The new year promises abundant potential for advancement across the broad swath of Federal government IT. Industry leaders shared their predictions for 2020 and beyond with MeriTalk, indicating the path to progress will often track uphill, and around plenty of curves. The big Federal IT issues for 2020 proper? How about multi-cloud architecture, artificial intelligence (AI) and machine learning (ML) adoption, 5G mobile and related security implications, and workforce upskilling, just to name a few. Asked to jump two years into the future and identify the biggest Federal IT areas we should have been looking at more closely in 2020, many of the broader security and infrastructure themes run in a similar vein.
Predictions are hard, which is why Nextgov turned to industry leaders with a simple request: Give us your boldest prediction for federal IT. They dove into specific initiatives like the Defense Department's Defense Enterprise Office Solutions contract and the General Service Administration's Enterprise Infrastructure Solutions contract as well as the pros and pitfalls of artificial intelligence. "Technology's (IT's) days as a mere enabler are over: in today's complex and dynamic environment, IT must lead the way. The relationship between technology and mission outcomes has never been stronger. As we move into 2019, we believe technology modernization will be the No. 1 priority for federal government agencies in the year ahead, which is why IT leaders will become business leaders and use their unique understanding of technology's potential and limitations to help their agencies envision the art of the possible."
The latest federal cybersecurity report holds little good news regarding the security posture of government agencies, and experts are not surprised by the findings. The Office of Management and Budget (OMB) and the Department of Homeland Security (DHS) developed the report in accordance with President Donald Trump's cybersecurity executive order issued last year. The report acknowledged the difficulties agencies face in terms of budgeting, maintaining legacy systems and hiring in the face of the cybersecurity skills gap, and it identified 71 of 96 agencies as being either "at risk or high risk." "OMB and DHS also found that federal agencies are not equipped to determine how threat actors seek to gain access to their information. The risk assessments show that the lack of threat information results in ineffective allocations of agencies' limited cyber resources," OMB and DHS wrote in the report.
You might know Moody's Corporation for its credit rating agency, Moody's Investors Service, trusted by bond investors for opinions on credit risk. But the company, founded way back in 1909, grew ever-larger, and in 2007, Moody's Analytics was established to focus on non-rating activities – including economic research, consulting services and software development. Today, the employees of Moody's Analytics include a large number of machine learning and deep learning experts. To find out more about the business, we quizzed one such expert, Ashit Talukder, head of machine learning at Moody's Analytics, who previously spent 12 years at NASA's Jet Propulsion Laboratory and served as the CTO of the US Department of Labor. Q: Most people know Moody's as a credit rating agency.
Federal agencies are looking to gain actionable intelligence and information from disparate data sources in a secure, scalable, and efficient manner. An emerging technology known as a big data fabric could provide those agencies with a unified platform "that accelerates insights by automating ingestion, curation, discovery, preparation, and integration from data silos," according to Forrester Research. Forrester recently identified the 15 most significant big data fabric vendors in "The Forrester Wave: Big Data Fabric, Q2 2018" by Noel Yuhanna and other Forrester analysts. The big data fabric market is growing because more enterprise architects see big data fabric as a way to increase agility and minimize the complexity that Federal agencies are struggling with today. The leaders in this field support a broad set of use cases, enhanced artificial intelligence and machine learning capabilities, and scalability features.