,1. How Data Drives Decision Making in
Machine Learning
This chapter explores the role of data in the enterprise and its influence on business decision
making. You also learn the components of a machine learning (ML) workflow. You may have
seen many books, articles, videos, and blogs begin any discussion of the ML workflow with the
gathering of data. However, before data is gathered, you need to understand what kind of data to
gather. This data understanding can only be achieved by knowing what kind of problem you need
to solve or decision you need to make.
Business case/problem definition and data understanding can then be used to formulate a no-code
or low-code ML strategy. A no-code or low-code strategic approach to ML projects has several
advantages/benefits. As mentioned in the introduction, a no-code AutoML approach enables
anyone with domain knowledge in their area of expertise and no coding experience to develop ML
models quickly, without needing to write a single line of code. This is a fast and efficient way to
develop ML applications. A low-code approach enables those with some coding or deep coding
experience, to develop ML applications quickly because basic code is autogenerated—and any
additional custom code can be added. But, again, any ML project must begin with defining a goal,
use case, or problem.
What Is the Goal or Use Case?
Businesses, educational institutions, government agencies, and practitioners face many decisions
that reflect real-world examples of ML. For example:
How can we increase patient engagement with our diabetes web app?
How can we increase our student feedback numbers on course surveys?
How can we increase our speed in detecting cyberattacks against our company networks?
Can we decrease the number of spam emails entering our email servers?
How do we decrease downtime on our manufacturing production line?
How can we increase our customer retention rate?
How do we reduce our customer churn (customer attrition) rate?
In each of those examples, numerous data sources must be examined to determine what ML
solution is most appropriate to solve the problem or aid in decision making. Let’s take the use case
of reducing customer churn or loss rate—using a very simplistic example. Churn prediction is
identifying customers that are most likely to leave your service or product. This problem falls into
a supervised learning bucket as a classification problem with two classes: the “Churn-Yes” class
and the “Churn-No” class.
From a data source perspective, you may need to examine customer profile information (name,
address, age, job title, employment statement), purchase information (purchases and billing
history), interaction information (customer experiences interacting with your products [both
,digitally and physically]), your customer service teams, or your digital support services. Popular
data sources of customer information are customer relationship management systems, system
ecommerce analytics services, and customer feedback. In essence, everything the customer
“touches” as a data point should be tracked and captured as a data source.
The nature of the decision you must make is tied directly to the data you will need to gather to
make that decision—which needs to be formulated into a problem statement. Let’s say you are in
charge of marketing for a company that makes umbrellas, and the business goal is to increase
sales. If you reduce the selling price of your existing umbrellas, can you predict how many
umbrellas you will sell? Figure 1-1 shows the data elements to consider for this option.
Figure 1-1. Data elements that impact a price reduction strategy to increase sales.
As you can see in this data-driven business illustration, your business goal (to increase sales) takes
on a new dimension. You realize now that to understand a product price reduction, you need to
include additional data dimensions aside from the selling price. You will need to know the rainy
seasons in specific regions, population density, and whether your inventory is sufficient to meet
the demand of a price reduction that will increase sales. You will also need to look at historical
, data versus data that can be captured in real time. Historical data is typically referred to as batch,
whereas real-time data capture is typically called streaming. With these added dimensions, the
business goal suddenly becomes a very complex problem as these additional columns may be
required. For any organization, there could ostensibly exist dozens of discrete data sources—with
each source requiring certain skills to understand the relationships between them. Figure 1-2 is an
illustration of this challenge.
Figure 1-2. A typical business data and ML experience today.
So what is your use case here? It depends. You would need to undergo a business decision-making
process, which is the process of making choices by asking questions, collecting data, and assessing
alternative resolutions. Once you figure out the use case or business goal, you can use the same
data to train machines to learn about your customer patterns, spot trends, and predict outcomes
using AutoML or low-code AI. Figure 1-3 shows our umbrella example as a business use case that
then leads to data source determination, ML framework, and then a prediction.
Figure 1-3. Business case that leads to predictions using ML framework.
An Enterprise ML Workflow
While decision-making processes help you identify your problem or use case, it is the ML
workflow that helps you implement the solution to your problem. This section presents a typical
ML workflow. In our ongoing umbrella example, you could use your data to train an ML model
using an AutoML service that provides a no-code solution for running unsupervised ML clustering.
From there, you could examine clusters of data points to see what patterns were derived. Or, you
Machine Learning
This chapter explores the role of data in the enterprise and its influence on business decision
making. You also learn the components of a machine learning (ML) workflow. You may have
seen many books, articles, videos, and blogs begin any discussion of the ML workflow with the
gathering of data. However, before data is gathered, you need to understand what kind of data to
gather. This data understanding can only be achieved by knowing what kind of problem you need
to solve or decision you need to make.
Business case/problem definition and data understanding can then be used to formulate a no-code
or low-code ML strategy. A no-code or low-code strategic approach to ML projects has several
advantages/benefits. As mentioned in the introduction, a no-code AutoML approach enables
anyone with domain knowledge in their area of expertise and no coding experience to develop ML
models quickly, without needing to write a single line of code. This is a fast and efficient way to
develop ML applications. A low-code approach enables those with some coding or deep coding
experience, to develop ML applications quickly because basic code is autogenerated—and any
additional custom code can be added. But, again, any ML project must begin with defining a goal,
use case, or problem.
What Is the Goal or Use Case?
Businesses, educational institutions, government agencies, and practitioners face many decisions
that reflect real-world examples of ML. For example:
How can we increase patient engagement with our diabetes web app?
How can we increase our student feedback numbers on course surveys?
How can we increase our speed in detecting cyberattacks against our company networks?
Can we decrease the number of spam emails entering our email servers?
How do we decrease downtime on our manufacturing production line?
How can we increase our customer retention rate?
How do we reduce our customer churn (customer attrition) rate?
In each of those examples, numerous data sources must be examined to determine what ML
solution is most appropriate to solve the problem or aid in decision making. Let’s take the use case
of reducing customer churn or loss rate—using a very simplistic example. Churn prediction is
identifying customers that are most likely to leave your service or product. This problem falls into
a supervised learning bucket as a classification problem with two classes: the “Churn-Yes” class
and the “Churn-No” class.
From a data source perspective, you may need to examine customer profile information (name,
address, age, job title, employment statement), purchase information (purchases and billing
history), interaction information (customer experiences interacting with your products [both
,digitally and physically]), your customer service teams, or your digital support services. Popular
data sources of customer information are customer relationship management systems, system
ecommerce analytics services, and customer feedback. In essence, everything the customer
“touches” as a data point should be tracked and captured as a data source.
The nature of the decision you must make is tied directly to the data you will need to gather to
make that decision—which needs to be formulated into a problem statement. Let’s say you are in
charge of marketing for a company that makes umbrellas, and the business goal is to increase
sales. If you reduce the selling price of your existing umbrellas, can you predict how many
umbrellas you will sell? Figure 1-1 shows the data elements to consider for this option.
Figure 1-1. Data elements that impact a price reduction strategy to increase sales.
As you can see in this data-driven business illustration, your business goal (to increase sales) takes
on a new dimension. You realize now that to understand a product price reduction, you need to
include additional data dimensions aside from the selling price. You will need to know the rainy
seasons in specific regions, population density, and whether your inventory is sufficient to meet
the demand of a price reduction that will increase sales. You will also need to look at historical
, data versus data that can be captured in real time. Historical data is typically referred to as batch,
whereas real-time data capture is typically called streaming. With these added dimensions, the
business goal suddenly becomes a very complex problem as these additional columns may be
required. For any organization, there could ostensibly exist dozens of discrete data sources—with
each source requiring certain skills to understand the relationships between them. Figure 1-2 is an
illustration of this challenge.
Figure 1-2. A typical business data and ML experience today.
So what is your use case here? It depends. You would need to undergo a business decision-making
process, which is the process of making choices by asking questions, collecting data, and assessing
alternative resolutions. Once you figure out the use case or business goal, you can use the same
data to train machines to learn about your customer patterns, spot trends, and predict outcomes
using AutoML or low-code AI. Figure 1-3 shows our umbrella example as a business use case that
then leads to data source determination, ML framework, and then a prediction.
Figure 1-3. Business case that leads to predictions using ML framework.
An Enterprise ML Workflow
While decision-making processes help you identify your problem or use case, it is the ML
workflow that helps you implement the solution to your problem. This section presents a typical
ML workflow. In our ongoing umbrella example, you could use your data to train an ML model
using an AutoML service that provides a no-code solution for running unsupervised ML clustering.
From there, you could examine clusters of data points to see what patterns were derived. Or, you
