo*rr"rrd pay increases for themselves, based on th.it contributkins. Employees
also may pu.li.ipur. in serting individual or group goals for which thiey can receive
bo.r,rr.r. R"r.u..h has found th"t .mploy"e participation in decisions about pay poli'
cies is linked to greater satisfaction with the pay and rhe job.rO And as pve discussed in
Chaprer 11, whln organizations expiain their pay structures to emplgyees,.the.cotn-
munication can er1hance employees' satisfaction and belief that the system is fair'
HRl Technology
Human resource departments can improve their own and their organization's perfor'
*"'r.. by appropriately using r-r..' t"ch.tology (see the "HR Oops!" box). New tech-
.roiogy r-,r"utiy i.r,rol,r", autJ-atian and. collaboranon-that is, using equipment and
information processing to per{orm activities that had been performed, by people and
faciiitating.l".tror-,i. Jommunication between people. Over thelast feq decades, auto-
,rrurio., hu", irrrprou"d HRM efficielcy by reducing the number of peopie needed to
per-
form routine tasks. Using automation can free HRM experts to concentrate on ways
to
determine how human resoufce managemenl can help the-organizatiorl meet its
goals,
,o t.ch.,ology also can make this function more valuable'rl For example, information
technoiogy irovicles ways ro build and improve systems for knowledge;generation
and
,t *i.rg, ir iurr of a iearning organization. Among the appiications .
Learning Analytics in Enterprise Performance ManagementTatainteractive1
http://www.tatainteractive.com/ : TIS is clear in its roadmap that learning analytics is gaining momentum and is here to stay while maturing over the next decade. TIS’ ability to harness the right set of data, transform data and use open source platforms to analyze data brings to our customers a high impact solution at reasonable investments. Visit http://www.tatainteractive.com/ for more.
This paper illustrates the similarities between the problems of customer churn and employee turnover. An example of employee turnover prediction model leveraging classical machine learning techniques is developed. Model outputs are then discussed to design \& test employee retention policies. This type of retention discussion is, to our knowledge, innovative and constitutes the main value of this paper.
Total Workforce Management 2013: The State of Time and AttendanceADP Marketing
The paper looks at addressing critical workforce management issues, it looks at the trends influencing time and attendance today, and how time and attendance interacts with other critical workforce management systems, including payroll, rostering, and absence management.
Total workforce management 2013 the state of time and attendanceADP Marketing
Find out how organisations are responding to today’s challenges in workforce management. This report looks at how time and attendance factors in employee engagement, what savings businesses are making with integrated systems, and how time and attendance plays a role in business improvement.
This presentation provides a high-level overview of BPM and where it is today.
It also touches on some of the core technologies and standards.
Its focus is on the four specific “Challenges” facing BPM and they are aligned to the four phases of the typical application development life cycle.
1. Discovery
2. Design
3. Development
4. Deployment
Comparative Analysis
Xxxxxxxx Yyyyyyyyy
ITM 619
xx/xx/xxxx
Dr. Webb
Microsoft Dynamics & SAP ECC6.0 Comparative Analysis
1
Introduction .................................................................................................................................................. 2
Software Platforms ................................................................................................................................... 2
Microsoft ............................................................................................................................................... 2
SAP ........................................................................................................................................................ 2
Human Resources ..................................................................................................................................... 3
SAP, Human Resources.......................................................................................................................... 3
Microsoft, Human Resources ................................................................................................................ 4
Comparison ................................................................................................................................................... 5
Table 1: Comparison of Systems ............................................................................................................... 5
Interpretation ............................................................................................................................................ 7
Considerations .............................................................................................................................................. 8
Culture of the firm/Nature of the business ............................................................................................... 8
Interconnectivity ....................................................................................................................................... 8
Costs (short term and long term) .............................................................................................................. 8
Conclusion ..................................................................................................................................................... 9
References .................................................................................................................................................. 10
Appendix A .................................................................................................................................................. 11
Microsoft Dynamics & SAP ECC6.0 Comparative Analysis
2
Introduction
This project is the analysis of two different ERP systems, Microsoft Dynamics GP10 and SAP ERP Release
6.0.
Soft.
The CorPeuM mission is to improve the execution of strategy!
The basis of all performance management is in administering an organization’s business activities (sales, marketing, production, product development, etc.) in an environment that is increasingly uncertain. As outlined in ‘What is Strategy Execution?’, a strategy execution system should support the way in which these business processes are planned and monitored. This will require a number of integrated application capabilities, including:
Business Modelling: The system should be able to model an organization’s current and proposed business processes that show how they are connected to achieve the organization’s purpose.
Metric Categories: It should be possible to view that business model in terms of a number of metric categories such as the resources it consumes, the risks being run, the workload being performed, and the outcomes that are generated. Measures from these different categories will need to be displayed in combinations. For example, to show whether an activity is worthwhile requires its costs to be shown, along with the work performed and any outcomes. In addition, these metric views should be tailored to those people responsible for particular areas of the business.
Methodology support: It should adapt to an organisation’s chosen management methodology. i.e. it should conform with the terminology used and the way in which planning activities are prescribed.
Initiative management: It should allow the creation, selection, approval and monitoring of projects/strategic initiatives that improve organizational performance and how they link to corporate goals.
Scenario planning: It should allow combinations of initiatives to be assessed and the side-by-side analysis of alternate business models, through which senior management can set future plans.
Dynamic reports and analyses: It should communicate plans and results through personalised reports, analyses, dashboards, scorecards and strategy maps but in the context of how well the plan is being executed, so that the future can be better managed.
Dynamic workflow management: The system should be able to cope with continuous planning and monitoring of execution, which intelligently involves the right people at the right time, from across the enterprise.
Most people would agree that these capabilities are essential for managing strategy and its execution. Similarly, most CPM software vendors would claim to have these, but as they say, the devil is in the detail.
„Барометър на нагласите“ е първото национално проучване, посветено на ангажираността на служителите в държавната администрация и на факторите, от които тя зависи. То е инструмент за определяне на субективни, общо споделяни мнения на служителите, относно различни аспекти на тяхната работа в държавната администрация като цяло.
В първото национално проучване „Барометър на нагласите“ 2019 г. участваха общо 6246 служители от държавната администрация. Проучването е представително по отношение на всички демографски и организационно-административни променливи.
In some organizations, there is a blending of responsibilities for internal audit with aspects of risk, quality, control, and compliance. This occurs, for example, when the CAE is given responsibility for enterprise risk management, or where the head of risk or compliance reports to the CAE. The importance of effective safeguards under such circumstances is at its greatest. The governing body’s added oversight of the CAE’s nonassurance responsibilities can be an effective safeguard.
ИНТЕГРИРАН ПОДХОД ЗА ОСИГУРЯВАНЕ НА СИСТЕМИТЕ ЗА УПРАВЛЕНИЕ НА КАЧЕСТВОТО (С...Светла Иванова
ИНТЕГРИРАН ПОДХОД
ЗА ОСИГУРЯВАНЕ НА СИСТЕМИТЕ ЗА УПРАВЛЕНИЕ НА КАЧЕСТВОТО (СУК), СИГУРНОСТТА НА ИНФОРМАЦИЯТА (СУСИ) И ФИНАНСОВО УПРАВЛЕНИЕ И КОНТРОЛ (СФУК)
AGILE MANAGEMENT AS AN ELEMENT OF THE MODERNIZATION OF THE TERRITORIAL STRUCT...Светла Иванова
Thanks to Steven Vale for translating English!
Agile Management of the Statistical Organization is one of the topical issues under discussion by the Commission for modernization the organizational framework and assessment to the High Level Group for modernization of official statistics at the United Nations. From the position of the Head of Territorial statistical division in the article, I presented my point of view on the possibility of such a type of management in the territorial structure of the NSI.
The article justifies the need of a flexible organizational culture. The main features of the Agile management and Scrum approach and how their application in the NSI's territorial structure would appear, while complying with the European Statistical Practice Code and the implementation of the ISO 9001 clauses, are presented. The Agile management philosophy is consistent with the phases and sub-actions of the common statistical production process model in the NSI.
Attention is also paid to risk management under the conditions of Agile Management, with presented detailed methods of risk monitoring and analysis of the causes of its occurrence. The role of internal audit beyond the context of the Public internal audit Act and its necessity in the territorial structures of such governance is also reviewed.
As the most suitable way of introducing the philosophy of Agile management in the structure of the NSI's territorial structures into the statistical production process, a hybrid model of management is shortly presented, where only in separate phases of the Common Statistical Production Process in the NSI is applied the Scrum Approach.
AGILE MANAGEMENT AS AN ELEMENT OF THE MODERNIZATION OF THE TERRITORIAL STRUCT...Светла Иванова
Agile Management of the Statistical Organization is one of the topical issues under discussion by the Commission for modernization the organizational framework and assessment to the High Level Group for modernization of official statistics at the United Nations. From the position of the Head of Territorial statistical division in the article, I presented my point of view on the possibility of such a type of management in the territorial structure of the NSI.
The article justifies the need of a flexible organizational culture. The main features of the Agile management and Scrum approach and how their application in the NSI's territorial structure would appear, while complying with the European Statistical Practice Code and the implementation of the ISO 9001 clauses, are presented. The Agile management philosophy is consistent with the phases and sub-actions of the common statistical production process model in the NSI.
Attention is also paid to risk management under the conditions of Agile Management, with presented detailed methods of risk monitoring and analysis of the causes of its occurrence. The role of internal audit beyond the context of the Public internal audit Act and its necessity in the territorial structures of such governance is also reviewed.
As the most suitable way of introducing the philosophy of Agile management in the structure of the NSI's territorial structures into the statistical production process, a hybrid model of management is shortly presented, where only in separate phases of the Common Statistical Production Process in the NSI is applied the Scrum Approach.
Consistency of the Generic Activity Model for Statistical Organizations (GAMS...Светла Иванова
Consistency of the Generic Activity Model for Statistical Organizations (GAMSO) with the Common Assessment Framework (CAF) and the Toyota's management model "4P"
Value of official statistics: Recommendations on promoting, measuring and com...Светла Иванова
The document presents for your comments the Recommendations on Promoting, Measuring and Communicating the Value of Official Statistics, including a measurement framework.
The Recommendations have been prepared by the UNECE Task Force on the Value of Official Statistics consisting of the United Kingdom (chair), Australia, Canada, Ireland, Mexico, New Zealand, Switzerland, Turkey, Eurostat, OECD, PARIS21 and UNECE.
The deadline for the reply is 24 March 2017. Please send your comments using the attached feedback questionnaire to anu.peltola@unece.org.
Subject to the positive outcome of the consultation, the Recommendations will be submitted to the 2017 plenary session of the Conference of European Statisticians for discussion and endorsement on 19-21 June 2017.
Подобряване на ефективността на регионалната структура на националния статист...Светла Иванова
В тази част на статията е представен бенчмаркингът като управленски подход за постигане на желания от ръководителите на ТСБ резултат - непрекъснато подобрение на ефективността чрез идентифициране и прилагане на най-добрите практики. Разгледани са ролите на директора на ТСБ, на главния счетоводител и началниците на отдели в процеса по бенчмаркинг и са дефинирани ключовите въпроси на бенчмаркинга, на които всеки от тях трябва да отговори, за да е успешна процедурата. За да се докаже, че бенчмаркингът може лесно да бъде официализиран като управленска практика в регионалната структура на НСИ, са разгледани подробно шестте основни етапа в процеса на сравнителния анализ независимо от неговата форма - планиране, работа с партньори, картографиране, анализ, действие и преглед.
Статията има за цел да докаже, че прилагането на концепцията за Lean управление ще помогне на новосъздадените ТСБ да рационализират процесите чрез преодоляване на причините за организационна неефективност, изграждане на системи за управление, възможности за поддържане на нови начини на работа и ангажимент на ръководните служители за непрекъснато подобряване на ефективността. Представени са и петте основни принципа на Lean управлението, пречупени през призмата на бизнес процесите в ТСБ, както и подходи, техники и инструменти, към които следва да се придържат ръководителите им. Етапите на внедряване на Lean управление в ТСБ - възприемане, оценяване, моделиране, действие и напредък, могат да продължат няколко години, през които удовлетвореността на потребителите и служителите, производителността, времето за изпълнение ще започнат да се подобряват.
ИПА - "Анализ на добри практики и изготвяне на предложения за въвеждане на гъ...Светла Иванова
http://www.ipa.government.bg/bg/publikacii_na_ipa
На база извършените проучвания в рамките на настоящото изследване са формулирани предложения, както за въвеждането на нови форми и елементи на гъвкави условия на труд, така и за усъвършенстване на прилаганите до момента в практиката, като те са съобразени със спецификата на действащата нормативна уредба в България.
Enhancing Existing Risk Management in National Statistical Institutes by Usin...Светла Иванова
7. Effective Risk management is fundamentally about appropriate decision making. We all make decisions every single day; some decisions will create threats or opportunities whilst some will mitigate threats. Risk management helps us take decisions which are appropriate to the level of risk we are willing to take.
http://www1.unece.org/stat/platform/display/hlgbas/2016+Workshop+on+the+Modernisation+of+Official+Statistics
Guidelines on risk management practices in statistical organizations 2.0Светла Иванова
These Guidelines are intended to help the implementation of a risk management system in statistical organizations.
This draft consists of two sections, whose index complies with risk management standard ISO31000/2009:
• Section 1 investigates the risk management system; and
• Section 2 focuses on the risk management process.
Трета есенна академия "Реформите в публичната администрация в огледалото на п...Светла Иванова
В края на октомври катедра „Публична администрация“ на УНСС, с подкрепата на Института по публична администрация, организира третата есенна академия „Реформите в публичната администрация в огледалото на публични мениджмънт“. Участие в академията взеха университетски преподаватели, представители на държавни институции и експерти от неправителствения сектор. На откриването присъства и изпълнителния директор на ИПА г-н Павел Иванов.
Дискусиите по време на тазгодишния форум бяха фокусирани предимно върху образованието, което предлагат българските университети и съответствието му на изискванията на модерната публична администрация.
С това си издание форумът затвърди позициите си като обединител не само на университетските институции, но и на всички организации, които се стремят към една нова и модерна публична администрация. Това бе подчертано и от г-н Павел Иванов, който оцени високо изпълнението на Меморандума за сътрудничество между администрацията на Министерския съвет, ИПА и висшите училища със специалност „Публична администрация“.
Opinion UNECE about statistical program for 2017, the Conference of European ...Светла Иванова
The annual statistical programme describes the concrete activities to be carried out in 2017 to implement the UNECE biennial statistical programme for 2016-2017. The biennial programme was adopted by the Conference of European Statisticians (CES) in June 2015 (document ECE/CES/2015/16) and approved by the UNECE Executive Office (EXCOM) in January 2016. The programme takes into account the outcome of the UNECE review carried out by EXCOM in 2012. The review acknowledged that “the UNECE Statistics subprogramme, the Conference of European Statisticians and its related subsidiary bodies work within current mandates in an efficient way, producing concrete results (methodological principles, recommendations, guidelines and databases) in a regular and ongoing way that have clear value added for the region and beyond, and that attract extra-budgetary funding including from outside the region.”
Relationship between ISO 9001:2015 and Scrum practices in the production and ...Светла Иванова
When it comes to quality management, each organization used as a tool for this well-established procedures and standards. In Agile Scrum is a management framework that thanks to its iterativnost has changed perceptions of project management and proven advantages of this type of management to traditional. It is interesting what happens when you meet ISO and Scrum in a flexible environment for managing statistical processes.
As seen ISO 9001 and Scrum are not two different things. In both cases the objective is to improve the process of producing statistical products and services. Concomitant administration may lead to a result - improving user satisfaction.
Regulation (EU) No 99/2013 of the European Parliament and of the Council on t...Светла Иванова
Предложение за Регламент на европейския парламент и на съвета за изменение на Регламент (ЕС) № 99/2013 на Европейския парламент и на Съвета относно Европейската статистическа програма за периода 2013 — 2017 г. чрез удължаване на програмата за периода 2018 — 2020 г.
Talent Management: Accelerating Business Performance - Right ManagementСветла Иванова
Right Management’s latest study provides a global overview of talent management trends, drawing on feedback from more than 2,200 business leaders and HR professionals in 13 countries and 24 industries.
Human resources management in modern statistics - Janusz Dygaszewicz, Central...Светла Иванова
Modern statistics means modern management of statistical surveys at each implementation stage, with an optimum use of resources, along with approximating the completion of the official statistics mission by increasing the effectiveness of the statistical production process.
Modern statistics is also a set of notions characterising individual phases and processes involved in the creation of statistical data in line with customers' needs and the principles of the activities optimisation and allocation of resources, and also the rational use of resources.
The innovativenes of statistics is reflected in the current adjustment of statistical production to the Generic Statistical Business Process Model (GSBPM), with the Integrated Statistical Business Process Model (ISBPM) being its equivalent in Poland.
The introduction of the CSPA-compliant corporate architecture also determines the actual development directions for statistical production, and the resulting needs to further expand the competences of statisticians.
Preliminary findings _OECD field visits to ten regions in the TSI EU mining r...OECDregions
Preliminary findings from OECD field visits for the project: Enhancing EU Mining Regional Ecosystems to Support the Green Transition and Secure Mineral Raw Materials Supply.
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Property appraisals completed in May for downtown Reno’s Community Assistance and Triage Centers (CAC) reveal that repairing the buildings to bring them back into service would cost an estimated $10.1 million—nearly four times the amount previously reported by city staff.
Working with data is a challenge for many organizations. Nonprofits in particular may need to collect and analyze sensitive, incomplete, and/or biased historical data about people. In this talk, Dr. Cori Faklaris of UNC Charlotte provides an overview of current AI capabilities and weaknesses to consider when integrating current AI technologies into the data workflow. The talk is organized around three takeaways: (1) For better or sometimes worse, AI provides you with “infinite interns.” (2) Give people permission & guardrails to learn what works with these “interns” and what doesn’t. (3) Create a roadmap for adding in more AI to assist nonprofit work, along with strategies for bias mitigation.
Jennifer Schaus and Associates hosts a complimentary webinar series on The FAR in 2024. Join the webinars on Wednesdays and Fridays at noon, eastern.
Recordings are on YouTube and the company website.
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The potato is a starchy root vegetable native to the Americas that is consumed as a staple food in many parts of the world. Potatoes are tubers of the plant Solanum tuberosum, a perennial in the nightshade family Solanaceae. Wild potato species can be found from the southern United States to southern Chile
Synopsis (short abstract) In December 2023, the UN General Assembly proclaimed 30 May as the International Day of Potato.
Jennifer Schaus and Associates hosts a complimentary webinar series on The FAR in 2024. Join the webinars on Wednesdays and Fridays at noon, eastern.
Recordings are on YouTube and the company website.
https://www.youtube.com/@jenniferschaus/videos
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2. Statistical Training Framework
Introduction
The Central Statistics Office of Ireland and the United Nations High Level Group for the
Modernisation of Statistics (HLG-MOS) identified the need for the development of a
Statistical Training Framework aligned to the Generic Statistical Business Process Model
(GSBPM). This Framework will facilitate statistical organisations in measuring and
improving the standard of statistical expertise within their organisation in line with the
GSBPM and accordingly should improve standards across the entire statistical system.
The Framework is built on 13 high level statistical headings which have been selected as
they represent key statistical skills for working in any statistical organisation. This document
introduces the rationale for the design of the framework, tracks its relationship to the
GSBPM model and details all 13 high level headings. It allows individuals to self-assess their
areas of strengths and weakness and targets their training needs more effectively.
Each heading has been broken down into three levels - Basic, Intermediate and Advanced.
These terms convey a certain level of learning. They broadly refer to statistical experts: but
they could be matched to different roles at different levels within the GSBPM (for example,
staff involved in statistical production but without a formal qualification in statistics). As an
example, the Advanced descriptors are at a superior level, the sort of skills you would
expect from a methodological expert or an individual who has worked in a specific area for
several years.
This Framework will work best in conjunction with a Skills Register. A Skills Register is a
database containing the skills, knowledge and expertise of the people within your
organisation. The combination of the skills levels required for the role, and the current level
of skill as identified in the register, will allow for the identification of the person’s strengths
and gaps. Training can then be more specifically targeted.
Each statistical organisation can draft a list of training interventions in order to improve
their skills, knowledge and expertise. It is recommended that these training interventions
encompass the 70/20/10 Model. The 70/20/10 Model is a learning and development reference
model which captures the three types of learning - experiential, social and formal - and
explains their relationship to one another.
3. GSBPM Model
Why the GSBPM model?
The GSBPM describesanddefinesthe set of businessprocessesneededtoproduce official statistics.
It provides a standard framework and harmonised terminology to help statistical organisations to
modernise theirstatistical productionprocesses, as well as to share methods and components. The
GSBPM can also be used for integrating data and metadata standards, as a template for process
documentation,for harmonising statistical computing infrastructures, and to provide a framework
for process quality assessment and improvement.
A recentsurvey1
reported that 60% of statistical organisations currently use the model. Of the 40%
whoare not usingit,75% of those have planstostart implementing its use soon. This indicates that
over 85% of all statistical organisations are or will use the GSBPMmodel.
The Statistical TrainingFrameworkisbasedonthe GSBPMmodel. Thismakesthe frameworkwidely
applicable as it is based on something which is respected and implemented in the majority of
statistical organisations,andwhich providesastandardised model for statistical business processes
in an end to end statistical production cycle.
1
Survey data availableat https://statswiki.unece.org/display/GSBPM/Uses+of+GSBPM
4. Statistical Training Framework
What this frameworkwill mean for statistical organisations & their staff:
The Statistical TrainingFrameworkisanecessaryresource whichwillformanintegral partof
strengtheningstatistical organisations overall statistical capability. While noone personisexpected
to possessall of the statistical skillslistedinthe frameworktoperformaparticularjob,a statistician
shouldgaina broad range of statistical skillsovertime.
The Statistical Training Framework will ensure all areas within the statistical process will get the
required focusandemphasison trainingspend.Onoccasionitisthe more publicareasthat have the
‘spend’ on the Specify Needs, Survey Design and the end element disseminate and evaluate.
A structuredStatistical TrainingFrameworkwill:
Form an Integral partof strengtheningstatistical organisations overall capability- will
measure statistical capability basedon the GSBPM in conjunctionwith the Skills
Registerand Role Definitions.
Aligntrainingacrossall statistical processeswithGSBPM
5. Identifygapsinstatistical levelsinorganisationsbutalsoacrossthe wider statistical
organisation
Assiststatistical organisations inWorkforce Planning
Allow statistical organisations identify
o Where trainingistakingplace
o Where overtrainingishappening
o Where undertrainingishappening
Allow statistical organisations todevelopclearlearningpathsforstaff througheffective
deliveryof statistical traininginterventions
Assistwithdecisionmakingonthe Mobilityof Staff
Provide staff withgreaterunderstandingof the range of statistical skills,knowledge and
expertiselinkedtoGSBPM
Maturity Model couldbe developedtoassesswhere statistical organisations are with
regardto trainingand the GSBPM
A structuredStatistical TrainingFrameworkwillgive staff:
A greaterunderstandingof the range of statistical skills,knowledge andexpertise
neededtoworkeffectivelythroughoutthe endtoendstatistical productioncycle,
alignedtothe GSBPM;
A greaterunderstandingof the role of statistical organisations andthe policyand
legislative environmentgoverningstatistical work
A structuredstatistical trainingpathforthe individuals
Helpwithdeveloping careerpathsandsuccessionplanning
An opportunity toshare theirknowledge andassistothersindevelopingtheirstatistical
skills.
6. Register Management
Basic
I knowthe differencebetweenanadministrative registeranda statistical register.
I understandthe datasourcesusedto constructthe register(s)insubjectmatterrelatedto
my role,andhave a basic ideaof theirqualitycharacteristics,particularlycoverage,
timeliness and(wheremultiple sourcesare used) linkage quality .
I understandthe importance of aregisterandhow it relatestootherelementsof asystem
of official statistics.
I understandthe unitsmodel underlyingthe businessregister,andhow the statistical units
relate toadministrative units.
I am aware of the needforharmonisedclassificationsystems,andknow whichonesrelate
to my role.
I understandthe principlesof manual andautomatedcoding,toclassificationsandforother
characteristicssuchas life events(forbusinesses,peopleandothertypesof unit).
I can use approvedregistersystemstoupdate unitsandtheircharacteristics,andstore the
associatedmetadata.
I can run a registerprovingexercise tovalidate the recordsonpartor all of a register.
Intermediate
I knowhowto linkmultiplesourcestogenerate orupdate aregister,andthe strategiesfor
dealingappropriately withunmatchedunits.
I understandhowsamplesare selectedfromthe populationonaregister,includingthe
principlesof coordinatedsampling, rotational samplingwith permanentrandomnumbers,
and systematicsampling.
I understandhowsamplinginformationand associatedpopulation information(asampling
frame) are created, storedandmanaged.
I knowhowsamplinghistoriesandcostinformationcanbe usedto manage respondent
burden.
I knowhowregisterinformationisbackedupandarchived,sothat it isavailable long-term
for analysis.
Advanced
I knowhowto undertake demographicanalysisusingregisterinformation.
I understandhowlagsinrecordingbirths,deathsandothereventsaffectthe registerand
differentstrategiesforincorporating theseinestimationprocedures.
I understandthe dangersof sample-basedfeedbacktoa register,andoptionstomitigate
these.
Where does Register Management fit into GSBPM model? (dark blue is central and pale blue is minor)
EvaluateDisseminateAnalyseProcessCollectBuildDesign
Specify
needs
7. Sampling and Estimation
Basic:
I understandthe difference betweenprobabilityandnon-probabilitysampling,andthe
advantagesanddisadvantagesof each.
I am aware of issuesof thatarise withsamplingframes(overcoverage,undercoverage,
duplicationetc).
I understandthe difference betweensamplingandnon-samplingerrors.
I understandhowweightsare usedtocalculate populationtotalsandmeans.
I understandhowweightsare usedtocompensate forunequal selectionprobabilitiesorfor
nonresponse.
For simple randomsampling,Ican draw the sample andestimate the populationmeanand
total of a givenvariable,the variance of these estimatesandconfidence intervalsforthem.
From a simple randomsample,Icanestimate apopulationproportionforagivenvariable,
and calculate the variance of the estimate,andconfidence intervalsforit.
Intermediate:
I understandthe followingfourtypesof probabilitysampling:simple randomsampling,
stratifiedrandomsampling,clustersamplingandsystematicsampling.
I am aware of the strengthsandweaknessesof eachmethod,andcanidentifythe most
appropriate methodforagivensituation.
For eachtype of probabilitysample,Icandraw the sample andestimate the population
meanand total of a givenvariable andconfidence intervalsforthem.
For eachtype of probabilitysample,Icanestimate apopulationproportionof agiven
variable,andcalculate the variance of the estimate,andconfidence intervalsforit.
I understandandcan implementratioandregressionestimationtoimprove the precisionof
sample estimates usingauxiliarydata,andhow to evaluate whetherthisisworthwhile.
For eachtype of probabilitysample,Icancalculate the requiredsample size forestimating
populationmeansandtotals,givenaparticularprecisionrequirement.
Whendesigningstratifiedsamples,Iunderstandcommonmethodsof allocatingthe samples
across the differentstrata,andthe advantagesanddisadvantagesof eachmethod.
I understandwhatthe designeffectof a sample is,andhow itaffectsthe effectivesample
size of the survey,andthe precisionestimatesof populationparameterscalculatedfrom
that sample.
I knowhowto calculate the designeffectof a clustersample.
I understandhowpost-stratificationcanbe usedtoadjusta weightedsample forcertain
variables(e.g.age/sex) tomake itconformto a knownpopulationdistribution.
Advanced:
I have a good understandingof multistage clustersamplesandhave experience in
implementingthem.
I knowhowto analyse complex surveydataanddeal withissuesof differential weighting,
stratificationandclustering.
8. I knowhowto use calibrationestimatorstoextendthe accuracyandconsistencyof
estimationwith auxiliarydata.
I am familiarwithsample coordinationmethodswhichmaximiseorminimisethe overlap
betweenseveral samplesdrawnsuccessfullyinapopulationthatchangeswithtime.
I am able to extendthe designbasedsamplingframeworktoinclude the modelassisted
approach,and understandthe advantagesanddisadvantagesof each.
I understandthe issuesthatarise frominformative non-response,andhow todesignand
implementstrategiestocompensate forit.
I understandreplicationmethodslikebalancedrepeatedreplications,jackkniferepeated
replicationsandbootstrappingandcanuse themto estimate variance incomplex surveys.
I knowhowto designrotatingsurveysandmulti-phase sample surveys.
Where does Sampling and Estimation fit into GSBPM model? (dark blue is central and pale blue is minor)
EvaluateDisseminateAnalyseProcessCollectBuildDesign
Specify
needs
9. Survey and Questionnaire Design
Basic
I knowhow use the GenericStatistical BusinessprocessModel (GSBPM) todefine stagesina
survey,andknowhowthe designsof the differentcomponentsinterrelate.
I understandthe role of questionnaire designinmaximisingthe qualityof the datacollected
insurveysandin administrative systems.
I understandthe needforclearlanguage inquestionnaires,withnotestoexplainconcepts
and definitionsprovidedwiththe questionstowhichtheyrelate.
I understandthe trade-off betweenquestionnaire lengthandrespondentparticipation.
Intermediate
I knowhowto make an assessmentof the optionsfora particularprocessbasedontheir
properties.
I understandhoweditcheckswithinanelectronicquestionnaireaffectrespondents anddata
quality,andhowto use themeffectively.
I knowthe principlesof designforon-linequestionnaires,andcanadaptthese to different
devices.
I understandthe differencesbetweenmodesandhow theyaffectrespondentbehaviour
(e.g. throughprimacyeffects).
I knowhowto programme an electronicquestionnaireinappropriate software,andhow to
include skippatterns,loopsandcross-checks.
Advanced
I understandhowtodesigna surveytakingaccountof the trade-offsbetweenthe elements
that make it upto achieve the requiredoutputswiththe righttrade-off betweenqualityand
cost.
I am able to undertake cognitive interviewing toassessthe qualityof questionnairesand/or
questions,andtoanalyse the resultingdata.
I am experiencedin qualitative analysistechniques.
I understandandcan applyconceptual modelsforinformationretrieval processesto
improve datacollection.
I am able to developandimplementstatistical measurementconceptsrelative tousers’
requirements(forexample ontopicssuchaswellbeing,sexualidentityandenvironmental
protectionexpenditure where there are nointernational standards).
I understandhowrandomisedresponse,CASIandsimilarapproachescanbe usedto gather
sensitivedata.
Where does Survey and Questionnaire design fit into GSBPM model? (dark blue is central and pale blue is
minor)
EvaluateDisseminateAnalyseProcessCollectBuildDesign
Specify
needs
10. Imputation and Non-response
Basic:
I understand the potential for bias which arises from non-response and the need to
compensate for missing data.
I understand the difference between unit non-response and item non-response.
I understand the difference between deterministic imputation and stochastic imputation,
and in which circumstances each can be used.
I understand the non-response mechanisms: Missing Completely at Random (MCAR);
Missing at Random (MAR) and Missing Not at Random (MNAR), and how each affects the
choice of adjustment procedures.
I understandhownon-response canbe reducedduringthe surveydesignanddatacollection
stages by implementing good practice, monitoring response rates and targeting important
units.
To deal with unit non-response, I understand the approach of calculating survey weights.
To deal with item non-response, I understand the approach of using an appropriate
imputation method.
Intermediate:
I can define homogenous response classes using modelling or segmentation approaches
I can implementandevaluate single-imputation methods for item non-response, eg mean
and mode imputationwithin homogenous imputation classes, regression imputation, hot-
deck imputation, predictive mean matching.
I understand and can calculate the components of survey weights: inclusion probabilities
(design weights) and non-response adjustments using inverse response rates within
homogenous weighting classes.
I can calculate nonresponse adjustment weights through post-stratification.
I understand when it might be appropriate to use imputation methods to adjust for unit
nonresponse.
I know how to ensure that imputed values are consistent with edit rules.
I can assessthe potential forbiasusingappropriate quality indicators(e.g.missingnessrates,
R-indicators).
11. Advanced:
I can calculate nonresponse adjustment weights using calibration estimators.
I can apply advanced methods for non-response adjustments using response propensity
modelling or segmentation algorithms.
I can account for extra uncertainty due to missing data when analysing survey data,
including hierarchical data, through multiple imputation.
I can calculate variance estimates using analytical expressions, multiple imputation or
replication methods to account for the extra uncertainty due to imputation.
I can designandimplementanadaptive designapproachtomaximise dataquality, and
understandhowprocessingandanalysisneedtobe adaptedtobe consistent.
Where does Imputation and Non-response fit into GSBPM model? (dark blue is central and pale blue is minor)
EvaluateDisseminateAnalyseProcessCollectBuildDesign
Specify
needs
12. Statistical Data Editing
Basic:
I understandthe conceptof an editrestriction(edits) andhow todefine themforboth
businessandsocial surveys.
I understandtypesof errors:validity,consistency,distributional aswell assystematicerrors
and randomerrors.
I understandfatal errors(hardedits) versusqueryerrors(softedits).
I understandthe basicapproachesof statistical dataediting:interactiveediting,selective
editing,automaticeditingandmacroediting.
I have knowledge of whenandwhere dataeditingcanoccur,e.g.at the time of data
collectionversuspost-processingtakingintoaccountcostimplications.
Intermediate:
I knowhowto evaluate the effectivenessof single edits,includingtheirsensitivityand
specificity.
I understandthe principlesof the Fellegi andHolt(1976, Journalof the American Statistical
Association 71 17-35) paradigm.
I understandapproachesthatsolve the errorlocalizationproblem.
I can implementimputationmethodsto adjustflaggedrecordssothattheyare consistent
witha setof editrules.
I can develop andapply astatistical score function totargetimportantunitsformanual
follow-up,andapplyautomated dataeditingandimputation tothe remainingunits.
I can applyproceduresinmacro editingforidentifyingoutliersandinfluential units.
I understandthe risksof over-editing.
Advanced:
I understandthe Fellegi-Holtalgorithm(categorical variables)/Fourier-Motzkin elimination
(continuousvariables)forvariable eliminationandfindingadmissibleintervalsfor
imputation.
I understanderrorlocalisationasamathematical optimisationproblem.
Where does Statistical Data Editing fit into GSBPM model? (dark blue is central and light blue is minor)
EvaluateDisseminateAnalyseProcessCollectBuildDesign
Specify
needs
13. Evaluating User Statistical Needs
Basic:
I maintaina catalogue of usersandusesof outputs,includinginformationonunmetuser
needs.
I am able to developexperimentalstatistical outputsandgatherfeedbackto helpthemto
develop.
I knowhowto obtainuserinformationandfeedbackfromarange of differentsources,
includingcomplaints,webmetrics,social media,customersatisfactionsurveys,media
monitoring.
Intermediate:
I can undertake auser consultation, includingprovidingevidence,summarisingand
analysingresponses,developingandimplementinganactionplan.
I am able to follow upandprobe userrequirements,andtogenerate innovativesolutionsto
meettheirneeds.
I am able to designandruna userengagementevent.
I maintainregularcommunicationwitharange of usersusinga varietyof channels.
Advanced:
I am able to undertake cost-benefitanalysisof arange of outputsand requirementsto
determine the rightbalance of outputstocovera range of userneedswithlimited
resources.
I am able to developalong-termworkprogramme todevelopmystatisticaloutputsin
supportof users’needs.
Where does Evaluating User Statistical Needs fit into GSBPM model? (dark blue is central and light blue is
minor)
EvaluateDisseminateAnalyseProcessCollectBuildDesign
Specify
needs
14. Index Numbers
Basic:
I am aware of “the index numbersproblem”, thatariseswhencomparingprices (or
quantities) betweendifferenttime periods,andsome practical solutions.
I understandhowindex numbertheoryisused inofficialstatisticsinthe productionof some
commonindicese.g.the ConsumerPrice Index.
I understandthe conceptsof base year,reference year,rebasingandchainlinkingforindices.
I can compare common algebraicmethodsof indexnumbercalculation includingLaspeyres
and Paasche indices.
Intermediate:
I knowthe stepsto take to rebase a fixedbase series.
I understandandcan applydifferenttechniquesthatcan be usedto chainlinkrebased
series.
I can calculate aggregate indicesfromelementaryones byapplyingweights,andknow the
difference betweengrossandnetweights.
I understandthe challengesinsamplingandcollectingprices,andhow discontinueditems
may be substitutedandincorporatedinanindex.
Advanced:
I understandthe differentapproachestocalculating aconsumerprice index.
I understandthe advantagesanddisadvantagesof the mainelementaryprice index number
formulae,i.e.the Dutot,Carli andJevonsindices.
I understandthe challengesin measuringprice changes whenthere are significantquality
changesinthe goodsbeingpriced,andsome commonapproaches todeal withthem,
includinghedonicregression.
I understandsome advancedtechniquesin index numbertheory,includingsuperlative
indices,the axiomaticversusthe economicapproach,the utilityfunctionandthe difference
betweencostof livingandcostof goods indices.
I can applythese advancedtechniquestoindex numberproblemsinofficial statistics using
appropriate software.
I understandthe challengesandopportunitiesof web-scrapedprice data,andwhich
methodscanbe usedtoincorporate themina price index.
Where does Index Numbersfit into GSBPM model? (dark blue is central and light blue is minor)
Regression
EvaluateDisseminateAnalyseProcessCollectBuildDesign
Specify
needs
15. Basic:
I am aware of the importance of regressionmodellinginproducingofficial statisticsand
understandsome examplesof how itisappliedinthe CSO.
I can undertake exploratory analysisof the relationshipbetween twonumerical variables
usingdescriptiveandgraphical techniques.
I understandthe terminology,conceptsandassumptionsbehindordinaryleastsquares
regression. (Inparticular,Iknow thatI shouldn’trunordinaryleastsquaresregressionon
time seriesof data.)
I can develop asimple linearregressionmodel (includingconsideringtransformation,
outliersetc.) usingstatistical software,andinterpretthe model diagnosticsandgoodnessof
fit.
I am aware of the differentregressiontechniquesthatare requiredfordifferenttypes
and/orcombinationsof variables.
Intermediate:
I understandvariable selectionstrategiesforchoosingpotential predictorsinregression
models(forward,backward,stepwise etc.)
I understandissuesthatarise with collinearitybetweenpredictorsinmultipleregressionand
methodstoidentifyandmanage it.
I can develop amultiplelinearregressionmodel usingstatistical software,andinterpretthe
model diagnosticsandgoodnessof fit.
I understandthe terminology,conceptsandassumptionsbehindlogisticregression.
I can develop alogisticregressionmodel usingstatistical software,andinterpretthe model
diagnosticsandgoodnessof fit.
I knowwhento applythese methodstotypical datasetsarisinginofficial statistics.
Advanced:
I understandthe terminology,conceptsandassumptionsbehindgeneralisedlinearmodels.
I can develop ageneralisedlinearmodelusingstatistical software,andinterpretthe model
diagnosticsandgoodnessof fit.
I understandthe terminology,conceptsandassumptionsbehindmultilevel models,andin
whichcasestheyare likelytobe useful.
I can developamultilevel modelusingstatistical software,andinterpretthe model
diagnosticsandgoodnessof fit.
I knowwhentoapplythese differenttechniquestoparticularsituationsthatarise inofficial
statistics.
I am aware of the possibilitiesof computer basedanaloguesof regressionsuchasmachine
learning,supportvectormachines,etc.
Where does Regression fit into GSBPM model? (dark blue is central and light blue is minor)
Time Series and Seasonal Adjustment
Basic:
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16. I understand the concepts and methods underlying the analysis of univariate time series.
I am aware of some examplesof time seriesmodelsandtheir applicationsincommon use in
official statistics.
I am aware of the special problemspresentedbyusingtime seriesdatainstatistical analysis.
I understand the decomposition of a time series into trend, seasonal and irregular
components, and can identify these components in a time series graph.
I can distinguish between the three types of data: time series, cross-sectional and pooled
data.
I understand concepts such as: stationary, weakly stationary, unit root test, random walk,
spurious regression, cointegration, time series models (AR, MA, ARMA, ARIMA), ACF and
PACF.
I can undertake seasonal adjustment (by estimatingunobservablecomponentslikethe trend
and seasonal effects, and removing them from a time series) using appropriate software.
Intermediate:
I can assess the assumption of stationarity for a time series, and transform it accordingly if
the assumption does not hold.
I understand the purpose of smoothing techniques in common use in time series analysis,
including moving averages and exponential smoothing methods.
I understandthe theoretical foundationsunderpinning the ARIMA model approach and can
estimate the parameters of the model using e.g. the Box-Jenkins approach.
I can apply these techniques using appropriate software, and critically assess the output
generated.
I create seasonal adjustment models and conduct diagnostic analysis/quality assurance of
the seasonal adjustment models.
I can produce forecasts by fitting time series models using appropriate software.
Advanced:
I understand some advanced techniques in time series analysis, including advanced
elementsof ARIMA model estimation and forecasting, spectral analysis, and the use of the
Kalman filter.
I can applythese advanced techniques to time series in official statistics using appropriate
software.
I can generate and quality assure forecasts of time series data.
Where do Time Series and Seasonal Adjustment fit into GSBPM model? (dark blue is central and light blue is
minor)
Statistical Disclosure Control
Basic:
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17. I understandthe legal andethical obligations to protect the confidentiality of respondents.
I understand the difference between ‘safe data’ and ‘safe access’.
I know the different types of disclosure, including identification, attribute and group
attribute disclosure.
I understandthe differenttypesof datasourcesarisingfromsurveys,censuses and registers
and appropriate methods for protecting their privacy and confidentiality.
I understand basic concepts of statistical disclosure control methods and their impact on
data utility.
I can evaluate different levels of protection for different types of statistical outputs,
including the use of Virtual Microdata Labs and remote access.
Intermediate:
I can calculate disclosure risk measures for different types of data: microdata and tabular
data.
I can apply statistical disclosure control methods to microdata and tabular data, e.g.
Suppression, rounding, perturbation, additive noise.
I can evaluate the utilityof the datafollowingthe applicationof statistical disclosure control
methods.
I am able to evaluate andcritique differentstatistical disclosure control methods depending
on the type of statistical output with respect to the amount of protection afforded and the
impact on the utility of the protected data.
Advanced:
I understandthe definitionof differential privacyanditscomparison to statistical disclosure
control.
I can adapt statistical disclosure control methods to new forms of data, e.g. social media,
geolocated data, time stamped data and new modes of dissemination, e.g. flexible table
builders.
I understand the properties, uses and risks of synthetic datasets and am able to generate
them.
Where does Statistical Disclosure control fit into GSBPM model? (dark blue is high and light blue is minor)
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18. Visualisation and Presentation of Data
Basic:
I can draw out main messages from data, analysis and research. I can communicate
informationclearlyandefficientlyviastatistical graphs, plots and infographics so that it can
be easily understood by different audiences.
I can perform exploratory data analysis and determine the most appropriate presentation
style and tailor it to the audience as required.
I am familiar with the graphical procedures available in appropriate software.
I know and use the principles of good table design and data presentation.
Intermediate:
I am a proficientuserof software packagesto developsuitable visualisation technologies to
summarise my data any convey concepts.
I have experience of using data visualisation tools such as Tableau, Python etc. to create
basic interactive charts.
Advanced:
I have experience of using data visualisation tools such as Tableau, Python etc. to create
sophisticated interactive charts.
I am able to design web pages to present data in an accessible way, and to critique the
presentation on existing pages.
Where does Visualisation and Presentation of Data fit into GSBPM model? (dark blue is high and light blue is
minor)
Data Matching, Integration and Administrative Data
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19. Basic:
I understand sources of data and the difference between data collected by the survey
agencyfor statistical purposes(survey data,censusdata) anddata collectedby Government
agencies for other purposes such as to record transactions (administrative data).
I understand the benefits of using administrative data in statistical systems and the
challenges it poses.
I can build andimplement a quality framework for providing checks on administrative data
accordingto qualitydimensions, eg. accuracy, reliability, timeliness, coverage, coherence,
comparability, clarity, linkability, missing data.
I can articulate privacy issues related to the analysis of administrative data for research
purposes.
Intermediate:
I understand the principles of probabilistic record linkage (Fellegi-Sunter 1969) for the
situation where the same data units from two (or more) data sources can be linked by
common variables.
I understand the principles of statistical integration where we build joint statistical data
based on marginal observations arising from two (or more) data sources which may not
include the same data units.
I understandandcan articulate a total-errorframeworkforintegratedstatistical data, which
provides a systematic overview of the origin and nature of the various potential errors.
Advanced:
I can apply imputation/adjustment techniques in the presence of constraints from
overlapping data sources and/or missing data.
I can implement the Fellegi-Sunter (1969) probabilistic record linkage.
I can implement a statistical data integration procedure to integrate data sources to form
joint statistical data from two (or more) disjoint data sources.
I know how to account for linkage and coverage errors in the analysis of linked datasets.
Where do Data Matching, Integration and Administrative Data fit into GSBPM model? (dark blue is high and
place blue is minor)
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20. National Accounts
Basic:
I understandthe differencesbetweenGrossDomesticProduct(GDP) andGross National
Product(GNP) and the three waysinwhichtheycan be measured(byoutput,income and
expenditure approaches).
I knowthe mainplayers(sectors) inthe National Accountsmodelof acountry’seconomy.
I knowthe differencebetweenconstantpricesandcurrentprices,andhow to convertfrom
one to the other.
I understandthe productionfrontierandhow itaffectsthe national accounts.
Intermediate:
I understandthe main(household,business,financial,central government) accounts and
howtheyrelate toeach other.
I understandthe conceptof input-output(supply-use) tablesandhow theyprovide a
mechanismforbalancing.
I am able to analyse and interpretrevisionsinGDPand itscomponents.
I am able to undertake benchmarkingandcalendarisationforsubannual inputstothe
national accounts.
I understandthe principlesof satelliteaccountsandhow theyfitwiththe mainaccounts.
Advanced:
I know the detailsof the EuropeanSystemof Accounts(ESA) and/orthe Systemof National
Accounts(SNA).
I am able to implementbalancingininput-out(supply-use) tables.
I am able to constructsatellite accounts.
I am able to assessthe strengthsandweaknessesof the datasourcesused(orwithpotential
to be used) inthe national accounts.
I am able to interpretandexplainthe outputsfromthe national accounts.
I understandhowthe outputsfromthe national accountsare usedin econometric
modelling,andtherefore howchangesinthe accountsaffectmodels.
Where does National Accounts fit into GSBPM model? (dark blue is central and pale blue is minor)
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