Data Analyst Career Paths: Future Industries & Roles

Data analytics is an expanding field with immense demand across various industries. Every business needs some form of information analysis, and roles are available for job-seekers at all levels. Are you a problem solver who’s good with numbers, Excel, and SQL? You might be a perfect candidate for the data analyst career path.

 

If you’re interested in data analysis, you’ve come to the right place. Scroll down to learn about the different industries and types of businesses you could work in. We will share more about what an analyst job would look like as you plan out your career path.


Operational Data Analyst Roles vs. Data Science Track Roles

There are two main routes you can take with a data analyst career path: operational or data science. 

 

With an operations role, your job is to analyze and interpret raw data, then translate it in layman’s terms. Your goal is to make sure your colleagues (including the CEO and management) can understand and use it.

Data science track roles combine statistics, computer science, and math to process, analyze, and model data. The goal is to translate the information into actionable insights, so your organization can respond accordingly and make informed decisions.

 

Beyond the key differences between operational and data science roles, the day-to-day experience of an analyst will vary depending on the industry you choose. Here’s a general idea of what you can expect from working in healthcare, marketing, or a startup role.

 

Healthcare Data Analyst Career Path

Healthcare data analyst careers can vary widely. You could be a finance analyst who focuses on hospital revenue or an operational analyst at an insurance company who works on customer success. 

 

You could find yourself analyzing patient experiences, insurance claims, treatment pricing, procedural outcomes, length of hospital stays, or wait times. There are even PhD-level roles for those with doctorates, in which case you might work exclusively on patient trial data.

 

This type of job involves assessing information to help the organization make decisions, improve care, streamline processes, boost efficiency, and reduce costs. Healthcare provides a varied job experience in a lucrative industry. Data analysts are often drawn to healthcare, as the industry is considered not only essential but also recession-proof.

 

Marketing Data Analyst Career Path

Since most businesses employ some level of promotional efforts, marketing and advertising data analyst roles are a bit broader. A marketing analyst will work closely with an organization’s internal marketing team and potentially outside firms or consultants for specialty services.

 

Regardless of the industry, data analytics in marketing often focuses on A/B testing to assess the effectiveness of two approaches. This type of role may also assist in:

  • Running and managing promotional campaigns
  • Web development efforts
  • Audience analysis
  • Targeting and retargeting ad campaigns
  • Implementing machine learning

 

Startup Data Analyst Career Path

Data strategy for startups is also a bit broad, as there’s a wide range of recently launched businesses in need of analytics. As a startup analyst, you might have a hand in A/B testing, audience segmentation, targeted campaigns, or other marketing efforts.

 

You could also be involved in the operations side, analyzing information such as:

  • Sales revenue
  • Company expenses
  • Client acquisition
  • Customer support
  • Customer success
  • Team productivity
  • Managerial processes

Then there are product analysts who prototype dashboards, analyze user metrics, and help optimize user experience (UX).

 

Data Science Roles

In a data science role, you might build models to integrate with product software. You could also be in charge of running regression tests, creating visualizations, or developing new product recommendations.

 

Sometimes sales, product, and marketing roles blend together at startups. For instance, an analyst could be in charge of compiling customer data, product use information, or impact data for sales presentations or investor pitches.

 

Why Be a Data Analyst at a Startup?

While there are opportunities at companies of all sizes, many recent grads and industry veterans alike are drawn to data analyst startup jobs.

 

Contribute Directly to Success

In the early stages of a business, collecting and analyzing data is crucial to company growth and overall success. Tech startups in particular have a pressing need for data scientists to build predictive models, analyze user behavior, and create innovative software solutions.

 

Work in a Close-Knit Team

As a data analyst at a startup, you’ll work with a close-knit team and support both the marketing and operational efforts. Interpreting customer behaviors from the beginning helps companies fine-tune their products and forecast future metrics.

 

Attractive Compensation Possibilities

While larger organizations offer a higher starting data analyst salary, there are benefits to being one of the first few employees at a company. You might have stock options as part of your compensation package and could end up leading a robust analytics department years down the road.

 

Advancement Opportunities

Like other startup roles, analysts sometimes wear multiple hats the first few years. That typically isn’t the case with more established companies. However, as the business grows, your role has the potential to evolve into a senior business analyst or data scientist position.


Answer These Questions to Choose a Career Path

From enterprise technology and software-as-a-service (SaaS) to healthcare, consumer goods, and gig platforms, nearly every industry relies on data analysis. When exploring a data analyst career path, answering these questions can help you figure out which industry and type of role to pursue.


What kinds of outcomes do you want to impact with your analysis?

If you want to help organizations make informed decisions to improve company processes, an operations role might be right up your alley. On the other hand, if you’re more interested in developing product-based solutions, a data scientist track role could be the way to go.


What kinds of business problems excite you most?

If you like finding solutions to internal problems, such as improving productivity or minimizing costs, you might consider an operational role. Operations data analysts also work to optimize customer experience, sales processes, and profitability.

For those excited about data patterns and software efficiency, a product analyst role might be suitable. And if you’re interested in researching and assessing market trends, you could be a good candidate for a market data analyst job.


Which types of analysis are you most excited to do?

Are you energized by the idea of performing A/B tests, managing campaigns, or running targeting and retargeting efforts? If so, a career in marketing analytics could be a good fit.

If you’re interested in pattern matching, prediction models, artificial intelligence, and data visualization, you might consider exploring the data scientist career path.


Average Salary Ranges for Data Analyst Jobs

Here’s a general idea of what you can expect to earn from different analyst career paths. (Keep in that mind salaries vary among industries and change from year to year.)

 

Operations Analyst

Starting salary: $40,000

3–5 years of experience: $50,000–$70,000

Career growth to operations manager: $100,000

 

Business Analyst

Starting salary: $50,000–$70,000

3–5 years of experience: $70,000–$100,000

Career growth to senior business analyst: $100,000+

Career growth to product manager: $100,000+

 

Data Analyst

Starting salary: $70,000–$90,000

Promotion to junior data scientist: $90,000–$100,000

Promotion to senior data analyst: $90,000–$110,000

Career growth to data scientist: $120,000–$200,000


Data Analyst Career Path Tips

Once you land your first data analyst job, use these tips to ensure you’re on the right path to career advancement.

 

  1. Connect with your peers inside and outside your organization, both in person and on LinkedIn.
  2. Pay attention to how your colleagues work, listen to their insight, and take notes.
  3. Get to know your industry, exploring the ins and outs of your organization, including each department and its primary competitors.
  4. Keep learning and developing your technical knowledge, whether on the job or by taking analytics and programming courses.
  5. Learn how to identify new data sets for each project.
  6. Attend data hackathons to practice quickly defining and solving problems. 

 

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