Will data analysts be unemployed in the future because their jobs are taken over by generative AI? Not completely. However, as companies dramatically rethink their approach to data analysis, many roles in this field should expect significant changes in their responsibilities. Generation AI rotate. How does this happen?
Background: What does a data analytics team do?
Let’s start by looking at the tasks typically performed by data analytics teams at companies that haven’t yet deployed generative AI to enhance their analytics capabilities.
Traditionally, data analysts have focused on integrating disparate data sources within a given organization and creating queries that allow business stakeholders to answer questions based on the data. This process was often complex and time-consuming for several reasons.
One reason was the challenge of mapping disparate data sources together and implementing the data transformations required to integrate and query them all. This task required deep expertise in data integration and analysis and took up a lot of the analysts’ time.
A second problem was that initial queries rarely fully answered business stakeholder questions because it was difficult to determine exactly what information the stakeholders wanted. As a result, queries became an iterative process, requiring analysts to adjust queries and generate new reports repeatedly until they finally arrived at the desired answer.
In short, the traditional data analyst role has revolved around complex data integration and querying tasks. This task tends to be tedious and time-consuming, and becomes even more difficult as the size and variety of data assets within a business grows.
How generative AI can transform data analysis
However, generative AI is fundamentally changing the data analysis process.
The main reason is that generative AI models allow business stakeholders to ask and answer data-centric questions without relying on data analysts to write the queries. As long as a generative AI model has access to relevant data sources, it can accept questions in natural language format and generate appropriate data queries based on them. This is exactly one of the use cases that solutions like Amazon Q are designed to address.
This approach to querying data has two important implications for the data analyst role. One is that it becomes less important to create data inventories and maps. Instead of integrating disparate data sources in traditional ways (requiring manual effort on the part of data analysts), companies can simply expose all relevant data to a generative AI model and let it decide how to query it.
Additionally, generative AI-based approaches to data analysis allow teams to iterate and refine queries much faster than if they relied on analysts to create queries manually. Instead of the time-consuming process of back-and-forth conversations between data analysts and business stakeholders, where the data analyst creates multiple queries and reports to provide the answers the business stakeholders are looking for, stakeholders can interact directly with the generative AI service and ask questions in different ways until the service generates the correct answer.
This is not to say that generative AI can interpret business needs better than human data analysts. Natural language queries are always ambiguous for generative AI models and humans alike. The advantage of generative AI in this context is that it can iterate faster and generate new versions of answers in seconds instead of hours.
The future of data analyst work
None of this is bad news for data analysts who are worried about their jobs. On the contrary, while generative AI has the potential to fundamentally change the core elements of traditional data analysis functions in many companies, the job of data analysts will become more rewarding and important in other ways.
Analysts within companies that embrace generative AI as the foundation for their analytics will shift their work to enable generative AI, rather than spending most of their time integrating and querying data. For example, an analyst leads the training of a model. It also plays a key role in enforcing data governance and security policies, determining which data-generating AI models can and cannot access. Or, if a very granular level of access is required, data analysts can help enable controls that give specific users access to specific data through generative AI services that may not be available to other users of the same service.
This task can be more rewarding than writing boring queries. This includes learning new data management skills. Therefore, data analysts who want to stay ahead of the generative AI revolution should focus on upskilling in this area.
Conclusion: A bright but different future for data analysts
If you’re a data analyst living in the era of the generative AI revolution, now is the time to rethink your role and the value you bring to your business. Gone are the days when the ability to integrate data sources and create complex queries was the ultimate and ultimate feature of data analysis capabilities. Going forward, the focus will be on features related to supporting and managing data paradigms that power generative AI models.
Ultimately, the work that data analysts perform in a brave new generative AI-centric world is likely to be more interesting, challenging, and distinctly different from traditional data analysis tasks.
Eamonn O’Neill Co-founder and Chief Technology Officer. lemongrass.
