This enables authorities to make smarter decisions with their data


  • Government agencies often encounter speed limits when adopting data-driven decision models.
  • Instead, they should use an AI-driven analysis system based on a knowledge model to sort data.
  • This helps companies make smarter decisions and achieve scalable results.

The digital transformation is driving new innovations in government. It relies on rich, accurate data, but government agencies often encounter speed bumps in adopting data-driven decision-making models.

This is because integrating data sources and feeding information into analytical systems on a large scale is not enough to become a data-driven agency. Agencies and their data scientists need to make sure they aren’t drowning in a deluge of data they can’t use, said Forrest Hare, developer of cyber operations solutions at SAIC.

“It’s easy to get data overload and decision paralysis when you integrate your data successfully,” he added. Agencies need to find an easy, repeatable, and scalable way to distill the numbers into meaningful knowledge.

“Fast, data-driven decisions require automated meaningfulness,” said Hare. “Without that, you need lengthy, tailor-made analyzes for every decision.”

When they think of automated making of meaning – also known as machine-based understanding – data scientists often turn to artificial intelligence (AI). Its ability to uncover patterns in large amounts of data makes it possible to navigate through a lot of incoming information and pull out nuggets of useful information.

Aligning a neural network to a large amount of data could produce some patterns, but it won’t help make sense of it, Hare said. Without understanding this data, the AI ​​will reveal patterns, but no more. It cannot infer their effects on the real world because it does not understand what they mean. It only sees them as collections of zeros and ones.

The components of a data-driven culture

In order to understand all of this data, we have to combine it with understanding – and a good culture starts with two elements: a knowledge model and a data management.

Our databases contain the assets we want to mine, but our understanding of this data relies on human subject matter experts who have spent years building tacit knowledge in their areas of expertise. In most cases this knowledge stays locked in the head. The good news is that we can unlock its value by extracting it through a knowledge model and presenting it formally.

A knowledge model encodes the accumulated knowledge about a particular area or discipline by recording the elements it contains and their relationship to one another. We need to produce these models in formats that can be read by machines so that they can absorb and use some of this human understanding.

An AI-powered analytics system powered by a knowledge model can infer the patterns it sees. Questions about domain-specific entities such as B. Answer patient readmission rates in hospitals or the reliability of intelligence sources. The AI ​​can use the knowledge model to extract the correct information from the data.

The other important component of a data-driven culture is data management.

“We need data security through effective governance that organizes and cleanses our data,” explains Hare. “That leads to better use of AI and leads to decisions that are not biased.”

How to achieve your data-driven goals

A lot of work is required to arrive at this utopia of machine understanding. It means creating a culture of openness in your agency and promoting the use of open standards and protocols for better integration. This allows you to integrate government data solutions with domain-specific knowledge models to promote better understanding.

This open culture also depends on collaboration between users and technology experts. “Working together enables better results in digital transformation,” said Hare. “The end users of the data know best how they want to use it, and that should drive the structuring of the knowledge models.”

One way to encourage collaboration is to use appropriate data sharing and discussion tools, but these alone are not enough. The buy-in of executives on the operational side is crucial if employees are to work together on the construction and use of knowledge models.

SAIC has its own methodology for creating knowledge models called COSIKnE. It started using this framework to transform the way the elements of the US armed forces view their data. According to Hare, any agency can benefit from incorporating human understanding into their knowledge framework and, through strong collaboration, drive better data usage and governance. On the way to becoming a data-driven agency, it can only be the difference between success and failure.

Learn more about how you can make your agency more data driven.

This post was made by Insider Studios with SAIC.


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