Part 2: The Government Fisheries Data Modernization Landscape

Gaining audience and interest with governments to consider data modernization is the first step. But once interested, the first question is nearly always: So, what does this whole process look like? This section provides an overview of the current fisheries data modernization landscape, including a generalized framework that articulates key elements of the process; we also provide insights into design principles that can help overcome known barriers to the successful implementation.

The fisheries data modernization space is highly dynamic, and there exist multiple frameworks and process guides, many directed at a specific element of data modernization (such as electronic monitoring or apps for fishers).  We have aimed to incorporate elements of these frameworks into our synthesis, and invite further discussions as we work to refine and develop harmonized approaches to meet the diverse needs of governments around the world.

The Process: structure and attributes of government fisheries data modernization

In order to better understand how government fisheries data modernization works, and where the process becomes stuck, we looked across eight different case studies of government fisheries data modernization around the world.  From this global view, we distilled patterns in how the modernization process unfolds and key attributes of these systems, including the human barriers to progress.  We offer here a draft framework (see Figure 3) that encapsulates these high-level commonalities, which can be used to inform a holistic strategy for implementing fisheries data modernization.

The Four Stages

The framework includes four stages—Initiate, Pilot, Establish, Scale— through which government fisheries data modernization generally progresses (Figure 2). In reality, the process is not so uni-directional. Instead, within any stage, new ideas or concepts may emerge that spark a new and separate work initiative that will undergo its own evolution; likewise, most launched initiatives are born from the convergence of multiple factors, making a “starting point” difficult to define. Regressions can occur where an initiative stalls and another, similar project with duplication of effort and redundancies emerges.  However, for the sake of discussion—and potential strategy development—we find these four stages useful in thinking through what is needed for a fisheries data modernization idea or concept to progress to an established, functioning, and (ideally) scaled solution within the context of governing agencies.(1)
Figure 2. The four Stages of a generalized government fisheries data modernization process. Stage 1: Initiate; Stage 2: Pilot; Stage 3: Establish and Stage 4: Scale. The process is not always so uni-directional, with many new ideas surfacing during Stage 2 and 3, which may kick-off a new process cycle; likewise, stalled initiatives often wind up reverting back to Stage 1 to regain momentum.  When new initiatives emerge out of Stage 4, they often will progress through the cycle again, creating the next layer of a spiral; or, they may be a replication of a solution into more fisheries and fleets with little need to pilot and thus, don’t re-enter the spiral.

Stage 1: Initiate. The first stage is where an idea or concept is first ignited and then socialized to garner interest, engage experts, and secure the support necessary to turn the idea into a testable pilot or working draft.  It’s about ideation, brainstorming, and shaping an idea or concept into a plan. In some instances, ideas may be “pushed” onto an agency by an outside party that may be a poor fit for the capacity or needs of the system.  Careful assessment during this phase is critical to evaluate which ideas are appropriate to move forward and which are not.
Stage 2: Pilot. The second stage in the process is where the idea moves from theory to a testable prototype.  For initiatives focused on technology deployment, this stage is often a pilot project; for policy developments, it may be initial drafts or committee meetings to vet and organize ideas.  This stage involves more doing, less talking. It also often involves capturing lessons learned and refinement of original approaches to make them more effective. Proof of concept and associated benefits may occur at small scales.
Stage 3: Establish. During this stage, the initiative or program takes root and becomes embedded in the day-to-day of an agency or individual’s workload. The data modernization initiative or program is no longer viewed as a pilot or temporary exercise but instead is now a part of regular operations. During this stage, benefits from the modernization process are realized and shared with multiple stakeholders.
Stage 4: Scale. The fourth stage of the process is when a data modernization effort is refined, replicated, or applied in a novel context.  This may mean expansion of a technology to a new fishery or fleet; it may mean incorporating a new type of analysis or feedback mechanism to increase the value generated by existing data; or it may be adding a new technology or tool. This is the stage where stakeholders no longer question if they should modernize, but begin to explore how they can continue to do so.  Often, this stage generates ideas that loop us back to the beginning in terms of process (Innovate arrow), but expand the scope of modernization by building out on what has already been accomplished (represented by the next layer of the spiral). As modernization progresses we anticipate that the drivers and enabling conditions may look different in more mature parts of the cycle.  Scale can also occur when  a solution is simply expanded to other fisheries or geographies without the need for further testing or refinement (Replicate arrow).

The Attributes

For each stage, we also identify seven attributes of the data modernization process that are important to account for when planning and implementing initiatives—consideration for each can help inform successful design of projects or programs.  For every scenario, the nature and number of factors within each attribute will vary; however, there are some key factors that appear to be common and thus, potentially critical for success, in each stage. These are noted in Table 1b-1g and shown in Figure 3.
Figure 3. Framework for government fisheries data modernization. Red Octagons note where numbered System Barriers tend to manifest during the process. The specific attributes for each stage are included with detailed descriptions provided in the text.  For Stage 4, funding type depends largely upon the type of scaling to occur and thus has been left as TBD. See Table 1d for further details on funding types.

Attribute 1: Primary Driver (PD). The catalyst that gives the initiative the initial or additional momentum required to maintain forward progress. There are often more than one primary drivers at play (Table 1b). They may take the form of a catalytic event or may be a tipping point reached after a slow build up of energy or effort. Primary drivers are critical in Stage 1, but can also be important levers to move a project into Stage 3.

Table 1b. Key Primary Drivers and Stage where we see them manifest


Attribute 2: Enabling Conditions. These are factors that nurture and support the progress of an idea or initiative. Without these factors in place, initiatives would run out of steam (Table 1c). These conditions also provide the critical resources necessary for any idea or initiative to realize its potential within a given stage.

Table 1c. Key Enabling Conditions and Stage where we see them manifest


Attribute 3: Funding. Different types and sources of funding are critical for long-term progress of data modernization initiatives. Understanding what kinds of support are needed at each stage is necessary to design and execute a realistic implementation plan (Table 1d).

Table 1d. Key Funding Types (FT) and Stage where we see them manifest


Attribute 4: Leadership. Success for all stages hinges on strong leadership. However, the type of leadership that is most effective varies across stages (Table 1e). As most government agencies are hampered by staff operating at overcapacity, having the right kind of leadership to motivate participation and attract key stakeholders is critical. Understanding these nuances in leadership needs can help practitioners identify who to engage, for what purposes, when.

Table 1e. Key Leadership Types and Stage where we see them manifest.


Attribute 5: Barriers (B1-8). These are the common and underlying challenges that emerged across multiple case studies. See the next section, “The Challenges,” for details on each barrier and Figure 3 for where they manifest in the modernization process.
Attribute 6: Tools. Each stage of the data modernization process requires different knowledge, skills, and resources. Through research into existing tools and resources, we uncovered a vast wealth of reports, toolkits, platforms and guides that can help with different aspects of government fisheries data modernization.
Table 1f provides a select list of tools that could be used to support data modernization efforts, from Initiation through Scale. Part 3 and Annex III of this report provide additional information regarding tools research, including a complete initial list of resources available for data modernization practitioners as well as an assessment of remaining gaps and system needs.

Table 1f. Tools and Resources by Implementation Phase (2)

Initiate (Tools provide background information guidance, and rationale for data modernization efforts)
Fisheries Monitoring Roadmap
Challenges, Opportunities, and Costs of Electronic Fisheries Monitoring
Guiding Principles for Development of Effective Monitoring Programs
Catalyzing the Growth of Electronic Monitoring in Fisheries: Building Greater Transparency and Accountability at Sea
Electronic Monitoring and Electronic Reporting: Guidance & Best Practices for Federally-Managed Fisheries, Discussion Draft
Electronic Monitoring White Papers
Electronic Monitoring Program Toolkit: A Guide for Designing and Implementing Electronic Monitoring Programs
Getting There from Here: A Guide for Companies Implementing Seafood Supply-Chain Traceability Technology
Good Practice Guidelines (GPG) on National Seafood Traceability Systems
Pilot (Tools provide technological and implementation guidance through research or lessons learned)
Fisheries Digital Data Collection Guide
An Inventory of New Technologies in Fisheries
Tools and Technologies for the Monitoring, Control and Surveillance of Unwanted Catches
Evaluating Electronic Methods of Fisheries Monitoring, Control, and Surveillance
Technology for Fisheries Monitoring and Surveillance
OECD Issue on Inventory of new technologies in fisheries (2017)
FOCUS (Fisheries Open Source Community Software)
Fishing Data Innovation Taskforce
The Ocean Data Alliance
Open Data Kit
Establish (Tools provide platform for assessment and expansion of existing data modernization systems)
Cost Recovery Guidelines for Monitoring Services
Project to Develop an Interoperable Seafood Traceability Technology Architecture: Issues Brief
Global Fishing Watch
Global Dialogue on Seafood Traceability (GDST)
Scale (Platforms or events for larger expansion, creation, and connection)
Too Big To Ignore: Global Partnership for Small-Scale Fisheries
Fisheries Innovation Fund
Fish 2.0
Fishtech Awards
The Marine Protection Prize
The Techstars Sustainability Accelerator
Fishackathon
Seafood and Fisheries Emerging Technologies (SAFET) Conference
Seafood Alliance for Legality and Traceability (SALT)
Attribute 7: Timeframe. Expectation management and appropriate budgeting are critical to ANY project success—and both require a realistic estimate of how long a project or phase will take. Because government fisheries data modernization is not a single process, but may be initiated and built upon via multiple diverse initiatives, it is impossible to define a specific timeframe for any stage in the process. The following are the minimum time horizons over which the different case studies progressed—on average—through these stages, noting that often, multiple initiatives operating over different time scales were happening simultaneously:

  1. Initiate: 2-4 years
  2. Pilot: 1-3 years
  3. Establish: 2-3 years
  4. Scale: ongoing

The Challenges

We define barriers as the underlying roots of a problem. Stuck points may include risks that are symptoms of larger failings or inefficiencies in the way a system works, so as to prevent optimal functioning. These barriers are not immutable conditions; they must be moveable and changeable.

Often, these barriers are challenges individual actors in the system are trying to resolve, but the integrated solution required is beyond the capacity of any single actor to implement. During our analyses, we noted the barriers that existed within each case study. We then looked for commonalities behind these barriers to identify recurring themes that emerged across multiple case studies. We call these System Barriers, and they point to critical challenges in the government fisheries data modernization space, which, if successfully resolved, could pave the way for real progress.
B1. Lack of Long-Term Planning & Vision
The concept of holistic data modernization is largely absent from government discourse. Instead, specific projects to address a discrete issue or threat are developed in the absence of any overarching strategy or vision. During Stages 1 and 2, little to no resources are spent on developing a financial plan to support long-term integration and adoption of new technologies, protocols, or analyses. The ability to appropriately budget and leverage one project to advance another is lost under these circumstances, as are other types of economies of scale.
B2. Inflexible and Rigid Systems
Governments are notoriously slow to change. Passing new protocols or policies, cutting through the red tape to secure resources and execute a pilot—all these things are heavier lifts within governance bodies. Add to this long-term staffers who have a vested interest to protect the status quo—especially if they’ve built or managed a legacy system and are considered the go-to expert—and introducing any change is really hard.
B3. Capacity Missing for Tech Adoption
Modern data systems require IT expertise, infrastructure, and at least some level of literacy. Unfortunately, these conditions are often not met in the system, limiting the viability of tech-based solutions until improvements can be secured around these conditions. This barrier is also related to general lack of resources in many countries where fisheries management—and especially modernization—are simply not a priority.
B4. Policy Prevents Progress
There are multiple ways that aspects of policy can thwart data modernization efforts. These range from simple logistical hurdles in hiring outside experts to conflicting decision-making practices among departments or across jurisdictions. Changes in policies—especially unexpected ones—as well as a lack of consideration for legislative consequences, can quickly and entirely derail initiatives. Again and again, we saw case studies where factors related to policy became major stuck points in the data modernization process.
B5. Data Ownership Confusion
Despite being inundated with data at every turn, in general, most people are not experts in strategic use of data. Whether it’s refusing to trust data protocols to protect sensitive information or only seeing value in data ownership, stakeholders from both industry and government sectors continue to be confused and often misled in their understanding of data ownership and sharing, creating a major hurdle to modernization initiatives, which depend on clear roles and responsibilities, as well as agreements (oftentimes, formal) around data ownership, contributions, and access.
B6. Missing and/or Perverse Incentives to Attract Participation
The benefit and the need for better data are often unrealized and thus, unrecognized by many actors in the space. In some places, this is due to a lack of regulatory or public demand; in others, scarce resources make it difficult to retain talent or impose new tasks on already-overburdened staff. Fear around consequences for sharing imperfect data—in government and industry sectors—also results in reluctance of stakeholders to engage. Similarly, fear of too much transparency can deter some from participation in a project.
B7. Insufficient In-house Expertise
Scarce resources, overburdened staff, inadequate training, and missing expertise all reduce effectiveness of data modernization initiatives. Currently, the vast majority of projects rely on outside expertise to provide IT support and training on data systems and analytics. This reliance increases costs and reduces learning opportunities as the experts are available only for a limited time and for specific duties. Language and cultural factors can also limit the effectiveness of outside experts who come in for one-off projects and don’t understand the local context. Developing skills and expertise with IT and data systems takes time, and is a worthwhile investment, but current models are limited in how they leverage pilots to build internal expertise—a resource that would return benefits again and again as data modernization progressed.
B8. Most-Recently Elected Leader Wants All The Credit
Whether it is within a sub-sector of a government fisheries agency or a ministry official or the president of a country, leaders want recognition for creating and executing impactful, sexy projects. Taking on the work of a previous administration is inherently unattractive from a PR lens. This aversion to inheriting former projects, combined with high government and leadership turnover rates in many countries, poses a threat to long-term investment and execution of data modernization initiatives which require longer time horizons to achieve success.

Design principles by phase

The following recommendations are based on synthesis of strategies, design principles, and practical solutions that emerged in the case study research. During our analysis, we worked to identify the underlying, universal concept that was being employed in order to craft a recommendation that is scalable to multiple locations and conditions. That said, some of these recommendations will be more or less applicable depending on existing enabling conditions and other attributes of and resources in the system.(3) Additionally, while we have categorized these design principles by stage, the majority cross multiple phases, such as “be transparent regarding expectations around timelines”. Where this occurs, we have categorized the design principle within the phase where it begins. For example, “be transparent regarding expectations around timelines” is found within stage 1 (Initiate), although it also applies across all later phases of implementation.
Table 2. Summary of Design Principles by Implementation Stage

Stage 1 (Initiate)

Set a Clear and Holistic Vision
Define the purpose of the modernization effort, why it is needed, and the vision for how it will support management and industry. Elements of a sound fisheries data modernization vision include:
  • Real-time or near real-time data management and benefits this brings to managers and industry
  • Sufficient (sometimes very simple) data to support management of data-limited fisheries
  • Reduced data entry and reporting burdens through automation
  • Seamless data sharing (interoperability) and useability across stakeholders
  • Advanced analytics to generate intelligence to support industry and government interests
  • Multi-stakeholder engagement and accountabilities
  • Verification and enforcement mechanisms in place
  • Embedded into fisheries management policy
  • Built-in ability for system and vision expansion and revision
  • Benefits to society

An effective vision also provides an opportunity to ensure industry stakeholders see benefit to their participation in the process. Fisheries managers, NGOs, and scientists often are the main advocates for modernized data collection and reporting technologies (e.g., electronic data, electronic monitoring, mobile applications) because they see the potential for obtaining more accurate, complete, and timely information, which can support more effective conservation and management. However, the fishermen and dealers who would be tasked with implementing those new technologies rarely share the same level of enthusiasm. A holistic vision-setting process must articulate the need for a new system, address users’ concerns, and finally, highlight benefits for all users.
Be Transparent Regarding Expectations Around Timelines
Strategy needs to be built upon realistic timelines to ensure alignment with stakeholder capacity, workflows, and expectations of participants. As noted in the framework, most stages require two years and up to achieve success and progress to the next stage. Helping participants understand the long-term play, and the short-term benefits that can be realized, is important to building trust and lasting partnerships.
Embed Adaptive Learning into Strategy
Build in capacity for updating strategy to allow for refinement, pivots, and adjustments based on changing contexts; including scientific, policy, and technology landscapes. Rigid systems lacking the ability to respond to evolving conditions will inevitably become outdated and unused.
Set Interdisciplinary Focus and Map Roles and Responsibilities from the Start
Engage key stakeholders from multiple divisions of government, industry, regional bodies, and civil society early on in the process to identify how the data modernization could benefit them and what concerns or challenges may need to be addressed. Consider:

  • Who collects, uses, and shares or reports on fisheries-related data, and how this initiative could assist in meeting their data needs.
  • Judiciary aspects—laws and regulations must be updated to be able to accept modernized data sources such as for evidence in court. Ensure experts from this sector are able to participate in project development.
  • Industry engagement is key, especially in sharing the purpose of a project and how it could benefit them.

Generating commitment to specific roles and responsibilities will help shape the strategy for moving workstreams forward. However, roles need to be crafted to fit existing capacity and abilities among participants, something that may take time to understand and define. Accountability must also be built in to ensure such responsibilities are being met.
Foster Relationships Between Key Leaders and Champions
Prioritize relationship-building to promote trust and generate a strong steering committee that can drive strategy forward. Creating alignment among leadership helps mitigate risk of efforts collapsing when a single champion is replaced or removed from the system. Slow, steady commitment to consensus building has been valuable in moving forward data modernization across multiple stakeholders and often is best done through in-person relationship-building.
Resource Beyond Pilot
While it is likely impossible to finance a full-sweeping data modernization initiative from the start, it is critical that a funding strategy accounts for resource needs beyond a small-scale pilot (Stage 2). Planning for embedding learnings, systems, and processes into daily operations will help leverage momentum from pilots to support longer-term progress and scaling of solutions.
Such strategy should include support for ongoing training for all supply chain actors; including both the rationale behind the use of new technologies as well as how to utilize new systems.
Consider the Role of An Intermediary
An independent group that can serve as a neutral, third-party data collection, storage, sharing and analysis hub can help increase trust, gain efficiencies, and support improved QA/QC of data. There are several examples where such an entity has played a critical role in shepherding multiple data modernization initiatives forward (PacFIN, ACCSP, National MIFC in Madagascar). This entity can be funded by the government without being a government body itself. Independence from government, NGO, or industry can prove powerful in expediting system design, building trust, and allowing for transparency in how data is collected, used, and shared.
Employ Human-Centered Design (HCD) and Co-Design
No technology or policy is perfect, so good design includes strong incentives for good behavior. HCD engages stakeholders, especially industry, to map their pain points and identify solutions that can help address these pressing needs. Other elements of HCD include strong feedback loops to continue to inform and gauge stakeholder response, as well as rapid prototyping to allow for continued refinement and improvement of the system based on user-feedback.

  • HCD can also surface other fears and concerns and give space to address them within pilot design.
  • Stakeholders do not always know what options exist to improve their system, so they do not know what to ask for. HCD can help stakeholders self-identify their specific needs.
  • HCD can also ensure projects clearly identify incentives for all users, including short-term gains. For example, for industry, publicly promote market and crew-safety benefits; for government fisheries scientists and managers, lead with data quality; for upper management, appeal to self-interest.

Utilize Existing Experts
A lot can be learned from those who have succeeded at developing collaborative data platforms, and those experts should be approached to consult on new projects attempting to achieve similar outcomes. These experts may be found in other sectors outside fisheries, or other countries. Similarly, leveraging existing models and refining them to suit regional and national needs can be a way to save on costs and increase likelihood of success by building on proven systems.

Stage 2 (Pilot)

Define Software and Hardware Parameters
Understanding the project goals—improved data collection? increase in stock sustainability? defense against IUU product? —as well as who the users will be (fishers, middlemen, fisheries scientists, fisheries managers, enforcement officers, etc.) will enable the implementing party to more effectively determine the type of software and hardware needs for the specific system. Consider the use of a public RFP process to solicit tech vendor support and force articulation of data and system needs.

Establish Pilots as Learning Opportunities
There is risk—for government officials as well as industry participants—to engaging in initiatives that can expose poor practice or imperfect data. To mitigate this risk and increase participation, fisheries data modernization projects can create clear MOUs (or other structures)to protect and benefit participants. Such structures and agreements state any infractions or poor practices that are observed are used as a learning opportunity, but cannot be used at that time for any penalty or prosecution. Examples of successful models include data amnesties for up to two years or ability to fish under a “research fishing permit” which can offer certain tax benefits.
Be Transparent with Data Flows and Share Data Quickly
Build into the modernization process a pathway for users to understand how and where data is generated, captured, and shared. This includes clear explanation and proof of how confidential data is secured. Processes also need to be put into place that allow stakeholders to see the results of implementation as soon as possible so they can verify the process is working as expected. In addition, information may be of value to industry before it is useful for governments—something officials can take advantage of to provide a service to seafood companies. Some potential ways to do this include:

  • Hold interviews with stakeholders to ask about what kinds of information is of value and in what format. Examples of valuable information may include summary data relevant for trip-planning, such as price of fuel/month, catch volume, or price over time.
  • Utilize tools such as Tableau or other simple, easy-to-use visualization software
  • Ensure pathways for those who submit data to be able to access their own data (and basic analyses on that data) early in project lifespan.

Engage Mentors for In-person Trainings and Long-term Support
Often technology companies will provide one or maybe two training sessions at the launch of a project, but long-term support is often at expensive rates and done virtually. The lack of in-house IT expertise in most countries demands greater, customized, and personal support of staff who are working to implement new systems that require new skills and tools. Close working relationships between an experienced fisheries IT professional and inexperienced officials can help build long-term capacity in culturally appropriate ways.
Enlist Experts Who Can Talk Cross-sector (It, Fish, And Policy)
The need for interdisciplinary approach to government fisheries data modernization means that inherently, there will be participants that do not speak one another’s language. Data and tech experts don’t understand seafood industry and fisheries KDEs; fisheries managers may not grock the technical aspects of a pilot; and policy experts don’t necessarily understand practical limitations or needs on the ground. Having experts that can effectively bridge the gaps among disciplines can help accelerate progress during implementation of a pilot or initiative.
Make Data Accessible and Useable as Quickly as Possible
Even if a pilot or initiative will require months to generate information that is impactful in terms of management or compliance, sharing newly generated data or analyses right away helps to build a sense of transparency and trust with participants. In addition, information may be of value to industry before it is useful for governments—something officials can take advantage of to provide a service to seafood companies. Some potential ways to do this include:

  • Hold interviews with stakeholders to ask about what kinds of information is of value and in what format. Examples of valuable information may include summary data relevant for trip-planning, such as price of fuel/month; catch volume or price over time.
  • Utilize tools such as Tableau or other simple, easy-to-use visualization software
  • Ensure pathways for those who submit data to be able to access their own data (and basic analyses on that data) early in projects’ lifespan.

Stage 3 (Establish)

Provide Enforcement Mechanisms to Ensure Compliance at Every Step in the Supply Chain
On-the-ground enforcement and verification mechanisms (human and mechanical) are necessary at each step in the supply chain to ensure new data and technology policies are carried out in practice, in order to quickly clamp down on cheaters (and learn how to refine system) and to avoid potential greenwashing.

Create Ongoing Training For all Supply Chain Actors
Training supply chain actors on both the rationale behind the use of new technologies as well as how to utilize new systems is especially helpful to get a pilot launched (Stage 2) and to move from pilot into more established phase (Stage 3). As new systems are tested and integrated in operations, new benefits as well as potential challenges will emerge. Industry actors need to be kept abreast of these developments so they can take advantage of benefits, help mitigate risks, and be reminded of the purpose (and long-term vision) of this work.
Ensure Trainings are Wide and Deep To Grow Institutional Knowledge
Include both high level (heads of MCS, science or statistics) and also mid-level, career professionals that are not political appointees in training and capacity-building during data modernization initiatives. Leadership brings legitimacy and helps secure a bigger vision by exposing leaders to the potential of new processes and systems. Career professionals can continue to share and train others as administrations change over time.
Promote Technological Communication
Enthusiasm about the promise of emergent technology to solve data problems can tend to discount or ignore the challenges associated with actually implementing that technology in a useful way. The ability to design and improve a data management program requires good communication among decision-makers, stakeholders, program implementers, and technologies. When information is not sufficiently articulated or understood, problems arise and resources are wasted. Technological communication, especially as it relates to data integration and the combining of disparate data streams, requires both human understanding (e.g., data requirements, purpose, goals) and machine understanding (e.g., standardized data fields, formats, programming language, etc.).
Anticipate and Budget for System Upgrades
Systems (and budget) to accommodate software upgrades—they always come, and it’s helpful to have planned for it.
Balance Stability with Adaptability
Pick a system and stay the course to allow for mastery by analysts and familiarity by users. However, in picking the system, government fisheries data modernization initiatives benefit from forward-thinking and proactive design that anticipates needs and demands as well as solutions coming down the pike. Conduct regular checks-in to assess where change may be needed, but limit time scales over which this happens to at least a year or two.

Stage 4 (Scale)

Build Legislation and Data Systems in Parallel
Create nimble policies and data systems that can both respond to emergent opportunities (technological or otherwise) and encourage the creation of future initiatives that support data modernization. Policy and data systems need to be designed in tandem to create the enabling conditions necessary for continued innovation. Staff turnover creates inefficiencies and capacity loss which hinder long term visions and project outcomes–once a system has been established, new policies can cement progress over the life of one administration to the next.

Balance Top Down With Bottom Up
Top down ideas (such as national-level policies or enforcement mechanisms) require the buy-in of all stakeholders, including project managers and community members. The same holds true for bottom-up initiatives (often the result of pilot programs). The ability to scale as projects progress is dependent upon participation and buy-in from all players throughout the design as well as implementation process.
Focus on the “how” and “why” of success in order to scale
Understanding why a system was successful (e.g., it solved specific problems; it increased efficiency; it allowed for real-time data collection) allows for practitioners to identify new contexts where the system could bring success. Similarly, it is equally important to understand the “how” of success (government support, influx of funding) before attempting to grow or replicate a system.

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1. The Environmental Defense Fund’s Fisheries Monitoring Roadmap, and Designing and Implementing Electronic Monitoring Systems for Fisheries, as well as TWDI’s Data Maturity Model offer variations on the Four Stages identified in this report, with different models ranging from 5 to 8 phases.
2. This is not a comprehensive list of Tools and Resources, rather, this a representation of the current availability of resources by phase. For a complete list, please see Annex III.
3. There are a number of existing best practice guidelines and design principles offered for EM/ER projects, Digital Investment, Fisheries Monitoring, and Government IT Modernization. EDF’s Designing and Implementing Electronic Monitoring Systems for Fisheries, Stanford’s Digital Impact Toolkit, Bradley et. al’s Opportunities to improve fisheries management through innovative technology and advanced data systems, and IBM’s A Roadmap for IT Modernization in Government are just a few examples. There are significant overlaps and similarities in principles encountered across these types of publications that a) point to a range of different organizations reaching similar conclusions independently, both within and external to the sustainable fisheries industry, and b) directly supports findings from the interview stage of our work, the results of which are presented here. For additional information regarding existing resources and how they can be utilized in the fisheries data modernization space, please see Part 4: Tools and Resources.