There are three reasons why I joined PsyFyi after 15 years in various maritime tech companies: Data, Technology, and Outputs. What attracted me was the approach to all three: KISS (Keep It Simple for the Seafarer)!
Tackling the elusive crew data
After working with shipping companies on data, software, hardware, integrations and analytics, I knew they are swamped with collected data (vessel location, engine performance, speed, fouling, etc.), all at high frequencies. From that, I realised two things:
The maritime industry is quite advanced when it comes to technical data—companies regularly collect it and use it to their advantage
Crew data isn’t collected on the same scale, even though we know that 75% of incidents onboard are impacted by human factors
Why the gap in crew data? Part of the reason was potential privacy and ethical concerns, but the bigger issue was technology. There are apps for crew feedback, but the power of data lies in relevance, frequency and scale - something that yearly crew questionnaires or welfare apps don’t offer.
To solve this, PsyFyi created SeaQ using popular messaging apps like WhatsApp, Messenger and Telegram. By using apps seafarers are already familiar with, the barrier to use is low, trust is built and there’s more willingness to respond. But engagement alone isn’t enough - clients carefully choose their questions to understand working conditions from both the onshore and onboard perspectives.
As daily questions are answered by thousands of seafarers across fleets, the dataset grows rapidly into millions of records. Suddenly, shipping companies have a live data feed from their seafarers, just like they have from the AIS.
Less is more when it comes to tech
Building a database, data pipeline, interfaces, and outputs is a major investment for any company. Shipping companies often end up with many solutions and many data sources, and often only large companies have the resources to build a comprehensive data processing architecture.
PsyFyi’s approach avoids adding complexity for either the client or the seafarer. By using the existing messaging apps to deliver questions, seafarers don’t have to install new apps or worry about being bombarded by notifications. Quick daily questions, minimal time spent answering, and hence minimal disruption to work keep response rates high over time.
For the client, this approach keeps also costs sensible as they aren’t paying for constant feature upgrades or maintenance of software. And while SeaQ isn’t critical to operations, we benefit from the cybersecurity of platforms like Meta, allowing us to focus on securing our databases. For both seafarers and shipping companies, implementing and using SeaQ is as frictionless as possible.
The ’so what’ matters most
While PsyFyi offers data, reports, and dashboards, the main focus is on providing the industry with valuable outputs. Raw data requires a lot of processing to generate insights, and most clients don’t have the people or interest to do this. The key is generating relevant and specific insights: too often, solutions offer the standardised outputs across the board (to enable scalability) with the expectation that clients will adjust their processes to fit the solution.
PsyFyi provides analytics that are tailored to the client’s expertise and expectations—if Descriptive Analytics is sufficient, that’s how we structure the reports and dashboards. If a client is experienced with modelling and statistics, we incorporate those elements into our outputs. And yes - we do use AI an enabler for the outputs, but the real work is done with the client to define what outputs will serve their interests best.
So there you go - PsyFyi’s straightforward approach to data, technology, and outputs is why I joined the team. We stay focused and deliver real value with minimal complexity.
TL;DR
Crew data is not consistently collected due to technology that would encourage sustainable usage.
SeaQ uses popular messaging apps that seafarers already use, to collect data through daily questions on topics relevant to the shipping company and the crew.
This data quickly amounts to powerful data sets of millions of records. It is used to build analytics (time series, correlations, regression) in the form of reports and dashboards customised for the client, allowing them to discover issues, implement changes, and monitor the impact.
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