Data analytics is a booming field — making a big difference in industries like finance, manufacturing, health care, and retail — and it has a job market to match. According to Technavio, the global data analytics market will see 13.54% compound annual growth between 2021 and 2026 — or almost $2 billion in growth difference, in large part due to the proliferation of advanced data technologies like machine learning.
Our Guide to Data Analytics Careers
While Technavio notes that much of this growth will emanate from Asia-Pacific (APAC), the growth in the US data analytics job market may be even higher. The US Bureau of Labor Statistics foresees 23% growth in the “Operations Research Analyst” — or data analyst — market over the next decade, with an additional 24,000 jobs up for grabs.
Those landing these jobs can expect above-average compensation: the BLS pegs the median salary for someone in one of these roles at $82,360, well above the $58,260 that they estimate the average American earned in 2021.
The best part? Becoming a data analyst doesn’t require you to spend a lengthy — and costly — period in school. Most data analysts only hold a bachelor’s degree and many break into the field after simply taking a data analytics bootcamp or another short course.
Of course, if you are interested in investing in an advanced degree, experience in data analytics can open up lucrative pathways in data science, where the average salary is $100,410.
But first things first: What kinds of jobs are out there for someone interested in data analytics, and what does an aspiring data analyst need to succeed? In this article, we’ll cover all this plus show you some real-life job postings so you can start figuring out if data analytics is the career path for you. But first, we’ll clarify what exactly data analytics is.
What is data analytics?
Data analytics is a discipline concerned with leveraging statistical analysis and data management tools and techniques to derive insights from data sets. There are four primary types of data analytics: descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics.
Descriptive Data Analytics
Descriptive data analytics seek merely to explain what has happened at a business — what is the story told by historical data? These findings often form the foundation of more advanced analysis. Oftentimes, the only tools needed for descriptive data analytics are basic mathematics, especially statistics. Data visualizations can also play a prominent role.
In retail, companies employ descriptive data analytics for tasks like tracking inventory and sales.
Diagnostic Data Analytics
Diagnostic data analytics are used by businesses to identify the causes of particular problems or successes. Often, this involves identifying and probing particular correlations in the data, forming hypotheses, and using statistical techniques like regression to prove or disprove these hypotheses.
In marketing, diagnostic data analytics can be employed to help understand why some campaigns are more successful than others.
Predictive Data Analytics
Predictive data analytics — the most common — are used by businesses to identify trends from historical data to predict what might happen in the future. Often, these predictions are accomplished through statistical modeling and machine learning, with help from techniques like data mining, in which analysts identify patterns from the big data sets being generated by human activity every day.
In manufacturing, companies frequently employ predictive data analytics to predict future buying patterns and manage their supply chains accordingly.
Prescriptive Data Analytics
Prescriptive data analytics are used by businesses to determine which actions they should take moving forward. Frequently prescriptive analytics make use of machine learning algorithms, especially when a company is employing big data to support their decision-making.
In logistics, prescriptive data analytics are becoming more and more important as companies like Amazon seek to deliver goods to millions of customers as efficiently as possible.
It can be difficult to keep track of the four main types of data analytics. Fortunately, Harvard Business School has a useful shorthand:
Descriptive data analytics answers the question, “What happened?”
Diagnostic data analytics answers the question, “Why did this happen?”
Predictive data analytics answers the question, “What might happen in the future?”
Prescriptive data analytics answers the question, “What should we do next?”
Data Analytics vs Data Science
There is often confusion between data analytics and data science due to the considerable overlap between the two fields. While it can be difficult to fully capture the nuances of the relationship between data analytics and data science, as job titles and data operations may vary greatly between companies and industries, we can generally differentiate the two as follows:
Data analytics involves using statistical analysis, tools, and techniques to extract insights from data. Data science, on the other hand, involves using scientific methods, algorithms, and systems to develop new tools and techniques and new ways to gather data for analysis. Data science often involves a combination of data analytics, machine learning, and other related techniques to analyze and understand complex data sets, while data analysts often employ methods and tools developed by data scientists. Check out our detailed guide comparing the roles and responsibilities of data analysts vs data scientists.
What Types of Jobs are There in Data Analytics?
We’ve covered the different types of data analytics, but who are the people getting it done? For the most part, jobs in data analytics will have the title “data analyst,” but there are some notable exceptions, including “business intelligence analyst,” “data engineer,” and “marketing analyst,” to name a few. Below, we’ll give an overview of each of these roles and provide some real-world examples.
Data Analyst — CoinTracker
Analyze ads and marketing data to develop user models and compute lifetime value (LTV) and effectiveness of different channels.
Work with engagement/churn/retention data alongside product teams to improve product features, measure usage, and manage user cohorts.
Help develop finance/long-term models and understand tax/portfolio plan purchase and retention behaviors.
Partner with support to super-charge analytics for support tickets, queues, wait times, and help support be a highly-efficient organization.
SQL programming knowledge/ability to write queries comfortably
Experience with Tableau/Mixpanel/etc. for visualization and dashboards
Curious mindset to dig into datasets and find issues/concerns
Statistical knowledge to understand p-values, confidence intervals, and impact of experiments
Collaboration skills to work on cross-functional teams
Business Intelligence Analyst
There’s substantial overlap between a data analyst and a business intelligence analyst. Indeed, sometimes it just comes down to what title a company gives a position. So what sets a BI analyst apart? Often, they are more focused on developing insights that would directly contribute to business goals: increasing the company’s revenues, decreasing its costs, expanding its market, or entering a new one. A data analyst, on the other hand, might work on more narrow or siloed projects such as improving operations or developing a product.
Business Intelligence Analyst — Nordstrom
Build, manage, evaluate, and evolve a suite of Tableau dashboards and other self-service tools for key stakeholders
Partner with business teams to understand their analytics needs and look for opportunities to enhance product offering
Collaborate with other BI Analysts and Engineers around data/table structures in order to optimize for Tableau dashboards and other self-service needs
Offer strategic guidance to both analysts and business teams on how to best leverage tools
Maintain efficient QA processes to ensure the accuracy and quality of data
1+ year of corporate experience with SQL (MySQL and Teradata SQL preferred)
1+ year of corporate experience building advanced Tableau dashboards for ongoing measurement and self-service needs
Ability to think strategically and assess key stakeholder requests
Experience taking large volumes of data and condensing it into a clean, insightful, and automated format
Demonstrated ability to QA reporting process to ensure data integrity and accuracy
While a data analyst focuses primarily on data analysis, performing data collection, preparation, and storage as necessary to support this analysis, a data engineer’s entire focus is on efficiently transforming raw data for this analysis.
Data Engineer, Advertising Technology — Spotify
Work across the ads business platform, contributing to the improvement of many different pipelines.
Build large-scale batch and real-time data pipelines with data processing frameworks like Scio, Storm, and Spark.
Design, develop, and maintain Java services.
Use best practices in continuous integration and delivery.
Help drive optimization, testing and tooling to improve reliability and data quality.
Collaborate with other engineers, specialists and partners, taking learning and leadership opportunities that will arise every single day.
Work in cross-functional agile teams to continuously experiment, iterate and deliver on new product objectives.
Knowledgeable and passionate about improving and building distributed data pipelines
Experience building high-volume/business-critical, distributed services in Java or Scala that ingest data across multiple sources
Knowledgeable about data modeling, data access, and data storage techniques
Understanding of system design, data structures, and algorithms
Familiar with current data engineering practices and curious about new technologies that help derive insights and value from data
A marketing analyst is a data analyst whose work focuses on a company's marketing efforts. This may involve analyzing the performance of marketing campaigns in different markets or through various media channels, such as print, app, or web, using tools like Google Analytics.
Digital Marketing Analyst — Shake Shack
New York, NY
Own the data orchestration, visualization, and analysis of Shake Shack’s marketing and product efforts related to web, app, kiosk, and customer relationship management (CRM.)
Gather and mine data from all relevant sources, including disparate data sources, which include Snowflake (via Tableau), Google Analytics, and external data sources.
Utilize CRM and data science applications such as Alteryx, to identify high opportunity guest segments to drive the marketing organization to increase frequency and retention.
Partner with external agencies to steer analytics requirements and gather data where needed from marketing partners and external applications.
Serve as a key resource for understanding the customer journey as it relates to marketing/product and own segmentation of database to optimize marketing efforts.
Develops powerful and engaging data visualizations (data tables, charts and infographics) for senior management and stakeholders using Tableau and Google Data Studio, along with ad hoc requests using software/tools such as Excel.
Analyzes data identifying trends and patterns related to guest (CRM) segments, marketing performance, web/app analytics, and user experience.
Forecasts and/or predicts guest behavior based upon trends, research and analysis.
BS in Marketing, Statistics, Mathematics, or other quantitative fields
Proficiency in data analysis, interpretation and presentation using MS Office tools
Strong SQL development skills
5+ years of expert-level experience with marketing analytics tools (e.g. Adobe Analytics, Google Analytics)
2+ years of Alteryx experience preferred
Strong analytical skills and comfortable working with large data sets
Critical thinking skills, writing skills, communication skills, consulting skills and ability to work within a team
What Skills are Needed in Data Analytics?
Although the requirements and qualifications differ for the jobs above, these job descriptions also make clear that there are some basic skill sets required if you want to be successful in data analytics:
SQL programming: Being able to program using Structured Query Language, or SQL, is a must if you want to work in data analytics. The SQL programming language allows you to store, manipulate, and retrieve data from databases.
Data visualization: Most companies also expect data analytics candidates to be able to create compelling, appealing data visualizations, usually using Tableau.
Data analysis techniques: Of course, visualizing data won’t get you anywhere if you don’t have a story to tell with the visualizations. This means you’ll need to have strong data analysis skills, which means a solid foundation in statistics and statistical modeling.
Soft skills: For every job in data analytics, you’ll also need to have keen soft skills, including teamwork, leadership, critical thinking, and communication skills.
We’ve covered what exactly data analytics is, the different jobs out there, and the skills you’ll need to land one. How can you get started? Although data analytics typically requires less advanced training than data science or machine learning, having relevant education is nevertheless crucial.
If you have the time and money for a four-year degree, majoring in computer science, applied mathematics, economics, finance, information technology, or, where available, data analytics, will set you on the right path.
If you don’t want to pursue a bachelor’s of science, or already have a bachelor’s degree, you might instead consider a data analytics bootcamp or certification. These are shorter and can usually be completed online, allowing ultimate flexibility.
If you’re already in the data analytics workforce or have a quant-heavy career and want to get ahead and learn some advanced analytics, you might consider a master’s degree in data analytics or business analytics.
Whatever your planned educational journey, we have some recommendations to help you on your way: