When I think about what it means to have a data-driven culture, I think of an organization where the use of data is championed anytime someone asks a question or makes a decision. If data is used to track progress, provide transparency, and measure success — to me — that is a true data culture.
From the beginning, Indigo Ag was conceived as a data-driven company. With a commitment to improve grower profitability, environmental sustainability, and consumer health through the use of natural microbiology and digital technologies, the problems we’re striving to solve require complex, systematic changes that can only be accomplished through the use of data.
Some of the ways we’re doing this today include —
Indigo Marketplace, a digital platform for buying and selling grain, enables growers to receive premium prices for producing high-quality crops more sustainably, and buyers to source grain with a range of characteristics.
We combine data from remote sensing technology (moisture probes, drones, satellites, etc.) with data from each farm to provide individual insights directly back to each grower. The provides a holistic view of not only what is going on in their farm, but also how that farm compares in aggregate to every other farm on Earth.
Seeking to improve a plant’s natural microbial makeup, we identify and sequence thousands of endophytes, using an approach called “focused sourcing.” Indigo scientists leverage sophisticated genomic sequencing and computational bioinformatics to catalog and assemble a world-class database of genomic information from these microbes. We apply algorithms and machine learning to this database to predict which microbes are most beneficial to the plant’s health.
To help drive these efforts, we leverage the Looker platform and currently have:
Some of our most popular dashboards showcase supply and demand within the Indigo Marketplace, grower profitability, and marketplace bid quality.
My entire career has been working in data and using data to help businesses and people answer complex questions. Since joining the Indigo team, I’ve had the opportunity to dive into agriculture data — which is the hardest data I’ve ever had to wrangle. At Indigo, it’s important to me to provide everyone with equal access to data to help them succeed and ensure better results for our customers.
As the primary liaison between end business users and the Indigo data platform, building our data culture here at Indigo has been a key focus for the Business Intelligence Platform Team. Some of the ways we’re driving platform adoption are through onboarding programs, workshops to further education, and continued maintenance and policing of the Looker platform.
The BI Team works with hiring managers to scorecard analyst positions, gather requirements for an onboarding deliverable, and then constructs a tailored onboarding program for the newly hired analyst. This program is designed to educate analysts in the use of Looker, address skill-set gaps through training, and expose analysts to relevant data all in the context of the deliverable.
Another responsibility of the BI team is to provide ongoing data education and communication for all business and operations units. The BI Team communicates out newly available data within the UDP, manages shared documents outlining where data is available, and facilitates data related workshops.
The BI Team is responsible for managing the Looker platform. This means reviewing PRs, curating explores, and encouraging the use of best practices. The outcome of a well managed Looker Platform should provide: intuitive and easy to use explores, well-defined developer areas, shared spaces that are easily navigated by audiences, and documentation for everything.
Among all organizations, there are common misconceptions about how to use data to make business decisions, which leads to challenges when trying to develop a data culture. For instance, if your organization wants to succeed with data as a whole, gatekeeping or siloing the data should be avoided. As long as it is in accordance with a privacy-by-design data access structure, relevant, vetted, and smart data should be accessible throughout an organization.
In addition to these, the three biggest misconceptions I’ve come across in my career are:
There is no such thing as bad data — just poor analyses or transparency. Bad data is often the product of poor processes, but even the data that is generated from poor process can be used to highlight where the process is breaking down.
Is a common request which typically means there needs to be a conversation with the stakeholder around ‘what are we trying to answer’? This allows us to provide ‘smart’ data — only the relevant data required to answer the question.
This one is expected, but it’s always worth mentioning. Without a robust reporting platform users find the need to replicate data outside of source systems which leads to shadow systems and trusted data sets living outside of those source systems.
By removing hurdles for using data and maintaining a culture of transparency whenever possible, building a culture where questions and decisions are based on data can begin to spark across the organization.
Join the conversation and share your own insights about data culture and data adoption in the Looker Community