How can data visualization help you choose the best agribusiness investments?

Agree. Without data, agribusiness are unable to plan adequately. Data is the basis for planning, projections and trend analysis.

Agribusinesses, operating at the nexus of agriculture and commerce, are undergoing a transformative shift propelled by the escalating importance of data. In this digital age, leveraging data isn't just beneficial; it's a strategic necessity for agribusinesses aiming to thrive in a complex and dynamic environment.

In agribusiness, there is a balance between technological and observational data. Technical data, raw and precise, informs trends, impute impacts, and resource management, crucial for strategic planning and risk mitigation. On the other hand, observational data offers a local, nuanced native perspective essential for sustainable practices and rural community engagement. Together, they allow informed, balanced decisions, improving productivity, profitability, and regeneration over degradation in the sector where all wealth originates. Land! Underscoring its fundamental role in humankind's well-being.

No data, No decision-making, unless you're playing. Agribusiness has a complex web but if you can be able to collect data. You can simplify the work by 90%.

Data is a first key to success in any walk of business now a days. Specially in Agribusiness, Data has much importance. with the help of real time data and a professional analysis decision for the crops availability at the right place and at right time can be made possible. climatic factors, resources management, social needs etc can be measured and act accordingly

Visualizing agribusiness data can be transformative, but it's essential to distinguish between data collection and processing. When collecting data, such as soil samples or NDVI for precision fertilization, we must consider the limitations of the data. For instance, while NDVI indicates plant health, it doesn't pinpoint the exact cause of a health issue—be it fertilizer deficiency, irrigation problems, or uneven soil conditions. Processing the data to make informed decisions is crucial. It's important not to act hastily based on initial data. Comprehensive analysis and understanding of the data are necessary to accurately determine the underlying factors affecting plant health and to implement effective solutions.

Effectivement, pour le cas de la Côte d'Ivoire, les données sur l'évolution de la production, des exportations, de la consommation, des prix locaux et l'exportation sont collectées au Ministère d'Etat, Ministère de l'Agriculture et du Développement Rural Concernant les structures de recherche agricole, à savoir le Centre National de Recherches Agronomiques (CNRA) possède des données sur le rendement et les potentialités des cultures pérennes et annuelles ainsi que les vivriers. En outre, des données sur la rentabilité des cultures pourraient être trouvées au Fonds Interprofessionnel pour la Recherche et le Conseil Agricole (FIRCA) et à l'ANADER Le Conseil du Coton et de l'Anacarde enregistrent des données sur la production, l'exportation

The array of data types available for visualization in agribusiness is transformative for decision-making. Crop yield and quality data enhance productivity and profitability. Market price and demand data optimize marketing and sales. Weather and climate data mitigate natural disaster and pest risks. Soil and water data promote sustainability and reduce environmental impact. Social and demographic data inform market dynamics. These insights empower us to make data-driven, sustainable decisions in a dynamic industry, ensuring competitiveness and responsible agriculture.

Data visualization can help investors choose the best agribusiness investment by providing a clear and concise representation of the relevant data. This can include information such as market trends, crop yields, weather patterns, and financial performance. By visualizing this data in an easily understandable way, the tool could display a graph showing the average yield per acre of different crops over time. This would allow investors to compare the performance of different crops and identify which ones are likely to be the most profitable. Similarly, a map displaying weather patterns and soil conditions in different regions could help investors determine which areas are best suited for certain crops.

Cada Fase de un Proyecto (AgroIndustrial) tiene sus Factores Claves (Indicadores Claves de Desempeño o Medidor de Desempeño / KPI´s) de evaluación que deben ser debidamente definidos en forma y fondo/tiempo-inversión-rendimiento, de acuerdo a la Especificidad del Proyecto. Las Matrices Claves: Producción (área sembrada), Productividad (Rendimiento por Área Sembrada), Calidad (Pre y Postcosecha), Objetivo (Mercado-Tamaño-Venta-Uso). Y como Transversales: Especie, Clima, Suelo, Nutrición, Drenajes, Plagas, Calidad de Cosecha-Producto, Contenido Nutricional, Fructosa, Normal Maduración, y más, han de evaluarse de manera multifactorial, geoestadística, trazabilidad, inversión-rendimiento económico y el tiempo – mercado en el que se oferta. .

In the context of agribusiness and the discussions so far, several types of data can be visualized to gain meaningful insights and facilitate decision-making: 1. Crop Performance Metrics 2. Weather and Environmental Data 3. Supply Chain Tracking 4. Market Trends and Pricing 5. Soil Health and Nutrient Levels 6. Financial Analytics 7. Farmers' Collaborative Platforms 8. Energy Usage and Efficiency 9.Regulatory Compliance Dashboard 10. Sensor and IoT Data 11. Predictive Analytics 12. Blockchain Traceability Visualizing these diverse datasets empowers agribusinesses to derive actionable insights, enhance operational efficiency, and make informed decisions across various aspects of their operations—from the field to the market.

Choosing the right data visualization tool is pivotal for effective data communication. Considerations like data source, format, purpose, audience, budget, and time constraints all play a role. Ensure the tool can seamlessly handle your data format. Tailor your choice to the intended audience's accessibility and interactivity needs. Budget and time constraints should align with the tool's cost, learning curve, and customization options. With a wide range of tools available, from spreadsheets to online dashboards, your choice should match the complexity of your data and the depth of insights required. This thoughtful evaluation ensures that your data visualization efforts are both impactful and efficient.

Lo que entra es lo que Sale. FONDO/Entrada. – (1) El Experto Identifique (2) los Datos a Evaluar (3) Indicadores/Kapis, (4) de cada Fase-Proceso-Proyecto-Especificidad (5) a medirse adecuadamente (Frecuencia/Representatividad), (6) usar una excelente herramienta de Análisis de Datos (SIG-Geoestadística), (7) y que el análisis final, se exprese de manera gráfica, en su interpretación correcta. & FORMA/Salida. – La visualización solo tendrá que ajustarse “al FONDO” que estará debidamente cubierto. La Visualización de Datos (Geopórtales y otros) están mayormente normalizados (formatos de archivos, programación y más), incluso las clásicas de Excel u otras, pueden integrarse. Lo demás es Inversión-Demanda de Visualización-Control de Datos.

Lo que entra es lo que Sale. FONDO/Entrada. – (1) El Experto Identifique (2) los Datos a Evaluar (3) Indicadores/Kapis, (4) de cada Fase-Proceso-Proyecto-Especificidad (5) a medirse adecuadamente (Frecuencia/Representatividad), (6) usar una excelente herramienta de Análisis de Datos (SIG-Geoestadística), (7) y que el análisis final, se exprese de manera gráfica, en su interpretación correcta. & FORMA/Salida. – La visualización solo tendrá que ajustarse “al FONDO” que estará debidamente cubierto. La Visualización de Datos (Geopórtales y otros) están mayormente normalizados (formatos de archivos, programación y más), incluso las clásicas de Excel u otras, pueden integrarse. Lo demás es Inversión-Demanda de Visualización-Control de Datos.

Choose first between a table or a figure, and then, if using a chart, choose a type of chart that works well for the number of variables, as well as their type (continuous or discrete). For more guidance on how to choose the type of graph, see the United Nations' guide to Making Data Meaningful.

Mientras, la Creación de los “Mapas” requiere de un alto nivel de proceso-análisis, la Visualización debería ser una interfase amigable que permita aprovechar (extracción) de la Información intuitivamente, que iría de mínima/Referencial a máxima/Cualificada, por cliente-necesidad, el tiempo que requerirá interactuar, el nivel de requerimiento, cantidad y calidad de la información, incluso bajarla, que se acople a otros programas-componentes, y más, lo que hará que se pase de una visualización sencilla a otras más “compiladoras o integradoras”, Ej. los Dashboards, con representaciones desde sencillas a complejas que abarcan: comparaciones, tendencias, proporciones, ratios-tasas y más, pues la idea es TENER LA IMAGEN INTEGRAL DEL PROYECTO.

Creating effective visualizations involves more than just choosing the right tool; it requires following best practices. Start by defining your objectives and questions to guide data selection and analysis. Choose the appropriate visualization type (e.g., bar charts for comparisons, line charts for trends). Use clear labels, colors, scales, and legends for readability and accuracy. Avoid clutter and distortion, such as excessive elements or misleading effects. By adhering to these principles, your visualizations will effectively convey information and insights to your audience.

Creating effective visualizations involves thoughtful consideration of both the data and the audience. Here are key principles to guide the creation of impactful visualizations: 1. Know Your Audience: Understand who will be viewing the visualization. Tailor the design and complexity to match the audience's familiarity with the data and their specific needs. 2. Clarify the Message: Clearly define the purpose of your visualization. What message or insight do you want to convey? Ensure that the visualization serves this primary objective. 3.Choose the Right Chart Type: Select a chart type that best represents the relationships within the data. Bar charts, line charts, pie charts, and scatter plots are examples of different types.

Less is more. Show only what is essential to make a decision. Do not put the user in the seat of a data analyst! Think about what the user need to get the job done and present just that in the most efficient and intuitive way.

Leveraging visualizations for agribusiness decision-making is a strategic advantage. Visuals offer a quick overview, aiding in issue and opportunity identification. They uncover data patterns, revealing causes and effects, aiding in informed choices. Comparative analysis through visualizations helps evaluate scenarios effectively. Additionally, they serve as persuasive tools, simplifying complex findings for stakeholders. In agribusiness, where decisions impact yields and profitability, visualizations empower data-driven choices, risk mitigation, and sustainable success.

Effectively using visualizations for decision-making involves integrating data insights into the decision-making process. Here are few guide on how to leverage visualizations for informed decision-making: 1. Define Decision Objectives: Clearly articulate the decision you need to make. Understand the key factors and variables that influence this decision. 2. Identify Relevant Data: Determine the data points crucial for the decision at hand. This could involve historical data, real-time metrics, or a combination of both. 3.Choose Appropriate Visualization Tools: Select visualization tools that align with the nature of your data and the insights you aim to convey. Common tools include Excel, Tableau.

Before you start putting up the data in a visualization tool, it is important to understand the goal of the process. You will have to alter what you show in the data according to the audience and what you want them to do next. For example, if your stakeholders want to see your campaign performance, you won’t show them data around the tools you invested in or the task list of the agency that you hired for the campaign

With the expanding volume of data thrown at agribusiness professionals in this era, data visualization has become an absolute necessity, to even process half of the information thrown at us. The danger is in the interpretation. The danger is also in being caught up in data, catch phrases and 'keeping up with the Jones's' and losing those things which have made the sector so successful. The agribusiness sector has been extremely successful in overcoming real issues, with real and innovative solutions for centuries. My advice is to share expertise and find trusted partners in the gathering and interpretation of data and allow yourself time to still come up with those innovative solutions for you, and which the industry relies on to develop