Innovation and innovation management in statistics

Thinking and keeping ahead of new data demand remains a core impetus for innovation in statistics  . This foresight makes innovation in statistics as old a subject as any other persistent concern in the national statistical system (NSS) like capacity development. The data revolution   provides a new opportunity for innovation in statistics that NSSs will have to embrace more and fast.

Innovation is not an aim in itself, but a tool to overcome challenges the NSSs are facing. The list of reasons to innovate is long but can be narrowed down to the most essential and compelling ones as follows:

  • Increasing and varied data demand (e.g. SDGs, granular, speedy/quick);
  • Persistent/perennial data (quality) gaps;
  • Rising costs of traditional data collection in relation to limited resources;
  • Mobility and other access impediments in data collection (e.g. topography, hazards, conflict, disasters, including pandemics); and
  • Outdated approaches and methods.

The availability of enabling factors such as technology (e.g. mobile phone, cloud computing, social media platforms, and data science etc.) and the potential benefits (e.g. smart statistics  , speed, granularity and accuracy of data, the efficiency of statistical business processes, long-term cost reduction, etc.) help strengthen the need to innovate.

Innovation in statistics covers innovation in data and the statistical business process, new technology use, and innovative statistical policies. More specifically, innovation pertains to the use of new or non-traditional data sources and methods, improvements in operational processes as per the Generic Statistical Business Process Model  , development and use of new technological products and applications, and new policies to manage and coordinate the NSS. 

NSSs will need to revisit policies and strategies, including the NSDS and explore how they can build a conducive environment for innovation. Some useful considerations may include the following: Are there resources in the NSO and other data producers to support creative thinking and to turn ideas into innovative products and services? Are there capacities or relevant knowledge and skills? How will NSOs manage the impact of innovation (e.g. change in management) in the NSS?

Innovation must be at the core of the modern NSS   and modern NSDS  . Some concrete actions could be:

  • Include innovation in the assessment phase of the NSDS
    • Identify data ecosystem stakeholders of innovation (potential partners). Step 1.1 | Step 3.3
    • Assess current statistical output in relation to smart statistics and the data ecosystem. Step 3.2
      • Identify key or priority statistical outputs that will benefit the most from innovation.
    • Assess the NSS in terms of:
      • readiness (institutional resources and capacities/competencies and skills), and 
      • openness (legal framework, policies, standards, systems, and technologies) to innovation. Step 3.1
        • Prioritise the NSO and data producers in priority sectors such as education, health, agriculture, macroeconomy and finance, labour and employment, prices, income, and poverty, environment, among others, in the organisational assessment.
  • Identify appropriate strategic goals and critical outputs on innovation in key or priority sectors or subject-matter areas. Step 4.2
  • Identify concrete actions that develop or apply innovation to address statistical issues in key or priority sectors or subject-matter areas, and the corresponding costs, key risk factors and mitigating measures at the national, sectoral/subject-matter, and agency levels. Step 5.1 | Step 5.2 | Step 5.3  
    • Actions may include, among others, the:
      • creation of an innovation unit or expansion of the scope of existing research and development or methodological unit to include innovation;
      • formation of an inter-agency working group on innovation in statistics to formulate a strategy, plan, policy document, standards, etc., and sharing of existing or proposed innovation initiatives;
      • innovation initiatives to improve data collection, management, analysis, and dissemination;
      • capacity development programs on innovation application in statistics; and
      • measures to manage the impact of innovation (e.g. change management).
  • Establish institutional partnership mechanisms (policies, standards, and arrangements) between NSS and data ecosystem stakeholders, initially in priority sectors and subject-matter areas. Step 6.3
    • Consider partnerships with selected stakeholders (e.g. Big Data sources, data science firms, academic and research institutions, private and civil society organisations) on developing and sharing innovations. 
  • Monitor and evaluate milestones and outcomes of innovation initiatives. Step 6.4 | Step 7.2 | Step 7.3
    • Identify learnings and areas for improvement.