Donor Data Management Dictionary

Donor Data Management Dictionary

Every nonprofit needs a data management plan, especially for their donor data. The plan needs to address a variety of topics, such as data hygiene and enrichment, data usage in various communications activities, input/output management, and more. To help with your donor data management planning, we have created a Donor Data Management Dictionary to help you make sense of all the data jargon as it applies to the nonprofit organization’s business needs.

Is more data jargon giving you a headache? Send us a note and we’ll add it to the dictionary. Please consider bookmarking the Donor Data Management Dictionary. Below is a selection of terms you will find there.

Big data – [big DAY-tuh], noun
Refers to data sets so large and complex that traditional database tools are not able to manage or process the data. Big data has become a topic of interest due to the large quantities of data (and meta data) now captured, stored, bought and sold as a result of the digital age in which we live, especially via eCommerce and social media. Growth in big data and its related needs/problems has fueled growth in the discipline of data science. Data brokers rely upon big data warehouses to store, manage and transfer data sets that they sell to customers such as nonprofit organizations looking to append their donor databases.

Cleaning / cleansing – [KLEE-ning] / [KLEN-zing], noun
The process of identifying, correcting, and/or removing corrupt, inaccurate or outdated records from a database. For example, de-duplication, the process of removing or consolidating duplicate records from a database, is a common data cleaning technique. Data cleaning tools range from simple duplicate record look-ups in CRM software to third-party applications designed for database management to custom software programming designed to address the specific needs of a unique data set. Also referred to as data hygiene, data cleaning is recommended as part of a larger donor data management strategy for nonprofits in order to maintain a higher quality of donor data over time. Maintaining the quality or “cleanliness” of nonprofit donor data is a challenge for any nonprofit due to the many factors contributing to the degradation of the data, from poor quality control on data input to the frequency with which all consumer data changes.

Data quality – [DAY-tuh KWOL-i-tee], noun
Refers to the accuracy of data and effectiveness of using data for its intended purposes in business operations, including decision-making and planning. Donor data quality is critically important to the success of nonprofit fundraising and communications. The standards for high quality data include: accuracy, completeness, consistency, uniformity and validity. Data quality best practices for a nonprofit include: developing a data management plan, setting a data quality budget, assigning clear responsibility for data quality to specific individuals in the organization, controlling data inputs, training data managers, mapping data inputs against specific purposes for that data, and CRM software selection.

Data science – [DAY-tuh SAHY-uhns], nouns
The study of extracting knowledge from data. Data science incorporates techniques and theories from a variety of disciplines including mathematics, statistics, computer science, data engineering, and system modeling. The goal of data science is to make it easier for other people, such as decision makers, to learn from, and take action as a result of, ever larger sets of data. Big data and data warehousing companies typically rely on the expertise of data scientists to manage and improve their products and services.

Degradation – [deg-ruh-DAY-shuhn], noun
Refers to the worsening of data quality over time. With assets like a donor database, degradation is inevitable. Why? Because of the many, sometimes unavoidable, negative influences acting on your data quality. These include: consumer data naturally changes as people change jobs, relocate, have families, and go through the normal cycles of life; data inputs are often flawed and/or manual, and the manual labor can be poorly trained; related data is changed, purged or updated; data migration to new systems such as CRM software.

Parse – [pahrs], verb
To analyze a field of data (i.e. string of characters) in order to associate the data into groups, typically for the purpose of separating one field of data into many. In donor data migrations, for example, a miscellaneous text field in a legacy system may have been used to store multiple types of data, which need to be identified and stored in separate fields in the new system.

*This article is intended to be a quick reference guide to introduce readers to a variety of data concepts and key terms. Many of the definitions in this article are found in, or are closely related to, reference sources readily available on the Internet, such as www.wikipedia.org and www.reference.com.

 

About Gary Carr

Gary is the founder and president of Third Sector Labs. With more than 20 years of experience delivering software and data solutions to a wide variety of clients, Gary turned his attention to the overwhelming problem of data. Third Sector Labs is committed to making sense of data for the nonprofit industry.

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