As 2018 comes to an end, we are taking some time to reflect on the trends and milestones throughout the year. Government agencies are quickly realizing that data is at the core of the IT mission. From managing data, protecting data, analyzing data, and leveraging it for outcomes, it has a significant impact that can’t be ignored. As vast amounts of data are being collected, technology solutions are emerging to analyze the data for actionable intelligence, with artificial intelligence (AI) at the forefront.
We asked Charles Fullwood, Senior Director, Sales Engineering at Force3 for his reflections on the year, and he told us, “2018 was the year of the infatuation with artificial intelligence and machine learning. While no one knows what it is exactly, everyone wants to talk about it.”
Fullwood, sat down with us and shared his thoughts on the three major trends and milestones that he sees from 2018. Here is what he had to say:
A Move from the Centralized Data Model to the Edge
In the past, data was collected by agencies from many different sources, and business intelligence tools were used to analyze the data, creating even more data that was stored in a centralized location, Fullwood explained. This brought the evolution of the data warehouse, where data is stored in a centralized repository. According to Fullwood, he now sees a big shift in focus on moving away from the central repository and today, he is finding agencies are starting to gather data at the edge. Instead of analyzing data from the centralized location, he is finding that agencies are performing advanced analytics on the data from edge devices and gathering the intelligence before consolidating it. “The reason for the change is that everyone from the federal government is doing analytics from the data. And it’s getting so large that the centralized data warehouse is no longer realistic,” Fullwood said.
Rethinking Data in the Cloud
The journey to the cloud and the increase of cloud adoption has been evident in 2018. As more agencies have implemented cloud projects and have moved workloads to the cloud, they have quickly realized that they need to rethink their data and data architecture. Fullwood, points to the trend of understanding the data linage. “What I mean by this is because data is coming from so many places – network and other sources – it is the concept of having assurance and knowing the source of the data and where it is coming from.”
Agencies also need to think about data governance and data sharing differently. “Cloud has created complexities that has us rethinking how we manage, share, and govern our data” he told us. Now that agencies are getting more comfortable in the cloud, Fullwood sees that data analytics taking place in the cloud has been the latest movement.
A Push for AI
In 2018, there has been a lot of advanced analytics projects that have started, with a big focus on data science as a new discipline, according to Fullwood. “Now we are in a machine learning era with a drive to have better precise models, which has led to new data science roles.” Using open source tools, the ability to create models that can be operationalized has taken time, but we are starting to get there.
One example of this is how agencies are using data scientists to create better predictive models in a security role. Fullwood says that many agencies are using data models in the area of security to help predict the next threat. While this trend will continue, there is now a growing demand for data scientists who can analyze the data and help create these predictive models. This growing reliance on precise data models will result in a greater demand for data architecture and scientist skills.
The Year of Data
Every agency out there is gathering data and becoming more precise with the data they are collecting. They are looking to see who is on their network, what they are doing on the network, and using analytics tools to predict future security threats. “It has been a year where every agency is trying to do more with the security data they have today,” Fullwood stated. And with AI and machine learning as disrupting technologies for 2018, Fullwood sees the trend for more precise data requirements only growing in 2019.