Who are Alteryx?

Enterprise data teams have long faced the same frustration: too much time cleaning and preparing data, not enough time acting on it. Alteryx was built to solve that problem, and over nearly three decades it has grown into one of the most widely used analytics automation platforms in the enterprise market. With more than 8,000 customers worldwide and over $1 billion in annual recurring revenue, it sits firmly at the serious end of the data technology market.

Whether you are exploring Alteryx for the first time or assessing it against alternatives, this profile gives you an independent view of what it does, who uses it, and how it compares. Viewpoint Analysis is a Technology Matchmaker - helping businesses to quickly find and select new enterprise technology. Our aim is to be the place enterprise buyers go to understand the software and technology market before speaking to vendors.

Who Are Alteryx?

Alteryx was founded in 1997 and is headquartered in Irvine, California. The company was taken private in December 2023 following an acquisition by private equity, and has since been operating under renewed leadership. Andy MacMillan joined as Chief Executive Officer in December 2024, bringing executive experience from UserTesting, Salesforce, and Oracle, and has been accompanied by a broader refresh of the senior leadership team across product, strategy, finance, and revenue functions.

Under this new leadership, Alteryx has repositioned itself around the concept of AI-ready data, arguing that the biggest barrier to enterprise AI success is not the AI models themselves but the quality and governance of the data feeding into them. The company surpassed $1 billion in annual recurring revenue in 2025 and was recognised in the G2 2026 Best Software Awards for analytics products, reflecting continued strong customer retention and satisfaction across its base.

What Does Alteryx Do?

Alteryx provides an enterprise analytics platform that enables organisations to prepare, blend, analyse, and automate workflows around their data, without requiring specialist engineering skills. The platform is built around a visual drag-and-drop interface that allows business analysts to build repeatable, governed workflows connecting multiple data sources and applying complex transformations without writing code. The result is that analytical work which once required weeks of IT involvement can be completed by a business analyst in hours.

The current platform is called Alteryx One, and it brings together data connectivity, preparation, analytics, governance, and automation in a single environment. At the input stage, the platform connects to structured, semi-structured, and unstructured data across files, databases, enterprise applications, and cloud platforms including Snowflake, Databricks, and Google Cloud. In the preparation layer, users cleanse, enrich, and transform data through a low-code interface. The analytics layer extends well beyond basic data preparation, covering geospatial analysis, automated machine learning, predictive modelling, and reporting.

A central part of the current Alteryx proposition is what the company describes as an AI Data Clearinghouse. As enterprises invest heavily in AI applications and agents, many find that unreliable or poorly governed data undermines the quality of AI outputs. Alteryx One sits between raw data and AI applications, ensuring that what enters an AI workflow is accurate, auditable, and traceable. The platform integrates with data lineage tools including Atlan and Collibra to give IT, compliance, and governance teams full visibility into how data moves through workflows and where it originates.

Alteryx also maintains a community of over 750,000 members globally, a peer-driven resource of shared workflows, solutions, and best practices that reduces implementation risk and accelerates onboarding for new deployments. The platform reportedly powers more than 380 million automated workflows annually across its customer base.

Alteryx Technology Areas

Alteryx One covers a broad range of data and analytics capabilities. The main product and solution areas include:

•       Data preparation and blending: connecting, cleaning, and transforming data from multiple sources through a low-code visual interface

•       Analytics automation: building repeatable, scheduled workflows that replace manual analytical processes

•       Predictive analytics and machine learning: automated ML tools and model building without specialist data science expertise

•       Geospatial analytics: location intelligence and spatial analysis capabilities built into the core platform

•       AI Data Clearinghouse: governed data pipelines that ensure AI-ready, auditable inputs for AI applications and agents

•       Data governance and lineage: integrations with Atlan and Collibra for end-to-end data traceability

•       Reporting and data storytelling: Auto Insights and reporting tools for sharing analytical outputs across the business

•       Cloud and hybrid deployment: Alteryx One supports cloud, on-premises, and hybrid environments.

More information on the broader data analytics technology market is available on the Viewpoint Analysis website.

Alteryx Competitors

Alteryx competes across several overlapping segments of the data and analytics market. At the self-service analytics and data preparation end, it sits alongside platforms that enable business analysts to work with data without heavy engineering involvement. At the enterprise AI and governance end, it competes with platforms positioning themselves as the trusted data layer for AI applications.

Understanding where Alteryx fits within this competitive landscape is important before committing to a shortlist. If you want to understand who else you should be considering for your data analytics requirement, the Viewpoint Analysis Longlist Builder is a straightforward way to get started. Answer a few questions about your organisation and requirements, and we will come back with a comprehensive report covering all the vendors worth putting on your list.

If you are already at the stage of running a formal evaluation, the Enterprise Software Selection Playbook covers how to structure a professional selection process from market review through to final decision.

•       KNIME: an open-source data analytics platform with a similar visual workflow interface. KNIME is a credible alternative for organisations that want flexibility and want to avoid per-user licensing costs, though it lacks the enterprise governance infrastructure and commercial support that large regulated organisations typically require. Best suited to technically capable teams comfortable with an open-source ecosystem.

•       Dataiku: a data science and machine learning platform that competes with Alteryx at the more technical end of the market. Dataiku is a strong choice for organisations with dedicated data science teams who need a collaborative environment for building and deploying models at scale. It is less focused on the self-service business analyst use case that is central to Alteryx.

•       Microsoft Power BI: primarily a business intelligence and visualisation tool rather than a data preparation platform, though many organisations use it alongside or instead of Alteryx. Power BI is the natural choice for organisations heavily embedded in the Microsoft ecosystem, particularly where the primary need is reporting and dashboards rather than complex workflow automation. Many Power BI users also use Alteryx upstream for data preparation.

•       Tableau (Salesforce): like Power BI, Tableau is focused on the visualisation and dashboarding layer rather than upstream data preparation. It is a strong competitor for budget and attention within analytics teams, but the two platforms are often complementary rather than directly interchangeable. Buyers choosing between Alteryx and Tableau are typically working from different starting points in their analytics journey.

•       Databricks: a unified data and AI platform aimed at organisations dealing with large-scale data engineering and machine learning. Databricks is considerably more technical than Alteryx and is typically chosen by data engineering and data science teams rather than business analyst communities. It competes more directly with Alteryx in the AI-ready data governance space, particularly for cloud-native organisations.

Alteryx Customer Examples

Alteryx has over 8,000 enterprise customers across a wide range of industries. The examples below are drawn from the Alteryx website and illustrate the breadth of use cases the platform supports, from mining and energy through to healthcare and professional services.

Anglo American (mining): the multinational mining company deployed Alteryx Designer to build analytics workflows for internal audit and operational processes, achieving a 97% improvement in fuel tracking accuracy. The platform's low-code approach allowed non-technical team members to build and maintain complex workflows without specialist data engineering skills, enabling the analytics function to scale across multiple business units.

KPMG (professional services): the Big Four firm has embedded Alteryx within its own advisory practice to deliver analytics-driven insights to clients and automate data-heavy audit and assurance processes. KPMG uses the platform to reduce the time its teams spend on data preparation, freeing capacity for higher-value analysis and client engagement.

Siemens Energy (energy): Siemens Energy uses Alteryx alongside AI capabilities to support its global data and analytics operations, automating thousands of hours of manual work across finance, operations, and reporting functions. The deployment is part of a broader digital transformation programme focused on making data-driven decisions faster and with greater confidence across the business.

NHS Supply Chain (healthcare and public sector): NHS Supply Chain transformed a time-consuming, manual tender evaluation process using Alteryx, reducing a process that previously took weeks of manual work down to seconds. The automation has allowed the team to focus on strategic procurement activity rather than data wrangling, improving both efficiency and the quality of procurement decisions for the NHS.

Suggested Next Steps

If you are researching Alteryx or the broader data analytics market, here are four ways Viewpoint Analysis can help you move forward.

Further reading. If you want to understand the wider data technology landscape before going further with any individual vendor, the Viewpoint Analysis Data Technology area covers the market in detail, including the different categories, key vendors, and what to look for when evaluating your options.

Build your initial longlist. Not sure who else you should be considering alongside Alteryx? The Viewpoint Analysis Longlist Builder asks you a few questions about your organisation and requirements, and we come back with a comprehensive report covering all the vendors worth putting on your list. It is a straightforward way to make sure you are not missing a strong option before you start shortlisting.

Run a quick selection process. If you are ready to move to a formal evaluation, the Viewpoint Analysis 30-Day Technology Selection service takes you from a standing start to a preferred vendor decision in a single month. It combines the best of our Rapid RFI and Rapid RFP processes, and we run the whole thing for you: writing the brief, managing vendor engagement, and guiding your team to a scored, defensible decision.

Speak to Viewpoint Analysis. If you would like to talk through your requirement and understand how we can help, request a call and one of our team will be in touch. There is no obligation and no vendor agenda. Just an independent conversation about what you are trying to achieve.