As the world adapts in the wake of the pandemic, business-as-usual is evolving and there’s an increased urgency to adopt more advanced technology, move to digital-enabled work, and leverage approaches like machine learning to improve and optimize business outcomes.
We find that many businesses are intimidated by the adoption of machine learning and predictive analytics, but it doesn’t have to be overwhelming. Companies don’t have to hire a crew of data scientists to implement and realize the value of machine learning. There are plenty of platforms and solutions that are providing business users access to advanced analytics approaches. We are at a tipping point—machine learning is being democratized.
The Democratization of AI and Machine Learning
Just as the personal computer went from inaccessible to ubiquitous, AI and machine learning technologies are becoming more and more accessible. The platforms and companies that support a democratized approach to automated machine learning are becoming easier to access and simpler to use.
A recent Forrester article tackles this topic and answers the question, “How do I democratize predictive analytics and ML for non-data scientists with AutoML?”
Every part of your business can benefit from having more predictive models. With AutoML, your domain experts can now become directly involved in the process of developing these models without any knowledge of ML. For example: One & All, the fundraising agency for nongovernmental organizations like The Salvation Army, brought the creation of donor selection models in-house with the help of Lityx, enabling the agency to reduce costs by 70%, increase donor giving by 8%, and increase turnaround from months to hours, while also opening up the use of data-driven targeting to a host of smaller nonprofits.
In other words, advanced analytics approaches are available to any size business, no matter their level of sophistication. With platforms like LityxIQ, data scientists, non-data scientists, and senior executives can build models, use predictive analytics, and automate machine learning without having to know a single line of code.
Best Practices When Defining Your AI Approach
Obviously, there are some best practices for the use of machine learning. Companies should be sure to consider and incorporate recommended best practices into their machine learning strategy. Another recent Forrester article outlines the five principles of AI to put into practice. Below are the five principles, summarized. The full article can be found here.
- Prevent bias and champion fairness in your models.
- Apply testing disciplines and practices to ensure trust and transparency
- Establish ownership for each area of the data process to ensure accountability
- Use AI for good, not evil
- Respect the privacy and security of the data
Take advantage of the momentum of this moment and kick off your process of adopting machine-learning practices to gain new insights and scale your business. It doesn’t have to be complicated; just getting started is a step in the right direction. Start using your data more effectively while we’re still in the early stages of machine learning adoption, and your company will come out ahead.
Here is what Forrester has to say on how to accelerate and scale new business insights from data with AutoML
Contrary to popular belief, not everyone’s looking to make predictions. More business users are looking for insights from their data to make better decisions and react faster to changes in their business. However, they don’t have the skills, time, or enough talented data analysts. To empower these individuals to leverage the power of ML for data-driven decision making. Consider a converged offering. Vendors like Lityx are going beyond their capabilities for building predictive models and are piloting features that help business users rapidly extract insights from data.
We’re currently offering free 90-day access to LityxIQ, our AutoML platform. If you’re interested, let us know!