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How can you analyze agribusiness data like a pro?

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Agribusiness data analysis is a vital skill for anyone who wants to improve their decision-making, optimize their operations, and gain a competitive edge in the agricultural sector. But how can you analyze agribusiness data like a pro? In this article, we will share some tips and tools that will help you collect, process, and interpret agribusiness data effectively.

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1 Choose the right data sources

The first step in analyzing agribusiness data is to choose the right data sources that are relevant, reliable, and accessible. Depending on your objectives, you may need data from various sources, such as market prices, weather forecasts, soil tests, crop yields, farm inputs, customer feedback, and more. You should also consider the quality, frequency, and format of the data, and how you can integrate them into your analysis.

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Shailendra Vishwakarma

LinkedIn Top Voice ??| Senior Data Analyst at Eaton | John Deere | 10K+ LinkedIn ?? | 1M + Content Views ?? | Google Certified Data Analyst | Power BI | DAX | Power APPS | Python | SQL | ArcGIS | Datamart | Dataflows
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??Selecting the right data is a crucial first step, the entire analysis hinges on this decision. In my experience with crop-related projects , data collection Stars prior 20 days of seeding till harvesting , there is great significance of collecting data on soil conditions, weather and yield. ??Ensuring data quality is paramount. Following the ETL process, we delve into rigorous statistical analysis, extracting valuable insights. Amidst the abundance of data, discerning what constitutes good data for analysis is of utmost importance.

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Igor V.

Empowering Digital Transformation through Data | Data Management & Analytics Advisor | Mentor
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Actually, I disagree with that statement. In agro-business like any other industry, you shouldn’t start by choosing the right sources, you should start by narrowing down the hypothesis that you are trying to prove, then once you have them settled down you start to work on acquiring the right either internal or external data sources. By focusing on the hypothesis rather than the data sources you also reduce the amount of “misleads” that a vast quantity of non-contextualized data would generate.

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Avani Patel

Senior Business Analyst |Business Processes Analyst | Data Integration | Healthcare Data | Auto Industry
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Like ..analysis of any industry or subject.. its important to understand structure of the business which helps identify different areas that creates value and helps make decision to see important areas. Identify different areas that creates demands and fullfill supply management along these two are being impacted. This would analysts a starting points that is needed.

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Sridhar Reddy

Sr. Business Intelligence Analyst
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One should always understand how business functions and what KPI'S will be considered for measuring performance. In another way, understanding the business leaders mindset in reading numbers would help in presenting the numbers.

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Keenan Lakey

Analytics Engineer with a foundation in Management Accounting and a passion for end-to-end automation
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The best place to start, is on the field. Understanding the real lay of the land will lead you to asking the right questions and the right questions will develop robust hypothesis.

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2 Use appropriate data analysis methods

The next step is to use appropriate data analysis methods that suit your data type, size, and purpose. There are many data analysis methods available, such as descriptive statistics, inferential statistics, regression analysis, cluster analysis, and more. You should choose the methods that can answer your research questions, test your hypotheses, and generate insights. You should also be familiar with the assumptions, limitations, and interpretations of the methods you use.

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3 Apply data visualization techniques

The third step is to apply data visualization techniques that can help you communicate your findings, identify patterns, and highlight trends. Data visualization techniques include charts, graphs, maps, dashboards, and more. You should use the techniques that can convey your message clearly, accurately, and attractively. You should also follow the best practices of data visualization, such as choosing the right colors, fonts, labels, and scales.

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4 Use data analysis software

The fourth step is to use data analysis software that can make your work easier, faster, and more efficient. Data analysis software can help you collect, organize, manipulate, analyze, and visualize data in various ways. There are many data analysis software available, such as Excel, R, SPSS, Tableau, and more. You should use the software that matches your skill level, budget, and needs. You should also learn how to use the software features, functions, and commands.

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Igor V.

Empowering Digital Transformation through Data | Data Management & Analytics Advisor | Mentor
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This fourth step should be your third one… to me you started by analyzing the data and then using data visualization tools to confirm the patterns and finds that you are getting from the data as well as creating a more friendly way to present your results

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5 Validate and verify your results

The fifth step is to validate and verify your results to ensure their accuracy, reliability, and validity. You should check your data for errors, outliers, inconsistencies, and biases. You should also check your analysis for logical errors, statistical errors, and ethical issues. You should also compare your results with other sources, such as literature, experts, or peers. You should also acknowledge the limitations and uncertainties of your results.

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6 Report and share your findings

The final step is to report and share your findings with your audience, such as managers, clients, or stakeholders. You should prepare a report that summarizes your objectives, methods, results, and conclusions. You should also use data visualization techniques to support your report. You should also share your findings in a way that is clear, concise, and persuasive. You should also invite feedback and suggestions for improvement.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Rolf Brandes

Analytics Consultant | Automotive | Retail | Louwman Group
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This article is so general it applies to any industry, although the challenges the agricultural businesses face are often very specific. One of the more basic differences between agriculture and other industries is that the level of digitalization is often much lower. They use elaborate software to monitor their machines, energy or climate but hardly ever go beyond using Microsoft Excel when it comes to examining their data. This is why I think their biggest challenge lies in convincing management that data is an important asset in the ongoing optimization of their processes. Many agricultural business have no issue with investing millions into machinery but remain apprehensive when it comes to investments in their data and insights.

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How can you analyze agribusiness data like a pro?

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1 Choose the right data sources

2 Use appropriate data analysis methods

3 Apply data visualization techniques

4 Use data analysis software

5 Validate and verify your results

6 Report and share your findings

7 Here’s what else to consider

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