Providing tools to make your data talk

                Problems occur for organisations when they start collecting data and then, after the data has been collected, ask themselves what they can do with it.  Often, they realise that they have not collected a key piece of data, or that they have collected too much, and so have paid for data they do not need.  So, our advice to clients is to be 100% clear on how you will use the data you collect BEFORE you collect it.  This can be a frustrating task at the start of a project when everyone is excited about what the data will show and just want to get going with the data collection, but it’s like building the foundations for a house – you spend a lot of time digging down with little to show for your efforts, but without this investment, the building will fall.  A clear data framework is the foundation for any project involving data collection.

                Once the data has been collected, you need to make the most of your investment.  Using the right tools to analyse the data helps increase the value of the project.  Sometimes we see investment wasted because the data that has been collected is not analysed effectively.  This is something we want to help clients avoid and so have developed tools on our web platform to help with data analysis.

                In a GeoTraceability System, all the data gathered on each smallholder (for example production, household, training, credit, payments, inputs provided) is recorded in a ‘supplier profile’ (see diagram 1).  It is also possible for the system to make automatic calculations using the data gathered – for example if you know the size of a farm and the annual yield, the system can automatically calculate yield per hectare.  If delivery data has also been collected, you can estimate the amount of side selling occurring.

Diagram 1: Supplier Profile

Diagram 1: Supplier Profile

In addition to viewing the data on each individual farmer, the results for all the farmers are aggregated.  The results can be viewed in a number of different formats: bar graph, pie chart, table, and thematic map.  At any point, you can capture the result, a graph for example, by saving it on a pin board (see diagram 2).  The pin board lets you group results under different categories, which you can manage.  The results can then be integrated into a PowerPoint or Word document at a later date.

Diagram 2: Pin board

Diagram 2: Pin board

                You can also combine different indicators and do a multi-criteria analysis.  For example, the producers who delivered in February, have over 40 years old and apply over $500 value of fertiliser.  This iteration creates a ‘story’ that you can save and re use after (see diagram 3). It can also be used to monitor project indicators, such as the number of women who have been trained on how to prune.

Diagram 3: User Story

Diagram 3: User Story

If the above functions don’t give you the insight you’re looking for, you can also us the Pivot Table on the web platform (see diagram 4).  Here different pieces of data can be seen in relation to each other as you make a simple matrix, for example, the distribution of age per gender.  But you can also add a third dimension, like production, to see if the gender and the age impact production.  You can express the result as the average production for female farmer in the range 21 to 30 years or the tonnage produce by this group.  You can also add a forth or a fifth dimension and so on to see if other factors impact production, like the household revenue or the average distance to the nearest clinic.

Diagram 4: Pivot Table

Diagram 4: Pivot Table

Once you’re happy with your analysis, you can save it on Excel on a click of a button (see diagram 5).

Diagram 5: Pivot table results in Excel

Diagram 5: Pivot table results in Excel

With the GeoT tools you can analyse your data from any angle.  For example, show me first the producers who grew peanuts on their farms, and then, looking at only this sub-section of producers, which containers shipped to my clients contain their cocoa beans.  Or the other way around: one client says they found peanut residue in a batch of cocoa butter they processed with the beans you supplied, so they want to know if some producers who have contributed to this batch grow peanuts.  This shows how you can easily understand the power of the System to prevent contamination or to manage product recalls, as an example.

You can learn more on data analysis by getting in touch with us (https://geotraceability.com/contact-us/).