2.2 The information cycle
The process of turning data into relevant, useable information that supports evidence-based decision-making can be represented as an information cycle (Figure 2). Information is obtained by collecting, managing, analysing, interpreting and reporting data, and forms the basis for designing interventions or formulating policies. All policies, plans and decisions require further data to assess how well they are working (monitoring and evaluation) and improve their design and implementation. Therefore, the process of turning data into information is a continuous cycle.
You will learn more about data collection, processing, analysis, and synthesis and reporting in the later data modules. For now, consider the following points:
Data collection
Data collection refers to all the steps involved in obtaining data from their source. Sources of data are described in more detail in the next section. It also involves decisions about which data to collect – which data units, where, and how many – this is referred to as ‘sampling’ and is covered in detail in the Sampling module.
Data management
Data management refers to a set of processes for preparing collected data into a form suitable for analysis. This includes processes such as data entry, checking for errors and other quality assurance (QA) measures, integrating two or more data sources, and creating indicator variables. It also includes ensuring data are stored securely and in accordance with relevant governance and ethical requirements (see the module on Legal and ethical considerations in AMR data), and can be securely accessed by authorised users (such as government veterinarians or hospital administrators who have responsibility for reporting). Data management systems, including database software and protocols, need to be designed to be user-friendly for data collectors and providers as well as data managers, and informed by their level of training and real-world working conditions. You will learn more about data management in the module on Processing and analysing data.
Data analysis
Data analysis is about identifying and summarising meaningful patterns in data that are of value to a range of stakeholders, from the data collectors and providers, to national governments and international organisations. Data analysis includes processes for describing and summarising data, investigating relationships between variables, and assessing whether there is evidence that one variable (an ‘exposure’ variable) is a ‘cause’ of another variable (the ‘outcome’ variable). In AMR data analyses, the exposure variables are usually those related to the potential risk factors (such as AMU), and the outcome variable is the resistance category or value.
Data analyses can be presented as statements, in tables, as charts, maps and even infographics and other pictorial forms. You will learn more about summarising and presenting data in the modules Processing and analysing data and Summarising and presenting AMR data.
2.1.4 Indicators
