From collection to cash, how to deal with data
When dealing with data, companies need to understand their path from data collection to selling valuable products and services, or they are just going to waste their time and money on consultants.
I’m going to talk about the relationship between two mythical companies, a large fertiliser supply and service company called Ferty and a large agricultural company called Farmo. We will follow Ferty as it collects and uses data to increase the value of its service and then builds new product opportunities.
Data needs to be clean
Data is more usable when it is relatively free of contamination. In the same way that cities don’t allow dirty heavy industry in their drinking water catchments, you should work hard to improve the cleanliness of data flowing into your system. You should be doing the difficult work of ensuring the data is captured as cleanly as possible and flows through to storage reliably, this is more sustainable than trying to clean contaminated or incomplete data sets later.
Example
Ferty manufactures and spreads fertiliser from its trucks directly onto farm fields. Ferty believes that collecting data is important to its business, so it instruments its trucks with a control system that allows them to report the type, quantity and location of fertiliser spread.
If they invest heavily in this project, do a good job of building this control system, physically re-engineer their trucks to allow accurate measurement, have a robust communications network to bring the data back and then store it in a way that is reliable and accessible, they will have clean, usable data.
Ferty could use this data for invoicing, analysing fertiliser application rates, predicting supply chains and providing insights to customers. However, if they didn’t re-engineer their trucks to measure accurately, their network was unreliable or they dump their data into a place that is not easily accessible, Ferty will never get a return on their investment.
Data needs to flow quickly to your customer
If you are collecting large amounts of data from doing business with your customers, you should probably prioritise building the pipes to get that data into their systems as quickly as possible. As you connect your systems to their systems, you will need to have a reliable, easy to consume source of usable data available. Also, it is important to understand that data is often most valuable to customers immediately after it is collected, because it can inform near real-time improvement and optimisation. This is why modern companies don’t just email reports, they build APIs.
Example
Continuing to think about our friends at Ferty, we already know they are collecting lots of high quality, detailed data about fertiliser application on farm fields. Now think about them delivering over thousands of acres of fields owned by Farmo. Farmo uses the latest agricultural management software, recording application of chemicals, growth and yield rate of crops and scheduling production to meet the needs of their customers.
Because Ferty stored their data in an accessible place, they are now well placed to offer API access to the fertiliser application data to Farmo. Farmo can take the data flowing in about fertiliser application and combine it with chemical application, growth and yield rate data and further optimise their farming operations. Not only that, because the data is near-real time they can re-schedule their equipment if the data shows fertiliser application is running earlier or later than expected.
Selling Bottled Data
In California bottled water is mostly sourced from public water sources (it is just tap water), but it is also about 560 times more expensive than tap water. It is important to understand that bottled water isn’t just another source of potable water like the tap is. Instead Coca-Cola Amatil supplies customers with a solution to their thirst, 300ml of cold, clean drinking water in a convenient to carry package right when and where they need it, whether it is by the beach, at the petrol station or in a cooler at a party.
If you are trying to create greater value from data, you need to understand how to package a low value, hard to consume commodity and deliver it to a customer as a solution to their problem, right when they need it most.
Example
Ferty is now in the comfortable position of collecting large amounts of raw data and supplying it directly to Farmo. Farmo combines their live feed of fertiliser data with other agricultural data to optimise their production. But Ferty realises they can also combine it with other data they have in order to provide some discreet, highly valuable insights.
So Ferty takes the experimental test data about soil types, rainfall and leach rates that they collected when they created each fertiliser and use it to build a model that predicts the optimum time to apply more fertiliser. Ferty builds this algorithm into a cloud service and is now ready to sell their new optimisation service as a product.
With this optimisation service, Farmo can pay a monthly fee to automatically submit their soil information and rainfall data and receive automated recommendations on the optimum date, rate and blend for fertiliser re-application. Farmo chooses to pay Ferty for this optimisation because applying the wrong blend, too early or too late wastes money, reduces yield or slows crop delivery.
Don’t just collect data, create value
Ferty was in a weak position prior to starting out on its data journey. Farmo was paying for Ferty to drive a truck and spread fertiliser, Farmo was investigating just buying their own truck and buying cheaper fertiliser. Ferty didn’t keep dropping its price, instead it found a way to provide a more valuable service.
It first focused on collecting clean, usable data about its operations for its customer, then it provided this data directly and allowed the customer to optimise their operations. After they did this, Farmo is not thinking about buying a truck and cheap fertiliser, because they are too busy thinking about how they can combine this new data with other sources to optimise their end-to-end farming operation. Ferty didn’t charge additional money for this, but they did manage to maintain their product and service premium.
After stabilising their market position and focusing on providing a valuable service to their customers, Ferty had the time to explore new product opportunities. In this case, Ferty had the capability to build an optimisation service product that could combine the research and testing they had done for their fertilisers with data their customers already collect. Farmo pays separately for the value this new product provides.
Ferty doesn’t mind that using their optimisation service might reduce Farmo’s fertiliser consumption, because they understand that in the long run their success is only possible if they help their customers succeed.
