In this lecture, we're going to talk about ML and Business Processes.
So, when we think about how we're going to get from
a No ML to ML solution in our organization this path,
I really want us to think about the evolution of a business process.
And here when I say a business process,
I'm talking about any set of activities a company must do
directly or indirectly to serve customers.
Organizations must continually improve
these processes which they do through a feedback loop, and this is really critical.
Oftentimes, when we think of business processes,
we forget that almost every one of them has a feedback loop,
and understanding that loop becomes really important to understanding
the role ML plays inside a large organization.
So, let me give you a concrete example.
Let's say we have a call center and the call center takes customer questions,
and they produces answered questions so you call your favorite local telecom,
and you say, "There's a problem with my Wi-Fi router."
And the call center answers it for you.
But the interaction doesn't end there.
We all know that after that call center hangs up they say,
would you like to answer the survey or they email you.
And what they do if you actually answer the surveys,
is they take all those answers,
and they extract new insights from them.
They get new canned answers,
new product promotions, or decision trees on how to handle future calls.
And then those new insights,
they get fed into HR or training reference manuals,
and the employees are retrained,
and those retrained employees may now go answer new customer questions.
And this is this kind of feedback loop that allows us to convert
operational expertise into better future outcomes.
Now here, I'm showing you a more general view of this.
So here, we have some input and some output to a process,
this looks similar to our example of what
ML is compared to software, that's not an accident.
And now, what we do is we take this output,
and we generate new insights from it,
and these insights are going to give us some new operational parameters
such as a new canned answer or a new product promotion.
And then we're going to tune the original process with updated instructions.
And here again, we have this flow but in a much more general way,
that could be applied to almost any business process in any organization.
And so, when we think about the path to ML,
I want you to think about how we're going to automate each one of these boxes,
each one of these cornerstones and the business process tuning.
So, in the first step for anything,
I'm going to explain more of these steps [in a] later slide.
But, the first step of any new business processes just individual contributor,
one person doing it,
and then you get multiple people doing it,
and then you digitize that process,
and these steps one, two,
and three, they all affect the core process itself.
But, as you know in the last decade or so, Big Data,
and Analytics, and Machine Learning have become very popular and very impactful.
And what happens there is we're trying to automate
the insight generation phases and the tuning phases,
and thus, we have automated the entire feedback loop.
So, I want to go ahead and define what I mean by these a little bit more detail.
So business processes that eventually end in ML,
typically go through five phases.
Now, they don't have to spend the same amount of time and each of these phases,
but skipping these phases as we'll see later,
usually isn't a good idea.
So, the first one,
what do I mean when I say, "individual contributor?"
So, a task or business process that's in
the individual contributor phase is performed by a single person,
and a great example is like the receptionist inside the office building.
This person will answer the phone,
maybe points people towards the bathroom,
just one of them,
and the task is not paralyzed or scaled at all,
usually it's very informal.
And then what happens is as the task or
the business process becomes more important to the company.
Usually, we start to delegate,
we get multiple people who are all performing the same task in parallel.
A good example would be like a store checker.
And what happens when we start to delegate is we have to start to
formalize the role and put in rules,
so that each store checker starts to behave a little bit more like the others.
So, there's some repeatability in the task.
Then we get to digitization.
A little bit of a marketing buzzword.
But, what I mean is that we take
the core repeatable part of a task or a business process,
and we automate it with computers.
Great example is an ATM.
ATMs can't do everything.
Right? You can't open a mortgage through an ATM,
but you can withdraw cash.
And because that cash withdrawal part of that business process,
where the interaction with the user is so repeatable and so well automated,
customers get a very high quality of service using ATMs,
and how many of us actually would walk into a bank to extract $40?
Almost no one. But after we digitize, what happens next?
Now, we move into Big Data and Analytics,
and the idea is here we're going to use a lot of
data to build operational and user insights.
So maybe, when I say operational,
a good example will be like, Toyota manufacturing.
So, Toyota is famous for their lean manufacturing philosophy,
where they kind of measure everything about their construction or their facilities,
and then they use that to tune each little knob in
the process to get better and better outcomes,
and faster and faster cars,
faster and faster time to delivery.
And you could do this for
your internal operations or you could do this to learn about your external users,
and this would be like marketing research on steroids.
And then, of course, we get to machine learning,
which is kind of we're going to represent the last phase in the path to ML.
And here, we're going to do is going to use
all this data that we had from the previous step.
We're going to automatically start to improve these computer processes.
And a big example here is YouTube Recommendations.
As you click through YouTube,
and you watch different videos,
and you like them,
or you don't like them, or you watch to the end or not,
the algorithm is learning in the background.
What are good videos,
what kind of videos you like,
how you are different or similar to other users.
What I want to do is we think about this path MLs.
I want you to take a moment, I want you to sketch
this diagram for a specific example from your organization.
It doesn't have to be an ML example.
Maybe you have digitized part of this business process, but not all of it.
What phases of the pathed ML is your example in?
Do you have another example too that's in a different phase?
