If you ask chatGPT "How is big data managed in the logistics process within an organization, and what are the recent advances?" a reasonable answer comes up:

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Managing big data in the logistics process within an organization has become increasingly critical in recent years. Here are some of the recent advances in this field:

1. **Real-time Data Analysis:** Logistics companies are now using big data analytics to process and analyze data in real-time. This allows them to make decisions quickly, optimize routes, and respond to changing conditions on the fly.

2. **Predictive Analytics:** Advanced algorithms and machine learning models are being used to predict future logistics trends, demand patterns, and potential disruptions. This helps in proactive decision-making and resource allocation.

3. **IoT Integration:** Internet of Things (IoT) devices, such as GPS trackers and sensors, are extensively used to collect data from vehicles, products, and facilities. This data is then integrated into big data systems to provide a holistic view of the supply chain.

4. **Blockchain Technology:** Blockchain is being explored for enhancing transparency and security in logistics. It helps in tracking goods through the supply chain, reducing fraud, and ensuring data integrity.

5. **Automation and Robotics:** Automation and robotics are increasingly used in warehouses and distribution centers. These technologies generate large amounts of data that can be harnessed for optimization and efficiency.

6. **Cloud Computing:** The adoption of cloud-based solutions has made it easier for organizations to store and process vast amounts of data. This scalability is essential for handling big data effectively.

7. **Data Visualization Tools:** User-friendly data visualization tools make it easier for logistics professionals to interpret and gain insights from big data, facilitating better decision-making.

8. **Environmental Considerations:** Logistics companies are using big data to optimize routes and reduce the environmental footprint by minimizing fuel consumption and emissions.

9. **Supplier and Inventory Management:** Big data analytics help in improving supplier relationships and optimizing inventory levels, reducing costs, and ensuring products are available when needed.

10. **Customization and Personalization:** Big data allows logistics companies to tailor services and products to individual customer preferences, providing a more personalized and efficient experience.

In summary, the management of big data in the logistics process has seen significant advancements in recent years, contributing to more efficient and responsive supply chains, cost reductions, and improved customer experiences. The ability to collect, process, and analyze data has become a crucial competitive advantage in the logistics industry.

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I would add that the biggest problems that companies face is attempting to exploit recent advances is lack of software development skills. The reason is as follows:

1. To exploit new technology the skill must be there to create a test and verify the benefit of the advancement.

2. To test a technology that requires big data as input, big data administration skills are a prerequisite.

3. As these skills are novel (big data is only 10 years old) organisations don't hire enough SW engineers to enable or managing experiments at this scale.

So what are these skills? Again ChatGPT provides a reasonable answer:

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Managing big data effectively requires a combination of technical, analytical, and business skills. Here are some of the key skills required to manage big data:

1. **Data Analysis:** Proficiency in data analysis is crucial. You should be able to extract meaningful insights from large datasets. Familiarity with tools like Python, R, and data analysis libraries is essential.

2. **Data Management:** Understanding data storage and management systems is vital. This includes knowledge of relational databases, NoSQL databases, and data warehousing solutions.

3. **Programming Skills:** Proficiency in programming languages, particularly Python and Java, is important for data manipulation, transformation, and analysis. Knowledge of SQL for database querying is also valuable.

4. **Big Data Technologies:** Familiarity with big data technologies such as Hadoop, Spark, and related ecosystems is essential. These tools enable the processing and analysis of large datasets.

5. **Machine Learning:** Basic understanding of machine learning is beneficial, as it can be applied to extract predictive insights from big data. Knowing how to use machine learning libraries and frameworks is an advantage.

6. **Data Visualization:** The ability to create meaningful visualizations using tools like Tableau, Power BI, or open-source options is crucial for conveying insights to non-technical stakeholders.

7. **Statistics:** A good grasp of statistical methods is essential for data analysis and interpretation. Knowledge of descriptive statistics, inferential statistics, and hypothesis testing is valuable.

8. **Business Acumen:** Understanding the business context is critical. Being able to translate data insights into actionable strategies and decisions that benefit the organization is a key skill.

9. **Data Security and Compliance:** Knowledge of data security best practices and compliance regulations is important, especially when handling sensitive or regulated data.

10. **Problem-Solving:** Strong problem-solving skills are required to address data-related challenges, such as data quality issues, scalability, and performance optimization.

11. **Communication:** Effective communication is crucial for conveying complex technical findings to non-technical stakeholders and decision-makers.

12. **Project Management:** Being able to manage big data projects, including setting objectives, timelines, and resources, is important for successful data management.

13. **Data Engineering:** Understanding data engineering concepts, including data pipelines, data cleansing, and ETL (Extract, Transform, Load) processes, is essential for data preparation and transformation.

14. **Cloud Computing:** Familiarity with cloud platforms like AWS, Azure, or Google Cloud is valuable, as many big data solutions are hosted in the cloud.

15. **Continuous Learning:** Given the rapidly evolving nature of data technologies, a willingness to continually learn and adapt to new tools and techniques is essential.

In summary, managing big data requires a combination of technical, analytical, and business skills. As the field continues to evolve, staying updated with the latest developments and tools is essential to be an effective big data manager.

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Whilst chatGPT lists all the general issues, it is important to recognize that reducing the number of technologies will significantly reduce the cognitive burden of the big data team. Unfortunately, and due to the rapid development of technologies navigating all the possible technologies leads to a jungle of options, where selection of tools for "big data" projects becomes critical. Statistics by Alex Yu (ByteByteGo) reveal that more than 50% of big data projects fail to meet expectations due to wrong choice of technology (e.g. poor tech stack choice) and hence require some form of re-engineering that causes project overrun.

In summary, the lack of skills and previous experiences leaves big data projects to the largest corporations which can afford the learning process.

Finally I recommend you to ask yourself: What makes big data "big"?

A sufficiently technical answer to that question will leave you informed about what is required to manage "big data" and subsequently "how to manage" to prerequisites.