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18 Data Terms Your B2B Sales and Marketing Teams Should Know

Posted June 14, 2021

18 Data Terms Your B2B Sales and Marketing Teams Should Know

Posted June 14, 2021

The typical vocabulary of data-driven revenue teams comes with a long list of data terms. Some you can understand easily; some meanings are harder to grasp. For a convenient reference, we created a quick glossary of data terms that may save you some time.

Of course, this isn’t a complete list of data terms. Please contribute your favorites and suggestions in the comment section.

How many of these B2B data terms do you know?

Aggregation: A process of searching, gathering, and presenting data.

Algorithm: A mathematical formula installed in software to perform analysis on data sets.

Automatic identification and capture (AIDC): The process of automatically identifying, collecting and storing data in a computer system.

Big Data: A term applied to larger and more complex data sets that traditional databases with low latency can’t handle.

Bidstream Data: The data passed with a bid request is called bidstream data. It originates from the publisher website or app and includes basic facts about ad units such as URL, device type, IP address and ad format.

B2B Intent Data: Data generated by B2B buyers when they do online research. In other words, it’s the collection of behavioral signals about a web user that provides insights into B2B buyers’ interests.

Data Lake: This is a centralized repository where you can store all your structured and unstructured data in raw format at any scale.

Data Normalization: It’s a process wherein data within a database is recognized in such a way that you can exploit that database for future queries and analysis.

Data Cleansing: The process of identifying and fixing inaccurate, corrupted, wrongly formatted, duplicate and incomplete data within a dataset is called data cleansing.

Data Minimization: It’s the concept of collecting, using, disclosing and storing minimal data to fulfill a specific purpose.

Data Mining: The method of finding anomalies, patterns, and correlations within large datasets to determine outcomes is called data mining.

Data Validation: Data validation is the process of validating the accuracy, clarity and details of data before you use, import or process it in business. It’s a critical step in data workflow to mitigate any project defects.

Database Append: It’s the process of matching your existing contact database against a third-party vendor’s database to fill any gaps or missing information.

First-Party Data: The information you collect about customers, prospects or visitors from your own digital properties like website, email, CRM and social media.

Standalone Publisher Data: It is related to the data collected exclusively from a publisher’s own portfolio of web channels.

Second Party Data: Intent data that you buy from other companies is called second-party data. It includes information on buyers’ behavior, action and demographics.

Third-Party Data: The data gathered from all digital touchpoints outside your company, like websites, content platforms, and social media channels, is called third-party data.

Zero-Party Data: This is the information that buyers intentionally or proactively share with your organization. It includes buying intentions, personal context, preference center data, and how prospects want the brand to recognize them.

To learn more about B2B data, its different applications, and privacy, try these articles:
Intent Data Use Cases: Try These 10 Popular Applications for Intent