Work Musings...

Picture Credit: Shahadat Rahman on Unsplash.com Digitization is more than a buzzword in the analytics' business vocabulary. Maybe not with as much bling, pomp, and mystery as AI, Machine Learning, or Deep Learning.  Automation, RPA, bots, hyper-automation, digital-twin are some of the newer terms that have become part of the business executive's vocabulary and wish list in the last few years. The level of sophistication is increasing with many of these newer technologies. However, there are many practitioners just recycling the terminology to describe the 'same' work and efforts regardless of the sophistication. I tend to think of digitization as a catch-all term for all of these efforts.  In this post, I'll touch on digitization maturity with business processes and the interplay with analytics maturity. First of all, digitization of business processes is a journey. It's more like an ultra-marathon where the finish line seems to get farther and farther away the

  Photos from Unsplash.com: Srikanth D (Indian flag)  and Danny Gallegos (USA flag) Friday, June 25th 2021 @ 10.26am... I took the oath of allegiance to become a naturalized citizen of the US. It is the culmination of a 21-year process of my move from India to the US: being an international grad student applicant to Virginia Tech to a non-immigrant (Student, Temporary work visa) to an immigrant (Permanent Resident/Green Card Holder) to a US citizen. The biggest time was spent in going from work visa to Permanent resident with the long wait times for an Indian citizen. It is the end of a lot of waiting, a lot of checking the status of various applications on the USCIS site, and a LOT of documents including a notarized statement from my mom attesting to my birth! The two overwhelming emotions I felt after the oath ceremony, getting my certificate of naturalization, and walking out of the office were Acceptance and Relief.  Acceptance: It felt really good to be welcomed and accepted as a

It's a blog after a 1.5-year gap...  Recently, I had the chance to attend a webinar/zoom panel discussion on Building Effective Data Science Teams organized by RStudio. It was an interesting conversation - I highly recommend spending the hour to watch if you are interested in this field.  I'll summarize some key takeaways from the discussions for me and some thoughts from my side.  1. Communication, Communication, Communication! The panel could not have stressed more the need for MORE communication between the data science team and the various stakeholders (business/functional team, internal customers, etc.)  Data Science(DS) teams/Software Developers tend to like to work in a cave and only like to emerge with a shiny (pun intended!) new object - this is a high-risk game. More communication with the stakeholders allows for the project to be focused/aligned with everyone's expectations and more importantly, creates a sense of 'joint-ownership' of the project and th

I would like to post updates here with samples of some of my data science work. This is the first post of such work done in R and published on RPubs http://rpubs.com/Prabha/National_Parks_1

This is the AI panel discussion content post. The panel was sponsored by RSM. George Casey  - Panel Moderator, Principal at RSM Larry Fulton  - Professor of Data Science at Texas State University Dale Sayers  - Cloud Solution Architect at Microsoft The discussion centered around the current use and best practices of ML/AI in industry. Here are some of the key takeaways and my thoughts from the panel discussion. This is also based on follow-up conversations with various members of the audience. 1. AI/ML application, implementation is still low and unclear to most organizations Based on my interaction with people at this conference, the companies present were closer to the beginning of the AI/ML journey. To be honest, there were not a lot of pointed questions from the audience. The discussions were more around specific software tools - ML/AI is not a piece of software or a toolset. The application of AI/ML has to align with the strategies of each organization and will have to t

Public speaking has been an aspirational goal. I've not had too much opportunity before nor have I actively sought it out. It was this 'thing' that looked sooooo cool and people doing it were really cool! However, there was a fear of failure that was overwhelming enough for me to not make that leap - public failure, nonetheless :) In the last year or so, I have applied to speak at a few conferences on the topic of Analytics/Artificial Intelligence(AI)/Machine Learning(ML) applied in the space of Supply Chain Management. What I have learned in many cases is that the conference organizers are engaged in a 'Pay for Play' scheme. A speaking slot is up for sale with the purchase of a display booth or being a sponsor of the whole conference. I am not judging the conference organizers - I fully realize, agree and support the commercial realities of putting on a show. The shock was the pervasiveness of this model and that clash against my naivety.  An opportunity poppe