Data is digital gold for the government and companies. The ability to distill vast amounts of data for uses like artificial intelligence, machine learning and analytics will define how government and industry evolve.

Actually harnessing data for those purposes is easier said than done. The government has massive amounts of data in multitudes of forms and repositories.

The Defense Department and other agencies are relying on private companies to make that data organized and usable for use.

“There’s tons of silos,” Nima Negahban, co-founder and chief technology officer at Kinetica, said during the discussion “Active Analytics for Government and Public Sector,” sponsored by Kinetica. “They aren’t going to go away, but being able to consume from those silos and correlate is what active analytics is all about.”

It’s not only about translating old data, it’s also about analyzing new data as it comes in.

Companies are helping the government go through data in real time to respond to national defense needs, emergency situations, and other mission critical challenges.

“For organizations that have a massive universe of data, they need to have the active analytics capability so that they can get visibility into their organization and the problems that they may be facing in real time. They need to be able to flexibly ask questions at the speed of thought,” says Nima.

With the sheer volume of data at the government’s disposal, it’s no longer enough to rely on static dashboards and historical results from business intelligence applications and traditional analytics platforms. New, dynamic solutions like active analytics give agencies the ability to build dynamic, data-powered applications that deliver real time intelligence in order to enhance operations and decision making.

Forward looking organizations are also looking to leverage the power of AI to filter through massive datasets and enhance the abilities and efficiency of human analysts. Advanced analytics solutions can help organizations to operationalize their models and move them from science experiments to deployments in the field.

“If you’re a data scientist you need to be able to comb through massive amounts of data to potentially identify those features that you’re going to use to generate a model. It’s the most fundamental part of the AI workflow because so much of the rest of it has been automated. That is the last remaining art form of the data scientist. That’s where they really show off their capabilities and that’s what Kinetica makes so easy for them,” says Nima.

Listen to the full interview:

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