The public sector has been a big IT spender in recent years, but a lot of that investment went to on-premises big data infrastructure. It’s time for an update, says one expert.
Government agencies — whether at the federal, state or local level — are big spenders when it comes to IT. But not everyone thinks these organizations spend efficiently.
“If the government doesn’t rethink how it’s going to manage and maintain what it has, it’s going to continue to spend more than it needs to,” said Ashwini Chharia, senior director of public sector services at Tokyo-based consulting and outsourcing firm NTT Data Corp.
Efficient use of data analytics infrastructure needs to be at the center of everything public sector agencies do to improve efficiency, Chharia said. And in some cases they are taking this approach. The federal government, for example, has scaled back IT spending in recent years, opting mainly forcloud-hosted data infrastructure systems that carry less up-front capital expense and ongoing maintenance costs.
Some cities, too, have made targeted investments in analytics. For example, the city of Boston has implemented several analytics-centric projects to do things like make street repairs more efficient and improve emergency response times. Much of the infrastructure is built around API connections to prebuilt services and partnerships with private companies.
But not every public sector entity is as progressive when it comes to implementing data analytics infrastructure. A June 2016 forecast from Gartner predicted that government IT spending would remain flat for the remainder of this year, including on analytics and data infrastructure tools — and this is after spending fell by 5.2% in 2015.
Chharia said the last decade or so saw public sector agencies make massive investment in on-premises technology that comes with huge management costs. Additionally, agencies often don’t get as much value out of these as they could because they don’t have the staff resources. This has led to a pullback in new spending, locking some agencies into situations where they pay too much to just sit on potentially valuable data.
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