商业智能分析师
就业数据在劳工统计局对这个职业并未具体提供，所以我们正在使用来自Data Scientists的数据。
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计算自动化风险
高风险（61-80%）：这个类别的工作面临着来自自动化的重大威胁，因为他们的许多任务可以使用当前或近期的技术轻松自动化。
有关这个分数是什么以及如何计算的更多信息可在这里找到。
用户投票
我们的访客投票认为，这个职业很可能会被自动化。 这个评估进一步得到了通过计算得出的自动化风险等级的支持，该等级预计有74%的机会实现自动化。
你认为自动化的风险是什么？
商业智能分析师在未来20年内被机器人或人工智能取代的可能性有多大？
情感
以下图表在有大量投票数据时会显示。这些可视化图表展示了用户投票结果随时间的变化，提供了情感趋势的重要指示。
随着时间（每年）的情绪变化
增长
预计"Data Scientists"的工作空缺数量将在2033内增长36.0%
更新的预测将在09-2024到期.
工资
在2023，'Data Scientists'的年度中位数工资为$108,020，或每小时$51。
'Data Scientists'的薪资比全国中位工资高124.8%，全国中位工资为$48,060。
体积
截至2023，在美国有192,710人被雇佣为'Data Scientists'。
这代表了全国就业劳动力的大约0.13%
换句话说，大约每787人中就有1人被雇佣为“Data Scientists”。
工作描述
通过查询数据存储库并生成定期报告来生成财务和市场情报。设计识别可用信息来源中的数据模式和趋势的方法。
SOC Code: 15-2051.01
资源
如果您正在考虑开始新的职业生涯，或者想要换工作，我们已经为您创建了一个方便的工作搜索工具，它可能会帮助您找到那个完美的新角色。
评论
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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