4.2.4 Situation samples

TABLE OF CONTENTS

• “Typical case” scenarios to support the reflexions
  • Why revert to a scenario-based approach?
  • Counter-examples that should be avoided
  • Who can use these scenarios? At what point?
  • How to Interpret and understand these scenarios?
  • Generic variations that applies to all scenarios
  • What scenario would be best?
    • Scenario A: limited responsibilities, dispersed among each project
    • Scenario B: Technical responsibilities applied to an intervention
    • Scenario C: Coordinated general responsibilities
    • Scenario D: Technical, specialised and coordinated responsibilities
    • Scenario E: Specialised and centralised responsibilities within a department

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“Typical case” scenarios to support the reflexions

Why revert to a scenario-based approach?

This document outlines the options for sharing program data management responsibilities, by specifying “traditional” roles, their functions, and by describing the ties that exist between these roles. A scenario-based approach thereby helps to link an organisation’s context, objectives, priorities and constraints to its program data management organisation chart.

The skills and know-how required to ensure that the responsibilities incumbent upon each role are detailed in the document created to help you implement the most suitable scenario for your organisation, drawing the link between the document The professional frame of reference put into practice: a detailed overview of program data management skills to help frame your HR needs and the present document.

Although the ideas and scenarios put forward in this document are intended to serve as examples, it is worth noting that they do not reflect the overall realities of civil society organisations as a whole and that this document does not purport to be exhaustive. The ideas and scenarios listed below are indicative and may be considered adjustable and used as groundwork for reflection.

Counter-examples that should be avoided

The purpose of this document is also to initiate discussion on the models presented in order for francophone CSOs to avoid creating “counter-productive” scenarios. Amongst these:

• Technical or performer profiles supervised by a person lacking the necessary skills to provide guidance on methodological and strategic issues.
• Absence of clear roles and/or skills in terms of data quality and protection, even though sensitive data is collected.
• Concentration of responsibilities and skills at the level of a single person, whereas grounds of intervention are numerous (i.e., understaffing issues). The scenarios put forward therefore make it possible to develop certain examples in the form of standard ideals, as a guide to good practices in the organisation of program data management responsibilities, and to avoid the above-mentioned pitfalls.

Who can use these scenarios? At what point?

These scenarios can be used by both headquarters and field teams, whether by a person involved in program data management or by an HR department who wants to understand how program data management skills and responsibilities can be distributed between different roles or positions within a team.

Among other things, these scenarios can be particularly useful when:

• Thinking strategically about the distribution of program data management responsibilities and associated competencies within an organisation or team.
• Thinking about the scope of roles or positions that have a program data management component and how they are supervised and supported.

These scenarios are found to be most helpful when used in conjunction with other documents of the HR pack:

• The professional frame of reference put into practice: a detailed overview of program data management skills to help frame your HR needs [Add link to section 4.2.3]
• A tool to implement the most suitable scenario for your organisation: which skills are needed for which positions? [Add link to section 4.2.5]

How to Interpret and understand these scenarios?

Each “typical case scenario” includes:

• A definition of the context, to understand in which case the application of said scenario is recommended.
• A description of a typical case organisational scenario, detailing the responsibilities of each “role” within the organisation and the relationships between them, including two parts:

For each scenario, the organisation at headquarters that seems most appropriate to oversee these responsibilities in the field is also described.

• A summary of benefits, limitations and risks, to understand whether the scenario is the one that best fits the organisation in accordance with its needs and priorities, and the risks associated with choosing an inappropriate scenario.
• A few ideas for adapting scenarios, in order to support adjustment of the typical case scenario to different contexts.

Generic variations that applies to all scenarios

The program data management, M&E and IT triptych

The responsibilities related to program data management evolve in an environment surrounded by posts with complementary skills and responsibilities; this is true for instance of teams charged with Monitoring and Evaluation (M&E), but also of IT teams (or even ICT4D or innovation).

The responsibilities (and consequently the necessary associated skills) of the people in charge of program data management are thus dependent on their environment. For instance, if an organisation has no IT department, then the role responsible for program data management shall also have to take an interest (and have the associated skills) in IT-related aspects of program data management. Conversely, if the organisation’s IT department is highly developed, the person in charge of program data management will likely have reduced responsibilities and limited space for manoeuvre, being under the constraint of strong IT policies; however, said person will still need to have certain skills so as to engage in dialogue with the IT teams – or even, where necessary, to influence IT policies. The same is true for M&E, program data management responsibilities are determined on the basis of the scope of responsibilities held by the M&E teams.

In some cases, program data management responsibilities are held by those in charge of M&E, a combination that, at times, is deemed logical and useful. This approach is thus reiterated in certain scenarios put forward in this document whilst underlining its limits, given the difficulty of finding a person having these dual skills. It is hence important to recall that the responsibilities and by association the skills covered by both M&E and program data management are quite distinct and that the scenarios presented below only focus on skills inherent to program data management.

The DPO’s role

Since the advent of the GDRP – General Data Protection Regulation – organisations have an obligation to recruit a Data Protection Officer responsible for ensuring respect of the legal framework, including compliance with the protection and application of security safeguards with respect to personal data, and for communication in the event of data breaches or risk of exposure. In practice, the scope – and above all the intervention capacities – of the DPO varies from one organisation to another, particularly when dealing with project data. Nonetheless, all organisations do not – yet – have a formal liaison with the DPO within missions, departments or projects. As such, the other posts or roles in charge of program data management are more or less concerned by strategic responsibilities for data protection on the basis of existing organisational arrangements on data protection.

What scenario would be best?

The purpose of this section is to describe the various key elements that allow us to quickly identify and distinguish the five typical case scenarios.

Scenario A: limited responsibilities, dispersed among each project

• Poorly articulated projects at organisational or mission level
• Each project has its own organisation in data management
• Data analysis needs are limited to operational decision-making and reporting (lack of general visualisation)
• No complex needs (such as case management) or sensitive data.
• Small amount of data to be processed (i.e., tables with less than 10,000 inputs)

Scenario B: Technical responsibilities applied to an intervention

• Highly specialised organisation in a particular subject area
• Existence of technological solutions specific to the sector of intervention (e.g., health or water)
• A technological solution that plays a central role in the organisation’s intervention modalities (e.g., patient monitoring software)
• Training of specialised personnel in the data management solution (or easy implementation); existence of dual expertise (sectoral and program data management)

Scenario C: Coordinated general responsibilities

• Need for coordinated management of program data (common strategy at national level or by multi-zone project)
• Projected or implemented deployment of M&E teams
• No complex needs (such as case management) or sensitive data
• Data volume remains rather low
• Data analysis needs limited to operational decision-making and reporting. Lack of complex visualisation, it is therefore not necessary to deploy a multitude of technical tools (e.g., 1 MDC solution with Excel is enough for management and analysis)

Scenario D: Technical, specialised and coordinated responsibilities

• Need for coordinated management of program data (common strategy at national level or by multi-zone project)
• Longitudinal data collection and analysis on an ad hoc basis
• Collection of sensitive data
• Significant amount of data
• Involvement of a few external players in data management (e.g., partners, open data)
• Complex and frequent needs for data analysis (maps, qualitative analyses, various graphs), involving a greater diversity of technical solutions.

Scenario E: Specialised and centralised responsibilities within a department

• Need for coordinated management of program data (common strategy at national level or by multi-zone project)
• Involvement of a few external players in data management (e.g., consortium)
• Data management features as a strong component of a program, which may involve one or more sectoral data management solutions
• Significant amount of collected data
• Complex data collection needs (such as case management) or large-scale collection of sensitive data
• Frequent needs for automatically updated analyses and visualisations (dynamic maps, dashboards)
• Wide variety of technical solutions (e.g., multiple MDC tools powering multiple management and visualisation tools)