When it comes to grant management and administration, data is largely fragmented.
Federal grants are siloed by agency with no systems in place to standardize data and spending information. Whereas, on the recipient side, many organizations lack the processes to truly sync performance data across departments and implementation partners.
The result is inefficiencies, unnecessary expenses, a lack of transparency and a greater chance for error. The DATA Act's passage remedies this, and will lead to a complete overhaul of grant reporting nationwide.
In this post, we discuss how data can be more effectively consolidated, leveraged and managed to help both nonprofits and funders better reach their goals.
Government-Wide Data Standards
Right now, the government lacks consistent data standards for financial, assistance and procurement information. This makes it difficult, if not impossible, to compare spending and performance across agencies as each reports metrics differently.
The introduction of data standards, a pivotal piece of the DATA Act, will rectify current challenges by introducing unique award identifiers (UAIDs) and markup languages to be used government-wide.
Standard UAIDs and markup languages would make data searchable, allowing policymakers and citizens to truly track the money, as well as enable big data analytics to better identify cases of fund misuse.
(Note: Machine-generated identifiers for items like agency code, award type and fiscal year would further improve grant-reporting standardization.)
Internal Data Management
A step in the right direction, government-wide grant standards will have a trickle-down effect on grant recipients. An organized approach to internal data management is needed to efficiently pull and report grant performance data in the proper formats.
To simplify compliance, grant managers should plan their internal processes around these required data standards, and maintain consistent naming conventions and filing structures organization-wide.
In doing so, we have found grant management software to be extremely helpful in centralizing all grant management information (i.e. activities, tasks, data and supporting documentation), and automating traditionally manual tasks.
Data Collection and Reporting Automation
Open, standard data makes compliance automation a greater reality. For example, by pulling from USASpending.gov and the System for Award Management (SAM), fields such as agency information, funding amount, project type, award data, and recipient name and address, can be pre-populated into reports.
With the right technology in place, reports such as budget, performance, time and effort, and outcome evaluations can also be automated. This saves the grant manager time when reporting back to funders.
Machine-readable data is that which can be easily read, parsed and understood by computers. Historically, award data has been submitted via a variety of formats, including word processing documents, paper forms, PDFs and spreadsheets. While these files are easy for humans to read, they lack the structural elements needed for computers to easily aggregate, verify and analyze the data.
Presenting data in a machine-readable way using extensible markup language (commonly referred to as “XML”) filing would reduce the amount of human effort required and further automate much of the reporting process.
In the Grants Reporting and Information Pilot (GRIP), our grant management software proved successful at generating bulk or batch XML filings of award data. Information was pulled directly from grant recipients’ existing management systems, and multiple reports were submitted via one XML file transfer.
While legacy systems are the largest hurdle in the race to machine-readable data, the technology exists today to streamline the way data is shared and reported, and some predict widespread implementation in the next five years.
For more on federal reporting changes and their impact on grant data, read our white paper, The Changing Landscape of Grant Reporting.
How does your organization consolidate, leverage and manage data for maximum effectiveness? Share your experiences below.
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