Data is everywhere. Yet, despite so much data at our fingertips, it can often be hard to find the information we need, make sense of it, and take action.

So the question is: How do we find or create the right kind of data to answer our core questions and generate valuable insights?

We're sharing our tips on how to use data from our certificate, Human-Centered Insights. Keep reading below to learn about why data is important today, how to choose the right data, tools and techniques for data analysis, and how to share insights from your data.

 

1. Introduction: Importance of data analysis in business
2. How data impacts business decisions
3. Choosing the right data: Qualitative vs. quantitative data
4. Data analysis tools
5. Data analysis techniques
6. How to extract customer insights from your data
7. How to share and present data with others
8. Conclusion: Data is for everyone

 

1. Importance of data analysis in business

As data becomes more essential to business, organizations are using it to make informed decisions, gain a competitive edge, and boost efficiency.

For those who can thoughtfully leverage data, there is immense opportunity for data-driven innovation and growth. Data at scale can be a rich source of inspiration and insight for the design of new products, services, systems, processes, and experiences.

 

 

2. How data impacts business decisions

IDEO U takes a human-centered approach to using data. Behind every data set are human architects—people who decide what to measure, what not to measure, and how to measure it. And often, humans are the ones doing the collection of that data. Data may look like some objective truth when you see it on a screen, but it comes from a human process.

This perspective allows us to think more creatively about how to draw insights from data, particularly in the early decision-making stages of innovation, because we start to think about the contexts in which we collect data, the systematic biases that the data might inherently have, and how to design around that. If the data points collected represent human behaviors from real people, we can start to imagine and empathize with those human stories behind the data.

Using a human-centered lens can help you design better products and services for people. Rather than just looking at data to validate ideas in the final stages or to measure the effectiveness of an idea, this perspective of data-driven decision making and data-driven design empowers us to use data to inform our choices earlier. Data can help us brainstorm, ideate, and think about new concepts. It can give us the information to shape big ideas and decide which directions to move.

---

Learn how to draw actionable insights from data in our 5-week course Innovating with Data.

---

 

 

3. Choosing the right data: Qualitative vs. Quantitative data

When it comes to data, people often think of tables and spreadsheets, but there are many different types of data. Here are definitions and examples of qualitative data and quantitative data:

• Qualitative data: Primarily descriptive. Examples include interview data such as text transcripts and audio recordings from field interviews, videos taken on cameras or phones, observations from design research in the field.
• Quantitative data: Primarily measurement based. Includes things that can be represented with numbers or a series of numbers, such as revenue over time, the number of people that visit a website, the length of time spent in the store.

Keep an eye out for data that was perhaps collected for another purpose but, with some repurposing, could be useful for your core problem. Working with existing data, repurposed to address the core problem, can be a quick and powerful way to innovate with data. If there's no existing data that speaks to your core problem, you can design and gather your own using surveys, questionnaires, interviews, observations, and other methods.

Here are some general tips when using data to generate both qualitative data insights and quantitative insights:

• Aim for the bullseye — Be thoughtful about what you measure and how you measure it.
• Diversify your lens — Collaborate with others to get varying perspectives on how to measure something.
• Seek inspiration — Learn from how experts have measured abstract concepts.
• Triangulate your data — If you can, try not to rely on a single measure, but instead measure quantities in multiple ways.
• Consider the context — Always question whether a change in a metric is due to some real underlying trend or just randomness in the world.
• Lean into your curiosity — When a metric doesn't seem right, dig deeper and investigate.

 

 

4. Leverage analysis tools to derive insights from data

There are many different data analysis tools, data visualization tools, and data mapping tools. Here are a few options:

• Microsoft Excel: Widely used for basic data analysis, Excel offers powerful features like pivot tables and charts to visualize and manipulate data.
• Tableau: A leading data visualization tool that helps transform raw data into interactive dashboards and visual reports, making insights easy to grasp.
• Python: A versatile programming language with robust libraries like Pandas and NumPy for data manipulation, and Matplotlib for visualizing insights.
• R: Known for statistical computing, R excels in data analysis and visualization, offering a vast array of packages to derive deeper insights.
• SAS: A specialized tool for advanced analytics, SAS is favored for its ability to handle large datasets and perform complex statistical analysis.
• MySQL: A relational database management system that allows you to store, query, and analyze large datasets, enabling efficient data-driven insights.

 

5. What are some data analysis techniques?

There are some key data analysis techniques and steps that you can incorporate into your practice. Here are some key things to keep in mind when working with data, during the data analysis process:

Define your problem statement

When using data, the best place to start is with a designer's mindset and ensuring you know the right problem to solve—which can be a big task. When innovating with data, many people will start with the data, whether thinking about what to collect or digging into existing data. But before even looking at the data, start with the problem. Instead of immediately optimizing for numbers like revenue and engagement, ask the why behind the problem.

Gaining a deeper understanding of the problem or question will help you get beyond any initial problem statements that presuppose a solution. And get comfortable with the fact that what seemed like one problem might actually be several smaller problems — that will likely have to be solved over time rather than all at once.

Set a goal

It’s essential to figure out what you’re trying to accomplish with the data. Ask yourself these questions:

• What problem are you trying to solve?
• What decision are you trying to make?
• What outcome are you ultimately hoping to achieve? Why?
• What data do you need?
• What’s possible or feasible in the timeframe you have?
• What kind of change will you prioritize for your organization?

Define expectations ahead of time

Before diving into data analysis, align with your team on what you’re expecting to learn. If you're using data for decision-making, try to get your team to pre-commit to how you'll move forward if you see one thing versus another in the data. Finally, when visualizing your data, choose a layout that allows you to make comparisons between what you expected to happen and what actually occurred.

Sketch the data

Sketching the data early is one technique that can help save time by giving a clear direction before diving into any data collection or analysis. It’s powerful to think about what the problem is first because ultimately, you want to create a solution that solves the right problem, rather than just using any kind of data to solve a problem that may or may not be the right problem to solve.

Try our free sketching with data activity to see this method in action.

Collaborate with others

One guiding principle when working with data is that data requires a lens. Having a diverse range of opinions, views, and inputs is essential to any creative problem-solving process. When you collaborate with others, you’ll get varying perspectives on how to measure something and how to interpret data.

 

 

6. How to extract customer insights from your data

In a data context, many people think of insights as solely whatever the data looks like. For example, if there is an upward trend, you might think the insight is that the numbers are going up. While this is true, customer data insights are not just observations of the data, but are discoveries of patterns that offer new perspectives.

We use insights to inspire new ideas and unlock opportunities to design new products and services to improve lives. When we develop a data insight, that insight can sometimes change how we see the world, including how we think, feel, and act. Developing an insight is part art, part science. When you uncover it, it's this "aha moment" of discovering something that guides you.

Wondering how to derive insights from data? One approach to finding insights taught in our course Innovating with Data is to look for outliers in the data. When you have a series of quantities, outliers are quantities that are way higher or lower than the majority. In more traditional data analysis, you might look at the outliers and then remove them. But when designing with data in a human-centered way, it's often interesting to look at the outliers, ask questions and get curious about why certain data points are so different from the rest.

 

 

7. How to share and present data with others

To bring data to life for your audience, it’s important to learn data storytelling. Combining data and storytelling satisfies the left and right brain needs and gives your audience the ability to understand your insights quantitatively and qualitatively. When you incorporate storytelling into your work, it allows you to share insights and ideas in a more human way.

All the data in the world won’t sway your stakeholders to take on a risky new idea if they can’t connect emotionally with what it means. To successfully present data to others, make sure you include deep research and insights about customers, data about what's happening in the market, and experienceable prototypes of solutions. It’s also helpful to give people an opportunity to discuss what they’ve learned in groups. At IDEO, we’ve shared data storytelling examples and data visualization examples in our work with clients across the healthcare, automobile, and finance industries.

 

 

8. Make actionable insights from data work for your team

Data is a powerful driver of innovation, but figuring out where to start can be daunting. The good news? You don’t need to be a data scientist to make insights from data. By using human-centered approaches, anyone can leverage data to uncover opportunities for innovation and gain a deeper understanding of people and problems.

Ready to take the next step in using data to generate impactful insights? Explore our Human-Centered Insights Certificate to move toward innovation today.

 

Key Takeaways

• Clearly define what you want to learn from your data and how it aligns with your goals.
• Select the right data analysis tools—such as Excel, Tableau, and Python—to efficiently interpret and visualize data.
• Focus on human-centered techniques that prioritize user needs and behaviors to extract meaningful insights.
• Convert your data findings into actionable strategies that can drive decision-making and innovation.
• Consider advanced resources like the Human-Centered Insights Certificate to further develop your ability to analyze and apply data effectively.

---