Donor Data Management Dictionary

This donor data management dictionary is intended to be a quick reference guide to introduce you to a variety of concepts and key terms as they apply to data, and more specifically, to donor data management. Many of the definitions in this article are found in, or are closely related to, reference sources readily available on the Internet, such as and

Analysis vs analytics – [uh-NAL-uh-sis] vs [an-uh-LIT-iks], nouns
These terms are often used interchangeably, but they are not synonyms. Analysis is the process of breaking down something complex into its simple forms or parts. Analytics is the process of discovering meaningful information and patterns from data. Think of analysis as the key to problem solving, while analytics is the key to predictive models and decision making. For the nonprofit, applying analytics to a donor database would help predict future fundraising results or identify donors with the capacity or interest to give more.

Append – [uh-PEND], verb
To increase the quality of your donor records by matching existing data against a larger database in order to obtain desired additional fields of data. Nonprofits often use third party data brokers who specialize in consumer big data to enrich their existing databases before launching a major fundraising campaign or annual appeal.

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.


Cloud data storage concept

Central data repository – [SEN-truhl DAY-tuh ri-POZ-i-tawr-ee], noun
A database storing data from more than one system. See data warehouse.

Cloud storage – [kloud STOHR-ij], noun
A model data management in which data is saved, retained and managed remotely over a network of computers physically located apart from the business that owns the data. The term “cloud” has been used for at least two decades to describe this model because the customers don’t really understand everything about the networks and computers on which their data is stored. If your CRM is online, odds are your donor data is “in the cloud”.

Contact database vs CRM – [KON-takt DAY-tuh-bays] vs [SEE-ahr-em], nouns
A contact database is very much what it sounds like, a simple database of contact information that can be used for communications such as direct mail, newsletters, emails and more. CRM, or Customer Relationship Management, is a software application that enables the management of the customer relationship. CRM software includes a database of contact information, and much more, such as donation history, event participation, scheduling of meetings and activities, notes, additional membership information, etc. Smaller nonprofit organizations traditionally use contact databases built from commercial database software like Microsoft Access, which over time becomes customized to evolve into more of a donor database system. Eventually, many small nonprofits “graduate” to a CRM software solution.

Most CRM software today is referred to as SaaS software and utilizes a cloud storage model (see terms below).

Data broker – [DAY-tuh BROH-ker], noun
A professional or organization that aggregates, analyzes and sells data to clients. Common uses of this type of information include market research, sales activity, and fundraising campaigns. Data brokers aggregate large amounts of data into data warehouses (see big data), where the data is stored, organized, mined, analyzed and sold. Data brokering is a multi-billion dollar industry that may become highly regulated in the future due to consumer privacy concerns.

Data cleaning – [DAY-tuh KLEE-ning], 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 conversion – [DAY-tuh kon-VUR-zhuhn], noun
The process of changing data from one format to another. Many events can require a data conversion, such as a software system upgrade, a special program that alters the data as part of a system integration project, or the export of data from one system and import into another. The latter is also known as a data migration.

Data degradation – [DAY-tuh 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.

Data enrichment – [DAY-tuh en-RICH-muhnt], noun
The practice of enhancing or refining the value of information assets, such a donor database. Examples of data enrichment include: data cleaning to remove corrupt or out of date records; appending existing records with new data; and associating records previously not associated. Think of data enrichment as the opposite of data degradation – either your donor data is getting better, or it’s getting worse. For a more detailed discussion about data enrichment, see Donor data enrichment, what is it?.

Data governance – [DAY-tuh GUHV-er-nuhns], noun
A set of rules or policies that encompass the people, processes and technologies required to create and maintain higher quality data assets for an organization. Data governance goals resulting from higher data quality include: better compliance with third party standards, decreased risk of regulatory violations, improved decision making, improved data and organizational security, and greater profitability.

Data hygiene – [DAY-tuh HAHY-jeen], noun
See cleaning.

Data map / mapping – [DAY-tuh map] / [DAY-tuh MAP-ing], verb or noun
Associating data elements or fields between two models. Or, a diagram that associates data elements or fields between two models. For example, in a data migration project from an old contact database to a new CRM system, the engineer should “map” data fields from the old database to the data fields in the new CRM’s database. In developing a data management plan for a nonprofit, the consultant should map the nonprofit’s strategic business needs (e.g. fundraising) against the donor data fields available in the nonprofit’s donor database in order to determine data gaps that should be filled through a data acquisition strategy.

Data migration – [DAY-tuh mahy-GRAY-shuhn], noun
The process of transferring data between data storage types, such as databases. The most common data migration tasks for nonprofits is from one CRM system to another. The more automated or programmed a data migration tasks can be, the more successful the results. Data mapping is an integral step of any data migration.

Data mining – [DAY-tuh MAHY-ning], noun
The process of discovering relationships and patterns in large data sets, then extracting information and presenting it in an understandable structure for further use. Data mining is typically conducted by data scientists applying custom queries across large databases (data warehouses). As an example, a nonprofit might benefit from the building of a data warehouse that combines their donor data with social media data and third party consumer data in order to mine – discover – relationships or donor interests not previously known.

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.

data visualization 2

Open Security Foundation visualization of 2010 data breeches

Data visualization – [DAY-tuh VIZH-oo-uhl-i-zahy-shun], noun
The creation of images and visual patterns representing data, and the study of such creations. The goal of data visualization is to communicate information clearly, simply, and effectively through images. Examples of data visualization techniques include bar and pie charts, stream graphs, treemaps, Gantt charts, and scatter plots.

Data warehouse / data mart – [DAY-tuh WAIR-hous] / [DAY-tuh mahrt], noun
A data warehouse is a large database used for data analysis and reporting. It is a repository of data created by one or more sources (operational systems). A data mart is a small data warehouse supporting a particular set/type of data. Data warehouses are typically at the base of any good analytics software system. Larger nonprofits often use custom-designed data warehouses to store large amounts of data from disparate systems (e.g., CRM database, third party event system, social media data) in an effort to perform data modeling (e.g. building better donor profiles to target potential donors more effectively).

Donor database – [DOH-ner DAY-tuh-bays], noun
A contact management database that includes donation history and other related fields. CRM software will include a donor database as part of a more robust, feature rich system.

Donor persona – [DOH-ner per-SOH-nuh], noun
A representation or example of a typical donor or constituent to your nonprofit organization. The persona is based on patterned behaviors and qualities as documented in your donor data. Donor personas are created through the analytics of segmentation – breaking down donors into two or more groups based on similar behaviors, qualities or characteristics.

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.

SaaS – [sass], noun
SaaS stands for “Software as a Service”. SaaS is a software licensing and delivery model in which software is obtained on a subscription basis, is hosted in a data center independent of the subscriber, and is accessed remotely over the Internet. For example, most CRM software today is provided by means of a SaaS model. SaaS products are examples of software and data that depend upon “cloud storage” (see term above).

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