Here's how you can effectively handle and analyze big data in import/export operations.

To handle big data in import/export, use technology to collect and analyze data, create easy to understand visuals, and predict future trends to make informed decisions.

The older I get the more I reference the basics. Check sums; log files; file formats; Readmes; and, I dunno, paying the vendor's bill. When things go wrong I start with what seem like stupid lines of inquiry but these are usually at the root of the problem. Was the file actually posted? The right file? You do realize that the vendor had a planned outage notification. No one told you that the file naming convention changed last week? Yeah I saw that the file failed but I thought it re-ran automatically. So-and-so is out on vacation? Uh, she runs a final manual process that is not yet automated. No not that one, the other one. Soylent Green is the perfect metaphor for all things dev-tech, and it is still people. Even AI cannot fix us.

I have always found it very fascinating, the need to always make use of right and optimum tools in the areas of data import/export operations. With the continues evolvement in the area of Generative Artificial Intelligence (AI), one is better geared up for a better and seamless process towards generating actionable insights for all stakeholders.

Según mi experiencia encontrar patrones de datos es muy importante para poder fijar los criterios de completitud y tomar decisiones con respecto al tratamiento de outliers.

No organization should ever discard otherwise usable data due to a lack of completeness. In my book even a single field should be retained. It's not like storage is an issue. Listen, the only thing that really matters on the topic of completeness - i.e., the presence of some text in a discrete record - is counts. I propose a competition where someone stymies me on the potential use of a single field of data. I cannot think of any datapoint that has zero commercial or academic application. And don't get me started on accuracy. The debate will quickly devolve into something along the lines of whether or not life is meaningful. Is it? Is it really? Finally, all data contracts I've ever read (thousands) disclaim accuracy - in ALL CAPS.

The importance of Data Quality cannot be overstated. Data Analytics and its valuable output insights are only as good as the input. Trash in; trash out. Incomplete data can be worked around, but bad data must be cleansed. The more experience I get, the more time I spend cleansing or checking data for projects, and then rechecking my finished projects. Invest in cleaning, or at least checking, and you’ll seem like a superstar who finds things no one else can. It’s worth the time.

Guardar en un catálogo de datos la trazabilidad entre las fuentes de datos crudos y los procesados, puede ayudar a mejorar la calidad de los datos. En los procesos de importación/exportación en mi experiencia hay que prestar especial atención a los campos con formato fecha que cambian su sintaxis entre cada lenguaje de programación

Normalizar los nombre de los campos de nuestros datasets es importante para poder integrarlos en una datawarehouse que nos permita comparar datos de la misma naturaleza pero de diferentes fuentes

To effectively handle and analyze big data in import/export operations, integrate advanced analytics and machine learning (ML) techniques. Use ML algorithms to identify patterns and predict trends in trade data, optimizing supply chain efficiency. Implement AI-driven anomaly detection to flag suspicious activities, enhancing security alongside encryption and access controls. Regularly update your team with the latest data security protocols and leverage predictive analytics to foresee potential risks and opportunities, ensuring your operations remain both secure and strategically informed.

To effectively handle big data should be done in an appropriate big tools for preprocessing and use integration platforms where the volume of data is very large, which apply between export/import operations.Data integration ETL tools for maintaining data quality and security. While predictive analytics are done with using machine learning models/techniques to generate real time predictions so decision can be easily made. Maintain data tools continuously.