The ABCs of Data Products: An Essential Beginner's Introduction
From Concept to Execution: Building Strong Foundations for Data Engineering Success
Data products are obscure. Everyone talks about them, but nobody really knows how to do them, and everyone thinks everyone else is doing them, so everyone claims they are doing them.
Perhaps you know that a data product is something based on data. You also know data products are supposed to grow your business.
In this week’s article, you and I will discuss what a data product is and how to build one. I will give you my definition of a data product and share some examples of data products. You will also learn my top 5 tips for building data products and get some frequently asked questions answered.
Now, some of you have noticed that DataOps is all about building better data products. This is what Data Gibbersish is all about.
I've dived deeply into some of the points you and I will discuss today. I will share some of these relevant deep dives. Please make sure you save these before the end of May because you may lose access to most of them.
Now, let’s start with a definition of data product.
Reading time: 8 minutes
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🤔 What Exactly is a Data Product?
One common misconception is that data products are only about generating revenue.
I have worked with people who live with the idea that data products are user-facing and can be sold to customers. If you are reading this, you know who you are.
Indeed, many data products can directly increase your bottom line. Yet most have a much broader scope and serve various purposes beyond monetisation.
A data product is any tool, application, or system that uses data to provide value.
It's not just about making money, although that can be a part of it. Think of a data product as something that turns raw data into valuable insights, helping people make better decisions.
A data product can be anything from a single table to a complex Snowflake data share. So, in a nutshell, a data product is a bridge between raw data and meaningful insights.
Ready to dive deeper? Let's look at some practical examples to see these concepts in action.
🌍 Real-World Examples to Bring It to Life
To make these ideas more concrete, let's look at some real-world examples of data products.
Example #1: Recommendation Systems 🛒
Imagine you're shopping online, and the website suggests products you might like based on your browsing history. It analyses your past behaviour, compares it with other users, and predicts what you might be interested in next.
This website’s recommendation system improves your shopping experience and increases your chances of making a purchase. It's a win-win!
Example #2: Data Pipelines 🚰
Think about a mobile app that tracks user interactions. Every tap, swipe, and scroll is collected and sent through a data pipeline. This pipeline processes the data, cleans it, and stores it in a database.
Analysts can then use this data to understand how users interact with the app, identify pain points, and improve the user experience. Based on the results, the product owners know what their next focus should be.
Example #3: Dashboards 📊
Now, picture you’re part of a revenue organisation and need to keep track of various financial metrics. This dashboard displays all the revenue data, breaking it down by region, product line, and sales channel.
Suppose you notice a drop in sales for a specific product. In that case, you can investigate and address the issue before it impacts your quarterly targets.
These examples show how data products can turn raw data into valuable insights, driving better decisions and improving outcomes. You may not sell these products directly to your customers, but they can help your business grow.
It's not only about what you build with data. It's also about how you build it.
Now, let's talk about how you can start your journey to building data products.
🧭 Tips to Kickstart Your Journey
Starting your journey in data engineering can be both exciting and overwhelming. Here are some tips to help you get started:
Tip #1: Understand the Business Problem 💼
Always keep the business problem in mind. Data products are meant to solve real-world problems, so it's essential to understand the context and objectives. I always ask myself these two questions:
What problem am I trying to solve?
How will this data product provide value?
Tip #2: Start Small and Simple 🧩
Begin with a small project to understand the basics. It could be as simple as creating a basic data visualisation or building a small database. The key is to start small and gradually take on more complex projects as you gain confidence.
Tip #3: Learn the Essential Tools 🛠️
Familiarise yourself with essential tools and technologies. SQL is crucial for database management, while Python is widely used for data processing and analysis. Data visualisation tools like Looker or Power BI can help you present your findings effectively.
Don't try to learn everything at once. Focus on mastering one tool at a time.
Tip #4: Follow Best Practices 🧑💻
When building your code, follow best practices. Write clean, readable code, document your work, and test your solutions thoroughly. This will save you time and headaches in the long run.
Tip #5: Collaborate and Seek Feedback 🤝
Don't work in isolation. Collaborate with others, seek feedback, and learn from your peers. Join online communities, attend meetups, and participate in hackathons. The more you engage with others, the more you learn and grow.
By following these tips, you'll be well on your way to becoming a proficient data engineer. But you might still have some questions. Let's address some common ones in the next section.
❓ Your Burning Questions Answered
Question #1: What Skills Do I Need to Develop Data Products? 🤹
To develop data products, you'll need a combination of technical and analytical skills:
👷 Data Engineering: Proficiency in data engineering is essential. This includes skills like SQL for database management, Python for data processing and analysis, and data modelling to design efficient data structures. You need to be well-versed in the tools and techniques used to collect, transform, and analyse data at scale.
🗂️ Business Acumen: Understanding the business context is crucial. You need to know how your stakeholders work and the challenges they face. This helps you design data products that address real-world problems and provide tangible value to the organisation.
💬 Soft Skills: Effective communication, collaboration, and problem-solving abilities are invaluable. You must communicate complex technical concepts to non-technical stakeholders, work closely with cross-functional teams, and approach challenges creatively.
🗺️ Project Management: Managing data product development projects requires strong project management skills. You need to plan, execute, and monitor projects to ensure they are completed on time and within budget. This includes setting clear goals, managing resources, and coordinating with different teams.
Question #2: How Do You Measure the Success of a Data Product? 📏
Measuring the success of a data product involves several factors:
🎯 Clear Metrics and KPIs: Define clear metrics and Key Performance Indicators (KPIs) to track the performance of your data product.
🙌 User Engagement and Satisfaction: Measure user engagement and satisfaction to understand how well your product is received.
🔍 Business Outcomes: Evaluate the impact on business outcomes, such as increased sales, reduced costs, or improved efficiency.
Question #3: What Concerns Should You Be Aware of When Building a Data Product? 🤨
Building a data product comes with its own set of challenges:
🔒 Data Privacy and Security: Ensuring data privacy and security is paramount. Make sure you comply with relevant regulations and protect user data.
🎢 Scalability and Performance: Your data product should be scalable and perform well under load. Plan for growth and optimise your solutions.
💡 Ethical Considerations: Be mindful of moral considerations in data usage. Avoid biases and ensure your data product is fair and transparent.
And that is everything for this week. Let’s wrap it up.
🏁 Summary
Data products are crucial in transforming raw data into valuable insights. Whether it's a recommendation system, a data pipeline, or a real-time dashboard, data products help businesses make informed decisions and drive better outcomes.
Remember to start small, learn key tools and technologies, and always keep the business problem in mind. Collaborate with others, seek feedback, and continuously learn.
The journey to becoming a proficient data engineer is a marathon, not a sprint. Keep learning, stay curious, and don't be afraid to experiment.
Until next time,
Yordan
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Having an understanding of the business requirements, running a POC and obtaining the key metrics is the key to building a successful data product as opposed to obsessing about deploying shiny new tech and vanity metrics. A helpful post :)
A comprehensive article as usual! Thanks for the mention, Yordan.