As your company grows, it’s easy to lose track of where you store data. Data silos exist everywhere, and employees have difficulty finding information when they need it. In addition, other issues such as inconsistent definitions, naming conventions, and element values lead to misinterpretation and misuse of data in reporting.
A data dictionary improves data integrity within your organization by supporting consistent terminology and data elements in your systems. Using a data dictionary encourages the trust and reliability of data. It also makes it easier for users to understand data elements, find information, and reuse information in the future.
A data dictionary is one of the first steps towards achieving data integrity. It provides a foundation for your data taxonomy, marketing taxonomy, data transparency, and org-wide data democracy to come to life.
In This Guide
In this guide, you’ll learn how data dictionaries are used, the benefits of using a data dictionary, and how to create one as you unfold enterprise-wide data standards.
What is a
A data dictionary is a centralized repository of information used to catalog and communicate the structure and content of a database or dataset, such as the names of measured variables or descriptions for individually named data objects.
Think of a data dictionary like a road map of information that’s not only a place to keep the information, but also provides context and describes how you’ll store information. A data dictionary shows the purpose of data elements and includes metadata information within the context of a project to guide accepted meanings and representation for each dataset.
A data dictionary is also the foundation of any marketing data taxonomy. It outlines the core business fields, different channel requirements and specifies valid input values for your taxonomy. It is an essential tool that ensures your organization is aligned on taxonomy requirements across various organizational teams.
Typical information in a data dictionary includes:
How are Data Dictionaries Used?
The data dictionary is essential because it provides detailed information about the content of a database.
The primary use cases for data dictionary include:
Define data objects
A data dictionary defines the data objects to each user in the database. It helps users know all the existing objects in the database and who can access them. It’s difficult to remember the names of data in a large database. Rather than racking your brain for this information, you simply need to remember a database object.
For example, you have multiple spreadsheets of user interview transcripts from different research topics. You need to access an interview, but you’re unsure what you’re looking for. The only information you have is the word “invasive.” You’d generate a query on your data dictionary to get all the table names with “invasive” and pick the correct result from the list generated.
The data dictionary outlines and controls access to database objects. Users with the proper access can see specific tables and views in the database while other data assets are masked from view.
The best way to control access to the data dictionary is to adopt a data governance framework. It’s a great way to define how data you store, organize, and use data within your organization. You can use a data governance framework to establish a policy for the following functions:
- How data is organized, stored, and retrieved
- Who has access to data
- Parameters on data usage to protect consumer data, minimize legal risks and comply with regulations
- Categorize levels of sensitivity for accessing data such as classified vs restricted or internal vs public
- Establish a standard to ensure the validity, accuracy and reliability of data so it’s trusted for decision making within your organization
Data dictionaries create a common language between disparate SaaS tools, distributed teams, agencies, partners, creators, and customers. In a post-cookie world of limited identifiers, the measure-and-attribute game levels up three or four notches.
By creating cohesion between varied data at its creation, it becomes a single, unified data set rather than a collection of data silos.
The Most Valuable Benefits of Creating a Data Dictionary
Creating a shared data language throughout your organization unlocks operational improvements that get to the core of your ability to make data-driven decisions.
By creating and implementing your data dictionary, you can expect:
- Better structure and information architecture throughout the database
- Fewer data inconsistencies across projects
- Increased trust in data integrity across all your databases
- Easier navigation, search, and discovery of your data
- The elimination of redundant data through optimal data management
- Clear, reliable documentation and transparency across teams
- Improved data analysis
- More meaningful metadata to keep everyone on the same page about the function of a data set
- More efficient fixes by the database administrator
Putting reigns on your data by defining it within a data dictionary also arms you to effectively conquer the inevitable: data sprawl.
That’s also where data standards come in.
The Role of Data Standards
Not by any means relegated to the world of technology, data standards help any large organization — from businesses to governments, universities, nonprofits, and others — speak the same data language to enable democratized access and maximum usability of large stores of data.
Data standards are documented agreements on format, use, representation, definition, tagging, structuring, and data management. It describes how data should be stored or exchanged to ensure consistency across different sources, systems, and users.
Your data dictionary can be queued up for use alongside data standards to create an enterprise-level taxonomy that unifies data across your organization
While it’s more challenging to develop than non-standards, digital data standards are more economical because you use the same code, publishing mechanism, and presentation to help users access information. Data standards also:
- Promote transparency and understanding of datasets
- Encourage reusing data and software for multiple purposes
- Ensure consistency in results during data retrieval
- Allow for data comparison even when it’s not standardized
- Reduce cost through reusability of data elements
The Data Standards Cloud eliminates data siloes. By unlocking new opportunities through rich data resources and applying data standards to input values, you increase the quality of data your team receives and empower every team to focus on what they do best.
Marketing teams also use data standards to improve data integrity across organic channels, minimize errors, and build information-rich campaign tracking links at scale.
Types of Data Dictionary: Active & Passive
How your data dictionary is managed depends on your organization’s data management structure and tech stack. While a passive data dictionary requires more setup and management, it also offers a more beneficial experience than simply relying on built-in capabilities. After all, data often needs a human touch to make sense to other humans.
Active data dictionary
Active dictionaries are very consistent and managed automatically by the Database Management System (DBMS). An active data dictionary is created within the database it describes, and changes or updates are automatically reflected in their host database.
Any modifications and alterations in the database structure reflect in the data dictionary through an automatic update by the DBMS.
The significant benefit is that an active data dictionary doesn’t need external maintenance software or hiring manpower since its self-updating and the DBMS automatically manages the dictionary.
Passive data dictionary
Passive data dictionaries are created as new databases and are unique from their described databases. Unlike active data dictionaries, passive data dictionaries are not easy to handle. Each time you modify or change the database, you manually update the data dictionary to match the information on the database.
Passive data dictionaries are primarily used to store metadata as a standalone central repository. Hence, it doesn’t impact the structure of the database.
Manual updates on passive data dictionaries require staffing a team to maintain the database. You also have to be careful when making changes to avoid the database going out of sync.
In either case, the data dictionary is your central reference point for understanding your data’s taxonomy.
Taxonomy is like a pattern that holds metadata — e.g., region, channel, department, dates. Having your data in a consistent pattern has obvious benefits.
But to understand — think, decode — your data via its taxonomy, a data dictionary defines and contextualizes the elements of the pattern. The data dictionary will tell you how to use and read your taxonomy.
Data Dictionary Examples
Developers consult a data dictionary when developing programs from data models. The goal is to understand where data fits in the DB structure, values it contains, and meaning in a real-world application.
For example, a fin-tech company could create a model of the data objects involved in online banking. They provide a data dictionary for their team of programmers explaining what each data item means, such as “opening balance” and “name of account holder.”
Here are some more examples of data dictionaries from the USDA.
Creating Your Data Dictionary
For data managed in text files, spreadsheets, or csv format, you’ll manually prepare the data dictionary. A spreadsheet is the best format to support machine readability. However, you can prepare your data dictionary as a pdf or doc format by embedding a data dictionary table in your document.
Central to your decision on document type and format is the ability for your teammates to use it. Choose the one they’re most comfortable working with.
What are the key elements of a data dictionary?
The components of data dictionaries vary but usually include the following elements:
- A list of names and definitions of database objects
- List of tables or entities
- List of columns, fields, and attributes
- Properties of data elements such as optionality, data type, indexes, and size
- Business rules for data quality or schema validation
- System-level and entity relationships diagrams
- Quality indicator codes
- Reference data
However, a data dictionary is more like a point of reference, because it’s not deployable or actionable. An evolved approach is to build a data taxonomy as a way to group and organize data. For data to work, it needs a framework to follow. A marketing taxonomy is that framework.
Marketing organizations create taxonomies to give marketers a structure to describe assets such as strategy, content, and landing pages. Building an effective marketing taxonomy requires organizational-wide collaboration. You must also consider your analytics, legacy data, and report implications.
The path towards a unified, org-wide data taxonomy will include the following stages.
Map it out!
Organize key stakeholders in each line of business (such as content, business intelligence, and analytics teams). Grassroots adoption is the first step to implementing a unified taxonomy.
Set a realistic timeline for building your marketing taxonomy and look for ways to be efficient such as aligning with an existing data management team.
Identify and understand your metadata.
This is essential to the taxonomy process because it labels information for proper organization and identification. Schedule discovery sessions to get a feel for the data you’ll capture in your taxonomy and how you’ll need to account for and define core business fields.
Click the banner below to begin creating your taxonomy and its data dictionary. Once developed, you’ll review the data dictionary with your organization’s stakeholders and implement it across the company.
A standardized approach to metadata and tracking codes is critical to data maturity and ensures you have a clearly defined taxonomy that employees can follow.
Activate your data dictionary through The Data Standards Cloud
Inconsistent, siloed data is a primary reason why projects fail.
Activating your data dictionary through The Data Standards Cloud raises the quality of data that your teams receive. Better quality data means less human error and time spent cleaning and translating data.
A lack of data standards or universal data taxonomy means teams churn bad data. Bad data translates into poor team collaboration, failed marketing campaigns, and hard costs to your bottom line.
Claravine helps you define, connect, and govern an enterprise data taxonomy that eliminates redundant information, ensures data syncs across systems, and enables more accurate analytics to improve marketing campaigns.
Even non-tech users who don’t understand coding can leverage templates to quickly draft, review, and edit data thanks to a built-in framework of data democratization. It enables everyone within your organization to understand enterprise data regardless of job function or technical capabilities.
Data Dictionary FAQs
Data dictionaries describe technical terms such as data fields, data attributes, and other data types. The information should be properly structured, organized, and easily understood.
Meanwhile, a business glossary defines terminology across the entire organization to keep employees on the same page while retaining consistent information.
While a data dictionary defines standards for multiple types of data fields, local markets can create custom fields based on unique local situations.
Standardizing data allows for flexibility and ensures interoperability between systems without affecting custom local setup.
The Data Standards Cloud is in English at the data description level, but it’s extensible to other languages. First, use the data dictionary as the universal data language across your organization.
Then, you can layer multi-language display labels to support local usage while maintaining interoperability and serving a diverse customer base.