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TDWI Upside - Where Data Means Business

TDWI 2016 Salary Survey: Gender Pay Gap Remains

According to TDWI research, the gender gap in BI-related fields widened in 2016. There are many reasons why women are earning less than men in these jobs.

As a woman who has been in the data field for a number of decades, I always read the TDWI salary survey with great interest. The report quantifies and interprets the compensation, roles, responsibilities, skills, and experience of individual BI professionals. It also provides detailed profiles of the 10 most common BI/data warehousing roles, examining age, gender, education, job satisfaction, salary and bonus, certification, background, and other characteristics.

TDWI has been collecting this data for years, and it is a great TDWI member benefit. I was able to get a sneak preview of the data from this year's report. The responses below are from the U.S. and Canada only -- approximately 700 responses with a female/male split of 28 percent to 72 percent.

Overall, the gender gap in BI-related fields widened in 2016. Men continued to earn more than women -- on average $116,528 to $103,589, a margin of $12,939. Similarly, average bonuses awarded to female BI professionals rose only 4 percent, to $13,472, and men continued to receive higher bonuses, averaging $18,258. The gender gap also increased for the number of men and women receiving bonuses, 71 percent of men to 66 percent of women.

The salary trend over time is shown in the figure below (bonuses not shown).

(Source: TDWI 2017)

I decided to delve into this data in a bit more detail to understand what might be causing this gap. For the purpose of this analysis, I examined U.S. respondents who work full time (about 600 respondents, with about a 70/30 split male/female). It is a complex picture.

Common Roles Are Different for Men and Women

Women are more likely to be in roles such as data analyst/data modeler (median salary $92K) or business requirements analyst (median salary $95K) than men. Women appear to earn more than men in the data modeler role. However, men are more likely to be in roles such as technical architect (median salary $128K) than women are. In other words, men are often in roles that pay more to start with.

In High-Paying Roles, Women Are Generally Paid Less

However, even in high-paying roles where there is no significant difference in representational gender proportions, women are often paid less than men are. For instance, the median salary for a male lead information architect in 2016 was $130K versus $113K for a female lead information architect -- even though the women had on average close to two years more experience!

Male BI sponsors had much higher median salaries than females ($186K versus $124K median salary) although they had the same amount of experience. In one exception, female BI directors were paid slightly more in 2016 than males ($148K versus $141K median salary), although women in this role earned significantly less in bonuses in 2016, potentially filling the salary gap.

Bonuses are Smaller for Women

Women in our study received less in bonuses than men. Their average bonuses in all roles but one are less than their male counterparts. (The one exception: data modelers earn bonuses that are $1000 higher on average for women than for men.) In some roles the bonuses are significantly less, such as BI director and business roles.

Women's Salaries Don't Increase at the Same Rate

I found it Interesting that although both men's and women's salaries increase with more years of experience and higher educational levels, the rate at which women's salaries rise is much slower than that for men (i.e., the slope is flatter). Additionally, at any education level, men are paid more on average than women with the same level of education. In fact, in some instances women with higher educational levels are paid less than men with lower levels of education.

Overall, the 2016 earnings ratio of women to men for BI-related jobs in this survey was 89 percent. This is in line with a 2015 AAUW study, "Solving the Equation: The Variables for Women's Success in Engineering and Computing," that used U.S. census data and found that the ratio of women's earnings to men's earnings in software development was 87 percent. Although this is better than the 2015 national average of 80 percent, it is still not good.

Overlapping Causes for the Gender Gap

There are numerous reasons why women may earn less than men in BI-related jobs. First, women may start off at lower pay (or in a lower-paying job) than a man with similar experience, either because of discrimination or the fact that they may not negotiate well for themselves. Perhaps their expectations are lower. For example, in this survey, the average salary for women who thought they were not paid fairly (about 25 percent of female respondents) was $97K -- for men that number was $109K (about 30 percent of male respondents).

Then there is the "motherhood penalty." Studies have shown that employers are less likely to hire mothers (including mothers who never left the workforce) and that mothers typically receive a lower wage than non-mothers do. (See the 2016 AAUW study "The Simple Truth about the Gender Pay Gap" for more information and further references.)

If mothers take a leave and take a new job, when they return to work they may make less than what they were making when they left. Additionally, if they come back to the same workplace but cut back their hours, they may not advance as quickly. Whether women are discriminated against for higher-paying jobs, start at a lower salary base, or take a leave, because they are starting at a lower base any raises they receive are building on this lower base, which makes it hard to catch up.

We, as women, need to better advocate for ourselves and that is a key takeaway from this analysis, aside from advocating for transparency in organizations and mandated parental leaves. Negotiate harder. Seriously ask about bonuses. Make use of crowdsourcing websites such as,, or Many employers will pay you less if they can get away with it. Don't let them.

About the Author

Fern Halper, Ph.D., is well known in the analytics community, having published hundreds of articles, research reports, speeches, webinars, and more on data mining and information technology over the past 20 years. Halper is also co-author of several “Dummies” books on cloud computing, hybrid cloud, and big data. She is VP and senior research director, advanced analytics at TDWI Research, focusing on predictive analytics, social media analysis, text analytics, cloud computing, and “big data” analytics approaches. She has been a partner at industry analyst firm Hurwitz & Associates and a lead analyst for Bell Labs. Her Ph.D. is from Texas A&M University. You can reach her at [email protected], on Twitter @fhalper, and on LinkedIn at

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