AI everywhere: Modern predictive analytics
It wasn’t that long ago that artificial intelligence seemed like science fiction – but AI continues to reshape business. According to ESG research, spending on AI and machine learning (ML) to transform data analytics will continue to increase in 2019.
AI, machine learning and deep learning have opened up opportunities to use predictive models in ways that were once unheard of in data science and analytics. The result is that predictive analytics is rapidly evolving. Here’s how.
The tipping point for predictive analytics
To some, predictive analytics has a reputation for being a complex series of tools best left to serious, highly-trained data scientists. Graphical user interfaces, workbenches and visualizations were a move in that direction. Now, with the convergence of AI, intuitive tools, predictive techniques, and hybrid cloud deployments, predictive analytics has reached the tipping point. A recent Forrester report describes it as the place where businesses can begin combining machine learning with knowledge engineering.
Predictive analytics has busted out of its data science shell. Companies of all sizes can integrate predictive analytics everywhere, take advantage of AI and analyze all kinds of data. Now it’s possible to embed predictive models into a variety of applications. You can infuse AI, machine learning, and deep learning anywhere you need it. The data you can analyze has expanded to include relational semi-structured documents, text, sensor data, streaming data and geolocational information. The data world is your oyster.
These big changes have opened the door to modern predictive analytics, which has more features and more benefits than once thought possible.
The features and benefits of modern predictive analytics
A recent IBM eBook about modern predictive analytics describes the current landscape as the place where classical statistical analysis techniques and new world of AI intersect. Pre-built AI services help you address common use cases quickly and efficiently, delivering value more quickly.
When you need custom models, you can use specialized tools and open-source AI frameworks to create seamless workflows for building, training and deploying predictive models. Three features can help you boost accuracy and mitigate bias: robust data governance; discoverability for models; and the ability to monitor and manage models in near-real time.
In addition, capabilities such as visual, intuitive modeling and decision optimization can yield you faster analysis to determine a next best action or build the best plan.
Most importantly, however, modern predictive analytics breaks down existing analytics silos. Your data scientists can examine business data, identify items of interest, develop data sets and design predictive models. Your operations team can then train, retrain, test, deploy and manage the models. Separately, the business experts in your organization can monitor the models, checking for runtime performance, bias, or things that need explanation, providing feedback and notifying your data science team when models might need retraining.
Put it all together, and it’s easy to see how modern predictive analytics can help your organization make smarter decisions, get to market faster and even disrupt your competitors.
Want to learn about implementing modern predictive analytics?
Evolving from departmental, small-group AI projects to an enterprise data science platform can put your business on a path to significant competitive advantage. Those who don’t seize the opportunity risk falling behind the curve. But some might not be sure how to begin. If you’re interested learning how to get going, our publication, A business guide to modern predictive analytics, is great place to start.