Today's business world runs on data.

Data is what helps companies measure, scale, predict, and optimize. It's how retailers sell more product, how marketers target new audiences, how HR firms predict churn, and how Netflix knows exactly which show you're likely to binge next.

And as the volume, variety, and velocity of data continues to grow, it's no surprise that demand for analytics talent is on the rise.

But of course being a hot commodity isn't the only reason to pursue a career in analytics. Here are some other perks of the job:

• Earn a competitive salary & benefits

• Build a versatile & highly transferrable skillset

• Explore a wide range of roles & specialties

• Flex your creative & analytical thinking skills

• Work on unique and challenging business cases

• Make a meaningful impact (at any level)

If any or all of that sounds appealing, you're in the right place.

At Maven, we help you build the skills you need to launch or accelerate a career in data. But we also realize how important it is to help you find the right type of data career, based on your own unique strengths and weaknesses.

If you take a step back and think about the "Data Analysis" landscape as a whole, you might see something like this:

This Venn diagram, which was adapted from a version shared by Flatiron School, plots a few of the most common analytics roles based on certain combinations of skills.

Based on this diagram, you'll find Data Engineers at the intersection of programming and BI, Machine Learning specialists straddling the line between coding and statistics, and Data Scientists sitting squarely in the middle.

Keep in mind that while this is a clean and convenient way to categorize roles, the real world isn't quite so cut and dry; in reality, you'll find BI Analysts running machine learning models, Engineers who hate to code, and specialized roles (like data viz specialists) which don't fit neatly into the mold.

The important takeaway here is that ALL of the roles under the data analysis umbrella focus on one single common objective: using data to make smart decisions.

The differences between them are all about the types of problems you solve, and the types of tools you use to solve them. For example, a BI Analyst might help drive revenue growth using performance reports or ad hoc analyses, while a Data Scientist might build a logistic regression model to help predict customer purchase behavior.

In this post we'll focus on four common analytics roles, and break down some of the key differences between them.

Whether you're an aspiring analytics professional or looking to make a career switch, hopefully this will help point you in the right direction.

Business Intelligence Analyst

Ask 10 different people what a Business Intelligence Analyst does, and you'll likely get 10 different answers. While there are many flavors of BI Analysts out there, the common thread is that they all help businesses make smart, data-driven decisions.

Generally speaking, BI Analysts are involved in the entire analytics workflow, from data prep and QA to data modeling, exploratory analysis, and visualization. They speak the language of data, translating raw numbers into the insights that stakeholders need to drive the business forward.

A BI Analyst role may be a good fit if you:

• Love analyzing data for insights and convincing stakeholders to act

• Enjoy solving a wide variety of business cases and open-ended tasks

• Want to build a deep skillset, from data engineering to analysis and visualization

Data Visualization Specialist

Data Visualization Specialists possess a unique and powerful ability to give data a voice. They use visual design and storytelling to bridge the gap between analysts and business leaders, making sure that nothing gets lost in translation along the way.

Unlike BI Analysts, a Data Visualization Specialist typically won't be involved in the entire analytics workflow. As the name suggests, he/she would more likely play a specialized role on a larger team, focusing more on analysis and visualization than data engineering or database administration.

Depending on the job, projects might include anything from ad hoc analyses to published infographics, executive reports, and ongoing performance dashboards.

A Data Viz Specialist role may be a good fit if you:

• Love designing visuals to tell stories and bring data to life

• Want to flex both your creative and critical thinking skills

• Prefer working with prepared data and under specific project guidelines

Data Engineer/Database Administrator

Data Engineers and Database Administrators (DBA) are commonly referred to as the "plumbers" of data science, spending much of their time designing and building the infrastructure, or "pipes", which help data flow from one system or platform to another.

Data Engineers and DBAs are often the unsung heros of the analytics world, handling much of the heavy lifting required to prepare raw data for analysis and visualization. This might involve creating ETL pipelines to extract, load, transform and consolidate data from various sources, building database schemas and tables to feed into BI platforms, or managing the data lakes that data science teams use for predictive modeling.

A Data Engineer or Database Admin (DBA) role may be a good fit if you:

• Enjoy building data infrastructure and engineering database systems

• Prefer concrete technical tasks over open-ended business cases

• Would rather build and design databases than perform visual or exploratory analysis

Data Scientist/ML Engineer

While BI Analysts typically focus on descriptive analytics, Data Scientists and Machine Learning Engineers typically skew towards predictive and prescriptive analytics, using data to test hypotheses and predict uncertain future outcomes.

Data Scientists typically excel in math and stats, rely heavily on programming languages like Python or R, and often deal with large, unstructured data sources in addition to static, tabular datasets. Deliverables tend to be algorithms and statistical models, compared to visuals, reports or dashboards.

A Data Science or Machine Learning role may be a good fit if you:

• Love to program and write code

• Enjoy math and statistics

• Can distill complex topics and communicate them clearly

For a full comparison between Business Intelligence and Data Science, check out this blog post.

Tips for Success

While each role skews towards a unique and distinct set of skills, remember that they all require some combination of the analytics trifecta: strategic thinking, technical proficiency, and communication.

Strategic thinking helps you evaluate and attack business problems, technical proficiency ensures that you have to skills to execute, and communication skills allow you to speak the language of your stakeholders and convince them to take action.

Build that trifecta of skills, and you'll be unstoppable.

So whether you're taking the first step or are already well on your way down the analytics career path, I'll leave you with these 5 tips for success:

1. Obsess over outcomes

You don't get paid to analyze data, you get paid to drive outcomes. If you aren't moving the needle, what value are you adding?

2. Master skills, not tools

Tools matter, but skills matter more. Don't focus on which tools you want to learn, focus on which skills you want to build.

3. Don't be afraid to specialize

Aim to go deep rather than broad, and focus on building 1-2 expert-level skills rather than 10 mediocre ones.

4. Don't ignore the soft skills

It's not all about formulas and code. Emotional intelligence, creativity and communication can take you just as far as technical skills.

5. Never stop learning

The data landscape is constantly evolving, and those who fail to adapt fall behind. Embrace lifelong learning, and you'll always stay ahead of the curve.