A New Approach for Complex Environments:

As a young business analyst, I started my first contract in a warehouse and operations plant. A hundreds of years old manufacturer that had no ERP (enterprise reporting platform) or inventory tracking yet was wildly profitable for decades (their products were sold in cans but not those you are thinking).

Me and a small team were tasked with onboarding an inventory tracking system in this old-school environment. After weeks of prep and training, we were on-site for dozens of shifts as we handed out barcodes and scanners to employees.

We noticed something off on the third day.

Inventory levels kept ticking down. Throughput was constrained and manual adjustments increased. We thought the sync was delayed for the first day, the barcodes were placed incorrectly, or the scanners were failing.

Then I kept walking the floor. In the packing room, I noticed scanners were sitting on benches and their chargers - not good.

I approached Lenny (floor manager) and pointed this out to him.
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“Out of sight, out of mind son” he replied.

I took a step back that evening and wondered about this problem.

Then, we bought holsters for each floor employee and let them customize their scanners.

Inventory levels went back to expected in a week. Problem solved for a couple hundred dollars.

In the ever-evolving fields of data and product development, especially in healthcare, we need strategies that break free from conventional boundaries. Not everything works when everything is meant to be a system in itself.

A first principle is a “basic, foundational proposition or assumption that cannot be deduced from any other proposition or assumption.”. This philosophical approach encourages us to strip problems to their fundamental truths, which is crucial for teams dealing with complex data and product challenges.

4 core aspects of data product management and first principles:

• Breakdown the problem into core components - question every requirement
• Create a data product strategy that accomplishes the mission - the intersection of customer and business value
• Build a reliable data pipeline - product thinking to process
• Win through others - be a coach

First Principles for Data Product Teams

1. Breakdown the Problem into Core Components - Question Every Requirement

First principles thinking in data product management starts with deconstructing complex problems into their fundamental elements.

Understanding the analytical (really the business) question - the so what or why - is critical.

Complexity is not the goal in and of itself. No one gets points for difficulty in business. Question every requirement. Break down every component into its parts.

Encouraging yourself and others to question the assumptions you have is critical. Think of an EHR. EHRs, at their core, are financial and billing tools and those with poor usability have been connected with more medical errors. On top of that, the average ER doc has to make 4,000 clicks in a single shift.

Does this have to be true? Why are all of these needed? Start with each factor of how something came to be and you are on the right track.

2. Create a Data Product Strategy that Accomplishes the Mission - The Intersection of Customer and Business Value

A successful data product strategy aligns with both customer needs and business goals. All employees should be focused on fulfilling the company's mission to profit or purpose, maybe both.

Most team members are working directly toward the business goal by a product or service to customers: building (engineers, data, and designers), go-to-market (marketing and sales), optimizing the business (analysts and resourcing), or helping customs (support).

Product management doesn't directly build or operate. The goal is to inform the operations to optimize the intersection of business and customer value. How does that get done? By defining a strategy - specifically a data product one at that.

This includes four components:

1. Goal and outcomes

If you don't know where you are going, you are already on your way

Go back to that first principle and a good data product manager will incessantly ask the hard questions in a quest of defining the mission they follow. What does the world look like for that goal or vision to be real?

Data Product Managers (DPMs) also need to know what other teams aim for. DPMs are intertwined deeply with other teams as your product is either an input or output of another. See it as insurance to make sure you are not colliding with others.

2. Customers

“The customer is always right” and “customers don’t know what they want” are both accepted business wisdom. The line between “inspiringly bold” and “foolishly reckless” can be a millimeter thick and only visible with hindsight.” - Morgan Housel

That goal may be lofty and well-intentioned, but if no one pays for it or those that do are too few and far between, then what is the point? Customer input is the quantitative and qualitative data you develop and a sense you must cultivate.

What are customers signaling? Not directly, but what are they hiring to get a job done, and how are you, as a data product, helping your customers make progress in the jobs they need to get done?

That signal is worth listening to and what you hear from customers and what they do is the ultimate validation of your goal and efforts.

3. Environment

Nothing is built in a vacuum. If you see an opportunity, others likely have as well.

When looking externally, think of PESTLE (Political, Economic, Social, Technological, Legal, and Environmental) - what changes affect your company, input data, and customers' interest?

While much focus is on the external factors in the environment (competitors and market forces), you should never discount the company environment and dynamics. Most importantly with this, understand the cyclical nature of teams and focus - everything is always in a state of change, whether you choose to see it or not.

Calm plants the seeds of chaos.

4. Constraints

Lastly, any product team needs to understand the limits of the team that is doing the building. Just as a captain must understand the crew, time, and fuel to make a journey successful, DPMs will also.

People are the biggest constraint for product teams but the second most important constraint for data product teams.

Data, as mentioned, and its myriad components are the largest constraint on a data product once the goals, customers, and environment are better understood.

• Is the quality appropriate?
• Is there governance, documentation, and observability?
• What are the biases and limitations?
• How stable and extensible is the data?

Money is a critical factor in determining the scope and scale of a data product. The budget allocated dictates the quality and quantity of resources that can be employed, including technology, tools, and personnel. These constraints require a balancing act between desired features and cost implications.

Acquiring new data sources, spending on third-party tools, investing in data engineering, or freeing up resources for GTM (go-to-market) is critical to determining a trajectory.

Just like any other product, you can't consider only the initial development but also the maintenance and updates. Scaling, enhancements, quality systems, ETL updates, and product adaptation to changing needs.

Time constraints impact the speed at which a data product can be brought to market. A limited timeline might necessitate compromises in product features or depth of data analysis.

In fast-evolving industries, especially in this age of generative AI, where everything seems to change by the minute, time is crucial to ensure the data product remains relevant and competitive. Delays in development can result in missed opportunities or outdated solutions by the time of launch.

Thanks for joining - see you next time!

Part 2 - Next time principles 3 + 4

• Build a Reliable Data Pipeline
• Win Through Others

Where to learn more

Recommend books, articles, and other resources for readers to deepen their understanding of First Principles Thinking.

• “The Great Mental Models” by Shane Parrish for versatile thinking frameworks.
• "Inspired" by Marty Cagan for his influential text in developing product thinking.
• "Same as Ever" by Morgan Housel for a broad psychological look at the things through history that never change.

🚀 Greetings, data enthusiasts! Kevin here, and today we're going to delve into the fascinating world of data products. In this age of information, data is an invaluable asset, and effectively utilizing it can set your organization apart from the competition.

So, let's dive right in and examine the essential components for creating successful data products that not only achieve your business objectives but also engage your users. Without further ado, let's embark on a journey to explore the anatomy of data products and the 10 key elements that will help you understand and build impactful data products

🤔 Data Product vs. Data As A Product: What's the Difference?

🧬 Data Product

A data product is any technological product or element that relies on data to achieve its primary purpose. Think of it as a tool that uses data to solve problems and provide insights.

📱 Data As A Product

Now, "data as a product" is when you treat data itself as a valuable commodity. It's like transforming data into a shiny new gadget that your customers (or internal team members) want to get their hands on. This approach helps democratize data by making it more accessible, usable, and valuable to everyone in the organization.

👉 Remember: a "data product" uses data to achieve its goal, while "data as a product" means data is the actual product being offered. Data as a product is a subset of a data product. Applying product thinking to data management.

Now that we've got that cleared up, let's move on to the main event!

🧬 10 Key Elements of Data Products

1. Adaptable 🌿

To create a successful data product, it should be adaptable and seamlessly integrate with various inputs and outputs. It should effortlessly expand to accommodate new data sources and adapt to changing user needs. For instance, a data product should have the ability to connect with different databases and platforms, ensuring its usability across multiple systems.

2. Discoverable 🔍

Make your data product easily found and accessible without being hidden or restricted. Users should be able to locate it quickly and understand how it can benefit them. This includes having clear documentation, intuitive navigation, and easy-to-understand metadata.

3. Specialized 🎯

A successful data product must address a focused set of valuable problems for external customers. By having a clear purpose, the product can be designed to effectively solve these specific problems. This ensures that the data product remains relevant, efficient, and valuable to its users.

4. Fortified 🏰

Security and stability are crucial for any data product. Your product should ensure reliable data handling and protect sensitive information. This includes having strong access controls, data governance rules, and thorough testing to ensure data integrity and security are enforced.

5. Contextual 📚

A successful data product should maintain a historical record of its purpose and problem-solving context, including a data dictionary and external references. This helps users understand the data product's history and evolution, making it easier for them to trust and use it effectively.

6. Engaging 🎉

To capture the interest of your target audience, your data product should provide relevant and valuable insights. This can be achieved through interactive visualizations, easy-to-understand reports, and personalized recommendations. By making your data product engaging, you'll encourage users to interact with it more frequently and derive greater value from it.

7. Loveable 💖

A successful data product should exceed the minimum loveable product standard, making it approachable and user-friendly. This means prioritizing usability, design, and overall user experience. By creating a product that users enjoy using, you'll increase adoption and drive success.

8. Scalable 📈

A successful data product should be designed with scalability in mind. As your user base grows or your data volume increases, your data product should be able to handle the increased workload without compromising performance or functionality. This includes considering the infrastructure, data processing, and storage requirements needed to support your data product in the long term.

9. Actionable 💡

Your data product should provide insights that drive decision-making and enable users to take action based on the information it provides. The key is to present data in a way that helps users make informed decisions, whether that's through clear visualizations, relevant metrics, or user-friendly interfaces. By making your data product actionable, you'll empower your users to leverage the insights it provides effectively.

10. Collaborative 🤝

A successful data product should promote collaboration among its users, encouraging them to share insights, discuss findings, and work together towards common goals. This can be achieved by integrating features such as comments, annotations, or sharing options within the data product. By fostering a collaborative environment, your data product will become a valuable tool that helps your users work together more effectively.

🌟 Real-life Data Product Examples

🎬 Netflix: Adaptable, Engaging, and Loveable

Netflix has transformed the way we consume entertainment with its data-driven recommendation engine. By analyzing user preferences and viewing history, Netflix provides personalized content suggestions to keep users engaged. The platform seamlessly adapts to new data inputs, such as user behavior, content availability, and device usage, to enhance its recommendations. Netflix's engaging user interface and easy-to-use features make it a loveable data product that keeps users coming back for more.

🧠 Einstein AI (Salesforce): Specialized, Fortified, and Contextual

Einstein AI is Salesforce's artificial intelligence solution that offers specialized data products tailored to various industries and use cases. By leveraging machine learning algorithms and natural language processing, Einstein AI enables organizations to automate processes, gain insights, and make data-driven decisions. The platform is fortified with robust security features, ensuring data protection and compliance. Additionally, Einstein AI maintains context by integrating with Salesforce's extensive ecosystem, allowing users to seamlessly access relevant data and insights.

🏠 Airbnb: Adaptable, Fortified, and Loveable

Airbnb revolutionized the short-term rental market with its data-driven dynamic pricing algorithm. This data product helps hosts optimize their rental prices based on factors such as location, demand, and seasonality. The algorithm continuously adapts to market conditions and user preferences, providing accurate pricing suggestions to maximize revenue for hosts. Airbnb prioritizes data security to protect user information and offers a loveable, user-friendly interface that appeals to both hosts and guests.

Conclusion 🚀

Creating successful data products requires a deep understanding of the product's anatomy and how it fits within your organization's ecosystem. By considering the key factors discussed above, you'll be well on your way to developing data products that not only achieve your business objectives but also delight your users. Remember, successful data products are adaptable, specialized, discoverable, fortified, contextual, engaging, loveable, scalable, actionable, and collaborative! Good luck! 🌟