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Group 5- Data Science in the wild




Questions:
- Who will be benefiting from having a deeper understanding of new fouling products? (e.g Customers, Other Departments, Scientists/Reasearchers, etc...)
A) People entering data, testing, Poeple making decisions (Which coating release as a product, Narrowing down to the best product, better products for different scenarios)

- Is there currently any available standard framework for decision making?
- Is there any segmentation based on location for data? (i.e. warmer water areas, cold water areas, etc)
- Which level of decision making is needed? Different level of decision making process requires different approach.
- How many different fowling products are there? + Would you be able to provide a list, with there best use case? + How often are changes made to anti-fouling products and how often are new products brought to market?
- What are the common problems amongst all products?
- How is is this decision making happening now? + Why is this not seen as the best solution?
- How often are changes made to anti-fouling products and how often are new products brought to market?
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A) Tests are standardised, but the way the data is captured is standardised.

A) 2 main Databases (A squared and C squared)

A) Standardised way of data collection, live results (dashboards), future proof. Lots of people have different ways of entering and reviewing data. Microsoft tech stack.

- Stakeholders
A) Some collaberation across borders. Handful of managers making decisions and around 200 people entering data/doin tests.

- Past failures
A) two databases tried are basic but works well. Need's to save time to save time. 

- Bringing wrong product to market
A) Issues with wrong paint. Claims went into millions, Novel problem wasn't tested for.


