Data is transformative. Better access to data continues to disrupt many industries, including healthcare, insurance, and finance. Most data analyst positions typically involve data extraction, data cleaning and using querying languages such as SQL. IBM’s study from 2017, The Quant Crunch – How the Demand for Data Science Skills is Disrupting the Job Market, found that higher paying skills such as an understanding of object-oriented programming like Python, MATLAB and predictive analytics can increase average salaries. Candidates with advanced skills or at least three years of work experience on their resume can earn an average salary of over $100,000 per year. This data analyst salary guide will help you understand how experience and industry impact a data analyst’s salary and some recommendations to increase earning potential.

The average salary for a data analyst depends on what industry they work in, number of years of experience, their educational background and whether or not they have in demand skills. According to the Bureau of Labor Statistics (BLS), advanced degrees usually correlate with higher salaries. A data analyst with a master’s degree or a doctorate could potentially earn a higher salary than a candidate with only a bachelor’s degree. Most positions require at least a bachelor’s degree or the equivalent with relevant work experience. Taking all of these factors into consideration, what is the average salary for a data analyst?

Salaries increase for workers with in-demand skills, those employed in high demand industries, and those who have at least three years of experience. Unsurprisingly, the median salary for data analysts is $69,162 according IBM’s study in 2017, which is 46% more than the national average.

IBM’s study found that data analyst positions are in high demand and can be difficult to fill. It also revealed that on average, data jobs stay open five days longer than the market average, which may indicate that some employers are willing to keep a job open and wait for a candidate that has the education, analytics skills requirements and industry or domain knowledge.

Differences in salary also depend on what industry an analyst works in and the demand for that industry. According to IBM’s study, the industries with the greatest demand are in finance, insurance, healthcare, professional services and IT. The most high demand industries account for 59% of all data science and analytics job demand: 34% percent of data analysts are needed in professional services, 25% are needed in finance and insurance, six percent are needed in information, and seven percent are needed in healthcare. Healthcare data analysts and clinical data analysts analyze and improve operations in a hospital or clinical settings. IBM’s study claimed that the need for these analysts increased by 54% in 2016.

Additionally, IBM’s study claimed that jobs in the top industries have higher average salaries than the average, $69,162. For example, financial analysts typically earn $83,209 per year, which is 20% more than the average. These positions are also projected to grow at a high rate, cost more to hire and are likely to suffer from talent shortages. Some analysts continue to learn more advanced techniques, programming languages and big data, or switch careers to more in-demand industries to further increase their earning potential and hireability.

Industry demand for senior analysts and managers is similar to the industry need for entry and junior data analysts. The table below includes the average salaries for some of the most in-demand data analyst occupations, including: business data analyst salaries, clinical data analyst salaries, financial data analyst salaries, human resources analyst salaries, and pricing analyst salaries with their projected growth by 2020.

According to IBM’s study, job listings for data analysts with at least three years of experience range between 53-89% of all listings and the average salary ranges between $67,396-$99,970. Candidates searching for entry data analyst or junior data analyst level jobs may see listings for salaries at the bottom or lower than this range. Required experience percentages and education requirements are higher for individuals who hold senior positions, such as advanced analysts (e.g. senior analyst) or analytics managers. For example, 57% of all postings for a senior financial analyst require the candidate holds a master’s degree or higher.

Senior data analyst salaries and analytics manager salaries are around 53% higher than the average salary of a data analyst with three years of experience. Some managerial positions, such as an analytics manager, may require domain knowledge as well as deeper analytical skills. They also perform a wide array of business functions, such as financial planning, accounting and budgeting, which may increase their earning potential. For some companies, these roles may be especially challenging to fill due to their specificity. When employers are looking to fill a position, from entry level data analyst to senior data analyst to data managers, they sometimes hire the best qualified candidate to learn on the job or complete additional training. Data analysts looking to increase their earning potential even further may want to consider broadening their skillset or switch careers to data science or data engineering. IBM’s study claims that the average salary for these positions are $105,676 and $108,808, respectively.

Analysts looking to stay within their industry and earn a higher salary may want to consider leadership training or taking on personal projects to demonstrate their experience and commitment to professional development. They may also even consider reskilling through either a bootcamp, analytics master’s program or becoming a data scientist.

IBM’s study confirms that there is a talent shortage in data science and analytics. As companies collect more data and as they introduce more advanced technology, more employees will be needed to make sense of it all. Recent graduates will only alleviate a part of the demand for analysts. It’s up to universities, bootcamps, companies and the current workforce to work towards creating a more data literate population.