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.
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:
Alteryx Machine Learning: ML Made Easy
Deep Feature Synthesis:
Drive Models to Production: