Data Science- Healthcare woes 2

In continuation of my previous post, even if you are PhD in data science, you have to deal with myriad functional heads who are as clueless to your role as you are to them. Therefore, it would require an immense amount of effort to break down the silos only to unlock the potential that the enterprise is generating.

Leadership is adept at managing the “environment” for favourable policies- those people who network effectively rise to the top, which doesn’t necessarily mean that they understand the facets of technology disruption completely. Here’s from the author:

These non-data proficient executives and managers are usually the ones to make important product decisions. In tech, top-down decision-making is still very prevalent. You may not have a “seat at the table” or be respected enough to be included in these decisions and your research may not be valued. Where does that leave you as a Data Scientist?

Unless the healthcare organisation has a data pipe (with all departments linked in), it would be impossible to get proper analytics.

This telling picture encapsulates everything here:


Here’s another sobering thought- why most projects don’t make it into production.

“One of the biggest [reasons] is sometimes people think, all I need to do is throw money at a problem or put a technology in, and success comes out the other end, and that just doesn’t happen,” Chapo said. “And we’re not doing it because we don’t have the right leadership support, to make sure we create the conditions for success.”

The other key player in the whodunit is data, Leff adds, which is a double edged sword — it’s what makes all of these analytics and capabilities possible, but most organizations are highly siloed, with owners who are simply not collaborating and leaders who are not facilitating communication.

Here’s another blurb and something in line with what I have always been saying- complicated user interfaces.

AI is not going to replace managers, she adds, but managers who use AI are going to replace those who don’t.

We’re starting to see that awakening of business leaders wanting to understand how machine learning works, and what AI really means for them, and how to leverage it successfully. And those leaders are going to be the most in demand, Leff said.

Another essential key to success, Chapo added, is keeping it simple.

“Oftentimes people imagine a world where we’re doing this amazing, fancy, unicorn, sprinkling-pixie-dust sort of AI projects,” he said. “The reality is, start simple. And you can actually prove your way into the complexity.That’s where we’ve actually begun to not only show value quicker, but also help our businesses who aren’t really versed in data to feel comfortable with it.”

Frankly, at times, I am inclined to believe that the suits represent the perfect example of a Dunning-Kruger effect.

No one wants to relearn a skill that would make them efficient.

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