Last week I started watching the Jack Ryan series from Amazon Prime because all my friends recommended the show and told me that it’s amazing and a good watch for an evening while dinner.
In the first episode, Jack Ryan made a very funny comment “I had extract information from two independent Databases, but I solved it with a single custom SQL query”. Oh my God! Really, can you obtain all the information about one terrorist with only one SQL query between two Databases? Great, you are the Special One.
Nowadays it is very easy to identify people, especially if you work with a Government agency. For example right now, I am at the airport to take a flight from Madrid to Toulouse and there are multiple cameras controlling the passengers, badges, crews, etc AI can recognize faces in real time, and if you turn out to be ‘the bad guy’, the camera device is sure to identify you and notify the police nearest to you. Are we losing privacy? Or is our security growing?
On the other hand, with natural language processing (NLP), you can have a very high understanding of conversations. In fact, this technology can work with many languages, using inputs such as emails, social networks, SMS, voice, photos, etc.
For a financial expert like Jack Ryan, there are a lot of possibilities to create a graph for transfer flow from the beginning to the end point. It can be done for example with Neo4j database.
The key is that you have to connect many different data sources to define the personal profile correctly. And for sure, the amount of unstructured data is higher than structured data, and you have to deploy efficiently workflows to ingest, transform and keep the information through this process, to be used after in predictive models or AI tools.
These kind of technologies help developers to perform and deploy these use cases successfully. The combination of all of them are providing huge possibilities and the number of use cases that can be solved with is growing exponentially. In my point of view, the main challenge is health. Improving therapies, genomic and for sure detecting patterns in diseases like cancer, VIH, and so on. Another area that could be improved is the Industrial manufacturing, all the production process must be monitored and combined with these technologies I am sure that the yield could be improved easily.
Years ago I had read an article about how Merck™ improved the yield of one production process using Big Data, it was over 2014. This project produced amazing results and the ROI was successful in a very short period of time.
All these topics and skills combined, could be help scientist to improve his own investigations. As an analyst, we need to join them and build together. This new scenario could open new areas of study and increase all the synergies between both. All these topics and skills combined, could be help scientist to improve his own investigations. As an analyst, we need to join them and build together. This new scenario could open new areas of study and increase all the synergies between both. The main point for a collaborative way of working between technical and Scientist is to build a very high level of applications to be used for the most important challenge, for example detecting cancer using image recognition. Another is to help genomic studies using Deep Learning
That means, even today not everything has been invented. And using different approaches you can manage it because if “you always do the same, your results always are equals”. Many people are in alignment with this way of work, and it is very complicated to change, but we can try. We have to take it strongly and shout “Yes, we can do it”. It is our time.
Be risky and go ahead, and
About Alberto Alonso Marcos
My name is Alberto Alonso. Actually I work with Sogeti Spain in Business Intelligence Department with Microsoft Technologies. My profile is very orientated to customer, and how the DATA can improve the organization. My first steps in the data management were in the Pharmaceutical Sector. (I´m pharmaceutical too). I worked hard to extract and built procedures for gathering all the information across the organization. Measurement all kind of events. Aggregating different sources like ERP, LIMS, HVAC, OEE tools, and productivity machine reports.
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