For the past 10 years, the most successful business applications of machine learning have been quite ethereal: online advertising (e.g. retargeting, website optimization), high-frequency trading, yield management...
Machine learning has often disapointed ventures in the gritty world of operations:
We have come to understand that for hardcore business challenges, machine learning works best when integrated into a larger continuous intelligence solution.
Here is Gartner's definition of continuous intelligence:
Continuous intelligence combines data and analytics with transactional business processes and other real-time interactions. It leverages augmented analytics, event stream processing, optimization, business rule management and machine learning.
Could that be the next big thing?
This page provides an introduction to the four main features of continuous intelligence, and links to related third-party resources.
Streaming data (or event streams, or time series) is the raw material of continuous intelligence: data that accumulates over time, one data point at a time.
High frequency is a possibility, but not an obligation. From an algorithmic and decision management perspective, daily sales data or weekly inventory data are the same as instant sensor data.
What matters is:
Collecting, storing, preprocessing and modeling streaming data is very specific. The following resources clarify these specificities.
When building continuous intelligence solutions, the single most important step is the definition of the target: what do we want to understand? which outcome do we want to affect? are there one or many data streams matching that definition?
But a key feature of continuous intelligence is that understanding the target will require processing other data streams for context. For example:
This combination of target and contextual data has three notable consequences:
Resources on machine learning and data selection:
Continuous intelligence places a unique burden on data analysis, for two reasons:
Continuous intelligence is decision-oriented: it is about helping managers select the right course of action when multiple factors and potentially conflicting goals are involved.
An important consequence is that continuous intelligence solutions require the combination of many types of models: