Measuring Worth: Business Intelligence Salary Metrics – To respond to a price survey. The results helped us understand what our subscribers are worth, where they are, what businesses they work for, what their concerns are, and what career development opportunities they are pursuing.
Although it’s sad to say that the research came at the end of the COVID-19 epidemic (although we can all hope), it happened at a time when the restrictions were loosening: we started to go to the public, to events. , sometimes attending in-person meetings. The results provide an opportunity to start thinking about what impact the pandemic will have on jobs. There is a lot of uncertainty about sustainability, especially in small companies: Will the company’s business model continue to be profitable? Will your job last year? At the same time, workers are reluctant to look for new jobs, especially if they want to be relocated—according to the rumor mill. Were these concerns reflected in the new models for work?
Measuring Worth: Business Intelligence Salary Metrics
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The survey was published in O’Reilly’s Data & AI Newsletter and was limited to respondents in the United States and the United Kingdom. There were 3, 136 valid responses, 2, 778 from the US and 284 from the UK. This report focuses on respondents from the US, with little focus on those from the UK. A small number of respondents (74) identified as US or UK residents, but indicated their IP addresses were located elsewhere. We did not use data from these respondents; in practice, deleting this data has no effect on the results.
Of the 2, 778 American respondents, 2, 225 (81%) identified as male, and 383 (14%) identified as female (as indicated by their best name). 113 (4%) indicated “other,” and 14 (0.5%) used “their.”
). Our audience is very strong in the computer industry (20% of respondents), computer hardware (4%), and computer security (2%) – more than 25% of the total. The audience is also strong in the states where these institutions are located: 42% of US respondents lived in California (20%), New York (9%), Massachusetts (6%), and Texas (7%), although only these countries are built. up 27% of the US population.
The median annual salary for workers who worked in data or AI was $146,000. Most earn between $100,000 and $150,000 per year (34%); the next highest salary level is from $150,000 to $200,000 (26%). The price depended on the location, with average prices in California ($176,000).
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The average price change over the past three years is $9,252, or 2.25% per year (assuming the last price is the same as the average). A minority of respondents (8%) reported a price decrease, and 18% reported no change. The economic uncertainty caused by the pandemic may be the reason for the drop in prices. 19% reported an increase of $5,000 to $10,000 during that period; 14% reported an increase of over $25,000. The IEEE survey reported that the average salary for technology workers increased by 3.6% annually, higher than our respondents.
39% of respondents reported promotions in the past three years, and 37% reported changing employers during that time. 22% said they are considering changing jobs because their wages have not increased in the past year. Is this a sign of what some have called a “big break”? It is generally accepted that technical workers change jobs every three to four years. LinkedInandIndeedboth recommend staying for at least three years, although they note that younger employees change jobs more often. LinkedIn reports elsewhere that the annual turnover rate for tech workers is 13.2%—which suggests that workers should stay at their jobs for seven and a half years. If necessary, 37% have changed jobs within three years which seems reasonable, and 22% said “they would like to leave their job because of the lack of salary” not very high. Remember that wanting to change and actually changing are not the same thing—and there are many reasons to change jobs besides pay, including flexibility in work hours and working from home.
64% of respondents attended training or received a degree in the past year, and 31% reported spending over 100 hours in training programs, ranging from formal degrees to reading in blog posts. As we will see later, cloud certificates (in AWS and Microsoft Azure) are the most popular and seem to have a significant impact on prices.
Respondents’ reasons for participating in training were very consistent. Most said they wanted to learn new skills (91%) or improve existing skills (84%). Data and AI professionals are passionate about learning—and learning is self-motivated, not organizational. Fewer (22%) said their jobs required training, and even fewer participated in training because they were worried about losing their jobs (9%).
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However, there are other factors at work. 56% of our respondents said they wanted to increase their “job security,” in contrast to the minority who were concerned about losing their jobs. And 73% said they received training or certification to increase their “usefulness,” which may add to concerns about job stability that our respondents don’t agree with. The pandemic is a threat to many businesses, and workers are rightfully concerned that they will lose their jobs after a bad quarter influenced by the pandemic. The need for increased employment may indicate that we will see more people looking to change careers in the future.
Finally, 61% of respondents said they entered training or received a certification because they wanted a raise or promotion (“increase in job title/responsibility”). It’s no surprise that employees see training as a way to get promoted—especially as companies looking to hire in fields like data science, machine learning, and AI struggle to limited number of qualified employees. Given the difficulty in hiring expertise from the outside, we expect an increasing number of companies to grow their own ML and AI talent internally using training programs.
To no one’s surprise, our research revealed that data science and AI professionals are predominantly male. The number of respondents who speak the story: only 14% identified as women, which is less than we expected, although it is the same as when we came to the meeting (after our live meetings ) and similar to other technical areas. . A few (5%) said they preferred the word “they” or others, but this sample is too small to make a meaningful comparison of value.
Women earn significantly less than men, at $126,000 per year, or 84% of what men earn ($150,000). That difference persisted regardless of education, as shown in Figure 1: the average salary for a woman with a bachelor’s or master’s degree was 82% of the salary for a man with the same degree. The difference was not significant for people with a bachelor’s degree or still a student, but it was still significant: women with a bachelor’s degree or a student earned 86% or 87% of the average salary for men . The difference in salaries was even greater between the people who studied themselves: in that case, women’s salaries were 72% of men’s. The second indicator is the only indicator that women earn more than men.
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Despite the difference in pay, a higher percentage of women had higher degrees than men: 16% of women had a bachelor’s degree, in contrast to 13% of men. And 47% of women have a master’s degree, as opposed to 46% of men. (If those percentages seem high, remember that many experts in data science and AI escape academia.)
Women’s wages also lagged men’s wages when we compared women and men with similar job titles (see Figure 2). At the executive level, the average salary for women is $163,000 to $205,000 for men (a 20% difference). At the executive level, the difference is smaller—$180,000 for women versus $184,000 for men—and salaries for women are higher than those at the executive level. It is easy to speculate about this difference, but we are at a loss to explain it. For executives, women’s salaries were $143,000 to $154,000 for men (a 7% difference).
Career progression is also a factor: 18% of the women surveyed are managers or directors, compared to 23% of men.
Before we move on from our consideration of the impact of gender on wages, let’s take a look at how wages have changed over the past three years. As shown in Figure 3, the same percentage of male and female respondents had no change (18%). But more women than men saw their wages drop (10% to 7%). Similarly, many men saw their wages rise. Increases are lower for women: 24% of women have an increase under $5,000 compared to 17% of men. At the high end of the price spectrum, the
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