The value of real-time data in analytics
Analysis is done to support decision making by individuals, organizations, or computer programs. Traditionally, data analysis has been done on batches of data, usually in long-running jobs that occur overnight and that happen periodically at predetermined times: nightly, weekly, quarterly, and so on. This not only limits the scope of actions available to decisions makers, but it is also only providing them with a representation of the past environment. Information is now available seconds after it is produced, so we need to design systems that provide decision makers with the freshest data available to make timely decisions.
The OODA – Observe, Orient, Decide, Act – loop is a decision-making, conceptual framework that describes how decisions are made when reacting to an event. By breaking it down into these four components, we can optimize each to reduce the overall cycle time. The key idea is that if we make better decisions quicker than our opponent, we can outmaneuver them and win. By moving from batch to real-time analytics, we are reducing the observed portion of this cycle.
John Boyd
John Boyd was a USAF colonel and military strategist. He developed the OODA loop to better understand pilot combat operations. It has since been expanded and is used at a more strategic level by the military, sports teams, and businesses.
By reducing the OODA loop cycle time, new actions become available. They can be taken while events are unfolding and not merely responding to them after the event has occurred. These time-critical decisions can range from responding to security log anomalies to providing customer recommendations based on a user's recently viewed items. These actions are extremely valuable because they allow us to quickly respond to changing events and are only possible because we can process the data in near real time. The following diagram, inspired by the Perishable Insights report by Mike Gualtieri, shows how time to action correlates to the data's perishability. Each insight has a corresponding action that can only be taken if the data is processed quickly enough – before the insight perishes:
Figure 1.2 – Perishable insights
The preceding diagram uses shopping as an example to highlight the key distinction between time-critical and historical analysis. Combining historical data and recent data is extremely valuable since it allows deeper insights and can be used to detect patterns and anomalies. The goal of stream analysis is to reduce the amount of time between an event occurring and the appropriate response.
