A gaping hole in skills with agri-data

Last week the Sprout team attended the agritech Mobiletech conference in Rotorua, New Zealand. The focus of the conference was for technology developers to meet with industry players. The two key take away's from the conference were:

  • Momentum is being created within Agritech in New Zealand,
  • There is an opportunity for someone to fill a gaping hole in capability around turning data into actionable insights for growers within New Zealand.

Connecterra, who presented at the conference, is an interesting start-up founded in the Netherlands. They’re developing a solution that they describe as a ‘fitbit’ for cows. The goal is to allow farmers to improve productivity of their herd through advanced insights generated by deeper knowledge of the cows’ behavioural cycle. Their CEO, echoed the same message we have been giving Sprout companies: ‘Farmers don’t want data, they want actionable insights.’ 

This brings me to what is clear to everyone - corporates, new businesses and researchers. Data capture is becoming a commodity. Sensors, IoT, data transfer and storage - on the spectrum of difficulty, this is now in the ‘easy’ category. 

The gap is in the analysis of this data and the creation of actionable insights. 

Post the conference the Sprout team had multiple discussions on this, and the outtakes were that there's a unique mix of skills required to generate this value for a new venture:

  • Domain knowledge – A person who understands how the data captured relates to the biological, or business process occurring and what decisions can be made from the data.  This could be a number of types of person: a grower, a consultant or an industry leader.
  • Data analysis skills – A person who can analyse the data and look for cause and effect relationships. The reality of this, is that these two people need to work in tandem.
  • A research mentality – When working through large datasets, a scientific approach needs to be taken. The team should start with a hypothesis of what they think the data could be telling them or doing. They should then get to work testing that hypothesis against the data collected. The reason I call this a research mentality, is because there is a high likelihood it will take multiple experiments on the data before someone generates any valuable insights from it. Once the insights are created, a product could then be considered for development.

What does this mean for new businesses or business units looking to take products to market using these technologies? 


Getting a good mix of skills as described above is critical. Then, allowing that team to take time to experiment with the data to create feedback loops with the customer and ascertain value.