Yes—low-code tools could let more people take part in analytics and some machine-learning work, but evidence does not show that a standardized “low-code data scientist” job has emerged or that specialists are being replaced. The 2023 prediction is best understood as a forecast of broader participation, not a proven change in who does professional data science.
What is a low-code data scientist?
“Low-code” describes an approach to building workflows through visual interfaces and configurable steps, with less handwritten code than a conventional programming workflow. It does not mean “no expertise required.” A person may use a visual tool to connect data, clean it, explore patterns, build a model, and present results while still needing judgment about what to ask and how to interpret the answer.
KNIME describes its Analytics Platform as open-source software for visual workflows covering data access, transformation, analysis, modeling, and visualization (KNIME Analytics Platform documentation). Its learning resources include paths for analysts and data scientists, from preparation and visualization to productionizing data apps (KNIME learning resources). Those capabilities illustrate how a workflow can be made more accessible; they do not establish a new occupation.
Other products have different scopes. Alteryx promotes low-code and no-code tools for data preparation and machine-learning models (Alteryx State of Cloud Analytics report). Microsoft’s Power Platform spans analytics, apps, automation, and websites; Power BI is its analytics product, not a synonym for all data science (Microsoft Power Platform documentation). These examples are not interchangeable platforms or an independent comparison of their capabilities.
Do these 3 things before closing this tab:
1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minute#1 Best Overall
Why the prediction seemed plausible
Several dated indicators pointed to momentum in both analytics and low-code software, although they measure different markets and kinds of activity.
| Indicator | What was reported | What it does—and does not—show |
|---|---|---|
| Data and analytics software | Gartner reported worldwide spending growth of 13.2% to $150.9 billion in 2023; its 2024 abstract said data science and AI platforms grew 29.3% that year, among the fastest-growing subsegments (Gartner market report). | Growth in software spending, not proof that non-specialists took over data-science work. |
| Low-code and digital process automation market | Forrester estimated a combined $13.2 billion market at the end of 2023 (Forrester low-code research). | A market estimate, not an audited count of workers or low-code data scientists. |
| Enterprise developer use | Forrester reported that 87% of enterprise developers used low-code platforms for at least some development work, based on its survey data (Forrester low-code research). | Use by developers in some development work; not the proportion of workers doing data science. |
| Earlier spending forecast | A 2022 report of Gartner’s forecast projected 19.6% growth in worldwide low-code development technology spending for 2023 and 30.2% growth for citizen automation development platforms (TechRepublic report on Gartner’s forecast). | Forecasts made before 2023, not confirmed actual growth figures. |
| Employee access to analytics | In Alteryx’s 2023 State of Cloud Analytics report, 98% of respondents said their businesses would benefit if more types of employees had access to analytics solutions (Alteryx report). | Respondents to a vendor’s report, not a finding that represents all businesses or measures actual adoption. |
Gartner analyst Jason Wong was quoted in the republished forecast report saying, “The high cost of tech talent and a growing hybrid or borderless workforce will contribute to low-code technology adoption” (TechRepublic report quoting Gartner’s forecast). That reasoning helps explain the prediction, but neither the quotation nor market growth establishes that a distinct low-code data-scientist occupation followed.
What people can do with low-code tools—and what still needs expertise
Visual workflows can reduce implementation friction for tasks such as combining data, applying transformations, exploring results, and building or using models. Depending on the platform and the work, they can make parts of analytics or modeling accessible to people who do not write much code.
But a usable interface does not decide whether a question is worth asking, whether a dataset represents the situation, or whether a model’s output is trustworthy. An AutoML review identifies continuing human involvement in defining prediction problems, creating appropriate training data, and choosing a suitable technique; research on low-code machine learning also identifies MLOps, model, and data concerns (Research on AutoML and low-code machine-learning challenges).
Recommended Free Tools
- Problem framing: translate a business need into a question a model or analysis can answer.
- Data quality: understand where data came from, what is missing, and whether it is suitable for the task.
- Method and validation: choose an appropriate approach, test its performance, and recognize when results are misleading.
- Operation and accountability: deploy and monitor systems, manage changes, and decide who is responsible for their effects.
Low-code can change how some work is implemented; it does not remove the need for statistical reasoning, subject-matter knowledge, or accountable review.
Will low-code replace data scientists?
The available figures do not answer that question by counting roles, measuring the share of data-science tasks performed by non-specialists, or assessing their competence. They support a narrower conclusion: low-code adoption and visual analytics tools may allow more people to contribute to parts of data work. That can expand participation without eliminating specialist roles, particularly where work depends on complex data, careful validation, deployment, or ongoing monitoring.
For a worker, “citizen data scientist” is more useful as a description of tasks than as a claim about a recognized job title. A person who prepares data or builds a first-pass model in a visual tool may contribute valuable work, while a specialist may still be needed to review assumptions, handle complexity, and put reliable systems into operation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What organizations need to manage as access expands
More employees able to build workflows also means more workflows and models to oversee. Microsoft’s governance guidance highlights oversight, security, and compliance; unmanaged citizen development can become shadow IT (Microsoft governance guidance; Low-code governance risks).
Best Value
- Set access controls for sensitive data and define who may publish or share workflows.
- Establish review and approval for models or analytics used in consequential decisions.
- Keep workflows documented and reproducible so others can check inputs, transformations, and outputs.
- Plan for monitoring and maintenance after a model or app enters use, rather than treating creation as the finish line.
How to judge a low-code data-science platform
Product documentation establishes what vendors describe their tools as supporting, but the cited materials do not provide a like-for-like independent benchmark. When evaluating an option, check the workflow and organizational fit rather than treating “low-code” as a guarantee of capability.
Quick Recap
- Tasks: Does it support the data preparation, analysis, modeling, and visualization you need?
- Skills: Which steps are visual, and where are coding or statistical judgment still needed?
- Connections and extension: Can it work with your data sources and extend beyond built-in components?
- Reliability: What does it offer for validation, deployment, monitoring, collaboration, and reproducibility?
- Governance: Can administrators manage access, security, and compliance requirements?
- Cost: What costs apply to the relevant edition, users, and deployment? The cited documentation does not establish a comparable price across these products.
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
