Models of enterprise intelligence
Published: May 27, 2019
We can see how Continuous Intelligence can work in practice. For example, consider a hospital using machine intelligence to detect and reduce the rate of patient sepsis (a contraction of infection during the hospital stay). According to Healthcare Business & Technology, patients too often contract an infection during their hospital stay and may ultimately die from sepsis rather than from the primary reason for their admission. Early detection of sepsis is essential to its effective treatment. Patient data such as vital biometrics, interactions with doctors and staff, and feeding schedules are collected by medical monitoring and other hospital systems. These data are captured in the electronic medical record (EMR) and hospital operational systems and used to alert healthcare staff to problems. Doctors periodically visit patients, review these data, and make decisions about altering treatment plans. Additionally, using historical data collected from many patients, the hospital’s data scientists have developed predictive models to monitor these data and trigger alerts when the model detects a likelihood of infection. These alerts demand immediate action by nurses, doctors, and medical staff. What’s more, the capture of new data elements enables caregivers to determine whether the infection is being treated effectively.
In this scenario, it’s critical that captured data are quickly processed into digestible information, which predictive models can consume to produce insight about patients. Doctors must quickly make decisions about patient treatment and corresponding actions must be taken by caregivers. The models and hypotheses informing the insight and decisions about patient treatment should be continuously reviewed as often as needed to iterate the processes in order to ensure the best possible clinical decision-making. This requires changes in business processes, organizational collaboration, technical practices, and investment in supporting technical infrastructure. Organizations that embrace these changes and investments are the epitome of “the intelligent enterprise” and stand to become the disrupting forces in their respective industries.
Unfortunately, in most organizations, this cycle is time-consuming, manually intensive, and laden with friction. But understanding how to improve is essential in becoming an Intelligent Enterprise.
As the model suggests, organizations on the path towards true intelligence can expect to encounter a phase boundary — which we call the “intelligence chasm” — as the gap along each of these dimensions that separates truly intelligent enterprises (the disruptors) from those that are ripe for disruption.
In this series of articles, we’ll explore the enablers we’ve seen in leading companies, for instance, the impact of culture, with respect to using data to drive decisions and actions. Broadly speaking these insight adoption cultures range from:
Most modern organizations fall somewhere in the middle, either: