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E S S n e t o n S t a n d a r d i s a t i o n http://cros-portal.eu/content/standardisation

Framework Partnership Agreement Number 61001.2012.003-2012.100

Specific Grant Agreement 1 (Number 61001.2012.003-2012.101)

Deliverable 2.1

February 20 2014

Deliverable 2.3

Refinement of the Business Architecture model elaborated by the Sponsorship on Standardisation

Nadia Mignolli (Italian National Institute of Statistics - Istat, Italy) Giulio Barcaroli (Istat, Italy)

14 March 2014

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Contents 1. Introduction 3 2. Background 4 3. Business Architecture definition and relations within the Enterprise Architecture framework

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4. Identification of homogeneous BA business lines with their related activities and products

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5. Business Architecture model 12 6. Description of fundamental infrastructures and guiding principles 14 6.1 Infrastructures 14 6.2 Principles 14 7. Future developments 17 References 18

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1. Introduction This deliverable1 illustrates a Business Architecture (BA) model that represents a refinement of the approach produced by the Sponsorship on Standardisation. Its purpose is articulated as follows: it is a concrete approach that can be adopted by any Institution producing official statistics

both at national and European Statistical System (ESS) level; it constitutes a starting point for a more generic Business Architecture model shareable both

within National Statistical Systems and at the ESS level; it represents a reference framework for all the activities related to standardisation.

This renewed model was formalised, deliberated and approved by the ESSnet on standardisation members; although it is not mandatory, its use is highly recommended so as to work in a more efficient and optimised way. In this context, the role played by the Italian National Institute of Statistics (Istat) concerning this refinement work is due to fact that it has recently defined its own Business Architecture model also on the basis of its deep involvement at two different levels: at the international level, Istat is actively participating in various groups (the UNECE High-

Level Group for Modernisation of Statistical Production and Services2 , the Statistical Network3, the Eurostat Sponsorship for Standardisation4), in particular in those projects dealing with standardisation and industrialisation (for instance, the Business Architecture Project Team of the Statistical Network5, the Common Statistical Production Architecture – CSPA of the UNECE High-Level Group);

at internal level, since 2010 Istat has launched a long term Programme called Stat2015, that is focused on the modernisation of the whole statistical production process and is producing relevant and deep organisational changes.

On the basis of the work done so far, this document illustrates a BA model which is aligned to shared decisions and strategies, starting from both the European best practices and some international standards, and explores the following main aspects: BA definition and relations within the Enterprise Architecture (EA) framework; identification of homogeneous BA business lines with their activities and products; definition of the BA model value chain; description of fundamental infrastructures and guiding principles; planning of future developments.

1 This deliverable is produced and edited by the ESSnet on Standardisation members together with the following senior statisticians of the Italian National Institute of Statistics (Istat), who participate in the Istat Business Architecture Task Force: Nadia Mignolli (also member of the ESSnet on Standardisation and of the Statistical Network), Giulio Barcaroli (also member of the ESSnet on Standardisation and of DIME), Piero Demetrio Falorsi (also member of the Sponsorship on Standardisation) and Alessandra Fasano (member of the Istat Business Architecture Task Force). 2http://www1.unece.org/stat/platform/display/hlgbas/High-Level+Group+for+the+Modernisation+of+Statistical+Production+and+Services. 3 http://www1.unece.org/stat/platform/display/msis/Statistical+Network. 4 http://www.cros-portal.eu/content/ess-standardisation-0. 5 It includes a number of important Statistical Institutes (as Canada, Australia, Italy, New Zealand, Norway, Sweden) and works on the definition of a common model for the official statistical community.

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2. Background The BA model described in this deliverable is inspired both by the Dutch Central Bureau of Statistics (CBS) (Bredero et al. 2009) and by the international standard Generic Statistical Business Process Model (GSBPM, Version 5.0) (UNECE/Eurostat/OECD 2013). The CBS model was carefully chosen after a comparative study of the different approaches of various European National Statistical Institutes/Offices. This comparison was also based on the preliminary analysis carried out within the ESSnet Project on COmmon Reference Architecture (Cora 2010). The Dutch example has been operating for several years and proving to be very successful: it clearly identifies four main business functions which are all relevant at the governance level, splitting policy and management. It distinguishes clearly design activities from those related to the current production process. Therefore, it is compliant with one of the fundamental principles underlying process automation (or industrialisation/modernisation), i.e. making the statistical process as efficient as possible. An industrialised process, indeed, is based on the independence of its implementation stage from the high competence of human resources involved in its design. In addition, the CBS model also represents the basis of the Sponsorship on Standardisation BA model, whose refinement is the main task of this deliverable. After this point, significant changes have been made in the model described here to the previous approaches, concerning mainly: reference definitions and concepts; the BA business lines, that were changed and incremented; the activities defined within the different business lines that have been aligned with those set

out in phases and sub-processes proper of the current version 5.0 of the GSBPM, recognised as a real standard and adopted at international level;

the fundamental principles and the general scheme that take into account the introduction of some important innovations and several relevant international initiatives, as the launch of a harmonised programme of standardisation within the whole European Statistical System (EU vision, that is evolving from 1.0 to 2.0).

The full consistency of this BA model with the current version 5.0 of the GSBPM ensures a good level of compatibility also with other international standards for official statistics, including the Generic Statistical Information Model (GSIM) (UNECE – HLG-BAS 2012).

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3. Business Architecture definition and relations within the Enterprise Architecture framework

The definition of Business Architecture should be included in the general framework of Enterprise Architecture. EA is the process of translating business vision and strategy into effective enterprise change by creating, communicating and improving the key requirements, principles and models that describe the enterprise’s future state and enable its evolution (Gartner 2012). Therefore EA can be defined as the reference model by which an organisation operates and is structured to achieve its objectives, whereby each lower layer is governed by a higher one by means of a requirement chain and of modelling processes. In this way, it is possible to move from a conceptual representation to increasingly operational and technological levels (Burke and Tuft 2012). In other words, Enterprise Architecture is about understanding all the different elements that make up the enterprise and how those elements interrelate. It is an approach to enabling the vision and strategy of an organisation, by providing a clear, cohesive, and achievable picture of what is required to achieve this target (Statistical Network BA Team Project 2013). As agreed within the Statistical Network and shared among CSPA, EA can be split in four different layers (see Figure 1), with the following definitions:

1. Business Architecture (BA) that covers all the activities undertaken by an NSI, including those carried out to conceptualise, design, build and maintain information and application assets used in the production of statistical outputs. BA drives Information, Application and Technology Architectures for an NSI.

2. Information Architecture (IA) that classifies the information and knowledge assets gathered, produced and used within the BA. It also describes the information standards and frameworks that underpin statistical information (e.g. GSIM, DDI, SDMX). IA facilitates discoverability and accessibility of available data and metadata, leading to greater reuse and sharing.

3. Applications Architecture (AA) that classifies and hosts the individual applications describing their deployment, interactions and relationships with the business processes of the organisation (e.g. estimation, editing and seasonal adjustment, etc.). AA facilitates discoverability and accessibility of available systems and tools, leading to greater reuse and sharing.

4. Technology Architecture (TA) that describes the IT infrastructures required to support the deployment of applications and IT services, including hardware, middleware, networks, platforms, etc..

As shown in Figure 2, these layers are each other interconnected by definitions, classifications, and concepts; data, metadata and paradata; functionalities, applications, services, interfaces and infrastructures. For each interaction a detailed description is provided.

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Figure 1 – The four layers composing Enterprise Architecture

Source: Statistical Network BA Project Team (shared also with CSPA), 2013. Figure 2 – Interactions between the Enterprise Architecture layers

Source: Statistical Network BA Project Team (shared also with CSPA), 2013.

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As a consequence, it clearly emerges that BA can be considered the strategic part of EA and is called to play a central role in a programme as complex as that of industrialisation and standardisation of statistical information production. One of the most relevant obstacles to the success of such an ambitious modernisation process lies in the fact that Statistical Institutes/Organisations presently tend to be characterised by multiple organisational models (concerning financial, technological, regulatory sectors, etc.). The use of different vocabularies and terminologies sometimes can lead to conflicting descriptions of the same entities. Production processes in most cases are also organised following the logic of non-integrated stovepipe models. This lack of homogeneity of language and organisational approaches makes the dynamics of change expensive and complex and draws the attention to the importance of achieving a unity of views, so that each part of the Organisations may undertake a work of innovation consistent with the objectives that should be achieved. It is therefore necessary that Institutes adopt a common language in order to enable all their components both to conceptualise the given situation (“as is”) and the one to be reached at the end of the evolution process (“to be”). The description of the two states (present and future) also allows designing a path towards possible changes in a more rational and measurable way, defining specific actions involving different skills that need to interact within a shared view of a tangible progress. In such a complex situation, indeed, language and intent consistency can merely be achieved starting from the conceptual level that represents the only common point of the various specific languages. This is the real reason for a hierarchical logic that moves from the conceptual to the operational level (involving a specific technical knowledge), also taking into account the adoption of a Service Oriented Architecture (SOA) approach6.

6 The Service Oriented Architecture (SOA) is based on structured sets of modules, known as services, that collectively provide the functions required by complex applications. Each service is designed to provide a compactly defined group of functions. This makes it possible to reuse an application in different ways by changing only the way an individual service interoperates with the other services that compose the application, instead of making code changes to the service itself (Bieberstein et al. 2005).

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4. Identification of homogeneous BA business lines with their related activities and products

The main step in the definition of a BA model is to identify homogeneous areas with respect to the aim of the activities carried out and the nature of the information processed and/or services that insist on this information. Using a language typical of this organisational approach (Giachetti 2010), these areas are called business lines and are defined in order to guarantee independence from the Institutes current organisational structure, so as to ensure their stability with regard to any future institutional processes of reorganisation. Presently, statistical Institutes often tend to replicate internally the same organisational model, instead of referring to a unique one at the enterprise level. This is a kind of stovepipe model characterised by strong heterogeneity (of procedural, methodological and technological approaches), lack of standards and redundancy of data and applications. On the contrary, in order to achieve the target of harmonisation, it is advisable for the involved organisations to: define their strategic objectives and plan the activities that allow to achieve them (Strategy); support functions that develop work programmes (Corporate support); design the processes corresponding to the planned activities (Design); organise the designed processes taking into account the operational constraints

(Management); implement the processes ensuring efficiency and quality (Implementation); provide capabilities to undertake all the above activities (Capability).

Consequently, six different business lines can be defined (Strategy, Corporate support, Design, Management, Implementation, Capability): each of them supplies the necessary products for carrying out the statistical processes. Each business line is characterised by specific activities that are fully aligned with GSBPM 5.0 phases and sub-processes. Each activity realises one aggregate service that transforms the input information into the planned output. In more detail, Strategy consists of high level strategic, externally focused, functions and activities. Its products include: agreements with other bodies/agencies and communication initiatives, policy and strategic plans, budget planning, portfolio of projects definition and reports from the management line. Activities carried out in this line correspond to GSBPM over-arching processes (OPs) and are: S1. Maintenance and consolidation of strategic relations (GSBPM OP Legal framework); S2. Strategic planning (GSBPM OPs Strategic planning and Human Resource management); S3. Policy definition (GSBPM OP Quality management); S4. Project portfolio management and budget definition (GSBPM OPs Strategic planning,

Statistical program management, Project management and Financial management). Corporate Support provides cross-cutting support functions and its products consist of: risk management reports, measurements of performance, audits and quality controls, regulations. Activities achieved in this line correspond to GSBPM over-arching processes (Ops) and are: CS1. Financial management (GSBPM OP Financial management); CS2. Internal and external data source management (GSBPM OP Provider management); CS3. Legal framework management (GSBPM OP Legal framework management); CS4. Human Resource management (GSBPM OP Human Resource management); CS5. Administrative management (GSBPM OPs Organisational framework management,

Legal framework management, Financial management).

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Design produces the meta-information essential for the functional organisation and for the statistical process control. Its products comprise: technical designs, action patterns, instructions, process indicators and their description. These are the activities: D1. Determine needs for statistical information (corresponding to GSBPM OP Statistical

program management and sub-processes 1.1, 1.2, 1.3, 1.4 and 1.6); D2. (Re)Design statistical outputs (GSBPM OP Metadata management and sub-process 2.1); D3. Check data availability/(Re)Design data sources (GSBPM OPs Provider management,

Metadata management and Quality management, and sub-process 1.5); D4. (Re)Design production system and rules (GSBPM OPs Knowledge management, Statistical

framework management, Metadata management and Quality management; GSBPM sub-processes 2.6 and from 2.3 to 2.5).

Management utilises the control information in real time. Its products embrace: scheduling of activities, description of results, state implementation, reports on the achieved quality and plans for the improvement and adjustment of the procedures carried out. Management-related activities are: M1. Planning (aligned to GSBPM OPs Metadata management and Quality management and

sub-process 3.4); M2. Monitoring (GSBPM OPs Metadata management and Quality management and sub-

processes 8.1 and 8.2); M3. Adjustment (GSBPM OPs Metadata management and Quality management and sub-

process 8.3). Implementation realises the value chain from the initial sources to the statistical information. Its products include: data and the metadata that describe them, as well as the applications developed to be used in the process. Implementation-related activities are: I1. Tool and application reuse/development and release for the production (corresponding to

GSBPM OP Statistical framework management and sub-processes 3.1, 3.2, 3.3); I2. Collect: preparatory stage (sample selection; set up collection) (corresponding to GSBPM

OP Metadata management and sub-processes 4.1 and 4.2); I3. Collect: run and finalise data collection, administrative source acquisition,

standardisations (GSBPM OPs Metadata management and Quality management and sub-processes 4.3 and 4.4);

I4. Process: integration, classification and coding, editing, imputation, new variables and statistical units derivation (GSBPM OPs Metadata management and Quality management and sub-processes from 5.1 to 5.5);

I5. Process: calculate weights and aggregates (GSBPM OPs Metadata management and Quality management and sub-processes 5.6 and 5.7);

I6. Analyse: validate, apply disclosure control and finalise outputs (GSBPM OPs Metadata management and Quality management and sub-processes from 6.1 to 6.5, 8.1, 8.2);

I7. Disseminate: produce and release dissemination products GSBPM OPs Customer management, Metadata management and Quality management, and sub-processes from 7.1 to 7.5.

Capability deals with development and management of the required capabilities (related to methods, procedures, standards and frameworks, IT systems and people skills) underpinning the statistical production process. Its products are represented by the Repositories of Human resource Competencies (RHC), of Data and Metadata (RDM), of standard Methods and Guidelines (RMG), of Tools and Applications (RTA). Activities performed in this line are: C1. Development and management of Human Resource competencies (aligned to GSBPM OP

Human Resource management);

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C2. Storage of data and metadata (corresponding to GSBPM OPs Data management, Metadata management, Quality management, the whole phases 1, 2 and 5 and sub-processes 4.3, 4.4 and 6.5);

C3. Management of the process for the development and evolution of standard methods and guidelines (aligned to GSBPM OP Statistical framework management);

C4. Management of the process for the development and evolution of IT tools and applications (aligned to GSBPM OP Statistical framework management and sub-processes from 3.1 to 3.3).

All the correspondences between the BA business line activities described above and the phases of the GSBPM 5.0 are illustrated in Figure 3.

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Figure 3 – Correspondences between BA business line activities and GSBPM 5.0

General over-arching processes

1. Specify needs

2. Design

3. Build

4. Collect

5. Process

6. Analyse

7. Disseminate

8. Evaluate

S1. Maintenance and consolidation of strategic relations Legal framework Management

S2. Strategic PlanningStrategic PlanningHuman Resource

Management

S3. Policy definition Quality Management

S4. Project portfolio management and budget definition

Strategic PlanningStatistical Program

Management Project Management

Financial Management

CS1. Financial management Financial Management

CS2. Internal and external data source management Provider Management

CS3. Legal framework managementLegal framework

Management

CS4. Human Resource management Human Resource Management

CS5. Administrative management

Organisational framework Management

Legal framework Management

Financial Management

D1. Determine needs for statistical informationStatistical Program

Management Customer Management

1.1; 1.2; 1.3; 1.4;

1.6

D2. (Re)Design statistical outputs Metadata Management 2.1

D3. Check data availability/(Re)Design data sources Provider ManagementMetadata ManagementQuality Management

1.5

D4. (Re)Design production system and rules

Knowledge Management Statistical Framework

Management Metadata Management Quality Management

2.3; 2.4; 2.5; 2.6

M1. Planning Metadata ManagementQuality Management

3.4

M2. Monitoring Metadata ManagementQuality Management

8.1; 8.2

M3. Adjustment Metadata ManagementQuality Management 8.3

I1. Tool and application reuse/development and release forthe production

Statistical Framework Management

3.1; 3.2; 3.3

I2. Collect: preparatory stage (sample selection; set upcollection)

Metadata Management 4.1; 4.2

I3. Collect: run and finalise data collection, administrativesource acquisition, standardisations

Metadata ManagementQuality Management

4.3; 4.4

I4. Process: integration, classification and coding, editing,imputation, derivation of new variables and statistical units

Metadata ManagementQuality Management

5.1; 5.2; 5.3; 5.4;

5.5

I5. Process: calculate weights and aggregates Metadata ManagementQuality Management

5.6; 5.7

I6. Analyse: validate, apply disclosure control and finaliseoutputs

Metadata Management Quality Management

6.1; 6.2; 6.3; 6.4;

6.58.1; 8.2

I7. Disseminate: produce and release dissemination products Customer ManagementMetadata ManagementQuality Management

7.1; 7.2; 7.3; 7.4; 7.5

C1. Development and management of Human Resourcecompetencies(Repository of Human Resource Competencies - RHC)

Human Resource Management

C2. Storage of data and metadata(Repository of Data and Metadata - RDM)

Data ManagementMetadata ManagementQuality Management

1. 2. 4.3; 4.4 5. 6.5

C3. Management of the process for the development andevolution of standard methods and guidelines(Repository of standard Methods and Guidelines - RMG)

Statistical framework Management

C4. Management of the process for the development andevolution of IT tools and applications(Repository of Tools and Applications - RTA)

Statistical framework Management

3.1; 3.2; 3.3

STA

TIS

TIC

AL

PR

OD

UC

TIO

N

Capability

Implementation

Activities

Generic Statistical Business Process Model (GSBPM version 5.0)

Management

Design

Business Lines

Strategy

Corporate Support

Source: Istat; ESSNet on Standardisation, 2014.

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5. Business Architecture model A statistical process represents a logically ordered chain of activities that can also be considered as a value chain, as each step increases the value of the statistical product. Business line activities and products are the main elements of a statistical process/value chain. At a high level of abstraction, activities are processes that can be distinguished in a statistical value chain, while products represent objects. If processes are ordered in a logical way, it becomes possible to represent a process chain, which is iterative and starts and concludes with stakeholders and common users. In Figure 4, the overall scheme is divided into layers identified by vertical rectangles indicating the different business lines. The three business lines corresponding to Design, Management and Implementation are those more directly related to the Statistical production process. Inside each layer, rhombuses are specific activities or business services (processes); the labels of each activity are indicated inside little white ovals, and refer to the labels of the activities described previously (see Paragraph 4 and Figure 3). Cylinders symbolise information products (data and metadata) or applications; the grey ones within the Capability line are common shared repositories. Finally, large ovals indicate actors and external entities. As illustrated in Figure 4 through all the interactions, the whole process is dynamic by nature and is realised through the iterative repetition of chain parts, which also depend on the possibility of reusing processes and/or information products. Changes to a node in the step sequence can impact on all subsequent ones but should have no direct impact on the previous stages. Figure 4 – Business Architecture model

Source: Istat; ESSNet on Standardisation, 2014.

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As explained before, the whole process starts with the analysis of the information needs expressed by stakeholders and common users. This generates a real business case that has to be evaluated by the Institute governance board (Strategy), in order to decide whether to include the related plan into the current portfolio of projects facilitated by the Corporate support. If the decision is positive, then the Design steps can be performed: firstly, the expressed needs are the basis for the design of the outputs required to meet them. Secondly, by considering these outputs, a recognition of the availability of the data sources necessary to produce them is carried out. In particular, great emphasis should be set on the reuse of existing data (especially from administrative and big data sources). Finally, the design of the production system can be executed, by primarily taking into account the available Repository of standard Methods and Guidelines (RMG), in order to define the process rules and the expected quality levels: this generates a set of metadata that will be considered in the management and implementation lines and will be stored into the Repository of Data and Metadata (RDM). Management steps define all operational and practical aspects of the production process (workflow definition). Alongside these steps, quality and efficiency constraints are set, thus originating additional metadata (also to be stored in the RDM) that will affect the implementation of the process. In this business line, quality is controlled step by step and aligned with that defined in Design. Implementation steps are those that realise the overall designed production process. Each step takes data and metadata as inputs and produces data and metadata as outputs. Each input is taken from the Capability line, through the RDM (with the exception of the data collection step), and each output is stored in the RDM (with the exception of the data dissemination step). In order to minimise costs and maximise quality, in this line the Repository of Tools and Applications (RTA), containing the certified tools and applications that implement standard methods, is used. To summarise, the described overall value chain can be divided in two main sub-chains: the first is given by Strategy, Corporate support and Design activities, the second by Management and Implementation activities. Once the production process is well underway, in the subsequent iterations of the same process the set of Management and Implementation activities may be fully automated and repeated on a regular basis for the release of specific statistical products. Capability line increases the general value of institutional assets and skills. The evaluation of the process is structurally included in all implemented steps in order to continuously improve efficiency together with product quality.

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6. Description of fundamental infrastructures and guiding principles In the described BA model, a central role is played by both infrastructures (elements enabling standardisation and industrialisation) and principles (general rules to be followed to ensure optimality).

6.1 Infrastructures As illustrated in Figures 4, the most important infrastructures needed for the efficiency and efficacy of the overall process are: the Repository of Human Resource Competencies (RHC), that gathers information concerning

employee skills; the Repository of Data and Metadata (RDM), containing input data, intermediate data and

output data ready for dissemination, with defined quality standards and metadata; the Repository of standard Methods and Guidelines (RMG), that contains the set of statistical

methods, recognised as standards, to be applied to processes; the Repository of Tools and Applications (RTA), including three distinct categories of software

(generic IT tools, reusable applications and ad hoc applications). Some activities carried out within Strategy, Corporate support and Management deal with human resources and make use of RHC. The information produced at the various stages of processing should be considered a reusable asset, to be stored in the available infrastructure of RDM. The definition of methods as standard, to be shared in RMG, has to follow a precise procedure that is to be adopted by the statistical organisation, also taking into account the international framework. For example, following this approach Istat has already adopted such a procedure which is being used in order to define the first standards in the course of 2013-2014 (e.g. methods for sampling estimates calculation, selective editing and seasonal adjustment methods). In the same way, the set of production applications of the Institute, i.e. those necessary to ensure the realisation of statistical information to be disseminated to external users, should be also considered as an asset to be preserved and shared, maximising the use and reuse, through the infrastructure of RTA. In this context, shared services and standards should also be defined and implemented, with regards to Information Technology (IT) and the way to organise all the activities.

6.2 Principles The whole BA model is led by fundamental principles that become real guidelines for the implementation of each business line activity, ensuring in this way the success of the standardisation process itself. Being compliant with these principles represents a very important driver to foster and enhance standardisation, both inside each Statistical Institute and at ESS level. At the moment, the definition of these principles is still on-going within the Statistical Network BA Project Team. For this reason, the principles listed in the following Tables 1 and 2 are provided in a general short form. A more detailed description will be proposed at a subsequent stage, taking into account any possible amendments. These principles are classified in two different groups:

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Decision principles, that guide the activities of strategic planning (see Table 1); Design principles, that guide the design of production processes (see Table 2).

Table 1 - Business Architecture decision principles

DECISION PRINCIPLES 1 Capitalise on and

influence national and international developments

Collaborate nationally and internationally to leverage and influence statistical and technological developments which support the development of shared statistical services.

2 Deliver enterprise-wide benefits

Design and implement new or improved statistical business processes in a way that maximises their value at an enterprise level.

3 Increase the value of statistical assets

Add value to the organisation statistical assets (either directly or indirectly) through improved accessibility and clarity, relevance, coherence and comparability, timeliness and punctuality, accuracy and reliability, and interpretability.

4 Maintain community trust and information security

Conduct all levels of business in a manner which builds the community trust. This includes the community trust and confidence in the statistical organisation decision making and practices and in their ability to preserve the integrity, quality, security and confidentiality of information provided.

5 Maximise the use of existing data/ Minimise respondent burden

Leverage existing data from all sources (e.g. statistical surveys or administrative records) before collecting it again. Statistical organisations are to choose the source considering quality, timeliness, cost and the burden on respondents. Statistical authorities monitor the respondent burden, and aim to reduce this over time.

6 Sustain and grow the business

Focus investment and planning on long term sustainability and growth, both in terms of the organisational role and position within its own community, as well as internationally.

7 Take a holistic and integrated view

Ensure data, skills, knowledge, methods, processes, standards, frameworks, systems and other resources are consistent, reusable and interoperable across multiple business lines within a statistical organisation.

Source: Statistical Network BA Project Team (shared also with CSPA), 2013. Considering both decision and design principles, some general suggestions emerge clearly and it is possible to highlight some key messages. To begin with, the whole statistical process is output and metadata-driven: the statistical process chain starts from the output desired (from required products) and goes backwards, defining the various aspects of the process. Metadata resulting from the design are the reference for the implementation of the production process. As a requirement, metadata have to be generally accessible and, as far as possible, standardised with regard to the types of units, the definition of concepts, classifications, quality characteristics, process, etc.. Another strategic indication regards the concept of re-use that is related to data, metadata, methods, tools and applications. It focuses on both what is produced within each organisation and what is issued outside, with particular attention to the standards defined at European and international level. It is referred to existing and available data, whose use is generally to be preferred over the decision to conduct a new survey: data should be collected only once.

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Re-use is fostered by the adoption of modular services that can be shared in different contexts and statistical areas (Service Oriented Architecture). Developments from scratch should be considered real exceptions. Table 2 - Business Architecture design principles

DESIGN PRINCIPLES 1 Consider all capability

elements Consider all capability elements (e.g. methods, standards, processes, skills, and IT) to ensure the end result is well-integrated, measurable, and operationally effective.

2 Re-use existing before designing new

Re-use and leverage existing data, metadata, products and capability elements wherever possible before designing new.

3 Design new for re-use and easy assembly

Design, harmonise and standardise all new data, metadata, products and capability elements for re-use, so they can be easily assembled and modified to accommodate changing user demands.

4 Processes are metadata driven

Ensure that design, composition, operation and management of business processes, including all input and output interactions, are driven via standard metadata and automated wherever possible.

5 Adopt available standards

Adopt open, industry recognised, and international standards where relevant and available. Statistical industry standards such as the Generic Statistics Business Process Model (GSBPM) and the Generic Statistical Information Model (GSIM) are examples. 6 Designs are output

driven Ensure the whole statistical process is output-driven. Output is the reference starting point; the statistical production process starts from the output desired, that is from required products, and goes backwards, defining the various aspects of the process.

7 Enable discoverability and accessibility

Ensure data, metadata, products and capability elements are discoverable and accessible to achieve the benefits from sharing and reuse.

8 Ensure continuous quality improvement

Evaluate and document quality at all stages of the statistical production process. This is to be defined and planned during design or redesign, monitored and assessed in each phase of GSBPM and in connection with intermediate and final data releases.

9 Foster industrialisation of statistical processes

Ensure the independence between design and implementation, thus promoting the industrialisation of statistical processes. The assumption of an industrialised process is that it can be realised by agents other than those who have designed it.

Source: Statistical Network BA Project Team (shared also with CSPA), 2013. An additional guidance is the one ensuring the independence between Design and Implementation, so as to guarantee the optimality of Design and to foster industrialisation of statistical processes. In this context, Design is performed only when needed, while a current statistical process is carried out on a regular basis, and Implementation of a new project involving several innovations requires a new Design phase. Finally, quality has to be evaluated and documented at the different stages of the statistical production process. It is defined and planned during Design or (Re)Design. It is monitored and assessed in each phase of GSBPM and in correspondence of intermediate and final data releases.

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7. Future developments The BA model illustrated above represents a real modernisation instrument, overcoming the limits of the “as is” situation within Statistical Institutes/Organisations: the existing information/organisational model is often very complex; most of the statistical processes are still organised as stovepipes, i.e. vertically integrated in

silos, only partially communicating with other processes; this emphasises heterogeneity of procedural and methodological approaches, which is not always justified by the variety of the covered topics;

there is a lack of standards regarding processes, methods and technologies; there is a great redundancy of data and applications; it is often necessary to provide ad hoc developments for the harmonisation of applications in

different environments. For this reason, in this document the renewed model of Business Architecture offers an integrated view of the statistical production process that can facilitate the realisation of congruent innovation paths. This permits the achievement of an adequate organisational flexibility, based on the independence between Design and Implementation, that is to be guaranteed since it is related to the fact that a process can be realised by different agents from those who designed it and also in order to make available skilled resources and to allocate them to innovations. These resources, indeed, in a non-harmonised situation, can run the risk of being involved in a suboptimal way, e.g. in the redesign of existing tools or in the mere repetition of current and structured processes. This becomes crucial especially in the present economic context: the acquisition of additional resources is not likely to be achieved and consequently innovations can be introduced only through efficiency improvements. In the medium term, the BA model can be made fully operational through a road map properly designed and scheduled, focusing particularly on the implementation of some of the basic infrastructures provided, both in terms of procedures (such as the management of the portfolio, the compliance assessment of the several statistical production processes with the BA and EA principles, with their subsequent validation, etc.) and in terms of shared services (the Repository of Human Resource Competencies, the Repository of Data and Metadata, the Repository of standard Methods and Guidelines, the Repository of Tools and Applications). In this road map planning phase, a particular attention is to be focused on: the governance, to provide the strategic directions, to coordinate, to validate and to monitor

all the necessary activities, also involving some institutional reference Committees (e.g., “Methodology”, “Quality”, “Innovation and Research”);

the organisational structure, for the different operational phases and to advise the governance arrangements for the basic infrastructure implementation;

the communication process, to share BA principles within Organisations and to disseminate their knowledge.

As illustrated in this deliverable, the Business Architecture can provide an important support to an integrated and well-organised implementation of all the useful necessary innovations, since it is to be intended as a common language, sharable and adoptable by NSIs. For this reason, it represents the foundation, fostering and intensifying the creation of a Business Architecture at ESS Level. This involves necessarily an active participation and the sharing of broader initiatives aiming at increasing integration at national, European and international level, so that this conceptual model may become a best practice to be widely disseminated.

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References Baldacci, E. (2013). Innovate or perish – Italy’s Stat2015 modernisation programme. 59th ISI-World

Statistics Congress, Hong Kong, 25-30 August. http://www.statistics.gov.hk/wsc/IPS085-P3-S.pdf.

Barcaroli, G., Falorsi, P.D., Fasano, A., Mignolli, N. (2013). Business Architecture Principles to Foster Industrialisation and Standardisation at the Italian National Institute of Statistics. 59th ISI-World Statistics Congress, Hong Kong, 25-30 August. http://www.statistics.gov.hk/wsc/STS048-P4-S.pdf.

Barcaroli, G., Falorsi, P.D., Fasano, A., Mignolli, N. (2013). A Business Architecture framework for industrialisation and standardisation in a modern National Statistical Institute. NTTS, Bruxelles, 5-7 March. http://www.cros-portal.eu/sites/default/files//NTTS2013fullPaper_218.pdf.

Bieberstein, N., Bose, S., Walker, L., Lynch, A. (2005). Impact of service-oriented architecture on enterprise systems, organizational structures, and individuals. IBM Systems Journal, Vol. 44, Number 4.

Bredero, R., Dekker, W., Huigen, R. and Renssen, R. (2009). Statistics Netherlands Architecture; Context of change. Discussion paper (09017), CBS Statistics Netherlands, The Hague Heerlen.

Burke, B., Tuft, B. (2012). Federated Architecture Diagnostic: Understanding the Four Levels of Enterprise Architecture Consolidation, Gartner. http://www.gartner.com/id=2053915.

Commission of the European Communities (2009). Communication from the Commission to the European Parliament and the Council on the Production method of EU statistics: a vision for the next decade.

Eltinge, J. L., Biemer, P. P. and Holmberg, A. (2013). A Potential Framework for Integration of Architecture and Methodology to Improve Statistical Production Systems. Journal of Official Statistics, Vol. 29, No. 1, pp. 125–145.

Eurostat, Essnet (2010). Common reference architecture - Cora. Questionnaire. WP2 Requirements collection & State of the art.

Eurostat, Sponsorship on Standardisation (2012). Progress report, Theme 6.10, 15th Meeting of the European Statistical System Committee, Luxembourg, 15th November.

Gartner (2012). IT Glossary. Defining the It Industry, http://www.gartner.com/it-glossary/enterprise-architecture-ea/.

Giachetti, R.E. (2010), Design of Enterprise Systems, Theory, Architecture, and Methods. Boca Raton, FL (USA): CRC Press.

Statistical Network Business Architecture Project, 2013. http://www1.unece.org/stat/platform/display/statnet/Business+Architecture+Project.

Statistics Netherland (2012). Strategy to implement the vision of the High-level Group for Strategic Developments in Business Architecture in Statistics, Conference of European Statisticians, Sixtieth plenary session, Paris, 6-8 June, Item 8 of the provisional agenda.

Struijs, P., Camstra, A., Renssen, R. and Braaksma, B. (2013). Redesign of Statistics Production within an Architectural Framework: The Dutch Experience. Journal of Official Statistics, Vol. 29, No. 1, pp. 49–71.UNECE (2011). Some ideas on standardisation by layers in official statistics, http://www1.unece.org/stat/platform/display/hlgbas/Some+ideas+on+standardisation+by+layers+in+official+statistics.

UNECE/Eurostat/OECD (2013). Generic Statistical Business Process Model (GSBPM version 5.0), http://www1.unece.org/stat/platform/display/GSBPM/Generic+Statistical+Business+Process+Model.

UNECE, High-Level Group for Strategic Developments in Business Architecture in Statistics (HLG-BAS) (2012). Generic Statistical Information Model (GSIM), http://www1.unece.org/stat/platform/display/metis/Generic+Statistical+Information+Model+(GSIM).

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van’t Wout, J., Waage, M., Hartman, H., Stahlecker, M., Hofman A. (2010). The Integrated Architecture Framework Explained. Why, What, How. Berlin: Springer-Verlag.