3 Data use and analytics in water quality management
This is a case study about the Division of Water (DOW), a local government agency in the State of New York, which has attempted to improve its analytic capabilities by developing efficient data management practices, suggest governance models, and identify analytic techniques potentially beneficial to addressing harmful algal blooms (HABs; see Figure 3.1) and high chloride concentrations(Choi et al., 2021).
The DOW faces challenges in using its legacy systems and traditional analytical methods effectively in addressing the problems of HABs and high chloride levels. DOW aims to enhance its decision-making processes through DDDM by improving its ability to gather and analyse data more effectively, beyond their current capabilities, to better inform policy decisions.
From this process, nine key factors across four overarching determinants have been observed and articulated as being crucial to consider by an organisation in implementing a comprehensive strategy for DDDM (see Note 3.1). These factors interrelate and influence each other, requiring a holistic approach to ensure successful adoption.
Data determinants
DOW bases its decisions on internal water data from sampling and assessments, supported by a quality assurance process ensuring reliability and compliance with federal standards like those of the Environmental Protection Agency (EPA). Despite these strengths, challenges include manual sampling processes, incomplete data coverage, missing values, compatibility issues, and interoperability problems that hinder seamless data exchange and system integration.
Data quality and coverage Ensuring robust data infrastructure is foundational, as it supports the collection, storage, and accessibility of high quality data necessary for effective analysis.
Compatibility and operability
DOW manages water-related data through interconnected teams responsible for producing and analysing information from various sources like lakes and streams. While collaboration is facilitated by multiple analysts and teams, this setup poses challenges in maintaining consistent and compatible datasets due to differing file versions and a lack of field locking in their proprietary Filemaker system, risking data integrity. Additionally, varying levels of observation across systems complicate integration efforts.
Data compatibility and interoperability ensure that information flows freely, efficiently, and accurately across different systems, which is vital for organisations to function well, innovate, comply with regulations, and adapt as needed.
- External data
DOW utilises external datasets to address complex environmental and social issues beyond its internal data. While this approach enhances knowledge creation by incorporating charts and maps that combine water chemistry with geographical data, it faces challenges. These include potential quality issues due to lack of control over external sources and incompatibility with specific analytical needs, as seen with United States Geological Survey (USGS) land-cover data not providing sufficient detail on farm types affecting water bodies.
Utilisation of external data potentiates and enriches an organisation’s existing information which can lead to better and richer insights that can be derived from them.
Technological determinants
Information systems and software
Analytical techniques Investment in both skilled personnel and advanced tools is essential to transform raw data into actionable insights.
Organisational determinants
Cooperation
Culture
Institutional determinants Engaging with external institutions and navigating legal frameworks can provide resources and support, or pose restrictions, respectively.
Privacy and confidentiality Addressing legal requirements regarding data protection is crucial to ensure comprehensive analyses.
Public procurement Navigating bureaucratic processes efficiently can accelerate tool adoption without unnecessary delays.
These key determinants are interrelated and interdependent. For example, if an organisation has strong data infrastructure (determinant 1) but lacks the right analytical tools or skilled personnel (determinant 2), their DDDM efforts will be hampered. Similarly, even with good internal structures (determinant 3), if external regulations make it hard to access necessary tools or collaborate externally (determinants 7 and 9), progress is still limited. Without proper stakeholder engagement (determinant 6) and user involvement (determinant 5), the organisation might develop solutions in isolation, leading to less effective decisions. Moreover, privacy constraints (determinant 8) can affect data availability, which in turn impacts analytical capabilities since data is a key input.
While DDDM is often seen as a technical issue involving tools and data, it’s also deeply influenced by organisational and institutional factors. This makes sense because any significant change requires not just new technology but also cultural shifts within the organisation to embrace these changes.
These determinants also influence the ability of an organisation to adapt over time. For example, if the organisation faces challenges in public procurement, which is a structural issue, this could create delays that affect the organisation’s overall strategy. Conversely, strong stakeholder engagement might mitigate some of these delays by providing alternative solutions or resources.
3.1 Leadership role
Leadership plays a critical part in driving organisational change. Without supportive leadership, many of these determinants could be obstacles rather than opportunities. For instance, if leaders aren’t committed to DDDM, they might not push for necessary cultural shifts or investment in new tools.
3.2 Balancing existing practices
The balance between existing practices and new methods is important. While the state agency was implementing DDDM, traditional approaches were still relied upon. This blend can be beneficial initially but may need careful management to avoid conflicts or inefficiencies as newer methods prove their worth.
3.3 Measuring success
How would this state agency assess its progress in implementing DDDM? They might look at metrics like the quality and timeliness of decisions, reduction in issues (like HABs), efficiency improvements, and user satisfaction. These outcomes can help gauge whether their efforts are paying off despite facing various challenges.
3.4 Conclusion
A tailored strategy that evaluates specific organisational strengths and weaknesses across these determinants is essential for effective DDDM implementation. This approach ensures that each organisation maximises opportunities while minimising challenges, leading to more informed and efficient decision-making processes.