Bartosz Garbaczewski's Profile

Sofia, I like your suggestion for members of the PA team to rotate and evangelize PA strategies in various parts of the organization. However, this could only be done on a smaller scale. Since the PA teams tend to be small today (at least I assume they are), I don’t think this can be the sole strategy to raise prominence of PA function within more mature corporates. I believe much depends on frequent communication of measurable impact achieved by PA work on different parts of an organization but this is so hard to measure due to lack of counter-factual. I don’t think any corporate will want or can run a perfect experiment in this context and measure incremental impact from PA work, and this reinforces story-telling and conviction around that PA function has indeed a positive impact and is worth retaining and building out.

Thanks Georgi for taking the time to read the blog post and share you reflections. I agree that to fully understand value of LPA one would need to measure its impact on the proverbial bottom line. My observation of what has been happening in the wider industry is that companies take a leap of faith and follow the new trend of building their data science muscles before even thinking how they can utilize this new function well. So before they even think about ROI, the companies should think strategically about the “why” and “how” of growing LPA within their own structures.

Hi Grace,
Thanks for sharing the article and thoughts around the new wave of “new collar” jobs. I agree with the challenges you pointed out in assessing the quality of credentials thousands of non-degree holders acquire through Udacity, General Assembly, Boc.io or CareerFoundry, but I think well-established companies will be able to gauge readiness of a candidate just like they do with degree-holding job seekers. Nevertheless, I am more concerned about smaller firms, which do not have well-established interview practices and HR functions — these may end up with many lemon employees instead of high-quality candidates. However, your article raised a bigger concern around the value of an expensive degree, say in data science or computer science, which takes years to acquire and arguably should equip graduates with more than just a technical skillset e.g., critical thinking, creativity, and judgement. Recognizing the fact that the job market is starved for technical and affordable talent today, many firms look for “shortcuts,” which can be costly long-term. I agree it will take time before we see the true value of a 100-hour long training on Python, JavaScript, or C++, but perhaps we need folks who can just code for hours on end and become good at it. I do wonder though, how this will impact employees who spent years earning their technical skillsets and credentialing it with a degree. We will need to wait and see…

Hi Gauthier,
It was an interesting read because it related to your personal experience as a surgeon, who could see with his own eyes how informational asymmetry was skewed towards the patient and resulted in suboptimal treatments. In the spirit of sharing my personal experience, I did have at least one surgery performed on me, which I wished had been done by a different doctor or not done at all. There is tremendous potential for use of people’s analytics in various medical fields, but I agree that there are major challenges there also — many of which we touched upon during class i.e., how far should data analytics go w/o having adverse outcomes (many of these were touched upon Ellen in the comments above). In the context of your article, playing the devil’s advocate, as a young surgeon without much experience in “hepato-pancreatic surgery,” I would be worried about my professional outcomes in a highly data driven environment like the one you envisioned. My medical malpractice insurance premium will go up, patients will always opt to have a surgery performed by a more experienced surgeon, then how will I ever acquire appropriate training to be deemed ready to perform hepato-pancreatic surgeries or any other types of surgeries? I think there are many things that can go wrong here, hence the use of data in medicine should be and — as a matter of fact — is regulated. Maybe some level of informational asymmetry here is needed to ensure we have enough aspiring future doctors enter the field w/o getting paralyzed with fear of how big data and ML can prevent them from succeeding long term.

Hi Dimitrios,
This is an insightful article, which shows how one of the largest fast-food chain restaurants utilizes big data to drive commercial decisions and delight a customer. As I read the article, I thought about a massive opportunity for McDonald’s to become a more effective and profitable business – some of which you related to in your blog. It was fascinating to learn how the company can better source quality recipes and foods if it works with data. Furthermore, the massive challenge of effectively managing a complex supply chain becomes easier; beyond what you mentioned, I think it can also help minimize waste and bring products at scale appropriate to local demand. This in and of itself can have secondary effects on customers e.g., you are now running a greener and more sustainable business aligned with the global agenda of reducing greenhouse gas emissions and protecting natural environments – and it matters to all parents and kids that flock to McDonald’s establishments all over the world. Over time, use of big data becomes the secret sauce of competitive advantage for the chain, because it translates into a tighter customer value proposition in form of better-quality menus, and attractive pricing due to leaner and more efficient operation. Finally, you touched upon people’s analytics in your article i.e., adequate staffing across numerous restaurants McDonald franchises. In my blog I talked about difficulty of building people analytics teams at mature corporates – I think it may be interesting for you to check out if you find time, because there seems to be a secret sauce to that also. According to McKinsey: most successful companies had one thing in common i.e., “rather than relying on generalist data science skillsets, [most successful] companies leveraged subspecialities including “natural-language processing, network analytics, and quantitative psychometrics,” and linked their work to strategic corporate objectives.” So the team that is behind of what you described in your article, may not necessarily be the one to run peoples analytics function at McDonalds if the company understood benefits of integrating a specialist data science skillset within its HR function.