Revolutions create significant change no matter the circumstance; there are clear winners and always definite losers; we have seen this with the dotcom bubble in the 2000s and the financial crash of 2008, which have given a notable and demonstrable change to the world and how we do business.
With agriculture and its revolution from a mechanical to a digital age, we will begin to see the winners and losers with the companies and technologies that work and operate in the sector. As we are in the first generation of AgriTech Tools and technologies (Read “A Common Data Framework for Livestock Agriculture”), there are many technical limitations and unfair business models that exploit data ownership. Many of the current generations of AgriTech tools and business models are built on extracting farmer data for value, thereby boosting those companies’ bottom lines.
As we assess the first-generation of AgriTech tools on the market, they are mainly insight-focused, with disparate data handling, user interfaces, Intelligence, black box algorithms, unregulated data quality, and continuing data literacy issues. We need to begin to ask if all these tools and business models can pivot to where farmers own their data pre- and post-algorithmic processing (Read “Farmers own their data”). Most farmers and non-farmers agree that the raw data produced on a farm is owned by the farmer (Fadul et al., 2022), so new business models and infrastructure must be provided.
The pivot in this revolution includes livestock farming, producers, and their farm teams. We need to see farms as true businesses, and businesses need to have the ability of independent business intelligence and ownership over their business data. Data to companies (Facebook, Google, etc.) who have utilized and invested in harnessing your data for their business intelligence have become the wealthiest and most valuable companies in the history of the world, using data as their primary business asset. Farmers and farming are no different if they want to take maximum advantage of their data; the risk is someone else having your data assets that creates a bottleneck for developing new insight or value from your farm’s data.
How is Farm-level Data an Asset?
There are no legal requirements or regulations for the disparate AgriTech on the market and the data they produce today. There is a need for a Common Data Framework for Livestock Farming. We have some standards with ICAR, Interbull, CDBC, DCWC, etc., but we need more specifics for the increasing number of AgriTech offerings coming to the market.
To better understand the value of the data, we have previously compared the data with rare-earth minerals, for which their market value depends on extraction, refinement, and transformation. Likewise, the value of an individual point of data is low but having a system in place to use millions and trillions of data points has significant value, both on and off the farm—data and analytics form part of the value chain.
In the value chain and with farm-level data, value is based on the quality of the product; the raw data is the product. More, higher quality and known validity of the raw data and its metadata will allow farmers to ask more questions to their data about their facility, herd, cows, business, etc., which increases its value to the owner and the potential value to others off the farm (Read “The V’s of Victory in Agriculture Big Data.”). Examples of where the value of data is derived:
- New intelligence that is unique to your business: welfare, sustainability, management, profitability
- Predictive, prescriptive, and diagnostics tools for health, welfare, management
- Digital audits and digital traceability give confidence to buyers and customers.
- Quality and consistent data allows for the marketing of your data to third parties who want to do research or develop new technologies
- Post algorithmic analytical data access
Farmers need to take back control of their data as their asset, which is what will allow them to become the farmers of the future and to pivot from a mechanical to a digital farming age. The most prolific companies understand how to formalize their use of data and analytics. Whether proactively using it to make informed decisions earlier or reacting sooner with specificity, farmers must understand the value chain for data analytics and the subsequent value it can bring to the farm.
With farm-level data and its volume, we are still in the gigabytes level of daily data at the farm. The individual farm’s data will have immense value when we move to terabytes and petabytes. If a service provider has access to multiple of these types of big datasets, it also represents tremendous value to them. It is in the services providers’ business model prerogative to have a leading product and be profitable, as is the wheel of capitalism. This means farms must wield their ownership and control over the data to hold its value, which can be facilitated through a next-generation farm data ecosystem (Read “Farm Data Ecosystems”).
In 2020, for example, it was estimated that the typical person created 1.7 megabytes of data every single second of every day (Becker, 2022). Moving deeper into precision livestock farming and its digital age, how much data will your farm or individual cow generate every day? In Europe, the value of our digital identities, the sum of all the digitally available information about us, will be worth $1 trillion. It’s not just people; ‘things’ are already generating data, which are predicted to create an additional value-add of $1.9 trillion globally over the next five years (Future Agenda, 2022). The US dairy market is projected to be worth $1 trillion by 2030; this growth isn’t going to come from more people in the sector or more cattle on farms, but Research by Accenture has shown that organizations with a strong data culture have nearly 2x the success rate and 3x the return from AI investments.
We now need infrastructure at the farm level for data consolidation (Read “Farm & Ranch Data Consolidation”), that is, a next-generation farm data ecosystem (Read “Farm Data Ecosystems”), allowing farmers to have ownership over the data, security, ability for independent business intelligence, and generate new value; farmers need to tap into that value which is part of their independent business intelligence and farm assets.
Think about your farm; you have a population of animals and all your records over many years from every aspect of the business that is valuable; once they are brought together, it lies with you, the data owner, to increase its value and subsequent utility. This is a long-term strategy with farms to understand your farm data and systems and build your own value chain.
The data from the current generation of tools producing data at the farm level within livestock are limited in their validation and regulation, with many black box algorithms, with limited understanding of the handling, storage, or use of your data once off-farm. The farmer needs to look for new ways to regain control of their data and stop these wild West raids on their disparate data; this is part of farm data ownership, pre, and post-algorithmic processing. Academic researchers should aid in identifying better raw data points that are of value to farmers and their data ecosystem.
Farming is the OG industry; if we can transition and pivot farms from a mechanical age, we can continue to be the greatest achievement of the human species and feed the world sustainably while targeting welfare, profitability, and communities around the farming system. Data is a farmer’s asset; we can overcome and succeed in farm-level digital and data literacy with agile models and the use of off-the-shelf software and hardware adoption and deployment with our data ecosystem, allowing us to have greater cross-industry pollination of skills and a greater career path for our staff in agriculture while building new value to the sector.
With the expected growth in the dairy sector by 2030, fueled by big data and IoT, we need to ensure that the most significant proportion of this value returns to farmers in rural communities, allowing them to be the winners of the digital revolution.
- Cue et al. 2021, Data Governance in the dairy Industry https://www.mdpi.com/2076-2615/11/10/2981
- Fadul et al. 2022, Addressing data bottlenecks in the dairy farm industry – https://www.mdpi.com/2076-2615/12/6/721
- Cooley. 2022 – Don’t sell your dairy’s data for pizza and a beer | Ag Proud
- Threats to Precision Agriculture (dhs.gov)
- Popular Mechanics. 2018, How Much Money Facebook Gets From Selling Your Data (popularmechanics.com)
- Invisibly. 2021, How Much is Your Data Worth? The Complete Breakdown for 2021 (invisibly.com)
- Becker, The fast company. 2022, Personal data value calculator: A state-by-state breakdown (fastcompany.com)
- Deloitte. 2020, Data valuation: Understanding the value of your data assets, Valuation-Data-Digital.pdf (deloitte.com)
- Coyle et al. 2020 – Valuing data – Bennett Institute for Public Policy (cam.ac.uk)
- Future Agenda. 2022, Value of Data – Future Agenda
- Accenture. 2022, Data Value & Data-led Transformation Services | Accenture
- Mawer. 2015 – Valuing Data is Hard – Silicon Valley Data Science (svds.com)