This article is the fourth in our 3-Minute Nutshell series. We answer FAQs about predictive analytics in just a few minutes of your time! Get up to speed on the key things you need to know to start your business’s journey toward AI success. Catch up with the series now:
- What is predictive analytics?
- Is predictive analytics the same as data science, machine learning, and AI?
- What skills do you need to do predictive analytics?
… and now: Can predictive analytics be automated?
Automated predictive analytics for efficiency and results
As I write this, my robot vacuum is cruising around my house. It’s dealing with a pesky household chore while I take care of other tasks. Using AI for automation makes my life more efficient. And fortunately, today’s automated predictive analytics software can bring greater efficiency to every business team.
Predictive analytics might appear much more complex than vacuuming your house. However, powerful AI techniques now make predictive modeling automatic, accurate, and accessible. That’s also the case even for data and business professionals without advanced data science knowledge. It's easy today to move beyond automated data analytics to embrace AI. Let’s explore how automating predictive analytics can work and why it’s such a valuable approach to predictive analytics.
Reviewing the predictive analytics process
In a typical manual predictive modeling project involving artificial intelligence, the workflow includes these steps:
- Predictive question and action: defining the question the business needs to answer and determining how they’ll act on predictions
- Data preparation: connecting to, cleaning, and combining data to get it into the appropriate format for machine learning
- Feature engineering: determining the correct variables that will work best in modeling and creating new ones as needed
- Model selection, evaluation, and optimization: training different models on historical data and comparing their performance, then choosing the one best fitting the specific business need
- Model deployment and monitoring: integrating the model into active business processes using current data while also assessing the model’s impact regularly to inform adjustments
That’s a lot of tasks to do manually! However, as the above graphic shows, nearly all this data pipeline and workflow can now be rolled into automated processes. To be sure, those automation tools tackle some of the most challenging, time-consuming parts of the data science workflow.
With automated data preparation, automated machine learning (AutoML), and automated machine learning operations (MLOps), nearly every step can occur without human intervention. (And don’t worry — we’ll spend a little time on the steps that currently aren’t.)
How to automate predictive analytics
Data preparation and feature engineering
Automated vacuuming for your house is cool. But automated cleaning of your data — well, that’s a massive leap forward for data professionals’ quality of life. In this stage of the process, connecting to your data becomes a matter of clicks, not code.
No need to manually aggregate data or risk introducing human error. Tricky data issues are detected and repaired painlessly. In addition, feature engineering is no longer a scavenger hunt for useful new features. Instead, it’s an unbiased, speedy process of automated feature creation, evaluation, and selection.
Data scientists still spend 38% of their time on data preparation and cleansing. Therefore, automating just these processes means 15 hours in a 40-hour week are freed up for more meaningful, enjoyable tasks.
Automated modeling with AutoML
AutoML — perhaps the most familiar type of automation in the predictive analytics process — pops up next. AutoML primarily builds, trains, selects, and optimizes models, selecting the right machine learning techniques for you effortlessly. Importantly, you don’t need in-depth knowledge of algorithms or coding experience. Instead, AutoML crafts the ideal model for your particular dataset and question. AutoML means you can skip endless tinkering with models.
Automated deployment and modeling
Finally, only some predictive models make it into production where they can have real business benefits. Some large companies have dedicated MLOps teams. But others don’t have those resources and would greatly benefit from automating deployment and monitoring.
Automating this stage includes evaluating models’ impact on key business metrics. Moreover, those details must be automatically updated regularly to reveal when adjustments are necessary. Automated model deployment and monitoring ensure that accurate, useful models get deployed and achieve business impact.
How automated predictive analytics supports businesses
With the help of automated predictive analytics and machine learning, businesses across industries can easily improve a variety of processes and outcomes. Here are some examples:
- Predictive analytics in retail is used to forecast consumer demand, optimize inventory management, and personalize marketing campaigns.
- Financial institutions use predictive analytics to detect fraud, assess credit risk, and predict market trends.
- Predictive analytics is used in the manufacturing industry to optimize production processes, predict equipment failures, and improve supply chain management.
- The transportation industry uses predictive analytics to optimize routes, predict maintenance needs, and improve customer service.
- In the telecommunications industry, predictive analytics is used to improve network performance, predict customer churn, and optimize pricing strategies.
- The insurance industry uses predictive analytics to assess risk, detect fraudulent claims, and personalize insurance policies.
- Predictive analytics is used in the e-commerce industry to personalize product recommendations, optimize pricing strategies, and improve customer retention.
What’s not automated in predictive analytics today
If you paid attention to the graphic above, you noticed a couple of things aren’t automated today, including the initial definition of a business need. Additionally, business leaders need to determine their intended actions based on models’ predictions and later refinements. (We’ll discuss this in an upcoming blog post.) To be sure, developing the right “predictive question” and planning specific actions are critical to predictive analytics success.
An example of a specific, actionable predictive question, as constructed with Pecan’s platform.
While automated predictive analytics tools are advancing, humans still have an essential place in this workflow. This “human in the loop” approach combines the best of AI-powered automation with human judgment and insight. Without a doubt, the combination maximizes the benefit of predictive analytics.
Just like me and my robot vacuum, humans and AI-driven automated predictive analytics can become partners. Together, we can achieve great results.
Ready to streamline your predictive analytics workflow and embrace the speed and impact of automation? Get in touch for a quick, easy use-case consultation.