Will AI Replace Data Analysts? A Guide For Data Analysts

Data analytics is a critical function for a data-driven organization. Yet despite the value and demand, analytics teams are often chronically understaffed. Recent advances in AI code generation and visualizations have prompted speculation that AI might fully replace data analysts within the next few years. 

However, AI replacement shortchanges the value of analysts, who serve as an organization’s partner in extracting meaning from data and uncovering data-driven advantages. Analysts are often the most technically adept employees within an organization, with mastery that spans coding and querying, visualization, statistics, and exploratory data analysis, and a breadth of numerical problem-solving techniques. The future of analysts and AI is not a question of whether analysts themselves will be replaced, but rather what components of their work will be replaced or augmented and what parts of their work will become more critical as AI becomes a trusted workplace tool. 

Generative AI is an opportunity for data analysts to amplify their impact on their organization. In this article, we discuss generative AI’s potential to influence the tasks and aptitudes required of an analyst. We take a forward-looking view on how you, as an analyst, can master AI collaboration for the success of your organization and your career. 

Summary of data analysts’ domains that AI can replace, augment, or influence 

Analysts do more than generate SQL queries—they are an organization’s experts on making meaning and advantage from data.  Here is a summary of data analyst work domains and strategic approaches to incorporating AI to supercharge your work.

Role of an analyst in an AI-augmented workplace

AI has already proven itself as a workplace tool for technical and non-technical professionals alike. LLM-based technology has shown remarkable promise within key analyst functions such as SQL generation, data retrieval, and facilitating information visualization. That said, AI can’t replace the entirety of an analyst’s work. Analysts have a nuanced role, spanning both the technical and the human side of making sense of data for an organization. 

Today, data analysts’ roles in an organization are a combination of three major components:

• Technical and computational execution: Translating business questions to code queries, performing numerical and statistical analysis, and generating information visualizations
• Context and insight: Understanding data and business needs well enough to see the “questions beneath the question” and help suggest the right metrics, data assets, mathematical/statistical analyses, and paths to data-driven impact
• Collaboration and trust: The ability to form collaborative partnerships with colleagues in which others feel confident and empowered to act on the analyst’s insights. 

AI shows promise in filling the technical aspect of an analyst’s role; indeed, current AI solutions can meet or exceed many of the technical service functions of an analyst. However, AI can only partially meet the trust component of an analyst’s work, and it falters greatly compared to a human analyst’s grasp of context and insight. 

At the same time, AI also has high potential to increase the breadth of an analyst’s role. Qualitative analysis, natural language processing, analytic prompting, scaffolded app development, and AI cost-benefit analysis are likely to present emerging needs and opportunities for analysts to impact their organizations.

Given the full scope of an analyst’s position, AI is unlikely to fully replace analysts. Instead, AI is likely to influence an evolution of the role away from technical fulfillment and toward insight generation, expert question-asking and collaboration, and impact generation. 

The human role of analysts as creative data professionals can’t be fully replaced by AI, but it is likely to be changed and augmented. If you’re strategizing about how to position yourself for a successful future as an AI-savvy analyst, you’re in the right place!

{{banner-large-3="/banners"}}

Preparing for AI’s influence over technical and computational execution

AI tools for executing the technical functions of an analyst are maturing rapidly. You can supercharge your work by learning which of your tasks can be passed to AI. Here are some best practices for working with AI toward faster technical execution.

Code generation

This is the most popular use of AI in the analyst domain, for good reason: Modern AI tools can generate sophisticated SQL in seconds. Try to expand the kinds of code generation work you outsource to AI:

• Use ChatGPT, Copilot, or Gemini for code assistance.
• Utilize full-code or no-code generation solutions, such as WisdomAI, to free your time to make a greater business impact. 
• Become familiar with natural-language-to-text solutions like Langchain tools and HuggingFace libraries. There’s no need to learn or try every tool or library, of course. The ChatGPT 4-series routinely outperforms specialized libraries in language-to-SQL, especially with minor syntactic corrections using regular expressions (RegEx). 
• If you aren’t yet familiar with regular expressions, a common protocol for text manipulation and search, this is a great time to learn it. You’ll find that it supports generated code by automating minor postprocessing edits, such as removing quote artifacts or formatting from LLM outputs. It’s compatible with SQL, Python, and R. Here’s a great quickstart reference. 

Root cause analysis and anomaly detection

Root cause analysis (RCA) has traditionally been one of the more tedious tasks that an analyst can be assigned because it typically involves manually looping through a data schema to find issues. Setting up automated RCA can similarly be complex, especially if your warehouse changes frequently. Agentic AI solutions can perform RCA and anomaly detections easily by systematically looping through a warehouse. WisdomAI, for example, has excellent capabilities for automated RCA and can fully automate root cause analysis workflows to loop through your data and detect anomalies. The time spent setting up an automated system quickly will pay dividends in the long term. 

Data extraction from data

LLMs excel at bridging the gap between qualitative and quantitative data in a way that can be tedious or out of scope for many business and data analysts. You can use LLMs to do the following:

• Named entity recognition and slot filling: An LLM can help preprocess unstructured or semistructured data into tabular or JSON formats, making a document’s information accessible for computational and aggregate methods. 
• Basic NLU, such as sentiment analysis: Previously, natural language analytics such as topic modeling or sentiment analysis could only be completed by analysts with Python or R experience and knowledge of language processing methods. AI has lowered the barrier to entry to these language-based analytics tasks. Consider this example prompt from https://www.promptingguide.ai/prompts/classification/sentiment:

"""Classify the text into neutral, negative, or positive
Text: {input}
Sentiment:"""

‍

This kind of sentiment analysis used to be the domain of NLP-specialist data scientists and analysts, using special libraries. Today this kind of work is well within the reach of any analyst who is proficient with basic LLM prompting. 

• Visualizations: This is typically one of the most time-consuming parts of an analyst’s job. Though an expert analyst can easily imagine a visualization, building and editing it takes time and expertise, and the inevitable requests for iterations or minor changes take more time still. AI solutions, such as WisdomAI, are emerging that vastly cut down on the time, labor, and code required to draft and iterate an information visualization from tabular data. For example, visualizations and iterations that would take dozens of minutes for an experienced analyst can be done in seconds on WisdomAI’s analytics platform. 

Preparing for AI’s influence over context and insight

Providing context and insight has always been the true mark of a great analyst. Given that it is a weak domain for AI, you can prepare to work with tomorrow’s AI assistants by doubling down on the value and impact of your own ability to translate your organization’s needs into insightful data questions. Use these strategies to maximize your impact.

Craft insightful queries

AI is competent at suggesting basic queries (e.g., “show me last month’s sales”), and it is starting to get more creative. For example, Facebook’s famous insight that people who reach out to 10 friends in 7 days tend to stay onsite requires quite a lot of human ingenuity. Similarly, true business advantage comes from asking novel and creative questions from your prompts, not simply the same KPI questions that the AI training has primed it to pose on its own.  

Business context

If the products in your field have multiple IDs, which one is most universal and reliable? If someone asks for revenue, do they expect to see gross revenue or net revenue? What constitutes a “lead,” a “churn,” an “undesirable loss,” or an “expected loss”? Which search string would return ALS in our table of medical indications: “ALS”, “amyotrophic lateral sclerosis”, or  “Lou Gehrig’s Disease”? 

An analyst’s work is filled with minor judgments that are informed by their domain knowledge and business exposure. Many of these distinctions are invisible to AI that has been trained for general purposes. AI applications and human users alike benefit from semantic layers that curate data and make it uniform and readable. You, as an analyst, have immense power over the success of AI measures if you master modes of preparing data to make the business context more uniform and more readable by AI. 

Data preparation and metadata

One of the most promising ways that you can collaborate with AI in the contextual domain is to become adept at data prep, data labeling, and metadata, reshaping the data to make it easy for AI to work with. Providing a semantic layer is not just a good practice for human analysts—it’s a crucial step in allowing AI technical features to work at their best, with one-shot logic and correctness. 

Data intuition

In time, good analysts develop a feel for the patterns inherent in their own data. They often have unique insights on where to search for a root cause. This intuition combines with data literacy in a way that is invaluable for an organization that expects to use data in its decisions. This domain-informed intuition does not translate reliably into the logic patterns of today’s LLMs, and a human analyst remains its best source. 

Preparing for AI’s influence over building trust 

Your impact as an analyst is a function of the trust that your business partners can place in your results. AI confounds this. To a colleague who is bullish on AI, it can make you seem like an unreliable middleperson to automatable information. To an AI-shy person, it can raise your profile as a trusted human steward of data and business intentions. As analytics moves toward an AI-enhanced profession, you need to know the limitations of AI as well, on behalf of your nontechnical collaborators. 

Hallucinations and deception

Hallucinations are a well-known peril of using LLMs, and fine-tuning and prompt overloading can increase hallucinations. The latest round of AI models seems to exhibit an increase both in hallucination rate and general deception. To make matters worse, hallucinations are not predictable, so a core AI result might look correct in a prototype but show an incorrect result once in service for months. Safeguards, such as limiting the scope of prompts and learning sound prompt engineering practices, can protect your work against the potential issues with AI. Prompt engineering research on hallucination prevention is a rapidly developing front, but there are a number of tried-and-true techniques. As a starting point, consider concrete “According to-” style prompting to connect the result to your warehoused data, as in this example from PromptHunt:

"""Ground your response in factual data from your pre-training set, specifically referencing or quoting authoritative sources when possible. Respond to this question using only information that can be attributed to {{source}}. 
Question: {{Question}}"""

‍

Natural language to SQL limitations

Turning natural language questions into executable SQL queries is one of the most important AI-related innovations in the analytics space, but it’s good to remember that this technology is challenging and still evolving. Question-to-query assistants perform much better on data that has few errors, standardized labeling, and a schema that makes intuitive sense to LLMs. 

An LLM’s schema-parsing logic is quite a lot like a person’s: It relies on good data hygiene, context, and human-interpretable labels and structures. For example, while a sharp analyst might know to use a column like “psps-24-wsgh-75-mwh,” an AI won’t. An AI also doesn’t know institutional customs, like a company treating any ID ending with “cb” as a special case. 

You can help your AI assistants be at their best by participating in data restructuring, cleaning, and naming so that it is understandable by humans, who require language-based titles,  and also by machines, which require uniformity. Alternatively, employ tools that have advanced features to curate SQL generation, such as WisdomAI’s safeguard to only query existing data. 

Automation bias

Counterintuitively, many people have more faith in analysis when AI is involved because we are socially conditioned to think of automated work as better or more precise. AI tools that include explainability measures to help you understand the model’s reasoning are a good step toward grounding others’ understanding and expectations in reality. Remember that using AI has not changed the nature of an analyst’s work. The trust you build depends on the veracity and insights of your results, whether you have collaborated with AI or not. 

Preparing for new roles for analysts in an AI-enabled workplace

As AI reduces the time that you need to spend on formulating and writing code, it also unlocks additional ways for you to create value in your organization. 

Qualitative analysis

A unique power of generative AI is that it bridges quantitative analytics and qualitative analysis. Qualitative analysis is often very useful to organizations, but they neglect it either because of the expense and difficulty of hiring great qualitative analysts, or because of a blind spot for qualitative work in general, which is common in primarily technical organizations. In general, analysts would not consider thematic analysis, summarization, claims detection, or other qualitative work to be part of their portfolios, even though this work is often very valuable to organizations. Using LLMs as analysis tools makes these functions accessible and feasible to automate for quantitative analysts.

As an example, consider this basic prompt to summarize the main topics of a week of user review posts. It can be used internally for quality feedback or externally in B2B applications for suppliers or clients. Before AI, this kind of work was extraordinarily difficult to automate.

"""The following is a list of user reviews of our company's app, posted in the last week. Provide a summary of the major themes that are included. Do not include a review theme unless it was mentioned at least twice. Provide examples for each theme. 
User reviews: {input}
Themes:"""

‍

Analytic prompt engineering

The short-lived “prompt engineer” profession has given way to a suite of prompt engineering research and best practices, including methods for analysts. An organization’s analysts who work with AI should also become experts in prompting for accurate and repeatable results. 

App building

Have you ever wanted to build an app of your own? App-building hasn’t historically been part of an analyst’s role, but AI is making it easier for you to create simple applications. As an entry point, consider Streamlit; as Ben Luks’ tutorial on Botpress shows, a simple data-connected chatbot can provide a lot of value for your organization internally, with very little background and setup. 

Cost benefit

AI costs can add up! An analyst is well-positioned to ensure that an organization is spending its money wisely by monitoring costs and quantifying the benefits of saved time, increased functionality, and more advanced productivity among the analysts themselves and the organization more broadly.

Upskilling advice for analysts

As the analyst profession shifts from coding expertise to more insight-generating activities, you can meet the future by preparing to use AI tools in the service of your own insights. Here are some general tips to help you surge forward in the future:

• Be an early adopter: Try new tools as they are released. Get comfortable with prompting. 

• Note your changes in productivity: Analysts are experts in quantifying value, and that goes for changes in their own workflows, too.
• Advocate for data readiness and design reform: If your organization’s data structure is in poor shape for AI schema parsing, consider adding a cleaned and ready semantic layer. Champion metadata and clear field naming conventions.
• Lean into your insights to offer value: Read business stories and data-driven success case studies to expand your inherent intuition. Remember Facebook’s insight about users with seven connections? Each time you read a new case about unique data insights, it expands your own mental landscape about unique ways to look for advantages in a dataset.  

{{banner-small-3="/banners"}}

Last thoughts

AI has shown great promise in augmenting the work of analysts, but it can not fully replace the value of intuition, business insights, and domain knowledge. These will remain your creative contributions in an analyst-AI partnership. 

As AI and analytics co-evolve, your value as a code producer will recede. Your value as a trusted and insightful partner will be amplified. 

Toward this future, today’s AI offerings are tools that free you to realize your true value to an organization: not a code creation specialist, but rather a trusted collaborator and master of data insight.