Earlier this summer, RapidMiner attended and spoke at Predictive Analytics World for Industry 4.0 in Las Vegas. During the PAW conference, we spent a few days meeting with cross-industry smart manufacturing and IoT experts to discuss trends and new technologies in DSML.
Here’s What You Missed at PAW for Industry 4.0
In case you couldn’t attend, we put together this blog post to share our experience at the conference and give you an inside look at our talk on the secret weapon every organization already has to power their AI success story.
Themes of the Conference
Our conversations with dozens of experts confirmed that while many businesses want to further their AI initiatives, they don’t currently have the data science resources needed to support them.
We talked to many organizations who want to make the leap from statistical analysis toward predictive analytics, but they’re currently:
- Not doing anything more sophisticated than ETL or
- Struggling with not having enough internal data scientists or
- Hesitant to embark on potentially risky use cases
There are lots of analysts out there who are still just starting to learn about data science. They want to do more, but they need an intuitive way to collaboratively build models. For many orgs, it’s less about the number of models generated and more about creating sustainable models that actually make it into production and provide value to the enterprise.
Where RapidMiner Comes In
Clearly, many attendees we talked to were all feeling the same way—actually getting business value from AI felt more like a pipe dream than a practical goal. We wanted to prove to them that it’s truly possible—they already have what they need to get started and be successful with AI.
Most organizations cite AI as a top company initiative, yet only 1% of models created today have their desired business impact. Why are these AI projects stalling or failing completely? We broke it down into three reasons.
- Lack of data science talent
- Lack of trust in models
- Lack of scalability
To overcome these challenges, organizations need to enable their built-in secret weapon: their people.
By upskilling your existing workforce and teaching SMEs, analysts, and other data users how to make an impact with data they’re already collecting, you can rely less on keeping up with the revolving door of data scientists or external consultants and create a well-rounded, data-driven workforce.
The results of a recent McKinsey survey found that user enablement set AI high performers apart from other respondents—57% of high-performing organizations enabled SMEs to understand the basics of how machine learning models worked, while only 35% of other respondents went through similar efforts.
We’ve seen a similar story play out with one of our clients (a global sustainability-focused paper manufacturer)—they were able to scale the impact of a small team of only three data scientists to more than 200 non-coders, completing advanced AI projects in less than 6 months (four times faster than they’d originally planned).
To Wrap Up
Following our presentation, we had several attendees come and talk with us about predictive maintenance, fraud detection, supply chain optimization, demand forecasting, and more use cases at their organization. Realizing they already had a capable workforce in place resonated with them— they just need a means to empower them to leverage their own data, with the right partner and right tool.
RapidMiner was started by PhD data scientists who understood that the power of AI shouldn’t be reserved for just PhD data scientists. As a no-code data science platform, we want to enable everyone in your organization to complete AI/ML projects—from making sense of your data to building models and AI-powered apps to drive better decision-making.
Ready to get started? Request a demo to see for yourself today!