What is Data Maturity? Why is it Important for Your Career?

Data maturity is a topic that I touched on in a LinkedIn post awhile ago. It’s something that I think is important for all kinds of data roles — data scientists, engineers, analysts, etc.

As you grow in data science, you must learn new ways to analyze, communicate, and impliment. This is critical as your data science understanding matures. Its easy to use templates or tutorials alone.

For real understanding, you’ll have to train a new mindset and way to think about business problems.

There’s 6 critical pillars I’ve found with data maturity:

1. Communicating Complex Concepts

Being a translator is the core of of data maturity. It’s being able to communicate complex tech, statistics, and analysis in a simple way.

Lots of us in the data space struggle with. It requires you to break down a concept for your audience. You need to know what to remove and what to keep. And also what information you need to reserve to answer the inevitable questions.

It helps to have a strong foundation in the fundamentals of the topic. This makes it easier to more effectively break things down and simplify complex concepts. You know the ideas that the audience needs to hear, learn, and understand.

In short?

You’re a translator and mediator.

By the way, that translation goes both ways. You will be always translating between both business users and tech teams. And part of that role is managing and understanding expectations — in terms they can understand.

It requires constant learning and practicing fundamentals to simplify.

2. Balancing Technical Requirements vs. Business Context

Balancing is the being able to move back and forth between the technical requirements and the business context.

Knowing how what you do fits in the larger picture is important to quality work.

Technical requirements may not match the larger business needs or strategy. Likewise, business context may not match the tech, tools, or human talent available.

This is a common scenario. Data maturity in this case means knowing opportunity cost. It means being able to:

• Getting more done with less
• Knowing when to advocate for more resources
• Constant examination of both the bigger picture and the work being done at a team level.

You will need to sacrifice one to get another. What you will need prioritize will

Knowing the difference and being able to prioritize one over the other? Value lies at being able to bridge this gap.

3. Shift to Active Learning

Shift from learning by memorization to active learning. Theory is a foundation, not a guide for skill. You have to know the right way to learn from experience. You have to learn the fundamentals, try new ways, then develop more efficicent and effective ways.

This means being able to:

• Reflect
• Evaluate
• Roleplay

Simply using a textbook or what someone else has done before is not enough.Its really thinking about the significance. Test the gaps in the reasoning, check and know the assumptions.

Roleplay scenarios in your head. What holds true? What does not?

Data science concepts are build on certain assumptions. Which may or may not hold true depending on the use case, systems, or nature of the industry you are in. Theory assumes that all factors are held equal. It makes it easier to learn.

Its important to consider the how theory applies to the context to the context of your data problem.

Theory is the basis of all learning, and application is experience. Learning is being able to adapt it to different circumstances.

Thanks for reading DataLife360! Subscribe for free to receive new posts and support my work.

4. Handling Abstract Use Cases

Handling abstract business use cases is another sign of data maturity. No two business use cases are a like, even if they are in the same industry. Nor are two machine learning applications.

An abstract business use case is a high level description for a business product. It usually general and does not go into specific details. It focuses on desired goal, with a desired outcome and preconditions that a data professional must follow.

These use case(s) can be vague or very complex.

To handle complex cases, it requires breaking it down into simpler parts or functions. For vague cases, you need being able to narrow the focus of the use cases.

Handling requires refining and thinking about use cases. Then breaking it down into more focused smaller use cases or scenarios. Then prioritizing. Not all use cases can be addressed right away. Some use cases are actually multiple ones under one business need.

Data maturity can be seen in how the data professional approaches it. A person with high data maturity is able to look at the underlying structure and context of the business use case. They break it down.

They are able propose an optimal and efficient solution, that takes into account development time, money, maintainability, and current team resources.

To find that takes experience, constant and deliberate practice, and reflection on experiences.

5. Creating Technical Requirements from Interviewing

Translating verbal statements into technical requirements and code is tricky. You must be able to interview users well, and ask the right follow up questions. Not only that, but you must get requirements from reading their assumptions.

I think I’ve struggled more with this than any other.

Its important to know how to:

• Identify the key business needs
• Define the Business Objectives
• Individual User Needs
• Technical Resources
• Validate Requirements

Key business needs or objects aren’t always obvious. What a user needs is not always what they want. Sometimes, they lack the vocabulary or knowledge to communicate that need.

Gathering info for technical requirements is an iterative process. These requirements should be specific, testable, and measurable, and they should provide enough detail to guide the development of the solution.

You need to know how far to interview, and what is acceptable enough to create tech requirements. Data mature people know where to stop iterating. Too much iteration too soon can result in complex and unreasonable technical requirements.

After creating tech requirements, they focus on checking in with both the users and builders. They attempt to strike a balance between the user need and teams availible resources. While trying to validate and user test the data product — reviewing the requirements with the business team, conducting user testing, and feedback.

Data maturity is seen in how you handle things before AND after you create the technical requirements. Its what you do to prepare prior to the user interviews, and how you check back in with them and building teams after the creating technical requirements.

6. Connecting Your Experience

“No experience goes to waste. Its how you use it,” was a phrase I was told as a child. This also applies very well to data maturity.

Your experience matters. Often things we learned in one job will translate into another. They serve as guide posts to the knowledge we need to research to get the job done. We all do it.

But at a certain point, we template experiences. We use them to solve business problems while disregarding the context of the experience. What worked good in the past is not always the most efficient, economical, or correct solution. It can create unmaintainable business products or solutions.

Data maturity here means you know how to filter that context. A data mature person can filiter through the business, engineering, and cost contexts. All the while using learned experience. They don’t template — they adapt.

They create a solution from one experience, and combine it with the experience from another. While acknowledging context of the current business problem — discarding experiences and knowledge that is irrelevant to answering it.

These experiences may not even be from the same industry or use case

Data mature people are creative about applying their experiences.

Conclusions

Data maturity takes constant training, practice, and experience. It is something that must be constantly trained and practiced. Developing it not only improves your current skill set, it expands it.

Exposing yourself to other data problems helps increase your range of creativity.

There’s more than one way to solve a math problem — which applies to data maturity. Its developing flexiblity and critical thinking. Its learning the fundamentals well so that you can see different ways of approaching problems.

Its the key to delivering value, lowering costs, and increasing efficiency.