Lessons learned from using a high-level, visualised Target Operating Model

(TOM)MODERNISATION PROJECT, STATISTICS NORWAY

AgendaStatistics Norway’s TARGET PICTURE explained

How we use this target picture

Lessons learned

The target picture shows SSBs future statistical production (2022)• The target picture is a conceptual model for statistical production in the future (2022)

• It contributes to implementation of the modernisation strategy, and shows how this supports other strategic targets

• It supports communication, prioritisation and decision making

◦ Provides a common platform for communication

◦ Aids understanding and focus on what we intend to change, how we will change and the organisational consequences of the change

◦ Contributes to distance discussions and decisions away from specific projects, technologies and implementations

3

SSB prepares models on different levels for different purposes• Conceptual models (what)

• Logical models, breaking down and detailing the conceptual model (how)

• Physical models (solutions)

• Models and standards are built upon reference models and use international standards

within the statistical domain:

◦ Generic Statistical Information Model (GSIM) for the information model

◦ Generic Statistical Business Process Model (GSBPM) for business processes

◦ Generic Statistical Data Editing Model (GSDEM) for processing data

◦ Common Statistical Production Architecture (CSPA) for services and data-/services flow

◦ The aim is to contribute to more internal and external cooperation, shared development

and reuse of components and services4

AS IS

PRODUCT ORIENTEDSILOES

TO BE

Business change

Three organisational impact issues1. Change of roles

2. Build new capabilities

3. Move capacity

Topic 1

Change of roles

20222017

Change of role: Data collectorsdesign and develop common data

collection and takes on more responsibilities to make data

available

Topic 1

Change of roles

20222017Change of role: Statisticians

design, build and change newstatistical products.

IT, Statistical Methods and SubjectMatter experts improves Statistical

Production Processes, services and tools

Topic 3

Build new capabilities

20222017Capability building: New Data Management

as a shared resource. Information and Process Design to guide

effective production and high- qualityproducts

Topic 2

Move capacity

20222017Moving capacity: Statisticians spend less time on Process and more time on Analyse. Major change in competence is needed,

and new methods and technology is threatening people and jobs

Lessons learned oncommunication1. The need and complexity of communicating an ambitious and

holistic business change is easily underestimated

2. To communicate a new target operating model must be part of the

common leadership knowledge and responsibilities

3. If the objective and rationale of business change are not

understood and widely accepted, the risk of failure is too high

4. Specific examples are needed to gain understanding – the proof is

in the pudding

Other key learning points• Statistics Norway has a long experience in changing and improving statisical

products, but must improve its capability of changing statistical production

• Statistics Norway did not discuss enough the impact and consequences of the

aimed business change. The TOM is complex.

• We failed to point out a clear responsibility for handling business change at

corporate level, taking lead on how to involve the business

• To be able of funding changes, we started to reorganise the business in order to

take out effeciency gains up front, without linking the changes sufficiently to the

TOM

Annex: More detaileddescription

of the ModernisationProgram

Data sourcesThe current shared solution for data collection is limited to Altinn Questionnaire services. Other data collections are handled in a decentralised way, especially for register data, and we have our own solution for interview data.

We will establish a common platform to receive data from different sources in different formats.

The aim is to reuse shared, public services and standardise technology, infrastructure and management of data from different sources irrespective of the type and format.

• We coordinate collection of data from questionnaires via Altinn with data from our own questionnaires/interviews

• We are preparing to use of Altinn’s distribution service to gather register data and larger volumes of file extractions/transaction data from businesses and the public sector

• We shall prepare to collect data from new types of data sources and specifically use technology designed to exploit the potential of these sources («Internet of things», «big data», social media, «roaming data», web-shopping, etc.)

Channels in…• Data from different sources are managed in an integration platform with services designed

to prepare source data for the shared data collection, offer services to manage external APIs

e.g. Amadeus and dialogue services for providers in the data collection process.

• The aim is to reduce the cost of using different channels and ensure that we have control

over deliveries and dialogue with data providers, e.g. to manage, notify and trace.

• Changes are managed one place

Processing of data through statistical production• Processing of data occurs in a standardised and efficient production from raw data, through

the shared data collection for transformation to a common internal format, control and processing of data, quality management and dissemination

• Statistical products and production are adjusted and documented according to described principles, procedures and routines

◦ Compliance and traceability

◦ Control over processes, data and their steady states

◦ Documentation integrated in production through the use of shared metadata, methods, solutions and services

Processing of data through statistical production

A shared data collection service to transform raw data to in-data • Control against the agreed

format – are the data in the expected form

• Transform the data to SSBs format

• Supplement metadata received with additional metadata

• Store data as in-data in new data model

• Acknowledge receipt• Automatic process, manual only

for specific cases • Controlled and administrated in

the phase 4 Collect workbench

A set of methods and services configured to prepare data for analysis • Statistical methods and

functions from shared library• Metadata automatically added• Data with rich metadata enable

and raise the quality̵ Information about data and

variables is easily available̵ Easy to locate data̵ Basis for automatisation

• Prepares for more cross-cutting and common processing of data for more purposes and statistics

• Automatically archived as defined in new data model

A set of shared methods and tools to analyse prepared data and make statistical data• Visualisation and graphical

presentation including maps• Integrated with text processing,

spreadsheet and presentation tools

• Integrated data of a known quality, prepared so that the best suited analysis tool can be used

• Statistical data is stored as defined in new data model

• Automatic archiving

Consolidated dissemination and multichannel integration platform disseminates statistics and statistical data in several formats• PX-web, mobile devices• API’s• RAIRD• Purpose made products can be

made available for different user groups

• Prepared products and data are archived automatically (statistics, narratives, statistical data, reports, …)

• Dissemination and publishing are controlled and administrated in phase 7 Disseminate workbench

Shared data collection Process Analyse Disseminate

• Circular process and direct access to processing during the entire production cycle. The process and analyse phases can be carried out iteratively until pre-defined quality targets are reached

• Process and analyse phases are controlled and administrated in their shared workbench

Shared workbench for control of continuous production- A self-service view on statistical production and analysis• Production is administrated and steered in a role-based workbench

• Based on a shared concept system, tool set and data access

• Individual adjustment of tools and methods for a specific statistic

◦ Supplement metadata

◦ Self-service methods library, editing to pre-defined quality targets

◦ Use shared analysis tools (visualisation, GIS etc.)

◦ Loading and transferring data , dashboard for collection and other control information

• Metadata is used to support and steer production

◦ Connect agents (those that make use of and carry out activities in statistical production)

◦ With products, processes and services (that can be configured and used to control statistical production and make it more efficient)

Specify needs, design and assemble new statistics- A self-service design and assemble new statistics• New statistics are designed and assembled in a self-service workbench

• Based on a shared concept system, access to data and shared components (methods, solutions, functionality and services)

• Self-service design of new statistics

◦ Define your own in-data

◦ Choose methods and solutions to be used from the common library

◦ Shared business functions (sample administration, delivery to Eurostat.)

• Methods, solutions and business functions are assembled and configured based on category and type of statistics

◦ Data source

◦ Collection method

◦ Type of variables

◦ Type of processing (controls, imputation, harmonisation etc)

• This means adjusting the workbench to the needs of the individual

Innovation and development• There will be a continuous need to develop new and improve existing methods, solutions, business functionality and

services.

• If a need arises in a new statistic that cannot be solved using existing components in the library, then a new

component will need to be developed.

• Innovation and development occur broadly through a cooperation between those responsible for the

◦ Business area

◦ Methodology

◦ Architecture

◦ Information management

◦ Quality and security

• New solutions and tools are developed or procured and made available as new components in the shared library for

use in statistical production

External cooperation, strategic leadership and coordinationCooperation occurs not only within SSB. We also cooperate nationally and internationally.

• The national aim is to coordinate and cooperate on definitions, shared use of data, solutions and services and to

contribute to an efficient and digitalised public sector, as well as the use and development of national shared

components that are also used in SSB.

• The international aim is to contribute to multinational statistics, shared models, standards, solutions and services

• Top management in SSB, together with SSBs board practise strategic leadership and coordination based upon the

following:

◦ National and international requirements and commitments

◦ Business strategy

◦ Target picture

◦ Established principles

Dissemination – Multichannel• The aim is to meet users with statistics and statistical data where they are, when they need it and on the devices that

they wish to use

• SSB is creating a broad approach and coordinated solution for dissemination of

◦ Statistics

◦ Analyses

◦ Data and metadata

◦ International deliveries

• And end-user services such as

◦ Querying of data

◦ Access to data analysis

◦ Subscription

◦ Support for decisions

Shared data – prepared for re-useThe aim is to change the current practise of production oriented data management to management of data as a shared resource

across SSB and statistical products.

• Data according to a common and complete information model for the entire statistical production in SSB (all statistical data and

data that describe roles, agents, processes etc in production)

• Standardised and consolidated data

• Data content should be documented according to common requirements on the structure and content

• Access to and control of data should be managed centrally (metadata managed access)

• Distributed storage, not only one database.

This will increase our ability to

• Utilise new data sources and use existing data in new ways and in new contexts

• Connect more data sources29

Control, management - tidinessThe aim is to establish centralised functions and competency centres that prepare, manage

and have control over shared functions that:

• Manage access control and administrate data (to data and role based workbenches)

• Management of data and population registers

• Follow up requirements and quality of metadata

• Long term storage of archive worthy data and information in cooperation with the National

Archives of Norway

• General support to understand and use data across statistical areas and solutions

20222017

Multiple data sources, heterogeneity in

20222017

Same data and functionality many places

20222017

Parallel production lines

20222017

Customized solutions for similar products