Icon: Too much and too little data: a dilemma for decision making
In a data-driven world, individuals and organizations grapple with a paradoxical challenge: navigating an environment characterized by both data overload and data scarcity. This dichotomy creates unique hurdles for data literacy and informed decision-making in multiple fields including nutrition.
Data overload
Data overload has become a significant issue. The sheer volume of available data can be overwhelming, often making it difficult to discern what is useful. Examples of nutrition data overload include:
- Dashboards that aim to include all available nutrition-relevant data and lack prioritization based on more specific data user needs
- Conflicting data on key outcomes such as stunting, anemia and intervention coverage collected using multiple instruments and/or similar but not equivalent metrics (e.g., household surveys, HMIS).
- Large national surveys that collect hundreds of indicators without a clear understanding of why different data points are collected and how useful they are.
- Disaggregation of routinely collected data that are not relevant to the analysis or use of the data by nutrition stakeholders (e.g. some gender disaggregation, age groups)
- Complicated data that may be useful to topical experts or researchers but are not generally understood by nutrition data users (e.g., agricultural production, market and price data).
When faced with too much data, analysts can find themselves struggling to extract meaningful insights, while decision-makers and other data users face challenges in acting on the information presented to them. The task of translating complex data – often collected using intricate and arcane indicators – into actionable insights becomes even more daunting when trying to communicate findings to less technical stakeholders. As discussed in an earlier blog post, though the use of AI technologies is rapidly expanding and affecting how data are used, it is not necessarily solving the more fundamental problems about having and using the right data
Data scarcity
On the flip side, there is a scarcity of nutrition data in different areas.
- Many countries lack data on micronutrient status needed to inform key nutrition policies, including large-scale food fortification.
- A long-standing focus on maternal and child undernutrition contributes to a lack of data on emerging issues such as obesity and diet-related NCDs. Nutrition data on adolescents, school-age children, adult men and elderly people are generally scarce.
- Data on progress in implementing multisectoral strategies and action plans at national and subnational levels is a significant gap. Similarly, there are limited data on the reach or coverage of nutrition interventions or actions, particularly outside of the health sector.
- Data on nutrition budgets and spending on nutrition activities are not widely available.
The absence of data can complicate evidence-based decision-making and contribute to misunderstandings about key issues. Factors contributing to these gaps include inadequate data collection practices, insufficient resources to support the work and proprietary information that limits availability or access.
Importance of data literacy
To address these intertwined challenges of data overload and data scarcity, individuals and organizations working to solve nutrition problems must be strongly data literate. In a previous blog we defined data literacy as the knowledge, skills and mindset to find meaning in data. The meaning in the data should help stakeholders better understand the nutrition situation and contribute to their ability to make sensible and informed decisions about nutrition policies and programs.
Recognizing the difference between what data says (or appears to say) and the significance of what it says can enable stakeholders to better understand and use data. Data literacy requires understanding the nuances of both the available and the missing data, including understanding specific construction of indicators, identifying the data priorities and advocating for the collection of essential data. It involves grasping the underlying public health nutrition principles and context that gives data its meaning and value. Triangulation of data across multiple data sources – which is as much an art as a science– is also essential to data literacy.
Fostering a culture of data use
Having the knowledge and skills needed to support data use is a critical step. However, creating a culture that embraces thoughtful data use is equally important. A robust culture of data use emphasizes collaboration among stakeholders, fostering an environment where perspectives on data are readily shared and openly discussed. It encourages people to identify how data can answer important questions as well as how data can shape those questions. A collaborative spirit combined with a commitment to critical thinking helps stakeholders identify meaningful observations, insights and findings from the data, whether there is too much or too little of it and whether it says activities are going well or poorly.
Need for balanced approaches
Ultimately, navigating the complex landscape of data abundance and scarcity requires a balanced approach. By cultivating strong data literacy skills and fostering a culture that values collaboration and critical thinking alongside sensible and sound approaches to decision-making, organizations can better manage the challenges posed by both extremes.
DataDENT’s Contribution
DataDENT is continuing to reach out to national and global stakeholders to understand how they define and strengthen nutrition data literacy. The priority is to understand what issues matter most at country-level and where to target efforts to strengthen the capacity to use data. The input from stakeholders is helping shape our efforts to support national multi-sector nutrition data strategies in Nigeria and Ethiopia and a Global Good for wider audiences.