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Applying data science

Watch Alex Cowan talk about applying data science and how one can take an existing product and make it better.
In this video, we’re interested in how you take an existing product and you make it better and better. Increasingly, one of the big advantages of an existing product over its newer competitors is that it has lots and lots of data about what users do with the product, how the product performs for the user. And they’re able to use that data to make the product and the overall user experience better and better. And I think that there’s three things that you should bear in mind as a product manager with regard to data sciences. One is how you instrument data collection into the way you build the product and
the way the interfaces that you have with the customer create data. So for support or when they call in, it’s really important to make sure that you’re creating that data, can’t do data science without data. Two, is just like you need to bring your development team strong inputs about here’s our customer, here’s what we see them doing out there, and here’s the qualitative and quantitive data we have about what they do. You want to bring those same strong narratives to your data scientist because that will help them form strong hypotheses and focal points about what avenues they pursue with the data science they’re going to do with you.
And then three, your data science will help you with this, but zero on in questions of interest. What will we most like to know about the user, be able to do for them. Because that’s how you will iteratively create a rich interface with your data science team or whatever data science resource that you have. So, there’s a kind of general view of what are the basic jobs of data science? There’s four of them. The first one is it can give you descriptive outputs. So what did the user click? And who was that user that clicked that thing? And how hot is this piece of equipment in the field, right now? And how fast is it working?
Or where is our truck or where is our Uber driver? These are descriptive things. And the sort of second level, this get sort of more abstract and more intricate as we go a long. The second level is diagnostic. So who clicked what? And under what conditions does this part tend to break? Or it was working really fast and it’s been working really fast at 3 PM for the last 200 days, all of a sudden it stopped, so maybe it’s broken or not working. Where does this car go over the course of the day? And then predictive is when we are able to look at patterns and predict what’s going to happen.
So we say, what if we have a bunch of this type of users on this site where we’re going to start posting ads and we know X Y and Z about them. Can we predict now what type of ad they are most likely to click on? Or this piece of equipment is working for certain amount of time and it’s got a certain age and it’s got a certain heat or whatever is relevant. When do we think it’s kind of break? Or where will this truck be at 4 PM today or tomorrow or whatever? And then prescriptive, kind of the most refined and it’s where just the machines intelligence can just tell us what to do basically.
So what ad should we run for this user? Just pick one. Or when should we go and replace this part, based on all the costs and predictions about when it’s going to break? And is this car on the right route or should it take a different route based on a tree fell over the road or something like that? Let’s loop through these for the example of Cooped Up LLC. We’ll look at this for their H1 business where they’re selling industrial feed and watering systems to existing factory farms. An example of a descriptive application of data science would be that they have sensors instrumented into the feed and running equipment.
And they are able to tell how fast it’s working or how hot it is or how many parts per million of antibiotics are running through the water feed system.
A diagnostic would be it looks like this part is broken because we know that all the other things are doing X and Y and it looks like it’s broken because it’s doing this unusual thing. Predictive would be we think that based on all these factors that we know about this
part is going to break at 3:30 PM, two weeks from now, and we predicted that.
And then that might be valuable because then we could then prescribe to them hey, in the next week we really need to come and replace this part, so that it doesn’t break. And wouldn’t that be great, because they could avoid down time and having to move chickens around, things like that. So those are some examples. My recommendation is if you want to learn about data science, go learn about it. It’s so fascinating and there’s a lot of great e-learning platforms, or programs at regular universities where you can learn about it.
As a product manager, it’s good to think about the kind of things that it might be able to do for you and how you get that data, how you present the right narratives to your data science resources. And then how you create a rich interface with them, where you can start to make your existing product better based on the data you’re able to collect from your users.

In this video, Alex discusses applying data science to improve an existing product. One advantage of an existing product over a new product is the large amount of data that has already been gathered for an existing product. The data can be categorized in four ways: descriptive, diagnostic, predictive, and prescriptive. Think about a product you have worked with and categorize the known data under these four descriptors.

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