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Home » AI-griculture Business
AI and Copilots

AI-griculture Business

Pablo MorenoBy Pablo MorenoMarch 31, 20213 Mins Read
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Many agri-business operations are looking for ways to increase productivity, efficiency and improve the supply chain of products. In general, terms, looking to improve the global operation of the business itself.

As long as the agri-business organizations are growing in production and diversity of products, they generate a higher volume of data that has to be better managed and administered, increasing the complexity of the operation.

External data from social networks and supplier networks, combined with machine sensors and labor equipment operating in the fields, together with traditional CRM systems, are impacting the way traditional agri-business companies are running their operations.

This is a great playground for Big Data Analytics solutions, as Big Data Analytics can improve demand forecasting and operating efficiency, improving the decision-making process in such a complex environment as agri-operations.

The right education of the workforce in Big Data Analytics, combined with the traditional agriculture skills allows the operation to put data in perspective and set up the right context.

Agri-business has a long list of metrics, however the most needed nowadays is the ability to collect processed information from increasing sources.

Let’s take a quick look at some areas where Big Data Analytics can impact any agri-business:

i. Improve forecasting of production of goods

ii. Optimization of seeds and cattle

iii. Supply chain management and improvement of delivery of goods

iv. Real-time alerts to improve decision making process

v. Integrated KPI for a better operation management

vi. Management of crops among multiple geographies and location

vii. Analysis of supplier/distributor

Big Data Analytics can be the foundation of a great variety of capabilities, including identification among correlation of field crops and weather, basic production data for irrigation, fertilization, and harvesting of the optimal crop, among many others.

Another great area where Big Data Analytics can be applied to agri-business is preventive maintenance of industrial equipment, so operational efficiency and cost management will be improved.

There are many discussions nowadays why so much technology can be or should be applied to agri-business when global trend regarding food and agriculture goods is moving towards ‘eco-friendly’ or ‘whole-like’ type of products.

The reality is that technology is not in conflict with the ecological production of goods, as Big Data Analytics is centered on data gathering from multiple devices, and not on how those goods and products are produced.

In addition, the availability of human capital for agri-business, the increasing demand globally for food and agriculture goods, and climate change are forcing the traditional operation style of agri-business towards something more digitalized.

Source: BBC

Also, the availability of productive land is more limited, that’s why it is more needed to improve every aspect of the agri-business operation, end-to-end, from management to distribution.

The good news is that the more technological the operation, the more efficient in terms of productivity and less resource-intensive, so it will help to lower production prices and increase quantity and quality of goods.

So, if you want a good professional future career, deep dive into agri-business; but if you want a brilliant future career, incorporate Big Data Analytics.

More DAC Content by Pablo Moreno

How to Interview a Data Scientist

Roadmap to Digital Transformation

Excel or Power BI? … It’s Not About the Tool

Text Analysis with Power BI

Follow Pablo Moreno’s DAC Profile HERE

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Pablo Moreno
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Business Data Scientist and Project Manager (Waterfall & Agile) with experience in Business Intelligence, Robotics Process Automation, Artificial Intelligence, Advanced Analytics and Machine Learning in multiple business fields, gained within global business environment over the last 20 years. University Professor of ML and AI, International speaker and Author. Active supporter of Open-Source software development. Looking to grow with the next challenge.

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