The top five strategies our clients use to make data actionable

Data is incredibly important to the success of many types of projects, including agricultural ones. For the data to be as effective as possible, however, it is important for it to be actionable. Continue reading to learn five strategies on how to make data actionable in your projects.

1. Know what to measure

It isn’t possible to measure everything in a project, but it also can be difficult to optimize what isn’t measured. Thus, it is important to measure the right things. Some development programs have dozens of indicators, which means implementers must decide which ones to focus on. Not all indicators have an equal amount of programmatic impact. Some are more likely to drive decisions, while others are focused on donor expectations. This makes it essential to know which indicators will have the most potential impact and are the most actionable.

For example, if you know the ultimate objective in a project you are directing is to link agribusinesses to financial institutions, then there are certain indicators that ought to be the focus of the project. You may want to focus on improving the agribusiness’s business acumen or governance. This focus will achieve multiple goals (i.e., improved production, improved labor rights, improved environmental management, etc.) while also giving the agribusiness the skills it needs to link to financial institutions.

2. Use segmentation to drive action

Data segmentation can be a very effective way to make data more actionable. By sorting data into groups based on shared attributes, you can more easily organize your data, which makes it easier to use. Different segments may require you to take different actions in order to create the most impact. Segmentation also allows you to determine which groups are the most interesting or important to your project, so you can then start digging deeper in those specific areas.

Heatmaps, such as the ones offered by the SCOPEinsight Business Intelligence Platform, can be very useful in segmentation.

For example, in the heatmap to the right, you can see that the agribusinesses are sorted by their overall level of professionalism, as determined by their SCOPE score. This makes it easy to determine which are professional, which are maturing, which are immature, and which are very immature. Knowing this information makes it much easier to target low-hanging fruit, such as the more professional agribusinesses, as well as build a targeted technical assistance program for weaker ones.

3. Make the most of benchmarks

Benchmarks allow you to contextualize your data, which gives it more meaning. By understanding how your data compares to other relevant data, you are better able to understand what the data itself shows. Benchmark data can show you how your agribusinesses compare to other agribusinesses in the same sector or in the same region. This allows you to better understand if your agribusiness meets relevant standards.

Benchmarking gives context to data by showing it in comparison to other relevant data.

For example, in the graphs to the right, you can see how the internal management of the assessed agribusiness, which is based in Viet Nam, compares to benchmarks in south-east Asia and the world. You can also see how the region compares to global benchmarks. In the overall dimension and in each of the subdimensions, the agribusiness scored lower than the regional and global benchmarks. This indicates that its internal management practices require improvement to make it competitive on both a regional and global scale.

4. Build a solid optimization plan

While data can help create impact, it is not enough on its own. It is important to have a plan of how you intend to use your data, which will help increase your efficiency. It is best to create a loose plan before collecting data, so you can be sure that you collect the data you will need. The details of the plan, however, can be created based on the data that you collect. This plan can be informed by information from segmentation and benchmarking, particularly if you are dealing with many agribusinesses at once.

For example, if you are creating a tailored technical assistance plan, you can use data to determine the strengths and weaknesses of your assessed agribusinesses and focus your assistance on the weakest aspects. You can also use segmentation to split the agribusinesses into groups based on their weaknesses, if your group is large and diverse enough for that to add value. An established program like the IFC‘s Agribusiness Leadership Program may help you to determine the best way of working.

5. Reassess to determine impact

Reassessments allow you to establish whether a project is making strides towards the set goals and objectives. Comparing your benchmark to your reassessment can clearly show what impact your project is making. If necessary, reassessments can act as a call to change the implementation strategies. They provide you with the information you need to make crucial decisions midpoint and avoid spending additional program money on approaches that aren’t working.

Reassessments can demonstrate what has been improved and where the program needs to focus efforts

For example, in the graphs to the right, you can see that, between the baseline and the reassessment, the assessed agribusiness has improved by varying extents in every dimension. The financial management dimension, for instance, has seen significant improvement and can now be considered professional. However, the market dimension saw lesser improvements and still can only be considered maturing. This may indicate that a next round of training should focus more on helping the agribusiness work in the market to improve that score further.

Are you interested in learning more about how to use actionable data to increase the efficiency of your agricultural projects? Contact us learn more about how we can work together.

Back to news