This is a really interesting article on some of the challenges facing enthusiasts for technologies such as Python in a "traditional" corporate environment.
I'm a really strong advocate for these technologies - and have been developing a notebook based product for use in the insurance sector - but there is a lot of resistance.
- There is very little awareness of the power of open source tools to do traditional data manipulation tasks;
- In addition to Excel there are both legacy and newer proprietary systems backed by consulting firms that have a strong hold over parts of the market.
On the other hand there are some areas where there is increasing adoption of Python and (especially I think) R to do statistical analyses that are difficult / impossible in Excel. Also DataScience tools and techniques are now being taught as part of standard actuarial courses.
Finally, firms are increasingly acutely aware of the risks of relying on Excel and are looking for tools with better control / testing environments.
I'm a really strong advocate for these technologies - and have been developing a notebook based product for use in the insurance sector - but there is a lot of resistance.
- There is very little awareness of the power of open source tools to do traditional data manipulation tasks;
- In addition to Excel there are both legacy and newer proprietary systems backed by consulting firms that have a strong hold over parts of the market.
On the other hand there are some areas where there is increasing adoption of Python and (especially I think) R to do statistical analyses that are difficult / impossible in Excel. Also DataScience tools and techniques are now being taught as part of standard actuarial courses.
Finally, firms are increasingly acutely aware of the risks of relying on Excel and are looking for tools with better control / testing environments.