(Initially posted on LinkedIn)
"When learning, explore widely.
When mastering, focus narrowly."
About 2 weeks ago, I shared (https://bit.ly/32862sq) a little about the journey I'm on. I'm especially interested in data, cloud / infrastructure, cybersec and web3 and leveraging these building products, platforms, and solving often complex and sometimes boring problems. However, each of these are broad and deep areas in and of themselves.
Knowing what to learn, what to focus on, especially in the deluge of data in today's world is not a trivial matter. This applies not just to an individual, but also to large and small companies and organizations across the world. Leveraging the right data to create business value can turn every company into a data informed/led company. Side note: merely migrating your data warehouse doesn't automatically turn one into a a 'data' company though ( https://www.gooddata.com/blog/not-every-company-data-company-yet/).
I'm curious to see how deep I can delve into the world of data in tackling problems, as I see that that a deep understanding of data from a technical and business perspective, along with cloud appears to be a necessary path on this journey. There are lots of things to learn, implement, and remember. As it turns out, I realize I've done some data related bits and pieces over the years without necessarily framing them as such. Writing hopefully will be clarifying for me and helpful for at least a few (see #4 below). Additionally, a key thing for me on this journey is ensuring the creation of value - which could be revenue, customer satisfaction, saved time, new insights, knowledge - in anything I do.
Talking of problems:
Complex Problems are easy to define, difficult to solve. more here- https://www.august.com.au/blog/what-is-a-complex-problem-anyway/ - At a global level, food Security probably falls here, along with climate change,
Boring Problems to me are tedious, mundane but often necessary - such as keeping track of personal finances or automating back office reconciliations, or regulatory compliance.
Thanks to all those that have pinged me! Looking forward to connecting more!
Lastly, some useful reads and resources:
If you want to become a data engineer, this is pretty comprehensive: https://awesomedataengineering.com/
Building software is hard. (I think engineering anything useful is hard in general) Conrad Akunga: https://www.conradakunga.com/blog/coding-is-easy-any-monkey-can-do-it-software-is-very-hard/
WQU's 16 week Applied Data Science Lab is now accepting rolling applications. 100% free: https://www.wqu.edu/programs/applied-ds-lab/
This Twitter thread on writing (I like 2): https://twitter.com/david_perell/status/1483432685448032259