Agribusiness data analytics and intelligence roles
Organizations should strive to have the correct collection of capabilities to utilize data generated, which will create a competitive advantage.
By: Lourival Monaco
It is more than clear now that data-driven decision-making is crucial for any company that aims to remain profitable and competitive in today’s markets. Agribusiness’ companies are no exception to this rule.
More than just basing their decision-making on data, a company needs to fully embrace the concept of competitive intelligence, in which the right data, at the right time, analyzed in the best form, communicated optimally, and acted upon will produce competitive advantage.
In order to achieve this, especially considering the impact and relevance of Big Data, embedded analytics, IoT, Machine Learning, and many other data and computational related aspects in modern agriculture, a new set of competencies are needed to fully leverage the data assets being generated in farms. Thus, a demand for analytically capable workers is being created in the agribusiness sector.
These new professionals will not solve all the new puzzles and produce all the answers for agribusiness companies moving forward on their own. They will need to be inserted in a structure that will add their competency to other more traditional aspects such as agronomical knowledge in a synergistic way. Also, a computational, but user-friendly environment needs to be created for this new horizontal and network-driven structure to fully function.
Finally, a data-driven, or even better, an intelligence-driven culture needs to be fostered in agribusiness companies. But in the same way “greenwashing” is a problem for companies that are pursuing sustainability, “datawashing” a company will not help. Data need to be the fuel for better business performance, not be in its way.
Considering this, a generic work structure or organizational chart for agribusiness companies that want to be more efficient in using data as the source of competitive advantage is proposed:
The roles represented in this chart are not necessarily performed by a person. They can be performed by a team, or more than one role can be under one person’s responsibility. The important takeaway here is the function that needs to be fulfilled. Each role has specific responsibilities:
·Chief analytics officer (CAO) or Chief intelligence officer (CIO): Here I’m using a C suite professional as an example, but this can be at a managerial or supervisory level. This role connects the decision-makers to the analytical and intelligence team. Usually, the intelligence team’s power to do things derivates from these sponsoring positions in the company and the CIO or CAO is the one to make this connection. On the other side of this coin, this role is also the focal point for intelligence and data demands. Also, this is the person that will manage the analytical team, demanding not only technical skills in analysis, agronomy and IT, but also managing people and connecting them to the rest of the company.
·Data scientist: This is a key role in the contemporary analytical team in an agribusiness company. This person/team will be in charge of data acquisition (in the best form possible), data cleaning, organization and analysis. The analysis can range from simple excel tables and graphs to very complex statistical modeling and computational simulation, utilizing softwares like R, STATA, SAS, Tableau, and many others. This professional is the one to make the first important data transformation – data into information.
·Technical/Agronomical Specialist: This a professional/team that will provide the bridge between the cold information from the numbers analyzed by the data scientists and the real agricultural world. Of course, this role demands some knowledge from data analytics due to the necessity of communication with data scientists, but this professional will not run the analysis. They will check the data and using their technical knowledge come up with insights of what the results will imply for the business, either theirs or their customer’s. There is a great synergy possible here when the agronomist and data scientist can speak the same language. This role is in charge of creating the reports that will be used by the decision-makers. This is the professional that will create the second data transformation – information into knowledge.
·IT specialist: As databases get larger, analytical tools get more sophisticated and the speed/automation of data gathering increases, the necessity for a data management ecosystem becomes more crucial. This role will provide the stage in which the data scientist will be able to develop their function to the best of their capacity and where the agronomist will create their reports. Also, this professional/team will be fundamental in the data collection and data organization steps of any intelligence effort. Maybe in the future, people will be more versed in technology and will not need as much support when working with data and data-based reports, but for the time being, it will be a part of this professional/team’s role to help people navigate this area.
·Collaboration network: This role is intended for any other person in the company that is willing to help with the analytics/intelligence team. More often than not they are providers of primary information, collected straight from customers, partners, competitors, etc. This kind of information not only is extremely valuable but also hard to find in any other way. The company’s culture plays a major role here because the collaborators need to be data/intelligence-driven to understand the importance of collecting information on the field or their daily work and providing it to the analysis team. Normally, the results of proving this information for analytical efforts are great and help the whole company, but they are indirect, which reinforces the necessity of a cultural focus on data in the company.
As it can be observed, this organizational scheme is very horizontal e network-like. The idea behind this is that each of these roles has specific knowledge and the result of their work as a team is greater than the sum of its parts. A data-scientist will learn some agronomy skills as he/she works with an agronomist, and vice-versa. The learning curve is exponential and will create more sophisticated and precise analysis as the team gets more acquainted with each other’s area.
Analytics and the resulting intelligence from it will drive the success of agribusiness companies moving forward, across the entire chain. Having the right set of capabilities to leverage the data resources that will be created is the key to achieve competitive advantage in this space.
By: Lourival Monaco
It is more than clear now that data-driven decision-making is crucial for any company that aims to remain profitable and competitive in today’s markets. Agribusiness’ companies are no exception to this rule.
More than just basing their decision-making on data, a company needs to fully embrace the concept of competitive intelligence, in which the right data, at the right time, analyzed in the best form, communicated optimally, and acted upon will produce competitive advantage.
In order to achieve this, especially considering the impact and relevance of Big Data, embedded analytics, IoT, Machine Learning, and many other data and computational related aspects in modern agriculture, a new set of competencies are needed to fully leverage the data assets being generated in farms. Thus, a demand for analytically capable workers is being created in the agribusiness sector.
These new professionals will not solve all the new puzzles and produce all the answers for agribusiness companies moving forward on their own. They will need to be inserted in a structure that will add their competency to other more traditional aspects such as agronomical knowledge in a synergistic way. Also, a computational, but user-friendly environment needs to be created for this new horizontal and network-driven structure to fully function.
Finally, a data-driven, or even better, an intelligence-driven culture needs to be fostered in agribusiness companies. But in the same way “greenwashing” is a problem for companies that are pursuing sustainability, “datawashing” a company will not help. Data need to be the fuel for better business performance, not be in its way.
Considering this, a generic work structure or organizational chart for agribusiness companies that want to be more efficient in using data as the source of competitive advantage is proposed:
The roles represented in this chart are not necessarily performed by a person. They can be performed by a team, or more than one role can be under one person’s responsibility. The important takeaway here is the function that needs to be fulfilled. Each role has specific responsibilities:
·Chief analytics officer (CAO) or Chief intelligence officer (CIO): Here I’m using a C suite professional as an example, but this can be at a managerial or supervisory level. This role connects the decision-makers to the analytical and intelligence team. Usually, the intelligence team’s power to do things derivates from these sponsoring positions in the company and the CIO or CAO is the one to make this connection. On the other side of this coin, this role is also the focal point for intelligence and data demands. Also, this is the person that will manage the analytical team, demanding not only technical skills in analysis, agronomy and IT, but also managing people and connecting them to the rest of the company.
·Data scientist: This is a key role in the contemporary analytical team in an agribusiness company. This person/team will be in charge of data acquisition (in the best form possible), data cleaning, organization and analysis. The analysis can range from simple excel tables and graphs to very complex statistical modeling and computational simulation, utilizing softwares like R, STATA, SAS, Tableau, and many others. This professional is the one to make the first important data transformation – data into information.
·Technical/Agronomical Specialist: This a professional/team that will provide the bridge between the cold information from the numbers analyzed by the data scientists and the real agricultural world. Of course, this role demands some knowledge from data analytics due to the necessity of communication with data scientists, but this professional will not run the analysis. They will check the data and using their technical knowledge come up with insights of what the results will imply for the business, either theirs or their customer’s. There is a great synergy possible here when the agronomist and data scientist can speak the same language. This role is in charge of creating the reports that will be used by the decision-makers. This is the professional that will create the second data transformation – information into knowledge.
·IT specialist: As databases get larger, analytical tools get more sophisticated and the speed/automation of data gathering increases, the necessity for a data management ecosystem becomes more crucial. This role will provide the stage in which the data scientist will be able to develop their function to the best of their capacity and where the agronomist will create their reports. Also, this professional/team will be fundamental in the data collection and data organization steps of any intelligence effort. Maybe in the future, people will be more versed in technology and will not need as much support when working with data and data-based reports, but for the time being, it will be a part of this professional/team’s role to help people navigate this area.
·Collaboration network: This role is intended for any other person in the company that is willing to help with the analytics/intelligence team. More often than not they are providers of primary information, collected straight from customers, partners, competitors, etc. This kind of information not only is extremely valuable but also hard to find in any other way. The company’s culture plays a major role here because the collaborators need to be data/intelligence-driven to understand the importance of collecting information on the field or their daily work and providing it to the analysis team. Normally, the results of proving this information for analytical efforts are great and help the whole company, but they are indirect, which reinforces the necessity of a cultural focus on data in the company.
As it can be observed, this organizational scheme is very horizontal e network-like. The idea behind this is that each of these roles has specific knowledge and the result of their work as a team is greater than the sum of its parts. A data-scientist will learn some agronomy skills as he/she works with an agronomist, and vice-versa. The learning curve is exponential and will create more sophisticated and precise analysis as the team gets more acquainted with each other’s area.
Analytics and the resulting intelligence from it will drive the success of agribusiness companies moving forward, across the entire chain. Having the right set of capabilities to leverage the data resources that will be created is the key to achieve competitive advantage in this space.