Big data and the OODA Loop

What a military theory teaches us about big data and analytics

By Bryan Harris, Director of Cyber Analytics R&D, SAS

Col. John Boyd, considered one of the greatest military minds of all time, didn’t have much combat experience – just a few missions in Korea. That was enough, however, to produce a breakthrough insight.

“Wondering why the comparatively slow and ponderous American F-86's achieved near total domination of the superior MIG-15's,” the New York Times wrote in Boyd’s 1997 obituary, “he realized that the F-86 had two crucial advantages: better visibility and a faster roll rate.”

That led Colonel Boyd to develop what he called the OODA Loop -- the repeated cycle of observation, orientation, decision and action that characterized every encounter. “The key to victory, he theorized, was not a plane that could climb faster or higher but one that could begin climbing or change course quicker,” the Times wrote.

It turns out that Colonel Boyd’s pioneering theory has great relevancy to today’s data “battlefield.” In recent years, technology advances such as smartphones and social media have given the world exponentially more data to look at, think about and analyze. Organizations struggle with how to find what information is useful.

Analytics can enable businesses to scour data for the useful insights that will help them gain competitive advantage. But traditionally, many organizations have tended to implement analytics from the bottom up – that is, they’ve started with the data and the technology before looking at their goals, defining the questions that must be answered to meet those objectives and the time frame in which they need to meet them. Only with those matters considered and decided on can organizations gain the most value from analytics.

Whether in a military or a business context, the OODA loop is based on the fundamental premise that if an organization can observe, orient, decide and act faster than its competitor, it can shape the environment to increase success. 

The OODA loop puts big data and analytics into a decision-oriented frame of reference that allows organizations to more effectively take advantage of these powerful technologies.

Here’s a look at the OODA loop and how it applies to big data and analytics.

Observe

The decision cycle starts with collecting data about the environment, people, competitors and circumstances. So the Observe step is all about gathering data from relevant sources.  To optimize this step, organizations need a strong data management discipline. Without that, it’s difficult to build sophisticated analytics on top of data.

The data management discipline involves: Inventorying data sources; understanding the semantic meaning of the data fields; mapping out how the data fields support decision-making in the organization; building a data catalog to store definitions of database objects; and cleaning the data.

Heeding the importance of the observation phase ultimately allows a company to more accurately focus resources and make better business decisions.

Orient

All individuals have different lenses through which they understand their environment and experiences. At the orientation phase, individuals apply context to the data collected, which creates situational awareness.

Helpful technologies to create this type of prioritization for data include stream-processing technologies, in-memory analytics and powerful visual analytic capabilities to score data so it can be organized and recalled better based on the organization’s business and workflow.

These technologies handle a number of functions: event-stream processing, which continuously queries data in motion to detect patterns and analyze events in real time; in-memory analytics, which allows complex data exploration, model development and model deployment to be processed in memory and distributed in parallel across a dedicated set of nodes; visual analytics, which leverages in-memory technology to allow identify patterns and relationships in data that weren’t evident before; and visual statistics, which combines predictive analytics with visual data exploration capabilities in a single, interactive environment to run models more quickly.

Decide

Once individuals understand data in context, they can identify courses of action and weigh the potential outcomes of those actions. The Decision step is all about carefully weighing the data gleaned from observations to enable the right decisions.

The first step in using analytics in this manner is to determine the type of questions the organization wants to answer.

Modern analytics technologies can automate cognitive thinking to change the way people make sense of information and make decisions. Rather than being programmed to anticipate every possible answer or action needed to perform a function or set of tasks, human-like analytics can sense, predict and infer. These analytics solutions can also process natural language and unstructured data and learn by experience to help humans make better decisions based on the best available data.

Act

After developing and assessing multiple options, the decision is put into action. All the data in the world is worthless if it can’t be used to accelerate effective action.

Big data and analytics can help compress the decision cycle. By enlisting the OODA loop, companies and governments can put themselves in an offensive rather than a defensive position against their opponents in the marketplace.

Conclusion

Col. John Boyd articulated foundational principles that have been in effect since ancient times – as long as people have been making decisions. They are especially relevant today in a world swimming in data.

OODA teaches us that only by determining which data is of value and applying analytics, organizations can decide and act more quickly. That equals victory in today’s business wars.