Use Case
AI and ML form a competitive advantage in any company. Using historical data to model predictions of future outcomes, you can start riding waves long before they’ve crested and enter new markets early. The key to harnessing the power of AI and ML is to bring them within the reach of knowledge workers across your organization.
Top-Line Growth
Put AI and ML tools in the hands of knowledge workers and enable actionable insights in hours, not weeks
Bottom-Line Returns
Reduce time investment and cost for data science projects
Workforce Upskilling
Increase knowledge workers’ data science skills without requiring advanced coding skills
Efficiency Gains
Reduce workload on data science teams and let them focus on high-value projects
Customer Experience
Get insights and implement business actions that impact your customers
Business Problem
In the not-too-distant past, it took a heavy lift from your data science and IT teams to derive insights from artificial intelligence and machine learning. Then it took months to operationalize those insights and start reaping the benefits. Meanwhile, other data science projects were added to the queue, creating a backlog. Some of them were overcome by events and some were dropped completely when priorities changed.
The struggle to establish AI and ML in your organization is exacerbated by the need to communicate what’s going on in the black box around data science projects. Convincing project stakeholders that the approach and the data are sound becomes half the work. An “analytics divide” opens up between the data science experts, who don’t know enough about business problems, and the knowledge workers, who don’t know enough about data science.
Alteryx Solution
Companies can bridge the analytics divide by putting AI and ML capabilities right in the hands of knowledge workers.
Analytics are designed to abstract the complexity and coding involved in AI and ML. Through easy-to-design workflows, knowledge workers can get from data sources to useful insights without needing to write code and understand computer science. Users upskilled in this way don’t have to wait on data science teams to create entire models; they can build, validate, iterate, and explore their own models. Plus, operationalization goes much more quickly as the users create visualizations and share their easily packaged results around the organization.
With Alteryx, you can:
- Quickly create ML Pipelines directly in an Alteryx Designer workflow using Alteryx Intelligence Suite
- Utilize the Alteryx Open-Source Library which includes trusted models such as Woodwork, Compose, Featuretools, and EvalML.
- Scale data science across your business with Alteryx Machine Learning
Alteryx Machine Learning: ML Made Easy
Deep Feature Synthesis:
Quickly develop robust models with automated feature engineering
Open-Source Libraries:
Implement trusted models such as Woodwork, Compose, Featuretools, and EvalML
Drive Models to Production:
Integrate model predictions into any analytic process with our end-to-end analytics platform
The Auto Insights Simulation: A Free, Personalized Interactive Demo
Anyone can experience limited Auto Insights functionality. In less than 5 minutes, you can take a spin through a Simulation of Auto Insights, experiencing features like Playbooks, Magic Documents, and What Caused This.
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