In God we trust, all others bring data
At Yara Digital Labs Brazil we have our own values. They are connected to the company’s values, but when we think about to have our own identity, we have to look to ourselves and understand what is more important for us when we face the customers’ problems. We are the children of a great father, but we have to walk with our own feet.
“In data we trust” is one of these values. Data-driven became one of the biggest fancy words nowadays — and, in talking like a religious way, has been used in the wrong way sometimes.
In my previous article, I bring to life my ideas about the future of farming. Into the core of the future how I see, as well as the core of the future of anything surrounded by technology, is data.
When you think about data, we mind about conversion rates, numbers of users, recurrent users and a lot of indicators. This is how we “communicate” with the product. Sounds like a just born father looking to your baby and trying to figure out what is happening into his mind, even when he was fallen in sleep. This indicators are very important and can guide us to increase the value delivered to our clients and users. But this article is not about that. Is about another kind of data.
The data, not to measure our product, also known as the data for “learn”, when you look to the “lean startup” cycle. What other reason to “measure” if not to “learn”? The data-set behind the future farming — or other innovation product or service or whatever — is the data-set for “build”. Maybe the more relevant question here: to make something capable of learning or adapting without more code lines. The data-set should be used not to tell our team how to increase the value delivered by our product, but to tell our product itself how to increase that for our customers.
The system itself can collect, apply and deliver value based on the data-set available. The big challenge is getting knowledge. We need to know how the things work and bring science here to help us to tackle this task. However, we don’t need to become an expert in this science. Our focus is on shaping the way to make the right data available to collect. The goal of the product is working on what data-set can be more relevant and possible to collect, how to interpret it, how to learn from it, and how to adapt it to become something new and useful.
The product team should work like the most dedicated science students, but with a bit different goal than other students. The goal is to understand the big picture. And then, they have to think about solutions that can learning and adapting itself to solve problems and deliver value to the end user.
There are a lot of buzzwords surrounding us today. Big data, machine learning, artificial intelligence. However, to apply these in the real world we have to focus on our purpose: learning. And there is something that I heard a long time ago and never forgot: the best way to learn something is teaching it to others. So, at this complex times maybe we have to step back and thinking about learning and teaching — and to use Big Data and Machine Learning is about it. To learn and teach.
Every shining new innovation thing that comes to us, is surrounded by a simple or even intangible interfaces. It happens with all the great products. Behind the big complexity that we trying to solve, for the end user, the simplicity is a key. Usability can be the difference between success and failure.
In this context, we have to create the ways to collect the right data from our sources, and “teaching” the services that we built, how to use that to accomplish the mission that it was designed for. The medical science is about learning of a complex system — our body. The agriculture is about learning of a complex system — growing food. The school is about learning of a complex system — teaching students. The problems are very similar but aren’t simple to tackle. When we talking about innovation in a complex system, learning is the key to start. We need respect and understand the complexity, and bring a great team to learn from this, bringing their own path to envisioning the context, the problem, and the solution. We aren’t mad scientists into their basement, mixing elements. The real world is out there, to understand and create experiments, to learning from the people involved in it, to listen more than talk. Go outside and listen to the user!
Bringing back the focus on data, our mission as a product team is to make this data available for our systems and teaching them to read and adapt to solve problems. While we create systems or products only for completing tasks, we don’t bringing any innovation at all to market. The innovation path is about teaching our products to learn from data that we made available, and then, solve the problems themselves. This is the best way we can evolve our products to continue solving big problems. In “data” we trust, but not only the data to listening or watching, but to learning, teaching and adapting, and finally solve the right problem in the right way.
