The cautious process to data driven change (1): Data for the benefit of all

More and more, data is used as an important asset and means to improve value chains and make the traceability of supply easier. This has made it an essential element of our work; with many companies we work on the setup or improvement of what we call a Management Information System (MIS): a system of collecting, storing, analysing and using data. Setting up such a system is a complex process and there are many aspects in the design and roll-out of the system that can affect success, quality and in the end sustainability of the system. In this blog series we discuss three aspects that can make or break the set-up of a successful data system. In this first blog we focus on the importance of making data insightful so every actor in the chain realises that in fact he collects and uses data for himself and the improvement of his product and business, which is vital for the data quality along the chain. The second blog will be about the ownership of (sensitive) data. In the last blog we will zoom in on the importance of flexible systems that can complement existing structures to get the maximum out of a MIS.

Information System as source of information for improvement

As mentioned, data plays an ever increasing role in supply chain, with traceability often as the driver.  Digital tools, databases and systems play an important role to establish this by organising the collection and storage of the data. With the focus on these two elements, we often see that many actors in the chain collect data because the next step in the supply chain is asking for it; farmers add data to a MIS, so that the end market knows where the products are coming from. Traceability is there but for the farmer groups the collected data has no added value except for, for example, a premium that is paid, because they do not use it or interpret the data in a good way themselves.

Often, this results in data collection being seen as extra work, making it a burden instead of a chance. The logical consequence is that data collection is often not a priority and only the minimum necessary efforts are put into it to make sure that the off taker is happy with the data that he/she gets. This can compromises the data quality, and thus the effectivity of the MIS.

Making data insightful

At FairMatch Support we believe that paying premiums for data collection/traceability will not make the difference for data quality in the long term. And it even misses the opportunity to use the full potential of a MIS. This is because all the data that is usually collected can also be of added value to the collector. He/she should only see this added value, which is often the crux. Because of this, an important element of our work at FairMatch Support is to show the people that we work with what data could offer to them. Concretely, this comes down to making the data that they use insightful to show what is in it for them. This can show them which suppliers are most important to them, where improvement can be made by their suppliers and inform business decisions/investments.

To give a concrete example that we touched upon: when somebody wants to give new beehives as a present and motivation to its suppliers, often the one who delivers the biggest volume is being picked. But it may turn out, when analysing the data, that another supplier may be far more productive, and, therefore, would actually be the one to pick to let the new hives have the biggest impact. In other words, being able to analyse and use the data makes it possible to make better business decisions, identify room for improvement and makes tailor-made training and coaching possible.

If actors, such as farmers and processors, understand that collecting data is also relevant to improve their business they have a reason to fill in data on time and correctly, fostering the data quality for the whole chain. And if they are also going to use and analyse data, it is also a tool for improvement instead of just for traceability. We really notice that when people understand the importance of data collection and analysis with a MIS, the discussions with them change radically and their idea about data change; from a situation in which they had no idea about their needs, they start making suggestions about other possibilities to use the data for in ways that can benefit their business. At that moment, more constructive conversations can take place about data, its use and what it has to offer for the (actor in the) value chain. And in such a situation, a MIS has a much broader and more sustainable impact than making produce traceable.

Are you interested in reading more about data driven change? Keep an eye on our website and social media channels for the next blogs in this series; the ownership of (sensitive) data and the effect of having a flexible system.