A robust data independent platform

Data is a commodity. What you do with it is the real value and knowing when that data is valuable is priceless.

But it’s what you do with the data that makes a for safe, effective, efficient, and profitable operations.

What are we doing to make sense of this data? What are we doing to help users know where to look? How does our approach to data aggregation, alerting and reporting revolutionize how both government and commercial operations centers conduct business?

We have embarked on a three-step approach to machine generated data value:

1. Alert users to breaking events for known areas of interest

2. Benchmark patterns of activity and alert to abnormalities in that behavior allowing users to look where they would never know where to look

3. Benchmark risk on a local, regional and global scale and alert to significant variance in risk

We recently finalized the initial codebase for a multi-variant alerting framework that allows users to identify areas of interest and set criteria that allows them to automatically be alerted to changes in volume over time, keyword and a combination of both for news, social media and nearly all data sources BlueGlass aggregates. Alerts are available as flags in the platform and as email and text alerts directly to the user. We call these alerts Sparks.

BlueGlass’ global news feeds exceed well over 200,000 reports per day multiplies easily as users enter in their own RSS feeds. Social media posts currently exceed a million per day and will increase by 10-fold in the next month. Even in a 24x7 operations center staffed by trained professionals the amount of content available is inhuman. Said differently, it’s physically not possible for a person to consume and draw relevant insights. A powerful and accurate alerting framework brings that inhuman amount of data down to human scale and our AI-driven report generator brings it all together.

We are not satisfied with solely having frameworks that look for anomalies and keywords based on user defined thresholds and areas. Companies, governments and militaries are rarely surprised where they know to look. Surprise happens where we are not looking. That is the problem that Activity GRiD will solve. Activity GRiD is a global multi-scale activity modeling and alerting capability that we are currently developing. Activity GRiD, which will start as a new component of BlueGlass, will baseline patterns of normalcy over a given geography, Spark alerts to anomalies in that data and identify to users what is happening in those areas allowing them to focus on locations of potential risk or opportunity.

The next phase of development after the Activity GRiD will be the Risk GRiD. In addition to identifying activity anomalies we are hard at work on designing machine learning algorithms that will derive persistent and short-term risk for all locations on the Earth. The model takes into account the aforementioned patterns of behavior from the Activity GRiD, stabilizing and destabilizing events, local and regional sentiment and other human factors and scores every point on the Earth with a rating that is effected by both time and distance.

More on Activity and Risk GRiD in the coming weeks…

What can all of this do to help both government and businesses run operations centers that increase their missions while reducing costs? The answer is simple … It’s about providing information dominance and creating opportunity at machine speed

Over the course of the past 20 years I have visited and worked in operations centers both big and small; Federal government operation centers charged with global issues analysis, local government operations centers trying to make sense of the chaos of a natural disaster or a security event, corporate operations centers trying to understand the activities around individual facilities or their global operations. They all have these things in common — lots of screens with lots of data that isn’t connected and more importantly isn’t automated. All requiring lots of people to operate. Most local and global operation centers are limited by increasingly archaic means of media and breaking events. 24x7 news channels on walls, multiple screens with social media feeds, multiple news terminals, and not to mention CCTVs and other proprietary data from internal operations — all needing multiple highly trained officers. Which begs the question, how is this a scalable scenario to cover special events of increasing volume of content? — add more people of course is the typical response. Instead leveraging Sparks and GRiD can help solve this problem.

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