A quick guide on indicator meta-data

Many M&E professionals don't value the utility of indicator meta-data when designing an M&E system. Most of us think that there are other essential elements of an M&E system that need to be included in an M&E framework. In my opinion, this is a big mistake. Ignoring the importance of indicator meta-data in an M&E system can be the root cause of the failure of an M&E system.

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Why is indicator meta-data important?

Meta-data is critical because it helps explain what exactly we are tracking. Meta-data is key to ensuring indicator data quality and consistency, and it should be completed prior to data collection to ensure that quality and complete data is collected for the indicator. The most commonly used tool to document indicator meta-data is called an ‘Indicator Reference Sheet'.

Which critical elements should be included in the Indicator Reference Sheet - IRS?

Indicator definition

The indicator definition explains the key terms and elements of the indicator. The precise definition of the terms ensures consistent interpretations and hence results in collecting reliable and complete data as per the indicator reporting requirements. Sometimes there are vague terms which are difficult to define. In order to avoid misinterpretation, additional guidance about what can be reported and what is not reported should be included in the definition section of the IRS.

The calculation method

One of the most important aspects of meta-data is the calculation method. This might not be an issue for most indicators because the data could be a sum or a percentage. However, for some indicators, we might need some complex calculation methods to derive the progress value.

In my experience, when designing M&E systems for complex and large-scale programmes, there are often indicators that involve complex calculation methods. It is strongly recommended that the calculation method is explained –and if possible, that we include examples so that those responsible for data collection and checking are aware of the correct calculation method.

The indicator type and the data type

It is also important to know the ‘type’ of an indicator. This type could be:

  • a snapshot: progress at a given time which is not added to the previous progress
  • incremental: progress during a particular time period which is added to the previously reported progress
  • cumulative: overall progress since the start until the cut-off date Clarifying and including such details are important for data quality and reporting.

Likewise, it is essential to know the data type for the indicator, such as:

  • integer,
  • decimal,
  • currency,
  • or any other unit

Indicator disaggregation

Disaggregation of data is another important meta-data point for an indicator. It defines what kind of breakdowns (e.g. gender, nationality, age group, etc.) will be used for the data collected for a particular indicator. The templates and tools for data collection and reporting are mainly designed on the disaggregation requirement. Any disaggregation category which may create confusion needs to be clarified or defined in the definition section of the meta-data.

Data flow

In order to know the data life cycle and to set clear roles and responsibilities of the staff responsible for data collection, data management, data checking, analysis, and reporting, the data flow process for the indicator needs to be summarized and included in the indicator meta-data. The data flow answers questions like:

  • what is the source of data
  • who collects data (and which tool is used to collect data)
  • who checks data
  • who enters the data into a database
  • what is the timing and frequency of data collection
  • how data management and reporting of data will be performed

Baseline and targets

Baseline and targets are also critical elements for the indicator meta-data. Few indicators require a baseline. However, when a baseline is required, it must be included in the meta-data if the data is available. If the data is not available, a brief note needs to be included to indicate when the baseline data will become available or which method will be used to assess the baseline value for the indicator. The source of data may also be useful information to be included in the meta-data.

Targets are critical; if possible, every indicator should include a target in the meta-data.

Data limitations

Finally, one particular aspect that most of us ignore when it comes to defining indicator meta-data is data limitations. We know that data quality is a big issue; therefore, donors and institutions give a lot of importance to data quality assessment (DQA). To the extent possible, indicator meta-data must include all data limitations for the indicator.

What is the result of providing detailed indicator meta-data?

Having all this information or meta-data about an indicator will result in establishing a robust M&E system. Indicator meta-data is key to ensuring data quality and consistency.

The team of ActivityInfo would like to warmly thank Mr. Maheed Ullah Fazli Wahid for this useful quick guide on indicator meta-data.

Maheed Ullah Fazli Wahid is a high-profile M&E expert with demonstrated experience in designing and managing M&E systems for multi-billion-dollar programmes focusing on humanitarian and development interventions. Currently, he is the Senior M&E System Manager for the EU Facility for Refugees in Turkey (FRiT), a programme consisting of over 100 projects covering projects in sectors such as Education, Health, Livelihoods, Cash Distribution, Protection, Municipal Infrastructure, and Migration Management.