Data Team Building 101: Hire This Profile First To Set Yourself For Success
A practical guide to building a high-performing data team, starting with the most critical hire and laying the foundation for long-term success and business value
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Hi there,
As a seasoned professional, I've seen numerous companies struggle to develop a successful data strategy. They often get caught up in the hype surrounding AI and machine learning, hiring Data Scientists without first laying a solid foundation. But I'm here to tell you that there's a better way.
In this article, I'll share my expertise on how to build a winning data strategy by starting with the right hire. I'll explain why hiring a Data Analyst is the crucial first step and how this role can deliver quick wins, build foundational knowledge, and set your company up for long-term success. By the end of this article, you'll clearly understand the different data roles and how to sequence your hires for maximum impact.
You and I will explore the four key data roles - Data Analyst, Data Scientist, Data Engineer, and Analytics Engineer - and their unique strengths and weaknesses. I'll also share my experience on why starting with a Data Analyst is the smartest move and provide actionable advice on how to get the most out of this role.
By following the approach outlined in this article, you'll be able to unlock the power of data and drive business growth. So, let's dive in and discover the secret to building a winning data strategy.
Reading time: 11 minutes
🎭 Defining Data Roles
Let's break down the different data roles. You have Data Analysts, Data Scientists, Data Engineers, and Analytics Engineers. Each has its own unique strengths and weaknesses. Understanding these roles helps you decide who to hire first.
Data Analyst 🕵️
A Data Analyst is like a visual storyteller. They can dig into your data, explain the current state of the business, and tell the story of how you got where you are.
Imagine you're a business owner and want to know how many leads you have or what your sales numbers look like. Who do you ask? A Data Analyst. They use SQL, BI tools, and Excel to build dashboards that tell stories. These stories help you understand your business and make informed decisions.
An analyst works closely with the business. They need to know all your operations and key performance indicators (KPIs). They're on the frontline when the business needs to act quickly. For example, if sales numbers are down, the analyst has to figure out why and provide recommendations to turn things around.
Data Scientist 🧑🔬
A Data Scientist is more like a fortune teller. They uncover complex business process relations to predict the future.
Data Scientists build statistical models using R and Python for predictive analytics. They're your go-to for deep dives into your data. They can predict trends and behaviours, but you have to be patient. It takes time to uncover insights. It's not something you can rush into.
Imagine you're a business owner wanting to know which customers will likely churn. A Data Scientist can build a model that predicts this, but it takes time. It takes time, effort, and a lot of data.
Data Engineer 👷
A Data Engineer is the builder. They build the infrastructure for everybody else to do their job.
Data Engineers use cloud services and Python to collect data from all sorts of systems in a single place. They're the backbone of your data operations. They focus on the technical side to make sure data is accessible and reliable.
Think of a Data Engineer like a plumber. They make sure the pipes are in place so the water can flow. In this case, the water is data, and the pipes are the infrastructure that supports it.
Analytics Engineer 🥷
The Analytics Engineer is a bit of a mystery. It's a relatively new job, and everybody understands it in a different way.
Usually, Analytics Engineers sit between Data Engineers and Analysts. They are the ones who bridge the gap between technicality and business focus.
Analytics Engineers are masters of SQL and data modelling. Their job is to make sure you can represent your data in a way that makes sense.
Their power lies in building robust data models. They structure data in a way that makes sense. They make sure data models are robust and reliable.
Think of an Analytics Engineer as an architect. They design the blueprints for the data infrastructure so it's scalable and maintainable.
💪 The Role of Your First Hire
Now that you know the roles, let's talk about your first hire. This is a crucial decision that can make or break your data strategy.
What Most People Do 🙅
Most companies get it wrong. They start with Data Scientists or something related to AI. Sounds fancy. But it's like trying to fly before you can walk. You need to start simple.
But why do companies do this? There are a few reasons:
The hype around AI and machine learning: There's a lot of buzz around AI and machine learning. Companies want to be seen as innovative and cutting-edge. They hope hiring a Data Scientist will allow them to tap into this innovation and stay ahead of the competition.
The promise of quick fixes: Data Scientists are often seen as magicians who can wave a wand and fix all the company's problems. Companies think that by hiring a Data Scientist, they'll be able to quickly solve all their data-related issues and get instant results.
The lack of understanding of data roles: Many companies don't fully understand the different data roles and what they entail. They think that a Data Scientist is a catch-all solution who can handle everything from data analysis to machine learning.
Yet, this approach can be harmful. Here's why:
Data Scientists are not miracle workers: While Data Scientists are skillful, they're not magicians. They need good data to work with and time to understand the business and its problems.
Data Scientists are expensive: Data Scientists are in high demand, so they're expensive to hire. You're wasting your money if you're not getting the most out of them.
You're putting the cart before the horse: By hiring a Data Scientist before you have a solid data foundation, you're putting the cart before the horse. You need to understand your data and your business well before you can start building predictive models and AI.
By starting with a Data Scientist, you're setting yourself up for failure. You'll end up with a frustrated Data Scientist who can't deliver the results you want, and you'll be wasting your money on a salary that's not delivering value.
On the other hand, as a (former?) data engineer, I used to believe companies needed to collect and structure their data before reporting. I thought they needed to start with a data or analytics engineer. I was so stupid!
What You Need To Do And Why 🦸
Don't get caught up in the modern data tooling hype. Start with a Data Analyst. A Data Analyst will give you quick visibility into how your business works.
Why start with a Data Analyst?
Quick wins: A Data Analyst can deliver quick wins, such as building reports and dashboards that help you understand your business. This will give you a sense of momentum and help you see the value in your data.
Foundational knowledge: A Data Analyst will help you build a solid foundation of knowledge about your business. They'll help you understand your data, identify trends and patterns, and develop key performance indicators (KPIs).
Low risk, high reward: Hiring a Data Analyst is a low-risk, high-reward move. They're relatively inexpensive compared to Data Scientists, and they can deliver a lot of value quickly.
What should you look for in a Data Analyst?
SQL skills: A Data Analyst should be proficient in SQL and able to write queries to extract data from your database.
Data visualisation skills: A Data Analyst should be able to create reports and dashboards that help you understand your data.
Business acumen: A Data Analyst should have a good understanding of business operations and be able to identify areas where data can drive decision-making.
Communication skills: A Data Analyst should be able to communicate complex data insights to non-technical stakeholders.
How to get the most out of your Data Analyst?
Give them access to your data: Ensure your Data Analyst can access all the data they need to do their job.
Provide clear goals and objectives: Give your Data Analyst clear goals and objectives, such as building a certain number of reports or dashboards.
Encourage them to explore: Encourage your Data Analyst to explore your data and identify new insights and trends.
Have them work closely with stakeholders: Have your Data Analyst work closely with stakeholders to ensure their insights are relevant and actionable.
By starting with a Data Analyst, you'll be building a solid foundation for your data strategy. You'll be able to get quick wins, develop foundational knowledge, and set yourself up for long-term success.
Next, hire a Data Engineer to build the infrastructure. Then, an Analytics Engineer will help with the modelling. You can grow your team of data professionals for years before hiring your first Data Scientist.
Think of it like building a house. You need to lay the foundation before adding the fancy features. In this case, the foundation is a solid data infrastructure, and the fancy features are the predictive models and AI.
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💭 Final Words
As the Head of Data Engineering, I've seen many companies start their data journey with different roles. Some start with a Data Scientist, hoping to unlock the secrets of their data with machine learning and AI. Others start with a Data Engineer, building a robust infrastructure to support their data needs.
But, in my experience, the companies that see the best results are those that start with a Data Analyst. It may seem simple, but having someone who can dig into your data, build reports, and tell stories with numbers is incredibly powerful.
I've seen companies where the Data Analyst is just dumping CSVs into Excel and building reports. It may not be fancy, but it's precisely what the business needs. The analyst is able to provide insights and answers to questions that the business has been struggling with.
But, the value of a Data Analyst goes beyond just reporting. They can also facilitate other conversations within the organisation. For example, how do you align leads between Sales and Marketing? What does a user even mean?
A Data Analyst can help answer these questions by bringing data to the conversation. They can help define what a lead is and how you track it. They can help identify the key metrics that matter and how to measure them.
Starting with a Data Analyst doesn't mean you're only getting someone who can build reports and dashboards. You're getting someone who can help drive business decisions and facilitate organisational conversations.
I've seen it repeatedly - companies that start with a Data Analyst can get quick wins, build momentum, and drive business growth. They're able to make informed decisions, and they're able to measure the impact of those decisions.
So, don't get caught up in Data Scientists and AI hype. Start with a Data Analyst and build from there. You won't regret it.
In addition to reporting, a Data Analyst can also help with other critical conversations within the organisation. For example:
Defining key metrics: A Data Analyst can help clarify the most critical metrics of the business and how to measure them.
Aligning teams: A Data Analyst can help align teams around a common understanding of the data and ensure that everyone is working towards the same goals.
Identifying opportunities: A Data Analyst can help identify opportunities for growth and improvement and provide recommendations on how to capitalise on them.
Facilitating data-driven decision-making: A Data Analyst can help facilitate data-driven decision-making by providing insights and recommendations to stakeholders.
By starting with a Data Analyst, you're setting yourself up for success. You're building a solid foundation for your data strategy, and you're positioning yourself for long-term growth and success.
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🏁 Summary
In summary, start simple. Hire a Data Analyst first. Get quick wins. Build from there. Once you clearly understand your business, you can make more informed decisions about future hires. Don't rush into hiring specialised roles without first laying a solid foundation.
Remember, data is a journey, not a destination. Building a solid data strategy takes time, effort, and patience. But, with the right team in place, you can unlock the power of data and drive business growth.
I've seen companies struggle with their data strategy. But, with the right approach, you can unlock the power of data and drive business growth. Take the first step today, and see the difference for yourself.
Until next time,
Yordan
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Great post! As a data engineer turned data team lead this is exactly in line with my experiences.
Most businesses want the Data Scientist to jump on the AI bandwagon but have a terrible data infrastructure to start from. So they hire a data engineer.
But in reality they need a good data analyst to explore the data, tackle the low hanging fruit and create the first return on investments.
Saving this for the next time a company asks me for an "AI person" 😅
Great post - almost analogous to one way of getting into a DE role - I reaped the benefits of business acumen understanding data by starting as a data analyst before going into DS/DE!