Business Intelligence Analysts
Employment data isn't available specifically for this occupation from the Bureau of Labor Statistics, so we are using the data from Data Scientists.
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Calculated automation risk
High Risk (61-80%): Jobs in this category face a significant threat from automation, as many of their tasks can be easily automated using current or near-future technologies.
More information on what this score is, and how it is calculated is available here.
User poll
Our visitors have voted they are unsure if this occupation will be automated. However, the automation risk level we have generated suggests a much higher chance of automation: 74% chance of automation.
What do you think the risk of automation is?
What is the likelihood that Business Intelligence Analysts will be replaced by robots or artificial intelligence within the next 20 years?
Sentiment
The following graph is included wherever there is a substantial amount of votes to render meaningful data. These visual representations display user poll results over time, providing a significant indication of sentiment trends.
Sentiment over time (yearly)
Growth
The number of 'Data Scientists' job openings is expected to rise 36.0% by 2033
Updated projections are due 09-2024.
Wages
In 2023, the median annual wage for 'Data Scientists' was $108,020, or $51 per hour
'Data Scientists' were paid 124.8% higher than the national median wage, which stood at $48,060
Volume
As of 2023 there were 192,710 people employed as 'Data Scientists' within the United States.
This represents around 0.13% of the employed workforce across the country
Put another way, around 1 in 787 people are employed as 'Data Scientists'.
Job description
Produce financial and market intelligence by querying data repositories and generating periodic reports. Devise methods for identifying data patterns and trends in available information sources.
SOC Code: 15-2051.01
Resources
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Comments
The bulk of my time as a BI analyst is spent on 2 things. The first is working with domain stakeholders without technical knowledge (marketers in my case). This can often be like herding cats. Lots of people will have lots of different opinions on what KPIs they want to track or how to track them. And often they don't really understand the data limitations of what we can and can't report on, so I need to be there to provide guidance. Much of my job is spent guiding these people along, often massaging their egos along the way, so that the wider group of people arrives at a consensus.
The second is ETL. Even with whole teams of data engineers and operations managers, data is very rarely centralised into a single and easy to understand model. I work with about half a dozen different types of data sources (from AWS to Google Sheets). Each of these have hundreds of different indexes and many of those indexes have hundreds of fields. A tiny fraction of these fields have any kind of documentation and so all you have to go on is the metadata and the name of the db managers who put it together. Actually tracking down the data you need requires getting really into the weeds and following up with multiple people to try to track down who actually knows where to find the data you're looking for. That's just nowhere near enough data for an AI to get a hold of the data it needs.
It is a field where people usually believe in other people rather than AI.
The ETL process is also a complicated, one which most AI is not nor ever may be able to handle, data needs to be cleaned and standardized before AI can take a crack at it, the "AI" and yes I have to put that in quotes does not understand the context of anything, it is a prediction model using gradient boosters that performs quite well under controlled circumstances, thrown into any critical thought role it starts to lose pace. Furthermore nobody who works in the AI space authoring models ands understands the inner workings of "AI" treats this as anything more than a highly sophisticated toy...maybe in another 10 years we can come back to this question and see if we should start to worry.
I spend half of my time maintaining, tweaking, and fixing automation jobs - these include dashboards, data sets, and database tables.
It requires a technical person who is also an expert in their business domain to translate business requirements into data or reports that others need.
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