Domain Knowledge

What is Domain Knowledge (Data Science)?

In data science, the term domain knowledge refers to the general background knowledge of the field or environment to which the methods of data science are being applied. Data science, as a discipline, can be thought of as the study of tools used to model data, generate insights from data, and make decisions based on data. They are generic tools applicable to many fields such as engineering, laws, medicine, finance, etc.

Data Science Subject Areas

Broadly speaking, data science comprises the three main subject areas:

1. Computer Science and Programming

Computational science and programming refer to studying computational tools like programming languages, software libraries, and other tools. The knowledge of programming is essential for anyone who wishes to apply data science to problems in their field.

2. Statistics and Machine Learning

Statistics and machine learning form the theoretical foundations of data science methods and algorithms. Understanding the theoretical underpinnings of data science is required to know the limits of the methods being applied and interpret the results of the data science process correctly.

3. Domain Knowledge

Domain knowledge is often a general discipline or field to which data science is applied. An expert or specialist in an area such as biotech is said to possess domain knowledge of that industry. The first two items in the list above are essential skills required by all data science practitioners and are common to all applications of data science, regardless of the domain.

On the other hand, domain knowledge is more specialised. The lack of domain knowledge makes it challenging to apply the correct methods and judge their performance correctly. In fact, the application of domain knowledge must be pervasive throughout the data science process to be effective.

Data Science Process and Domain Knowledge

Here, we will discuss how domain knowledge applies to every part of the data science process. The data science process can be divided into four sub-processes as described below. The following figure summarises the data science process:

1. Problem Definition

The first step in any data science is defining the problem to be solved. It starts from a generic description of the problem and involves defining desired performance criteria. Defining the problem is an easy step for a simple problem like predicting credit default. The problem definition is simply predicting the probability of default based on the data on past borrowers. On the other hand, consider a problem in robotics or medicine, where a person without any domain knowledge cannot even define the pattern they are looking for in the data.

2. Data Cleaning and Feature Engineering

Most data collected in any field is seldom clean and ready for use. Preparing the data for the modelling process is data cleaning and feature engineering. Data cleaning and feature engineering involve transforming the data. Incorrectly transformed data can lead to spurious results.

For example, while analysing the relationship between stock price and financial results like cash flows, one might scale down cash flows. However, the scaling would introduce a look-ahead bias in the data as the naïve scaling process will use future data to scale past data. It will lead to spurious results in any analysis based on incorrectly transformed data. Further, domain knowledge is required in choosing the correct features from the data, which will provide the most predictive power.

3. Model Building

The model-building step involves fitting a model to data. The model built here is used to solve the problem defined in the first step. The choice of an appropriate model is essential to the success of the data science process. Again, this choice depends on the field of application and is enhanced by strong domain knowledge.

4. Performance Measurement

Performance measurement is the final step in the data science process that involves measuring how the model performs on new data or out of sample data, which was not used while building the model. The choice of performance metrics and thresholds is primarily driven by domain knowledge.

For example, when building a model to predict credit defaults, a false negative (predicting a potential defaulter to be in good credit) is costlier than a false positive (predicting a non-defaulter to be a defaulter). Such asymmetries will differ across disciplines, and it would be hard to detect them without domain knowledge. Further computing the costs from model failure can only be accurately estimated by a person with domain knowledge.

Case Study: Predicting Credit Card Delinquency

This section will look at a case study that illustrates the importance of domain knowledge. Predicting credit card delinquency is a common problem in consumer finance, where a credit card provider must decide whether to issue credit cards to a particular customer. It also helps the provider make risk assessments and strategic decisions.

We will look at a small data science project that aims to predict delinquency in credit card customers. The data consists of about 100,000 individual customers with data on ten attributes, including one indicating whether the customer was delinquent. Beginning with the problem definition, we will go through the various steps involved in the data science process.

Step 1: Problem Definition

In this case, the problem is easy to define. Predict the value of the delinquency indicator.

Step 2: Data Cleaning and Feature Engineering

Data cleaning and feature engineering is an essential part of the process in our case. This is because the data is imbalanced, meaning that it does not have an equal representation of delinquents and non-delinquents. In fact, the data has 93% non-delinquents, which is expected in the real world as most people do not default on their credit card debt. This imbalance can affect the choice of model and performance metric used. It will also affect the quality of the model.

A domain expert in credit risk would be aware that such an imbalance would create the need for methods to tackle the issue. A simple solution would be to resample the data, which creates a balanced but smaller dataset. This trade-off between the quality and quantity of data requires domain knowledge to identify and adjust.

Step 3: Model Building

When building a model for predicting credit delinquency, a domain expert would be familiar with past approaches to the problem. In this case, the literature includes extensive use of logistic regression. Thus, it can provide a good starting point for model selection and a baseline to benchmark new approaches.

Step 4: Performance Measurement

Selecting good performance measurement criteria is critical in developing a credit risk model, as an incorrect metric can lead to a wrong model being selected. In our case, where the data is imbalanced, a naïve model that labels all customers as non-delinquent will have very high accuracy. Still, in the process, it may label all defaulters as non-delinquent.

Such a problem requires a model that correctly predicts most of the defaulters while misidentifying only a few non-delinquents. It should be reflected in the performance metric. Further, in this case, mislabeling a delinquent customer is costlier than mislabeling a non-delinquent, and such a trade-off requires a proper understanding of the costs and risks involved. It cannot be a completely objective decision based on a singular metric, and subjective judgment based on domain knowledge is required.