How Do You Get Business Acumen and Domain Knowledge While Learning Data Science?

Domain expertise and business acumen are different. Business acumen covers how businesses and marketplaces work. It covers the dynamics of companies and how they operate. Domain expertise covers how industries and functional areas within the business work.

Business acumen is focused on value creation and value return which are described by the value stream. Domain knowledge focuses on the workflows that create value. Both allow data professionals to produce higher-value projects. We make business decisions as part of the data workflow, so business acumen and domain knowledge help data professionals connect what we build to the business.

These are gap skills, and few data professionals have them. At every stage in your career, business acumen and domain expertise will set you above others, giving you more opportunities for advancement and new roles.

Domain Knowledge

The most common questions I get asked are:

What domains are there?

How do you choose a domain?

I will answer both at the same time. I recommend one of three methods to select a domain; maturity, functional area, or use case. Each of these lists is based on surveys by Accenture and IBM on AI maturity and applications. Businesses are most interested in these, so your career prospects will be the highest.

AI maturity:

• Tech

• Automotive

• Aerospace and Defense

• Life Sciences

• Natural Resources

• Retail

• Utilities

• Manufacturing and Industry

• Banking and Financial

Functional Area:

• HR

• Customer Service and Support

• Manufacturing

• IT and InfoSec

• Supply Chain

• Marketing

• Sales

• Finance

• Legal

Use Case:

• IT Operations, Hardware, Network, and Software Management

• Fraud Detection

• Customer Care Experiences

• Business Workflows and Processes

• Inventory Management

• Supply Chain Efficiency

• Workforce Automation and Digital Workers

• Monitoring and Control With Smart Meters or Physical Structure Monitoring

• Asset Utilization Optimization

• Transportation and Route Optimization

• Wearables For Training and Maintenance

• Sensor-Based Manufacturing and IoT

• Autonomous Vehicles and Drones

• Contract Management and Review

• Fraud Detection

It is essential to remember that these are just the most popular and front of mind for senior leaders. These are not the only industries, functional areas, and use cases. Healthcare is a growing field of interest but has not matured very quickly due to privacy and regulatory constraints. Gaming uses AI, but the industry is relatively small, and use cases are currently limited. That will change as the Metaverse gains traction.

Robotics is another significant opportunity and growth area. It has not gained wide adoption yet and is only top of mind for a small number of companies. That will change over the next 5 years.

Just because I did not explicitly call something out does not mean machine learning is not being used in that space. Do not let this list limit your areas of focus. These are just starting points, and each one has significant depth.

How do you start to learn about your chosen domain? Set up a Google alert for the area + machine learning. “Fraud Detection Machine Learning” is an example. You will get an email every day with posts about that specific area. They will give you business thinking from popular websites. Most posts are complete trash, but they contain the keywords and language senior leaders use when discussing each domain.

Set up a second layer of Google alerts using those key terms, and you will start getting more granular content about your domain. Read 1 or 2 posts per day for 3+ months, and you will have a solid conceptual understanding of each domain. You will be able to speak in the domain’s vernacular and to the major themes or needs.

The other side of domain knowledge is technical. You understand the problem space better by reading posts written by people working in the domain, and now you need to understand the solution space by reading work by data scientists working in the domain.

The posts you will get sent through Google alerts will have some software vendors mentioned. Bookmark the major software vendors in the space and look at their websites weekly. Read blog posts. Track products and features. Gartner is another source of information about the software landscape. The Big 5 consulting companies are another excellent source for domain-specific content.

Search for the domain on arXiv and papers with code. Reading the abstract and conclusion of 1 to 3 papers per week will help you focus on the technical approaches and open challenges facing data scientists working on your chosen domain. For example, computer vision is a significant area of focus in robotics and manufacturing use cases. Digging into research will give you the model architectures to focus on while learning data science, so you will be best equipped to solve relevant problems.

Business Acumen

Business acumen covers 4 main areas:

• Business Strategy

• Business Management

• Economics

• Communications

Business strategy and business management are two sides of the same coin. Strategy is the study of leverage and advantage in competitive, zero-sum games. Management is the study of strategic planning and implementation.

These are the core concepts of value creation and value return. The business takes some inputs, transforms those inputs into a product, and generates revenue from customers who pay more for the product than it costs the company to produce. Businesses document the first two concepts in their value stream. The third is established in their monetization strategy.

There are 3 main areas of focus for learning the strategy and management sides of business acumen:

• Business Model

• Operating Model

• Technology Model

A quick Google search for business model will return lists of the different types. This one is comprehensive. Business models explain what the business monetizes. Operating models are more specific to the individual company and explain how the business creates value and delivers it to customers.

The technology model is a newer strategic concept that explains how the business leverages technology to produce value. Think of the operating model as physical value creation processes and the technology model as digital value creation processes. A business’s applications of data science fall under the technology model.

Economics is the study of the marketplace businesses operate in. The three main areas to focus on are:

• Macro Factors

• Micro Factors

• Competitive Strategy

Business strategy and management explain the internals of the business, while economics focuses on the external forces that influence the business. These external forces must be included in or removed from some models and are critical to understand.

Communications are the capabilities we use to interact with people in a business setting. These are customers, stakeholders, users, senior leaders, customers, and any other group the business touches.

The purpose of communications is to exchange information to drive some business outcomes. The major communications concepts are:

• Communications Objectives

• Communications Discipline

• Communications For Impact And Persuasion

• Facilitating Communications In Groups

General courses are available to teach business strategy, business management, and economics. These are meant for people going into each field, so they are not specific to data professionals or your role. You will have to synthesize what you are taught and figure out how to apply it in your daily work.

I am biased towards courses that teach business acumen specifically for data professionals because I teach them. I see a gap in business acumen education because traditional business and economics courses teach legacy business concepts. Data changes the way businesses operate, so I teach a more targeted set of courses. I focus on 3 main areas:

• Making The Transition From A Tactical To Strategic Mindset

• Value-Centric Development And Frameworks For High Business Value Opportunity Discovery

• Technical Strategy And Applications Of Machine Learning In Business

My courses combine technical and business with an applied focus. That is what companies are looking for but have a difficult time articulating their needs.

You can get on-the-job experience and education too. Internships provide an opportunity to get familiar with the role and business specifics. Most people focus entirely on the technical role during an internship.

Talk to people from different organizations. Learn about their jobs and ask about their biggest challenges. Ask about their expectations for technology and data. Get to know their problems, pain points, and highest-value needs.

Sit in on meetings with senior leadership and listen to their communications style and framework. Compare and contrast effective communicators with people still developing their communications capabilities. That will give you a firm handle on what to develop and the worst practices to avoid.

Non-technical industry conferences are another excellent opportunity to gain real-world business acumen experience. There are opportunities to network with people in the field. Presentations cover the most significant challenges and opportunities. Look for conferences with workshops that provide some hands-on learning opportunities too. Conferences are as close to work experience as you can get without doing the job.

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Business acumen and domain knowledge are a journey. You can use them to stand out and land your first role, but no one expects you to be an expert in your first role. It takes years to gain proficiency and depth. I teach technical strategists, and no one expects a recent graduate to be one. Hiring managers want people who are value-centric and understand how to deliver projects connected to business needs. They need data professionals who can communicate with technical and non-technical audiences.

Business acumen and domain knowledge start as foundational constructs. The best environment to improve both is on the job. You will advance quickly from understanding the core constructs to being proficient at applying them to your job. While they are critical capabilities, no one is fully baked and an expert from day 1. Your goal is knowledgeable and capable, not perfect. The technical applications are the most essential piece of landing your first role. Learn the problem space but spend 80%-90% of your time learning the solution space.