Welcome to Gather and Trust the Right Data. In today’s data-driven world, making smart business decisions depends on finding, evaluating, and using the right data—not just more data. This course is designed to help you cut through the noise, avoid common data pitfalls, and build confidence in your decisions by focusing on data that is relevant, reliable, and actionable. Through hands-on practice and real-world examples, you’ll learn how to clarify your business questions, identify the best data sources, and ensure your analysis leads to trustworthy insights.

Getting the right data starts with asking the right questions. In this unit, you’ll use concepts from the HBR Guide to Data Analytics Basics for Managers to learn how to focus your data search so you avoid wasted effort and zero in on what truly matters for your business decisions.

Before you start gathering numbers, pause and clarify what kind of data will best answer your question. Sometimes, the answer is already in your existing reports or dashboards—perfect for routine or historical questions. For example, if you want to know last quarter’s sales by region, your CRM or sales dashboard likely has what you need.

Other times, you’ll need to collect new data, such as running a quick survey or interviewing customers. This is especially useful when you’re exploring something new, like gauging reactions to a just-launched feature. And when you need to test cause and effect, a simple experiment or A/B test is often the best route. For instance, to see if a new email subject line increases open rates, you might split your list and compare results.

A practical rule: If the decision is urgent and routine, start with what you have. If it’s new or high-stakes, consider new collection or experiments.
For example, "Should we expand our pilot program?" might require both existing usage data and a short survey of pilot users.

When deciding whether to use existing data or collect new data, consider not just the speed and convenience, but also the reliability, cost, and potential risks. Observational data—such as reports generated from your company’s day-to-day operations—are often easier and less expensive to obtain, but they typically only show correlations, not causation. Experimental data (like A/B tests) can provide stronger evidence about what causes what, but they are usually more expensive, time-consuming, and may raise ethical or privacy concerns. For example, even a simple experiment can have unintended consequences for your brand or customer trust, as seen in high-profile cases where companies tested changes on users without their knowledge.

Before launching new data collection or analysis, always ask: What are the costs, risks, and potential benefits? Consider not just financial costs, but also the time required, the complexity of integrating new data, and any legal or ethical implications—especially when handling sensitive or personal information. Sometimes, the best approach is to start with clean, structured data you already have, as unstructured or messy data can require significant effort to clean and analyze.

By weighing these factors up front, you can focus your data search on sources that are not only relevant, but also practical and responsible to use.

Not all data is equally useful. The key is to focus on sources that are most relevant to your specific decision. Internal sources—like your company’s databases, transaction logs, or customer feedback—are often the first stop. If you’re trying to improve customer retention, your churn reports and support ticket logs are likely more valuable than broad market data. Sometimes, external sources such as competitor benchmarks or public datasets are necessary, especially when you need context or want to set targets. For example, if you’re setting prices for a new product, competitor pricing or industry reports can be invaluable.

It’s easy to fall into the trap of wanting to pull every possible dataset in the event that it will help your decision. Instead, ask yourself: Which two or three sources are most likely to help us make this decision? For example, if you’re deciding whether to move a recurring meeting, calendar attendance logs and employee feedback are likely more relevant than unrelated sales data.

Vague requests like "Get all the data on our users before Friday" often lead to confusion and wasted time. Instead, clarify the decision at hand, the specific group or timeframe you care about, and the most relevant metrics. A focused request might sound like: "Please pull last quarter’s active user counts for our top three products, broken down by region. We’re deciding where to focus next quarter’s marketing."

To see how this looks in practice, here’s a short dialogue between two colleagues:

• Jake: Hey Jessica, can you get me all the data on our users before Friday? I want to be ready for the leadership meeting.
• Jessica: Sure, Jake. To make sure I pull what’s most useful, can you clarify what decision you’re making in the meeting?
• Jake: We’re deciding which user segments to target for our next campaign.
• Jessica: Got it. Would it help if I focused on active users from the last six months, broken down by age group and region?
• Jake: Yes, that’s exactly what I need. Thanks for narrowing it down!

In this exchange, Jessica demonstrates how to move from a broad, overwhelming request to a focused, actionable data pull by clarifying the decision, timeframe, and relevant segments. Notice how she avoids unnecessary work and ensures the data will directly support the business need.

By narrowing the scope, you protect your team’s time and ensure the analysis is actionable—not just interesting. Once you master this approach, you’ll spend less time chasing numbers and more time making smart, evidence-based decisions. In the upcoming roleplay session, you’ll get to practice scoping a data request and narrowing down an overly broad ask—an essential skill for any analytics-savvy manager.