Data Decisioning
Blog: Decision Management Community
Data Decisioning was recently founded by Peter Schooff and John Morris, two veterans with decades of success in enterprise technology. Why? “What’s more important than data to today’s enterprise? The decisions a company makes based on that data will determine their success in the marketplace. In fact, improving decisioning is one of the central arguments for implementing a big data or AI solution in the first place. Our goal at Data Decisioning is to help make sense of the massive volumes of data that threaten to overwhelm companies every day.” Their website provides good resources for DM practitioners. Link Read for example this new article about “Lowered Cost Of Prediction“. Here are a few quotes:
“If AI-based predictions are much less expensive, why should we care? Just because we “can” doesn’t mean we “should”. The wonderful thing about less expensive predictions is that business is brimming over with use cases and business cases for prediction. In fact, this is a basic premise of the Data Decisioning website. Business is all about decisioning. And this is where your business savvy and business experience come in. Because opportunities for applying AI-based prediction technology won’t find themselves. And AI certainly won’t find it for you. It’s your opportunity!”
“How does one actually use AI in business? We know AI can help us with predictions. And we know that there’s a data life-cycle, whereby we acquire data, wrangle and store it, and then use our deep learning AI tools to find the patterns we seek…
The goal of your AI business program is to figure out how to manufacture prediction artefacts. A prediction artefact is an executable prediction model, an model instantiated in software. The model takes data inputs and then predicts various system states, to which the organization will schedule the right responses. In terms of frequency, we build AI models “periodically” — but we likely use prediction artefacts “every day”! We started with an AI deep learning opportunity in our work process, and then we built a predictive tool for that point — where the predictive tool is a manufactured artefact of our AI program. As part of building this artefact, we trained our AI on data. And then once trained and fine-tuned, we are ready to deploy our new capability!”
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