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First steps with program data toolbox

3.3 Improving my organization’s Information Management


3.3.1 What are the first steps to improving Information Management within my team?

Improving information management within your team first requires a focus on improving the overall data literacy. Data literacy is the ability to read, work with and analyze, and form arguments with data. According to the IFRC, data literacy is “the basic skills, knowledge, attitudes, and social structures required for different populations to use data”. Data literacy is the basic requirement and first step for organizations to use data to make evidence-based decisions. As such, improving data literacy within an organization can have a multitude of benefits, from improving collaboration, to improving performance and efficiency, to promoting accountability and transparency.

There are a number of ‘first steps’ that the team can focus on to improve their data literacy. For example:

  • Review and train your team on data literacy using IFRC’s Data Literacy Playbook.
  • Review different standardized indicators that could be useful to your program using People In Need’s Indikit.
  • Focus on how to better clean your data with Excel through guidance from section 3. Getting clean and usable data of the Data analysis toolbox.
  • Understand how to disaggregate data for your analysis using this resource by Indikit.

For more information on the concept of data literacy, please see CartONG’s data literacy series of blog posts on the topic, found here.

Besides working on data literacy, some organizations might be interested to get immediately hands-on regarding innovating their IM and start running pilot projects. It is possible to identify “quick-wins” where organizations can see benefit with little effort. One example could be the deployment of a simple MDC tool for existing program data collection activities. The amount of time and effort saved will surpass the efforts required to deploy the digital tools (see relevant MDC questions, including: “3.4.9 Does MDC improve my program data’s quality?”.

Whether you choose to be hands-on or not, making the topic as “organizational-specific” as possible based on the activities of your organization can make a lot of sense rather than staying on the general concepts.

3.3.2 Can’t we just hire a consultant to build the system/dashboard we want?

A dashboard can be a very effective method of displaying program data to a wide audience to increase overall understanding of the program outputs and impacts. However, technical external actors are not necessarily the silver lining for an improved system to manage your program data. It can often be considered that investing in IM can be completed as a one-off consultancy, where the technical solution in itself (such as a dashboard) will solve all problems. Dashboards, like IM systems in general, require regular maintenance if any issues arise, which means internal teams must be aware of and able to manage basic IM processes as well as the technologies behind them.

As such, choosing solutions for different steps in the data management cycle (see definition in the glossary) often should focus on the capacity of the team, as well as ensure that they feel some level of ownership of the data. Given the sometimes limited technical skills related to data within the sector and/or high staff turnover, it is usually better to adopt simpler tools that require little training and that can be managed properly by the team after the consultant has carried out his/her assignment.

Sustainability is a key factor that must be considered when looking for improvements in program data management. Focusing on data literacy among the team for long-term benefit is essential (see the above response to the question: “3.3.1 What are the first steps to improving Information Management within my team?)”. Once organizations have reached certain levels of data literacy, it will become more and more easy to implement certain solutions internally based on the many IM technologies that are available today.

To go further on this topic, see :

3.3.3 Should Information Management systems be implemented at the project or mission-level?

Project data management should be designed as central and as straightforward as possible to promote data comparability overtime and across different projects. On the other hand, the local use and ownership of data should be encouraged as much as possible. The more local teams are truly empowered in the use of data, the better the dynamics will be on data collection, data quality, data reporting, sharing data and findings with communities.

Projects may have a wide range of program data that is needed for certain activities that do not necessarily correspond to other activities. For example, indicators relating to WASH activities are not always relevant to food security projects on a multi-sectoral project, and different teams may be managing and implementing these different activities. In addition, partner organizations may collect data using different tools, including potentially asking questions differently relating to the same indicators.

However, an overall IM system should be established to include information from all activities. Doing so will greatly assist in strategic decision making and resource prioritization, while also reduce time spent on data consolidation and reporting.