In other words, who has the greatest need for data scientists and data analysts and would be willing to hire people virtually? And what kind of projects do you think make sense in such a context?
I'm with Tom. I built out internal dashboard for the last company I worked for.
Few of the challenges of "out-sourcing" are around security of data and DEFINITELY volume of data. Access large volumes of data remotely can be painstakingly slow that is why companies like Zoho and Google big query limit the rows of data.
Another critical component, in my opinion, if big data is the individuals' familiarity with the business processes and the data model. It was extremely difficult for me to present insightful metrics as a part of our dashboard because our financial, client, and transactional data were in different systems lacking a good foreign key relationship.
One approach that I thought valuable was data analysis As a service, where you provide existing transaction reports from stripe, quick books, shopify etc (so you've got standardized incoming data) run pre-programmed algorithms to provide new vs. repeat shoppers, average ticket value between new and repeat customers, recency frequency monetary analysis among many other types of reports to generate.
Hope this helps!
There are three major challenges with the scenario you describe:
1) the data is usually not in a format that is ready to be chewed upon (less critical)
2) The data-science tasks are usually not well-defined by the people who need them (very critical)
3) The process tends to be iterative and not on-shot.
The only successful situations I'm aware of that is close to the one you describe is competitions/benchmarks where the task is crystal-clear and the data is ready-made (like the Netflix one or many others run in the research community and by the government).
In these competitions, issues (1) and (2) are addressed, and you may hire a person so that they can iterate on it and continue the work (issue 3).
In certain domains and projects there's less iteration needed, so if the problem to be solved is well-defined and data is well-prepared, it can be done successfully. I've been in several situations where I "ordered" a data-driven algorithm and plugged it in a live system.
I would copy Kaggle initially (80%) and innovate (20%). The key is to get to Product / Market Fit asap, then scale.
Andrew Chen ( https://clarity.fm/andrewchen ) goes over this nicely in his presentation Zero to Product Market / Fit
So typically it would depend on the complexity of the project and how easily accessible is the data to determine the likelihood and possibility of outsourced data analysis.
For example, it is much easier to look at a ecommerce website and provide analysis than to go into a retail shop that has both online and offline data sources that needs to be analyzed.
As an web analyst, I tend to get projects to help identify opportunities and support marketing activities. There is limitations due to the amount of data available and it would usually need to be looked at on a case to case basis.
Big data can include data multiple departments (sales, accounting, operations) to something as simple as web analytics. For complex projects, you face issues such as security, accessibility, longer timelines and iterations which would probably mean an inhouse resources would make more sense.
Hope this helps. Be happy to hop on a call to address any additional issues.