1 What is Business Analytics?
By the end of this chapter, you should be able to:
- Describe what business analytics is and how it supports decision-making.
- Distinguish between descriptive, predictive, and causal analytics—and identify when each is appropriate.
- Explain how business decision-making has evolved in the age of data.
- Reflect on the ethical responsibilities involved in using data for business decisions.
- Recognize the role of the business analyst as an interpreter who blends data, business knowledge, and storytelling.
- Adopt a growth mindset as you begin developing your own analytical skills.
1.1 What is Business Analytics?
Businesses have always had to make important decisions: Who should we hire? Where should we invest? How much should we spend on advertising? In the past, these decisions were often made based on experience, intuition, or the authority of senior leadership—a dynamic sometimes known as the “HIPPO” effect: Highest Paid Person’s Opinion.
That’s no longer enough. Today’s businesses operate in faster, more complex environments. Fortunately, they also have access to something that previous generations did not: data. From customer transactions to social media posts, microsecond stock price information to supply chain records, nearly every part of a business now generates data. A single online purchase creates dozens of data points: what the customer viewed, how long they hesitated, which payment method they chose, even how they arrived at the site. The challenge isn’t collecting it—it’s knowing what to do with it.
This is where business analytics comes in.
Business analytics is the use of data and analytical tools to support better decision-making. It combines statistical thinking, technological capability, and business insight. But it’s not just about building models or generating dashboards. At its core, it’s about helping organizations answer the fundamental question that drives all business strategy: What should we do next?
“Let the data tell its story.” — Gregory Crawford, Chief Economist at Zalando
That story is ultimately what business analytics is trying to uncover. Analysts help businesses listen to what the data is saying—about customer needs, regulatory risks, investor sentiment, competitive dynamics, or operational bottlenecks—and translate that into actions.
But the essence of decision-making remains what it has always been: reasoning through uncertainty, weighing trade-offs, and telling a compelling story about what to do. The difference today is that we can do it better—with data as our guide, structure as our method, and analytical tools that help us move beyond educated guesswork toward informed action.
Business analytics and data science overlap, but they are not the same. Data science often focuses on building general-purpose models and technical innovations. Business analytics stays grounded in context-specific decisions. A good business analyst is less concerned with predictive accuracy in the abstract and more concerned with whether an insight leads to a better business outcome.
1.2 How Business Decision-Making Has Evolved
Business decision-making has gone through a quiet revolution. While the core challenge—deciding what to do—hasn’t changed, the tools, habits, and expectations surrounding decision-making have shifted dramatically. Business analytics sits at the center of this transformation.
We can think of the evolution in four broad stages:
Stage 1: Gut Instinct (The HIPPO Era)
For most of the 20th century, decisions were dominated by experience, instinct, and authority. The most senior person in the room often had the final say—regardless of what the data might suggest. This is the “HIPPO” model we mentioned earlier: Highest Paid Person’s Opinion. While experience remains valuable, this approach often lacks transparency and makes it difficult to learn from both successes and failures.
Stage 2: Simple Data Support
With the rise of digital systems, businesses began generating and storing data—and using it to create basic reports and forecasts. Dashboards and spreadsheets became common tools for tracking performance. But in this stage, data was mainly descriptive: it helped explain what had happened, not what to do next. Think monthly sales reports or annual budget reviews.
Stage 3: Advanced Analytics
As computing power increased, so did analytical ambition. Businesses began using predictive models and machine learning to forecast future outcomes and identify hidden patterns. This allowed for more proactive decisions—anticipating which customers might leave, deciding when to issue stock to raise capital, or optimizing prices dynamically. Yet even the most sophisticated model is only as useful as the decision it ultimately supports.
Stage 4: Experimentation and Causal Learning
The frontier today is not just prediction—it’s learning what actually works. Companies now use A/B testing, field experiments, and causal inference to evaluate strategies and understand the true impact of different choices. This lets firms go beyond correlation and ask the crucial question: What happens if we actually do X instead of Y?
“Nearly every decision we make about our product and business is guided by member behavior observed in test” (i.e. an experiment). — Netflix Tech Blog
“Our success at Amazon is a function of how many experiments we do per year, per month, per week, per day.” — Jeff Bezos
This shift toward experimentation represents more than just a technical advancement—it’s a cultural transformation. It reflects a mindset that values curiosity, iteration, and evidence over hierarchy or habit. Modern business analytics is as much about learning and adapting as it is about measuring and predicting.
1.3 What is Business Analytics?
We’ve spent quite a bit of time talking about business analytics without actually defining it. Time to fix that.
Business Analytics is the practice of using data and models to solve business problems and improve decision-making.
It’s not just about collecting numbers—it’s about using them wisely. In today’s data-rich environment, the real challenge is knowing how to extract insight, frame decisions, and take action in a way that creates value.
“Data are widely available; what is scarce is the ability to extract wisdom from them.” — Hal Varian, Chief Economist at Google
That’s where business analytics comes in. It combines technical skills with business understanding to move from raw data to real-world impact. A business analyst doesn’t just answer what happened—they help organizations understand why, predict what might happen next, and evaluate what to do about it.
The process usually involves five key steps:
- Defining the Problem — Every good analysis begins with a sharp question. What decision needs to be made? What are the options? What does success look like?
- Collecting and Cleaning Data — Data is rarely ready to use out of the box. Analysts spend significant time gathering relevant data sources and ensuring they’re accurate, complete, and reliable.
- Analyzing Data Appropriately — With the right tools—from simple summaries to predictive models—analysts identify patterns, test hypotheses, and quantify trade-offs.
- Communicating Insights — Even the best analysis fails if no one understands it. Analysts must tell persuasive, relatable stories with data—stories that clarify the decision at hand and guide action.
- Acting to Create Value — Ultimately, analytics only matters if it leads to better decisions. The goal is not analysis for its own sake, but to generate insights that drive strategy, improve operations, control costs, better serve customers, or deliver stronger returns to shareholders.
Business analytics is more than a technical field—it’s a way of thinking. It reflects a broader evolution in business: away from gut instinct alone, and toward evidence-based decision-making. But even as the tools evolve—from dashboards to machine learning to experimentation—the fundamental goal remains the same: to make smarter, more confident choices in an uncertain world.
1.4 The Three Types of Business Analytics
Business analytics isn’t one-size-fits-all. Different business problems require different types of insight—and therefore, different analytical tools. A helpful way to understand the field is to distinguish between three broad types of business analytics:
1.4.1 Descriptive Analytics: What happened?
Descriptive analytics focuses on summarizing and reporting what has already occurred in a business. It answers questions like:
- What were our total sales last quarter?
- Which products had the highest return rates?
- What were the top 10 ASX-listed companies by shareholder return last year?
Tools used here include summary statistics, tables, visualizations, and dashboards. These methods don’t make predictions or test strategies—they organize past data in a way that helps managers understand performance, spot trends, or identify anomalies.
Think of this as the rearview mirror of analytics. It helps you see where you’ve been.
1.4.2 Predictive Analytics: What might happen next?
Predictive analytics goes a step further: it uses past data to forecast future outcomes. This includes questions like:
- How many customers are likely to churn next month?
- How are annual depreciation expenses likely to change over the next three years?
- Will the RBA cut interest rates next month, and if so by how many basis points?
Methods here often involve statistical models and machine learning techniques that identify patterns and extrapolate them forward. These models don’t guarantee what will happen, but they give decision-makers a probabilistic view of likely outcomes.
Importantly, predictive models do not answer causal questions. A forecast that sales will increase next quarter doesn’t tell you why—or what would happen if you changed your pricing or launched a new campaign.
Think of this as the weather forecast of analytics: useful for planning, but not a substitute for experimentation or causal understanding.
1.4.3 Causal (Prescriptive) Analytics: What will happen if we act?
This is where things get more strategic—and more difficult.
Causal analytics (sometimes called prescriptive analytics) tries to answer counterfactual questions: What would happen if we did X instead of Y?
Examples include:
- What impact will a 10% price increase have on demand?
- How will merging with another firm affect our share price?
- Would switching to a new supplier improve delivery times?
These questions can’t be answered by simply observing correlations or running a predictive model. They require methods designed to isolate cause and effect—such as randomized experiments (A/B testing), instrumental variables, difference-in-differences, or other econometric techniques.
This is the decision engine of analytics: it helps businesses not just anticipate the future, but change it deliberately.
1.4.4 Key Insight: Different Problems, Different Tools
Each type of analytics serves a different purpose:
| Type | Core Question | Typical Tools | Business Use |
|---|---|---|---|
| Descriptive | What happened? | Summaries, visualizations | Reporting and monitoring |
| Predictive | What might happen? | Forecasting, classification | Anticipating future trends |
| Causal (Prescriptive) | What will happen if we act? | Experiments, causal inference | Strategy and decision-making |
Choosing the wrong tool for the question can lead to misleading conclusions. For example, a model that predicts which companies are likely to identify and correct mistakes in their previously published financial statements is not the same as understanding why those mistakes occurred—or what internal controls or governance changes might have prevented them.
To be a great business analyst, it’s not enough to know how to use the tools. You need to know which tool matches which question.
10 min
Matching Analytics Types to Business Questions
Now that you understand the three types of business analytics, let’s practice identifying which type of analysis different business questions require.
For each business area below, we’ve provided examples of questions that fall into our three categories: Descriptive (What happened?), Predictive (What might happen?), and Causal (What will happen if we act?).
Some examples are filled in to get you started. Your job is to complete the missing entries by thinking about what questions a business analyst in each area might ask.
Complete the Table
| Business Area | Descriptive | Predictive | Causal |
|---|---|---|---|
| Sales | How much did we sell last quarter? | [Your turn: What might happen in the future?] | [Your turn: What question would test the impact of changing something?] |
| Marketing | [Your turn: What happened in the past?] | Who will buy next? | [Your turn: What question would test the impact of changing something?] |
| Hiring | [Your turn: What happened in the past?] | [Your turn: What might happen in the future?] | What if we change recruitment practices? |
| Finance | [Your turn: What happened in the past?] | Will we meet next quarter’s revenue goals? | [Your turn: What question would test the impact of changing something?] |
| Supply Chain | How many units were delivered on time? | [Your turn: What might happen in the future?] | [Your turn: What question would test the impact of changing something?] |
5 min
Classify These Questions
For each question below, identify whether it requires Descriptive, Predictive, or Causal analytics:
- “Which marketing channels generated the most leads last month?”
- Type: _______________
- “How will our share price change if we replace our CEO with our current COO?”
- Type: _______________
- “Which customers are most likely to upgrade to our premium service?”
- Type: _______________
- “Which of our product lines account for the largest share of corporate overhead expenses?”
- Type: _______________
- “Would offering free shipping increase our conversion rate?”
- Type: _______________
- “Will analysts issue a buy/hold/sell recommendation for our stock next week?”
- Type: _______________
Remember: the same business problem can often be approached from multiple analytical angles. The key is being intentional about which type of analysis will best serve the decision you need to make.
1.5 The Power and Limits of Analytics
Business analytics is a powerful tool—but it is not a crystal ball. At its best, analytics sharpens our thinking, clarifies options, and supports better decisions. But it also has limits that every analyst must understand.
1.5.1 Good decisions depend on good data
It’s a simple principle, but an essential one: garbage in, garbage out. Even the most advanced model cannot save an analysis built on poor-quality data, vague problem definitions, or biased assumptions. Clean, relevant, well-structured data is a prerequisite—not a guarantee—for good insights.
Principle: Garbage in, garbage out.
Analytics doesn’t make decisions for us. It supports decision-making. The quality of that support depends entirely on the inputs we provide and the care with which we interpret the results.
This is why, later in the course, we devote specific chapters to how data is collected and how it is stored and managed—because those choices shape everything that follows in the analytical process.
1.5.2 Analytics doesn’t eliminate uncertainty—or judgment
All models are simplifications. They help us focus on what matters, but they also leave things out. A forecast can offer probabilities, but never certainties. A causal estimate can quantify impact, but only under specific assumptions. The world is more complex than any spreadsheet or statistical model can fully capture.
This means that even with data:
- Uncertainty remains. Analysts must often make decisions under incomplete information or ambiguous results.
- Trade-offs persist. No model can resolve tensions between short-term gains and long-term investments, or between efficiency and innovation. These are managerial choices, not statistical ones.
- Judgment is essential. Good analysts don’t just run models—they think critically, question results, and tell coherent stories that connect the data to the broader context.
Woolworths, Australia’s largest supermarket chain, learned this lesson during the ACCC’s 2024-25 supermarket inquiry. Despite having access to massive amounts of checkout data and sophisticated forecasting systems, Woolworths admitted to significant shortcomings in the accuracy of volume forecasts provided to fresh fruit and vegetable suppliers.
As CEO Amanda Bardwell acknowledged during ACCC hearings, the company had access to detailed checkout data that should have given them “a firm understanding of likely future demand.” Yet former Managing Director Natalie Davis admitted that an internal review found Woolworths had “failed to provide volume forecasts to suppliers that were as accurate as possible.”
The problem wasn’t the technology—it was how they used their data. Davis explained that Woolworths had failed to apply new analytical capabilities as they became available, essentially letting their forecasting methods lag behind their data collection abilities.
Key lessons:
- Garbage in, garbage out: Having lots of data doesn’t automatically lead to good predictions
- Tools must match the problem: Fresh produce forecasting requires different approaches than predicting demand for shelf-stable goods
- Implementation matters: Even good analytical capabilities are useless if they’re not properly applied
Source: ACCC Supermarket Inquiry Final Report, March 2025
1.5.3 The analyst’s role
Being a good analyst means more than knowing the right techniques. It means understanding the business problem, evaluating the quality of the data, and making thoughtful decisions about how to present and interpret results. It’s about being honest when uncertainty is high, and being clear about what the data can and cannot tell us.
In short, analytics gives us leverage—but it doesn’t replace leadership. The best decisions come when rigorous analysis meets sound judgment.
1.6 Ethics in Business Analytics
Business analytics is not just a technical activity—it is also a human one. Every decision about what data to collect, how to analyze it, and how to act on it involves ethical considerations.
1.6.1 Data use raises ethical responsibilities
As businesses gain more powerful tools to collect and interpret data, they also take on greater responsibility. Three core ethical concerns should guide any data-driven initiative:
Avoid bias.
Algorithms and models can reflect—and even amplify—existing biases in the data. For example, a hiring model trained on past decisions may replicate patterns of discrimination unless actively corrected. Ethical analytics involves detecting, understanding, and mitigating such bias.Protect privacy.
With more data comes more risk. Businesses must handle customer, employee, and user data with care. That means complying with legal requirements (like the GDPR or Australia’s Privacy Act), but also going beyond compliance to ask: Are we using this data in ways that our stakeholders would consider reasonable and respectful?Promote fairness.
Decisions driven by analytics—such as who gets a loan, a promotion, or a targeted offer—can have real consequences. Ethical analytics means being transparent about how decisions are made and striving to ensure that outcomes are just and inclusive.
Ethical analytics isn’t just about what you can do with data—it’s about what you should do.
These issues will return in later chapters—especially when we discuss data collection, machine learning, and A/B testing. In each of those contexts, ethical decision-making is not an optional add-on, but a core part of responsible business practice.
In Southeast Asia, ride-hailing platforms like Grab have implemented algorithmic management systems to coordinate labor, match drivers with passengers, and set dynamic pricing. While these systems enhance efficiency, they have also raised concerns about transparency and fairness.
Drivers often report a lack of clarity on how decisions are made regarding ride assignments and fare calculations, leading to feelings of disempowerment and uncertainty about their earnings. A study by the Carnegie Endowment for International Peace highlighted that such algorithmic management can disrupt traditional labor relations, making work more individualized and reducing opportunities for collective bargaining.
The opaque nature of these systems can exacerbate existing inequalities, particularly for workers in marginalized communities who may lack institutional support. This case underscores the importance of embedding ethical considerations into the design and implementation of analytics systems, ensuring that they promote fairness and transparency for all stakeholders.
Source: Carnegie Endowment for International Peace
1.6.2 Ethics is not optional
Firms that ignore these responsibilities may face backlash, regulatory penalties, or long-term reputational damage. But beyond risk avoidance, ethical analytics also presents a positive opportunity: to build trust, foster loyalty, and support a more responsible form of business.
Ethical thinking doesn’t happen at the end of an analysis. It must be embedded at every stage—from how we frame questions to how we communicate results. Throughout this course, we will return to this theme as we encounter specific ethical trade-offs in modeling, measurement, and experimentation.
Done well, business analytics can help firms grow in ways that are not only efficient, but sustainable and fair. It’s up to analysts to ensure that data is used wisely—and responsibly.
1.7 Becoming a Business Analyst: The Interpreter Role
By now, we’ve seen that analytics is not just about algorithms and datasets—it’s about decisions. And good decisions need more than numbers. They need interpretation, judgment, and communication. This is where the business analyst comes in.
Business analysts sit at the intersection of data and decision-making. They are not just technicians or model-builders—they are interpreters. Their job is to make data meaningful to the people who need to act on it.
“MBAs who have good business intuition but also speak the language of statisticians are rock stars.” — Susan Athey, Professor of Economics at Stanford
While this quote mentions MBAs, it’s not just about the degree. It’s about the blend of skills—business understanding, analytical fluency, and communication—that define a high-impact analyst. And that skillset isn’t reserved for postgrads. A well-trained undergraduate business analyst with this toolkit can add enormous value from day one. That’s exactly the kind of foundation this course is designed to build.
1.7.1 Analysts are modern storytellers
Humans are natural storytellers. For thousands of years, stories have helped us make sense of complexity—turning scattered events into patterns, and patterns into meaning.
Today’s business analysts continue this tradition. But instead of myth or anecdote, they use data. The tools may be newer, but the mission is timeless: to tell clear, truthful, and compelling stories that help others see what is happening—and decide what to do next.
That story might be about why sales are falling, how investor sentiment is improving, or what might happen if the firm raises prices or changes strategy. But in every case, the analyst’s role is the same: to build a bridge between the data and the decision.
1.7.2 Core skills of the business analyst
To succeed in this role, analysts need a blend of skills:
Business understanding
You must understand the context: What is the decision? What matters to stakeholders? What constraints shape the options?Data analysis
You need to clean, explore, and model data using the right tools. This includes both descriptive and predictive methods—and increasingly, causal tools to support strategy.Communication and storytelling
You must craft narratives that are not only statistically sound, but also understandable, relevant, and actionable.
These skills don’t develop overnight. They grow through practice—through working on real problems, engaging with real data, and reflecting on how analysis shapes action. In the next chapter we will devote more time to to thinking about the skill set of a modern business analyst.
1.7.3 Collaboration is essential
Business analysts don’t work alone. They collaborate with data engineers, who build the infrastructure and pipelines that make analysis possible; with business stakeholders, who define the problems and act on the results; and with other analysts, who bring complementary expertise and perspectives.
The analyst is the glue—the person who keeps the analytical process connected to business goals, ethical standards, and practical decisions.
1.8 How to Approach This Course (And Have Fun with It!)
By now, you’ve seen what business analytics is really about: using data to make better decisions, combining technical tools with human insight, and communicating clearly to create value. You’ve also seen that becoming a business analyst means developing a wide-ranging skillset—one that draws on statistics, strategy, storytelling, and judgment.
But knowing what the role requires is just the start. Now comes the fun part: learning how to do it.
This course is your first step into the world of business analytics—and it’s meant to be challenging, empowering, and fun.
We’ll move from data to narrative. From raw spreadsheets to compelling stories. From abstract problems to concrete insights that help real businesses make smarter decisions.
You won’t just learn how to analyze data. You’ll learn how to think like a business analyst: someone who blends technical skills with business understanding and communicates insights clearly and persuasively.
Our goal is to help you develop the mindset of someone who is curious about the world, rigorous with evidence, and focused on impact. Someone who doesn’t just look at data, but learns from it—and helps others do the same.
That’s the point. You’re not expected to know everything now.
Analytics is a skill you build through doing—not memorizing. Come to class with a growth mindset: it’s okay to be unsure, to try something that doesn’t work, to ask a question that feels basic. That’s how you learn.
What we ask of you:
- Be curious
- Be open to feedback
- Be willing to try, fail, and improve
- Ask questions—lots of them
- And support each other as we learn
This course will give you a solid foundation in the skills of a business analyst. But more than that, we hope it gives you a new way of seeing the world: as a place full of data, decisions, and opportunities to think better.
Next, we’ll explore the key skills that make a great business analyst—and begin to develop the mindset of someone who approaches business problems like a scientist.