The future of agri-data: Predictive analytics

Over the past 11 years, we have collected an enormous amount of data about agribusinesses (cooperatives, agri-SMEs, producer organizations, etc). We have combined this data with predictive analytics to create tools that allow clients to assess specific risks that agribusinesses face. Clients will now pinpoint specific areas of vulnerability with a clear focus through our business intelligence platform.

Predictive analytics calculates future chances

Predictive analytics is the science of using past and current data to predict future events by finding and identifying patterns. An everyday example would be planning around a chronically late friend. If a friend is always running late, you will probably assume that friend will arrive to future events late. Therefore, you may alter your plans accordingly (for example, tell them to arrive earlier than necessary to compensate). This is a basic example of predictive analytics: your data regarding your friend’s past lateness allows you to predict some of their future actions.

With larger, more complex datasets, the process of predictive analytics becomes accordingly more complex. Nevertheless, the technique can be used to identify many trends and offer solutions to many complex problems. Once you’ve decided what trend or problem you want to focus on, the first step is to collect and mine your data. The data must be clean and reliable for optimal results. This data is then input into a statistical model that, when run, will generate predictions. This model can be refined as needed to increase the accuracy of the predictions. Once the results are deemed accurate enough to be actionable, they can predict future behavior or prepare for likely scenarios.

SCOPE data is robust and reliable

When doing predictive analytics, the data used is vital to the success of the predictions. For example, there must be enough data to create the model for it to make valid predictions. Therefore, the more available data to go into the models, the more accurate the outcomes will be. For the past 11 years, our clients have completed over 5,000 assessments. This high volume of data means that we have enough data sets to fill our models, increasing their predictive accuracy.

As well as data volume, data quality is also paramount. Unreliable data will lead to unreliable predictions. At SCOPEinsight, we have the highest quality data about agribusinesses available in the sector. In addition, we have established many procedures and checks to ensure the quality of our data.

Our work with predictive analytics

Our Business Intelligence team, recently, has been working to utilize predictive analytics to assess common risk factors for agribusinesses. Currently, we are working together with NewForesight Consulting to create a data model that can predict the likelihood that an agribusiness can provide a living income to its members. We have used our vast dataset to train and validate the model. It focuses on the questions from a SCOPE Basic assessment that apply to the topic; for example, the living income model looks at questions about yields, side selling, awareness of market risks, etc.

After the model analyzes the data input, we can provide risk analysis, provided on a scale of 1 to 5. This process is nearly instantaneous. We can also provide information regarding which key attributes are the most important to improve. The beta version of this model will be available through our Business Intelligence Platform in early 2022.

Easy risk analysis with quick and reliable results

This sort of analysis is beneficial for strategically pinpointing the areas of an agribusiness that require support and improvement. For upstream market players, this can be the difference between making a profit or losing income.

The information can also validate certifications and other similar programs by showing that certified agribusinesses are more likely to, for example, provide a living income to their members.

Living income is only a first step. We are also working on another model to predict an agribusiness’s likelihood of gaining access to finance. Also on our radar is joining our data with others’ data to create cutting-edge predictions.

Are you interested in working together on a predictive model? Contact us today to discuss co-development options.

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