copy the linklink copied!Chapter 6. Case Study 1. New Zealand Our Land and Water National Science Challenge
This case study provides a practical example of how digital tools can be used to improve understanding of nutrient sources and their attenuation pathways, and agriculture’s impacts on water quality outcomes and policy options for management of water quality impacts, as part of a complex national innovation initiative.
copy the linklink copied!Context: A new approach to sustainable, productive agriculture in New Zealand
New Zealand’s Our Land and Water National Science Challenge (the Challenge) is a mission-oriented,1 government-funded, research and innovation programme, which aims to “enhance primary sector production and productivity while maintaining and improving our land and water quality for future generations”.2 The Challenge, which commenced in January 2016 and is ongoing, is comprised of three Research Themes3:
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Greater Value in Global Markets
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Innovative and Resilient Land and Water Use
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Collaborative Capacity
The second Research Theme (RT) – Innovative and resilient land and water use – is the primary focus of this case study. The goal of this RT is “to help land managers to grow the profitability and yield of productive land uses within the allowable environmental limits by providing widely applicable science and tools to understand the ‘off-farm’ environmental risks associated with a specific area of land.” This goal is set within the context of New Zealand’s 2014 National Policy Statement – Freshwater Management (NPS-FM), which sets statutory requirements for freshwater bodies and requires Regional Councils to meet these objectives.4 This RT will “evaluate, model and assess land and water resources and the environmental, social, cultural and financial suitability of land use practices. [It] will look at new technologies, concepts and enterprises that enable individual and collective land and water users and regulators to best adapt to market signals, to derive optimal value chains and achieve their primary production targets within community and regulatory limits.”5 Thus, this RT will assist land managers, communities and regulators.
To achieve its goals, the RT is comprised of a number of research programmes (>NZD 1 million investment) and smaller projects (refer to Table 6.1).
The Challenge as a whole envisages a new approach to fostering a primary agriculture sector that is both productive and sustainable; captured in the idea that “having the right enterprise in the right location at the right time will deliver the right outcome for individual property owners and catchment communities”. The Challenge aims to enable New Zealand to “move from considering land use capability (generally driven by production potential and other factors such as off-site environmental impact) to land use suitability where economic, environmental, social and cultural factors are considered together” (Our Land and Water National Science Challenge, 2015, p. iv[1]).
copy the linklink copied!Use of digital technologies in the Innovative and Resilient Land and Water Use Research theme
The problems
The key goal of the Innovative and resilient land and water use RT is to move to a Land Use Suitability (LUS) framework for New Zealand agriculture. Existing efforts to manage land for (environmental) sustainability are based on land-use capability (LUC) classifications. LUC classification defined as “a systematic arrangement of different kinds of lands according to those properties that determine its capacity for long-term sustained production” (Lynn et al., 2009, p. 8[6]). Data requirements for LUC classification therefore relate to on-site physical and environmental characteristics. In contrast, the Land Use Suitability (LUS) classification which the Challenge aims to produce integrates “information about the economic, environmental, social and cultural consequences of land use choices” (McDowell et al., 2018[2]), and thus requires substantially more, and different, data than was needed previously. Thus, achievement of this Research Theme’s objective requires a number of different information gaps6 to be filled. Key gaps include:
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Information about natural processes (e.g. nutrient and other contaminant pathways), including their spatial and temporal characteristics.
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Information about how producers and other land managers respond to incentives (both policy and other incentives).
These information gaps also prevent the targeting of existing policies to take into account local contexts. For example, whereas many researchers note that nutrient or other contaminant loss factors (from agriculture and other sources) vary widely depending on location-specific factors, current implementation of New Zealand’s National Policy Statement of Freshwater Management (2014) applies uniform contaminant loss factors “to all areas of land as there are not the tools or frameworks available to link contaminant losses from different parts of a landscape to different levels of water quality impacts downstream.”7
Further, the existing research landscape is characterised by fragmented and asymmetric information: often, data sets and digital modelling tools are accessible only by the researchers who work with them directly. This leads to duplication, confusion over the role of different models and research efforts, and impedes effective translation of research efforts into change “on the ground” (McDowell et al., 2017[7]). In addition, licensing issues with some of the datasets mean data sharing between researchers could be difficult. Case study participants observed that in a collaborative setting, the researchers can settle for a common minimum data that is accessible to all, but which may not be the most up-to-date dataset.
Digital solutions
The Challenge is making use of a number of digital tools to address the information gaps and asymmetries identified above. In some cases, pre-existing tools are being repurposed to help achieve Challenge objectives; in other cases, Challenge funding is being used to enhance pre-existing tools or build new ones. These tools constitute an important part of Challenge activities, but it is important to recognise that they are being developed and used alongside other (non-digital) activities.
Table 6.2 provides a description of the main digital tools being developed or enhanced under the Innovative and Resilient Land and Water Use Research Theme, using the classification of digital technologies presented in the project main report (Table 2.1 in the main report). This table includes several tools which are being advanced through co-innovation (Box 6.1) at the same time as the Challenge tools and which support the Challenge research programmes, but which do not receive Challenge funding. This table does not provide an exhaustive list of all digital tools developed using Challenge funding, as the project is ongoing.8
A central tenet underpinning the Challenge is that its objectives will not be achieved unless Challenge participants and stakeholders work together collaboratively (Our Land and Water, 2018, p. 4[4]). Recognising that there is insufficient documented evidence of the benefits of collaboration, the Challenge includes a range of specific efforts to measure these benefits and advance understanding of how collaborative processes can be improved.1 The Challenge implements a new way of working, termed “co-innovation”, which replaces the existing “funder-provider” model. Co-innovation is defined as “individual land managers, primary production sectors, iwi,2 communities, policy makers and scientists all working collectively to identify priority issues and create enduring solutions.” (Our Land and Water National Science Challenge, 2016, p. 4[8]).
Co-innovation involves a much closer relationship with stakeholders than existing approaches. The intent is that this closer relationship will produce research that is fit-for-purpose, relevant and will be used and championed within stakeholder networks.
The Challenge defines several different dimensions (and example metrics) of co-innovation:
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Co-design: Research questions are developed with stakeholders and signed off as relevant. The Challenge maintains a record of co-designing all programmes with stakeholders.
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Co-development: This generally involves scientists physically co-locating with stakeholders and stakeholders co-investing. Across the wider research landscape we have seen an increase in the frequency of collaboration by 66% (from 1.6 institutes per research programme in 2015 to 2.6 in 2017), while Challenge-funded programmes maintain an average of 5.3 collaborations.
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Co-production: Investment in and extension of outputs into outcomes is sustained by stakeholders co-authoring Challenge documents. During the first two years of the Challenge, more than 50% of academic outputs were co-authored with stakeholders.
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Co-innovation: Outcomes are promulgated by stakeholders, for example a close working relationship with science enables a stakeholder to reach sensible water quality limits
The Challenge aims to test the hypothesis that using co-innovation in science can lead to quicker, more robust and enduring outcomes. In particular, it aims to halve the time taken for an idea to be at its maximum level of use from 16 years (Kuehne et al., 2017[10]).
However, some participants noted that because co-innovation is inclusive and deliberative, the process may in fact take longer compared to a situation where researchers develop a solution with little to no input from users and then “push” the solution to users. This raises a question about whether there is a trade-off between designing solutions which are “better” (in the sense of being more robust, enduring or fit-for-purpose) versus “quicker”, and how to measure these different dimensions in order to evaluate and compare different innovation approaches. The Challenge will also be testing this aspect of co-innovation.
← 1. See in particular work done under the third Challenge Theme—Collaborative Capacity.
← 2. Iwi is “the focal economic and political unit of the traditional Māori descent and kinship based hierarchy”.
Source: http://archive.stats.govt.nz/methods/classifications-and-standards/classification-related-stats-standards/iwi/definition.aspx, accessed August 2018.
Managing data and interaction between digital tools: a vision for a data ecosystem
The many and varied research projects under the Challenge as a whole, and within the Innovative and resilient land and water use RT specifically, are producing a “growing diversity, complexity and volume of data” (Medyckyj-Scott et al., 2016[11]). From the start of the Challenge, it was recognised by the Challenge Chief Scientist and Leadership Team that gathering this data into a shared “data ecosystem” is one of the greatest sources of potential value added for the Challenge as a whole. In 2016, a group of experts from the New Zealand public service and the research sector collaborated to produce a “white paper” on the design of this data ecosystem. The data ecosystem is explained as “a system made up of people, practices, values and technologies designed to support particular communities of practice [in which] data is valued as an enduring and managed asset with known quality” (Medyckyj-Scott et al., 2016, p. v[11]) and defined (Medyckyj-Scott et al., 2016, p. 5[11]) to encompass:
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Policies regarding data management planning, data custodianship and curation, legal frameworks, and the use of externally sourced data;
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Procedures and processes to execute those policies and manage data;
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A data governance framework and organisational structures;
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Engagement with data consumers and stakeholders; and
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Technology platforms that will support data collection, storage, description, analysis, linking, delivery and curation.
The data ecosystem is proposed to be “supported, enabled and facilitated by a federated infrastructure in which data may be collected from traditional sources and new technologies, curated, published, analysed, modelled, linked, used and reused but accessed through a single point of access, from its authoritative point of origin, with discovery and visualisation tools” (Medyckyj-Scott et al., 2016, p. 21[11]).
Efforts to date have focused on developing a standard for metadata. However, the Challenge recognises that the issue will cost more than it can afford and that the solution must endure beyond the life of the Challenge (due to end in 2024). Therefore, the Challenge has engaged with central government agencies to act as repositories for data and modelling efforts, such that outcomes can be driven from the legacy of Challenge science.
copy the linklink copied!Lessons learned
Lesson 1. Multi-dimensional integration of digital and other tools is needed to ensure efficiency and effectiveness
Interoperability9 is an important consideration when building new digital tools or enhancing existing ones, and has long been identified as a key factor for efficiency and effectiveness. However, this case study demonstrates that more is needed than interoperability to ensure efficiency and effectiveness: digital technologies need to have clear roles with definable “added value” relative to other tools and relative to policy and programme objectives. This is encapsulated in the notion of making digital tools integrated, both with other tools and with other programmes or initiatives than the one under which they are developed. Dimensions of this integration include:
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clearly articulating how a new tool complements existing tools, including by considering whether a policy or programme objective can be achieved via leveraging an existing tool (potentially with enhancements) versus building a new tool;
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acknowledging that digital technologies are only one part of a broader solution;
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acknowledging that multiple digital tools are needed to accomplish complex policy objectives (e.g. models at different timescales, digital platforms to enable different users to use the same data or model for different purposes, etc.);
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considering potential uses of technologies that are broader than the current programme or initiative, and what design features will help ensure the re-usability of digital tools (in addition to re-use of data).
Case study participants identified two institutional design features that were instrumental in assisting the Challenge to achieve this integration:
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The co-innovation approach: as outlined in Box 1.1, the Challenge uses a co-innovation approach which actively includes a diverse range of stakeholders, right from the beginning of project design and throughout projects. This enables the relevance of research questions and likely outputs to be tested ‘up front’. It also increases the ability of Challenge participants to identify what type of new tools might be needed (e.g. digital tools or other tools), whether new tools are genuinely additional to existing tools (i.e. because creators and users of existing tools are included in the design process), and how different tools relate to each other.
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The data ecosystem ‘white paper’: the question of ‘[w]hat are the best data structures for land and water information to achieve the Challenge Mission?’ was actively considered from the outset of the Challenge. This helped ensure that all project proposals, including proposals for new digital tools, actively considered both existing and recommended data structures and existing data tools.10 As part of this process, the data ecosystem team conducted a collaborative workshop in 2015 (i.e. before the formal commencement of the Challenge) about digital tools to ensure stakeholder’s experiences with existing tools, particularly in relation to challenges, were taken into account (Medyckyj-Scott et al., 2016, pp. v, 11[11]).
Lesson 2. Monitoring and modelling should be viewed as complementary
Often, monitoring and modelling happen as two separate streams of work, and modelling is often described as being needed in the context of incomplete information. This implies that modelling is only needed because of data deficiencies; that is, that monitoring and modelling are substitutes.11
In many cases, data gaps are likely to persist: monitoring of all physical variables of interest is unrealistic, despite advances in sensors, Internet of Things devices (e.g. “smart” agricultural machinery) and remote monitoring technologies which enable much broader physical monitoring at lower cost than previously. Therefore, there will still be a need for models to attempt to “bridge” these gaps.
However, even if all necessary physical measurements could be obtained via monitoring, modelling may still be needed for a variety of functions, such as attributing physical impacts to non-physical drivers (particularly to policy drivers, so that policies can be evaluated), and modelling future scenarios to make ex ante policy assessments and improve planning.
Thus, modelling and monitoring should be viewed as complementary: modelling both uses data and allows for analysis in the absence of data.
Lesson 3. Ensure new digital tools do not create new information asymmetries
While the Challenge aims to produce a range of digital tools and information products which address existing information gaps, there is also the need to develop digital tools and effective stakeholder engagement strategies to ensure that production of new knowledge does not inadvertently produce information asymmetries. (This could potentially occur, for example, if only researchers involved in creating new knowledge or tools had access to them. The Challenge acknowledges this risk and addresses it via its co-innovation approach.
Lesson 4. Creation of dynamic, updatable digital tools can lessen the need to “reinvent the wheel” and better match users’ needs
Reflecting the dynamic nature of many factors relevant to land management decisions, there is strong demand for up-to-date information. Previously, many tools were relatively static, making them less useful and prompting periodical attempts to “reinvent the wheel” (to make tools which better suit users’ needs, which may have changed). Therefore, tools that can allow for rapid update of information better match demand for information, and as such are likely to be used more, both now and in the future.
Lesson 5. Embrace different levels of Data Management Maturity to fit different contexts
There are different levels of Data Management Maturity (DMM);12 it may not be necessary to advance all (or any) participants to the highest level of data management in order to achieve programme objectives. Also, it will take time to progressively move through the different levels of DMM. Strategic planning for transitioning through these levels (including planning for different stakeholders—whether individuals or organisations—to move through levels at different speeds) can be helpful for: (i) identifying the current situation (i.e. which participants are at which level), (ii) identifying which level(s) participants eventually need to reach for the programme or policy goal to be achieved, and (iii) improving the overall level of maturity while still allowing for flexibility and not imposing too high transition costs.
It is also important to recognise that moving towards more advanced levels of DMM may require attitudinal change. For example, the Challenge’s Data Ecosystem white paper (pp. 16, 29) identified that “experience shows that one of the major obstacles in the cultural change is the view that data belongs to “me” and that it is not treated as an asset”. The authors concluded that “it is unlikely that maturity in handling data will emerge if in other ways participants lack a strong sense of community.”
Lesson 6. Ensure initiatives generate “additional” benefits by using a mix of old and new technologies
Digital technologies have been in this case study used both to improve and enhance the functionality of existing analytical systems (e.g. upgrading the NZ Water Model), and to provide wholly new tools (e.g. LUS classification and Physiographic Environments of New Zealand GIS layers) that support decision-making process that were not previously possible. This enables the Challenge to avoid duplication and “reinventing the wheel”, while still ensuring that the tools are fit for purpose. This requires a thorough understanding of the existing analytical tools.
Lesson 7. Digital tools can be used to foster collaboration and overcome traditional roadblocks created by conflicting views and values
Development of new digital tools often requires greater collaboration between different individuals and organisations, and across disciplines. Also, there needs to be strong links between new or enhanced tools developed within the initiative (in this case, within the Challenge) and other tools (e.g. NIWA digital stream network layer).
Digital tools are being successfully used to help parties with different interests and incentives to build consensus. For example, the OVERSEER® nutrient model, which is being enhanced under the Challenge and aligned programmes (e.g. to be made spatially explicit by MitAgator), has been developed using co-innovation and can be scrutinised by all interested parties. It functions as an “authoritative point of truth”, but is able to be updated with the latest available science and incorporate innovations (e.g. new data sources from new sensor technologies).
Lesson 8. Digital tools can enable new information-rich policy paradigms rather than simply improving the granularity of existing information-poor paradigms
Many existing approaches to land use planning and managing environmental impacts are fundamentally based on a recognition that there are substantial information gaps and that assumptions are needed to bridge those gaps (Macey, 2013[12]). Land use capability (LUC) planning is one important example of these existing approaches. While the LUC approach provides “an indicator of the productive versatility of land parcels for a range of land uses and identifies key constraints such as erosion” (McDowell et al., 2018[2]), the focus is on determining what a given land parcel is capable of producing. This approach does not explicitly account for spatial linkages or for policy objectives such as objectives relating to downstream receiving water bodies. Because information on aspects such as nutrient transfer pathways and landscape attenuation capacity has been missing, existing watershed management policies tend to be based on LUC assessments and generally apply uniform approaches to different land-use types. While improved data can help these approaches to become more granular and allow for some degree of targeting (e.g. to focus mitigation or remediation efforts on areas where erosion potential is highest), it is difficult to explicitly take into account complex spatial and dynamic relationships within the LUC framework.
Digital tools such as those being explored in the Challenge can enable new approaches such as the land-use suitability (LUS) approach which are able to explicitly account for these complex spatial and dynamic relationships. Such holistic approaches, while still in their infancy, hold out the promise of designing policies which take into account a much greater degree of complexity, including the ability to evaluate synergies and trade-offs between multiple policy objectives.
References
[6] AgResearch; Landcare Research; GNS Science (ed.) (2009), Land Use Capability Survey Handbook - a New Zealand handbook for the classification of land, http://www.landcareresearch.co.nz (accessed on 7 August 2018).
[13] Geraci, A. (1991), IEEE standard computer dictionary: a compilation of IEEE standard computer glossaries, https://dl.acm.org/citation.cfm?id=574566 (accessed on 7 August 2018).
[10] Kuehne, G. et al. (2017), “Predicting farmer uptake of new agricultural practices: A tool for research, extension and policy”, Agricultural Systems, Vol. 156, pp. 115-125, https://doi.org/10.1016/j.agsy.2017.06.007.
[12] Macey, G. (2013), “The Architecture of Ignorance”, Utah L. Rev, Vol. 21/1627.
[7] McDowell, R. et al. (2017), Research landscape map for the Our Land and Water National Science Challenge (2nd Edition), http://www.ourlandandwater.nz (accessed on 17 August 2018).
[2] McDowell, R. et al. (2018), “The land use suitability concept: Introduction and an application of the concept to inform sustainable productivity within environmental constraints”, Ecological Indicators, Vol. 91, pp. 212-219, https://doi.org/10.1016/J.ECOLIND.2018.03.067.
[11] Medyckyj-Scott, D. et al. (2016), Our Land and Water National Science Challenge: A Data Ecosystem for Land and Water Data to Achieve the Challenge Mission, AgResearch, Hamilton, New Zealand, http://www.ourlandandwater.nz/assets/Uploads/Our-Land-and-Water-Data-Ecosystem-White-Paper.pdf (accessed on 7 August 2018).
[3] NIWA (n.d.), Our land and water and NIWA’s role, https://www.niwa.co.nz/freshwater-and-estuaries/freshwater-and-estuaries-update/freshwater-update-75-november-2017/our-land-and-water-and-niwas-role (accessed on 23 July 2018).
[4] Our Land and Water (2018), Our Land and Water Research Book 2018, http://www.ourlandandwater.nz/assets/Uploads/Research-Book-OLW-2019.pdf (accessed on 7 August 2018).
[5] Our Land and Water (n.d.), Sources and Flows: Managing contaminant pathways & attenuation to create headroom for productive land use, http://www.ourlandandwater.nz/assets/Uploads/sources-and-flows.pdf (accessed on 7 August 2018).
[8] Our Land and Water National Science Challenge (2016), Addendum to: Our Land and Water Toitu Te Whenua, Toiora Te Wai National Science Challenge: 2. The Revised Research and Business Plans, http://www.ourlandandwater.nz/assets/Uploads/Our-Land-and-Water-revised-research-and-business-plan-2017.pdf (accessed on 7 August 2018).
[1] Our Land and Water National Science Challenge (2015), Our Land and Water - Toitū Te Whenua, Toiora Te Wai National Science Challenge: Revised Research and Business Plans, http://www.ourlandandwater.nz/assets/Uploads/our-land-and-water-revised-plan-2015-web2.pdf (accessed on 7 August 2018).
[9] Rissmann, C. et al. (2018), Integrated landscape mapping of water quality controls for farm planning – applying a high resolution physiographic approach to the Waituna Catchment, Southland, Fertilizer and Lime Research Centre, Massey University, http://http:flrc.massey.ac.nz/publications.html.
Notes
← 1. “Mission-oriented policies can be defined as systemic public policies that draw on frontier knowledge to attain specific goals or “big science deployed to meet big problems”, https://ec.europa.eu/info/sites/info/files/mazzucato_report_2018.pdf, accessed June 2018.
← 2. http://www.ourlandandwater.nz/, accessed June 2018.
← 3. http://www.ourlandandwater.nz/assets/Uploads/Research-Book-OLW-2019.pdf, accessed June 2018.
← 4. Our Land and Water Revised Plan 2015, p.35. The NPS-FM is an overarching national policy for freshwater management, whose objective is “that the overall quality of freshwater within a region is maintained or improved and Regional Councils have to meet its statutory requirements. The NPS-FM links to the National Objectives Framework (NOF) that outlines the water quality objectives that Regional Councils have to meet, along with the proposed Environmental Reporting Bill increasing the demand for enhanced environmental monitoring and reporting.” (p.14)
← 5. Source: http://www.ourlandandwater.nz/the-challenge/innovative-resilient-land-and-water-use, accessed August 2018.
← 6. Refer to Chapter 2 of main report, which presents the conceptual framework for analysis and identifies that information gaps, information asymmetries, transactions costs and misaligned incentives as sources of fundamental problems for agri-environmental policies, which digital technologies can help ameliorate or overcome.
← 7. http://www.landandwater.kiwi/Projects/Theme%202%20Sources%20and%20Flows.docx, p.7.
← 8. See also the Challenge Research Landscape Map, available online at: http://www.ourlandandwater.nz/resources-and-information/strategy-and-plans/, accessed August 2018. This document details 350 related Challenge-related projects of >NZD 50 000, some of which involve digital tools.
← 9. Interoperability can be defined as “the ability of two or more systems or components to exchange information and to use the information that has been exchanged” (Geraci, 1991[13]).
← 10. Table 3 in Medyckyj-Scott et al. (2016, p. 11[11]) enumerates existing digital tools that the Challenge will interact with.
← 11. In particular, discussions of the use of modelling to support water quality policies for agriculture often centre on the notion that nonpoint sources (including agriculture) are sources for which it is not possible or prohibitively costly to measure and attribute emissions to particular sources (farms).
← 12. Data Management Maturity (DMM) is a concept and framework for analysing institutional capacity to manage and make beneficial use of data assets. The DMM framework assesses data management practices in six key categories that helps organisations benchmark their capabilities, identify strengths and gaps, and leverage their data assets to improve business performance. See (Medyckyj-Scott et al., 2016[11]) and https://cmmiinstitute.com/data-management-maturity, accessed September 2018.
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