JEDI will ease DOD’s FITARA woes, but when?
The latest FITARA scorecard released by the House Oversight Committee criticizes the Department of Defense yet again.
Of the eight categories covered in the report, DOD’s letter grade was an “F” in five of them. Citing data reported to Congress and multiple GAO reports, the Committee took DOD to task for failing to break up IT projects into manageable chunks, identify duplicative IT spending, optimize software licensing agreements, and move away from costly legacy IT investments.
DOD’s FITARA scorecard
In a response to the committee, DOD CIO Dana Deasy noted the challenge of addressing the complicated thicket of IT spending in an agency as large as DOD. He was able to foreshadow progress in one particular area, however – data center consolidation.
The Data Center Consolidation Initiative (DCOI) was created in 2014 as a way of trimming down the out-of-control costs of data centers throughout the Federal government. Since the program’s creation, DOD has closed 834 data centers, with an additional 1,112 closures planned for the current fiscal year.
This massive shift in resources begs the question – how will DOD meet its computing needs as all of these data centers are eliminated? Enter the JEDI program, which is designed to move a significant amount of computing power away from both data centers and private clouds and into a COTS cloud offering. The financial advantages of this approach are clear – JEDI will finally allow DOD to pay for compute based on what it actually uses instead of its perceived capacity requirements.
Key takeawaysThis key takeaway was generated through LLMs crawling the page and coming up with an overview of the content.The article reviews the House Oversight Committee’s latest FITARA scorecard that sharply criticizes the Department of Defense for failing in multiple IT management areas, receiving failing grades in five of eight categories. It highlights operational problems—poor project decomposition, unidentified duplicate spending, suboptimal software licensing, and reliance on legacy IT—that drive excessive costs and inefficiency across DOD. The piece notes DOD progress on data center consolidation (834 closed, 1,112 planned) and describes the JEDI cloud program as a strategic shift to commercial off-the-shelf cloud services to buy compute based on consumption rather than capacity, reducing cost and legacy dependence.
What are the main failures identified by the FITARA scorecard for the Department of Defense?
The FITARA scorecard criticizes DOD for failing to break up IT projects into manageable components, which complicates oversight and delivery; not identifying and eliminating duplicative IT spending across the department; failing to optimize software licensing agreements to reduce costs; and continuing costly investments in legacy IT environments. These shortcomings resulted in DOD receiving an “F” in five of the eight evaluated categories, according to data reported to Congress and multiple GAO reports cited by the House Oversight Committee.
How has DOD progressed on data center consolidation and what operational question does that raise?
Since the Data Center Consolidation Initiative (DCOI) began in 2014, DOD has closed 834 data centers and has plans to close an additional 1,112 in the current fiscal year. While consolidation reduces the overhead and operating costs of maintaining many distributed facilities, it raises a critical operational question: how will DOD fulfill its computing and processing needs as those physical data centers are eliminated? The article frames this as a key challenge driving DOD’s cloud procurement strategy.
What role does the JEDI program play in DOD’s IT modernization and cost management?
The JEDI program is presented as DOD’s approach to shifting significant compute capacity away from both its remaining data centers and private clouds into a commercial off-the-shelf (COTS) cloud offering. The intended operational and financial benefit is to enable DOD to purchase computing resources based on actual consumption rather than estimated or perceived capacity requirements, thereby reducing wasteful spend tied to legacy capacity planning and enabling more efficient, scalable access to compute resources.
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