IT officials addressing outstanding technical issues to align with President Trump's data objectives
Oak Ridge National Laboratory, one of the United States' premier research institutions, is facing a significant challenge in the form of legacy systems and data silos. These barriers, according to the lab's principal deputy CIO, Mark Pettit, present a notable hurdle for progress.
Several IT leaders have highlighted bureaucratic inefficiencies, particularly in addressing technical shortcuts or quick fixes that have since become pressing issues. Pettit, likening data to 'the new oil', emphasised its importance for returns on investment and AI capabilities. He also underscored the need to avoid creating new technical debt.
In a similar vein, the Environmental Protection Agency (EPA) is grappling with a siloed mentality that leads to unnormalised data, creating additional work when using AI or similar technologies. The EPA's chief information officer, Carter Farmer, is leading a campaign to break down these data silos, believing that data-first strategies will help the agency more effectively pay down its technical debt.
Farmer views this task as a data-first strategy, involving data in the very beginning of the design process. This approach, he believes, will position the EPA for better tools, faster service, and stronger security.
Meanwhile, the White House's goal in promoting data sharing is to eliminate bureaucratic duplication and inefficiency, while enhancing the Government's ability to detect overpayments and fraud.
Lawrence Livermore National Laboratory is also focusing on reducing technical debt through a series of initiatives. They are building digital twins to accelerate data sharing and AI work. However, dealing with technical debt and breaking down data silos remains an ongoing challenge for the lab.
The efforts to eliminate data silos under the Trump administration were led primarily by officials at the Centers for Medicare and Medicaid Services (CMS). They oversaw data-sharing initiatives with private tech companies to improve access to healthcare data, implicitly dealing with technical debt by promoting interoperability and data integration through new apps and technologies.
At Oak Ridge National Laboratory, Jay Eckles, division director for application development, stated that they don't have a data lake or a data warehouse, but instead have a data landfill. The lab is working on building strategic data stores to manage various forms of data, both structured and unstructured.
The lab is experiencing success in reducing some data silos, with a sense of urgency throughout the administration. However, the task of dealing with legacy data, equally important in terms of contributing to technical debt, remains a significant challenge.
In conclusion, the elimination of data silos and technical debt is a nationwide effort, with numerous institutions taking steps to modernise and improve their data management strategies. This is crucial, as to the degree that data is the rocket fuel that drives artificial intelligence, getting the data house in order is crucial for progress at institutions like Oak Ridge National Laboratory and the EPA.
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