In the future, will data analysts find themselves unemployed due to generative AI displacing jobs? Not quite. However, many roles in this space should expect major changes to their duties as businesses dramatically rethink their approach to data analytics in response to the generative AI revolution. How will this happen?
Background: What Do Data Analytics Teams Do?
Let's begin by exploring the tasks typically performed by a data analytics team in a business that hasn’t yet adopted generative AI for enhancing analytics capabilities.
Traditionally, data analysts focused on integrating various data sources within a given organization and then creating queries to enable business stakeholders to answer questions based on their 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 necessary data transformations to query all of them in a consolidated fashion. This task required deep expertise in data integration and analytics, consuming much of analysts' time.
A second issue was that the initial queries rarely fully answered a business stakeholder's question, due to the difficulty in determining exactly what information the stakeholder wanted. As a result, queries became iterative processes, with analysts having to tweak queries and generate new reports repeatedly until they finally arrived at the desired answer.
In short, traditional data analyst roles have revolved around complex data integration and querying tasks. The work tends to be tedious and time-consuming, and it becomes even more challenging as the scale and diversity of data assets within a business increase.
How Generative AI Can Change Data Analytics
Generative AI, however, is poised to fundamentally change data analytics processes.
The main reason is that generative AI models can allow business stakeholders to ask and answer data-centric questions without relying on data analysts to write queries for them. As long as a generative AI model has access to relevant data sources, it can accept questions in natural language form and then generate appropriate data queries based on them. This is precisely one of the use cases that solutions like Amazon Q are designed to address.
For the data analyst role, this approach to querying data has two profound implications. One is that it reduces the importance of creating data inventories and maps. Instead of integrating disparate data sources in the traditional way – which entailed manual effort on the part of data analysts – businesses can simply expose all of their relevant data to generative AI models and let them decide how to query it.
In addition, a generative AI-based approach to data analytics makes it possible to iterate and refine queries much faster than teams could if they relied on analysts to write queries manually. Instead of a time-consuming process where a data analyst and a business stakeholder hold a back-and-forth conversation, and the data analyst writes multiple queries and reports in an attempt to provide the answers the business stakeholder seeks, the stakeholder can interact directly with a generative AI service to ask a question in different ways until the service produces the right answer.
This isn't to say that generative AI can interpret business needs any better than human data analysts. Natural language queries are always ambiguous, for generative AI models and humans alike. The advantage that generative AI has in this context is its ability to iterate faster and to generate new versions of an answer in a matter of seconds instead of hours.
The Future of Data Analyst Jobs
None of this is bad news for data analysts who may be worried about their jobs. On the contrary, although generative AI is likely to upend core elements of the traditional data analytics function within many businesses, it will make data analysts' jobs more rewarding and important in other ways.
Instead of spending most of their time on data integration and querying, analysts within a business that adopts generative AI as the basis for analytics will shift toward work that caters to enabling generative AI. Analysts will take the lead in model training, for example. They'll also play key roles in enforcing data governance and security policies, which shape which data generative AI models can and can't access – or, in cases where highly granular levels of access are necessary, data analysts will help enable controls that allow certain users to access certain data via generative AI services, which may not be available to other users of the same service.
This work will likely be more rewarding than the tedium of writing queries. It will also involve acquiring new data management skills – which is why data analysts who want to get ahead of the generative AI revolution should now be focusing on upskilling in this realm.
Conclusion: A Bright, but Different, Future for Data Analysts
If you're a data analyst living through the generative AI revolution, now's the time to rethink your role and the value you bring to business. Gone are the days when the ability to integrate data sources and write complex queries was the be-all and end-all of the data analytics function. Going forward, capabilities related to supporting and managing the data paradigms that power generative AI models will become central.
Ultimately, the work that data analysts perform in the brave, new, generative AI-centric world is likely to be more interesting and rewarding, and it will certainly be quite different from traditional data analytics tasks.
Eamonn O’Neill is the Co-Founder and Chief Technology Officer at Lemongrass.
