I'm 7 people into working towards my 100 conversations. It is said that you need to have 100 conversations as you solidify your business / startup idea. So this is where I am, seven conversation in. If you know anyone who works with ocean data or works for a business that has an interest in oceans, I'd love to talk with them.
Given the time restraints of being deep into a large data / database migration project, I consider February has been a good month for conversations. It provided me a good view into the horizon of ocean data. I followed the conversations that were presented to me without me directing the focus. For this is the first month, and I have yet to gain clarity of the gaps of where I need more information. This makes sense given I am at the beginning and don't know what I don't know. Now that it is the end of February I have identified the need to talk with customers of ocean data. This could become a focus for March. The conversations for February unfolded in the following order, with the following summaries and highlights;
I reached out to a past co-worker in a leadership position within PropelICT. PropelICT is an Atlantic Canada e-accelerator for tech startups. The conversation was very encouraging and initiated my application to their April cohort. Looking forward to their support in the coming months (and years).
Highlights:
- The idea of 100 conversations.
- My first suggested conversation contact.
- Being a candidate for their e-accelerator.
eOceans (https://www.eoceans.co/)
I spoke with one of the principals of eOceans. Time very well spent, Thank-you! So many details to be digested from this conversation. This organization clearly understands ocean data and where it intersects with social media! A bulleted list seems the best to call out the highlights;
- There are many open standards and organizations working in this space. The data standards seem to be "standardizing" and there are many organizations working toward bringing the data standards together. More open organizations are contributing than the closed proprietary types. CIOOS is the standout for Canada. EU and US are much further down the standards and open data path than Canada.
- Both ends [(data storage and end-points (IoT)] of the data collection are well serviced with lots of business and startup activity. It's the middle were the greater opportunity exists. It's with the data integration with consideration for all the standards and granularity. "It would be nice to dust off a 10 year old data set and be able to easily use it".
- Working with ocean data initiatives is very project based and finding the revenue sources / the business model for an open reference architecture for the digitization of oceans could prove difficult.
Highlights:
- Many open organizations already working in the ocean data space.
- The business side of what you are exploring (reference architecture) may be difficult, so much work is project based and gov't funded. A reference architecture seems like an NGO or consortium kind of thing.
- Middle ground of software and data integration could be a big need given my skillset.
Mentorship
Super fortunate to reconnect with an older friend who has loads of experience; small devices, programming, data, startups to a favorable exit, machine learning, etc... many skills that align well with what I am doing. And on top of all this, I really enjoy the meandering conversations we share!
The one area where there is a strong overlap towards my ocean data focus and the mentors previous experience with the integration of data. And yes he confirmed, integrating data from different devices to a common standard is a lot of work for creating a single view into a broad data realm.
Highlight: He agreed to provide me mentorship within this endeavour. So great!
New Brunswick Ocean Strategy: Our Opportunities in the Blue Economy
This was an excellent online conference put together by the Ocean Supercluster. What I did most was listen, and a good thing too... I have so much to learn. I really liked the breakout sessions where there was more individual participation. Some names, and acronyms are becoming more familiar too me.
Highlight: A small list of contacts I could reach out to. All good!
TechNL (https://www.technl.ca/)
I spoke with one of the leaders in TechNL and we talked about what I am wanting to do with data, in particular, ocean data. The conversation pointed towards two relevant contacts;
- Petroleum Research of Newfoundland Labrador (PRNL) and how they are always focused on innovation within the Oil and Gas.
- OceansAdvance which is focused on the Newfoundland and Labrador Ocean Technology sector.
Highlight: That if I am going to be successful in this endeavour I am going to need partners. The time required for setting up an organization isn't the best place for me to be focusing my time at this stage of the startup. And given the nature of this startup needing to work in the open, the partnership route may be the best way to go...
Canadian Integrated Ocean Observing System (https://cioos.ca/)
So fortunate to have the attention of two CIOOS employees! They were so gracious a provided a broad and deep amount of information regarding the state of ocean data. Super helpful! CIOOS clearly knows the data. The best way to summarize my conversation is by including the important questions and there answers;
With ocean data where is the greatest pain?
Resources as in financial and skills / knowledge.
At the more general project level; governance and the people who know how to organize and stewardship data through its lifecycle. This is more a reference to the industry in general... it's a project issue. And having the ability to integrate with a project that happened years ago...
Do open data standards have an influence?
Absolutely! There are many references to open data. Most of what we deal with are open.
How easily integrated are the existing data sets?
It’s getting better. It can be difficult to get an older data set and want to integrate it. These older sets often lack the granularity or metadata that makes it easier to ingest. There is a definite need here at a project level. Developing an expertise here could become a strong business.
Most initiatives within this space are project based. Which makes it difficult for longer initiatives that have some data sustainability. Rarely are there long term funding initiatives.
Highlights:
- So many acronyms, references and URLs. The CIOOS folks provided me many references all pointing in the right direction. Reference to some of the ISO standards.
- The need for better stewardship of data so as data ages it still has usefulness.
Pisces Research Project Management (https://piscesrpm.com/)
Another fortunate conversation with a person deep into ocean data and with the added bonus of being very technical. This was a contact I harvested from the New Brunswick Ocean Strategy Conference. There are may topics I could summarize from this outstanding conversation, much of the information confirmed things I discovered from the previous conversations described above. This is good!
I did pitch my idea about mooring buoys as a fixed points of data collection, and having these buoys like the personalized weather stations that have become so popular. This employee loved the idea.
The exciting part of this conversation was the discussion of the technical stack used within the open data within the oceans sector. It was good to add this to the knowledge I had of the proprietary technical stack used when I was managing the software engineering dept. at Provincial Aerospace.
What is the most common tech stack for Ocean Data?
This person has extensive experience working with Government Organizations and Academics. From what they have seen the most common, and emerging, technology stack includes;
- Python
- Assorted data storage approaches. Often NOT an RDBMS.
- QGIS is common.
These are the tools he finds most effective and common. Using QGIS pushes you into the geo representation of data. Much ocean data requires different kinds of models, more 3d, more oceans… not necessarily geographic, etc.
The ability to prove models with real data is the biggest need from a technical perspective. This is why python has such good traction. It is easy for non-programmers and also rich enough for programmers. A good language for data, and useful across the technical skills working with data.
NetCDF is the most common data-store. Also CSV and proprietary data storage. Remember data people are mostly not programmers or overly technical.
Also take a look at CKAN (https://ckan.org/)
What are people looking for from a technical perspective?
- Proving models with real data.
- Integrating data
Highlights:
- A deep discussion about the technical stack. The preferred programming languages, data storage, integration approaches, and technical issues.
- Confirmation that integrating data and proving models is an area of software development opportunity.
Lessons Learned
- A reference architecture for the digitization of oceans is not enough to hang a startup or business upon at this time! Where I do believe it is still a good idea that will form through time. There is so much work already going on for a common open architecture that another doesn't need to be started. I truly believe a reference architecture will emerge, it is a; when it will happen, not if it will happen.
- There is a big need for technical and software development skills and knowledge in the data engineering space of ocean data. I believe the opportunity exists for a software development / data engineering consulting firm with the specialty of ocean data.
- The idea of an anchored (or fixed) buoy for ocean data collection is very compelling too me. Kind of like the personal weather station but as a fixed mooring buoy. Anyone who has a mooring buoy could replace it with the data buoy, and have real-time data about the conditions at the buoy in preparation for mooring.
Next Steps
- March will be the month of broadening my reach. I need to talk with a broader section of people working in the oceans space. I need to find potential customers for the processing and software development in, and around, ocean data.
- I need to start building software tools for the processing of ocean data. I need a reference technology stack showcasing our abilities to work with data.
- I need to start developing an elevator pitch for the ocean data software consulting firm. I need customers and revenue to get the real feedback to focus the business mission.
