The Power To Predict Is Mighty
Yogi Berra once said, "It's tough to make predictions, especially about the future." It is tough indeed, but enterprises that can make probabilistic predictions about customers, business processes, and operations will have an edge over enterprises that can't. These predictions don't have to be macroscopic to be consequential. Predictions about what a customer is likely to buy next. Predictions about marketing content that will resonate with a prospect. Predictions about the next best action to take in a business process. Predictions about when an expensive asset is likely to break down. Virtually any customer journey, business process, and even strategic decision can be made better if permeated with the power to predict.
Predictive Analytics And Machine Learning Solutions Make It Possible
Yes, making accurate predictions is tough, but predictive analytics and machine learning (PAML) solutions provide data scientists and developers alike with the tools to make it happen. Forrester defines PAML solutions as:
Software that provides data scientists with 1) tools to build predictive models using statistical and machine learning algorithms and 2) a platform to deploy and manage predictive production models.
The Forrester Wave™: Predictive Analytics And Machine Learning Solutions, Q1 2017
In our 23-criteria Forrester Wave evaluation of PAML solutions, we identified the 14 vendors — Alpine Data, Angoss, Dataiku, Domino Data Labs, FICO, H2O.ai, IBM, KNIME, Microsoft, Quest Software (Statistica), RapidMiner, Salford Systems (now MiniTab), SAP, and SAS — and researched, analyzed, and scored them. The report also includes trends in data science and the other options enterprises have to create predictive models.
Forrester clients can access the report to see how each provider measures up in order to make the best choice for their specific needs. If you are not a Forrester client, then you may be able to get a free reprint by visiting the website of one of the vendors mentioned.
Happy model training!