Data Standardization as the Catalyst For Artificial Intelligence Accuracy

By Debbie Ferguson, CFRE

As data analytics have become the norm for fund development, artificial intelligence (AI) is beginning to see its heyday. Commonplace for decades in the corporate sector, machine learning and artificial intelligence are finally emerging in the nonprofit world, providing valuable insights which may anticipate donor behavior and readiness leverage your data and allows you to pull data from different sources and systems to convert it into one standard format. This makes it easier for users to analyze the data. Data standardization provides more accurate analytics and reliable reports, as well as moreconsistent data from all your sources to make a gift. Several innovative prospect research providers have developed and released powerful AI tools that deliver these advanced capabilities to both large and small fund development teams. These solutions, however, rely heavily on one common element: access to clean data. Artificial intelligence draws its value on being fed a stream of fresh data, capturing the right datapoints in the right place at the right time. Thus, data standardization drives accuracy for AI tools.

Data standardization is a key process that makes sense of your data by bringing it into a consistent format. Data standardization makes it easier to leverage your data and allows you to pull data from different sources and systems to convert it into one standard format. This makes it easier for users to analyze the data. Data standardization provides more accurate analytics and reliable reports, as well as more consistent data from all your sources.

Artificial intelligence draws its value on being fed a stream of fresh data, capturing the right datapoints in the right place at the right time.

Disparate and fragmented data is common for nonprofits. Several factors contribute to inconsistent data including staff turnover, database conversions or migrations and even simple changes in database architecture. Additionally, some valuable data, such as email open rates or event attendance, might not even reside in your primary database. Aggregating these siloed datapoints with your primary constituent data empowers stronger analytics, including AI. Thus, the impact of clean and consistent data is immeasurable. Conducting a complete database assessment can aid in determining the overall health of your data. Initially running a report of your top 100 donors may reveal incomplete and disorganized data. In addition to checking for the number of records with complete contact information, checking constituent coding and gift histories and reviewing past actions such as types, categories, descriptions and statuses will give you a good idea of the robustness of your current data.

Next, assessing your organization’s general data strategy and maintenance procedures helps identify inconsistencies in areas such as prospect and solicitor coding, duplicate record management, appeals engagement and solicitation history. Once your operations team has initially reviewed all of these areas within your top 100 donors, as well as evaluated your existing policies and procedures, a more comprehensive and broader look will be necessary.

Preparing for the implementation of an AI tool, however, is more than pulling a flat file of demographic data and gift record histories. Precision philanthropy, a term coined by Associate Vice President of Prospect Development at City of Hope, Nathan Fay, requires consistent input, verification and management in addition to accurate, well-structured data. Buy-in for data standardization is necessary at all levels. Leadership must be willing to fully support the resources—both personnel and time—required to make the most of their new technologies. Database administrators will need to find value in the usage of advanced analytics and not feel threatened by artificial intelligence. Frontline gift officers need to believe metric-driven reports accurately demonstrate their work prior to making the effort to consistently document their actions.

Clean data not only powers better reports but can also build trust throughout your entire team. Assembling a data governance committee will further help ensure the continued accuracy of your purchase as well as protect the value of your investment. Having a shared language around data and a collective commitment to preserve the standards will build trust and bridges across teams. These committees work best when the participating team members include stakeholders from leadership, philanthropy operations and frontline gift officers.

Artificial intelligence has the potential to absolutely transform your fundraising capacity. Siloed, inconsistent and fragmented data will prevent AI from working as accurately as possible. It’s time to prepare your database for implementation of AI tools to drive accuracy, maintain the value of your investment and elevate your mission.

Key Terms

• Artificial intelligence (AI): the ability of computers to mimic human thought and perform tasks in real- world environments. AI-enabled programs can analyze and contextualize data to provide information or automatically trigger actions without human interference. Common tools using AI today include smart devices and voice assistants such as Siri®.

• Machine learning (ML): a subset of the broader category of artificial intelligence which refers to the technologies and algorithms that empower systems to identify patterns, make decisions and improve themselves through data. Machine learning is a pathway to artificial intelligence.

• Database architecture: the overall design of a database, including the settings, tables and distribution of datapoints throughout the system.

• Database assessment: the practice of reviewing a database for consistencies and gaps in important datapoints.

• Data governance: the process of managing the availability, usability, integrity and security of a database, based on internal data standards and policies that also control data usage and input.

• Data standardization: the vital process of bringing data into a common format that allows for collaborative research, large-scale analytics and implementation of advanced tools and practices.
  

Accordant White Paper_Data Standardization for AI Accuracy.pdf

Download PDF • 465KB

About the Author: Debbie Ferguson, CFRE, is a Senior Consultant for Philanthropy Operations and Data with Accordant. She specializes in best practices for data solutions, integration and governance as well as patient program evaluation, creation and development. She can be reached at Debbie@AccordantHealth.com or through LinkedIn.