Do you have technical questions or need support for your Qognify installation?
June 4, 2018
JAN TERJE BY, GUEST BLOGGER, RACOM // JUNE 04, 2018
Data alone is just that – data. But when structured, it creates knowledge. It provides a higher-level view beyond just the here and now. Where once we used data mostly to understand what has already happened, we can now use to not only predict what might happen but also how we can prevent it.
Risk is defined as the potential of losing or gaining something of value. With today’s technology’s ability to capture, correlate and structure massive amounts of data, organizations can know what might happen and the variables that may impact that event. It’s risk management at its highest level. And that’s why the utilization of big data has had such a transformative impact on security and operations. These are both areas whose focus is on smooth operations and ensuring business continuity through the elimination or mitigation of risk.
Understanding risk can dramatically impact the way you prepare and address it. For example, in a Smart City Solution, sensors can identify traffic congestion, that’s called Descriptive Analytics. All of the data surrounding the congestion and many others in the area can be captured and analyzed, and this is what we call Diagnostic Analytics. It tells us why or how something happened and allows us to identify indicators that could lead to similar events in the future.
A traffic congestion, a thing of the past?
Photo by Jeremy Yap on Unsplash
With this knowledge, data can be used to predict events. Predictive Analytics identifies potential probable events based on current conditions or indicators. If you can predict an event based on a deep understanding of the risks involved, then you extract Prescriptive Analytics. They let you know what you need to do in order to deter an event from happening by changing the conditions. Continuing with the traffic analogy, Prescriptive Analytics would advise traffic control that by diverting traffic at a particular time and place, probable congestion and accidents could be averted.
While the above example increases safety for citizens, data and analytics are highly effective in improving security by reducing risk. Let’s say a bank has been hit by a string of ATM robberies. The bank can use analytics to establish patterns or indicators based on time, severity, location, and methods. Once indicators are identified, the bank can deploy precise security measures, such as increased patrols during particular times or when certain indicators happen in efforts to apprehend the thieves.
This sequence of understanding can be applied to almost any scenario. At airports, analytics can identify gathering crowds that lead to delays, dissatisfaction and potential security risk and why and how they occur. If certain times of the day, week, weather conditions and shift changes are indicators of impending congestions, when these indicators converge, airport management can be alerted to take prescriptive or mitigating action.
In the same way that analytics are used by organizations to increase security or performance, they can be used to improve operations. With data coming from both internal and external sources, organizations can use it to ensure smooth operations and business continuity.
As we know, weather can cause massive disruption. Traffic is another disruptor. Combine the two and organizations can face being slowed down or even stopped by environmental conditions and staff’s inability to get to work. The impact of these conditions can be mitigated with advanced data analytics. For essential organizations such as rail, airport or critical infrastructure, understanding the risk based on external and internal data allows them to take prescriptive action based on predictive analytics. That might mean changing schedules, routes or ensuring staff availability by housing them on site in advance of the event.
Maintenance is sometimes a struggle for large organizations.
Photo by Michael Weidner on Unsplash
Data is what powers knowledge and insight. It is what allows us to improve overall conditions and better respond to singular events. The biggest obstacle to leveraging data no longer exists. We now have the computing power and technology to extract its value in the form of informational, descriptive, predictive and finally prescriptive analytics. And these contain the knowledge to save lives, costs, time and resources.