What is a Data Dictionary?
Learn everything there is to know about data dictionaries so you can leverage them provide relevant context and create transparency across teams.
March 6, 2023
Workplace collaboration and the desire to become a data-driven business are inextricably linked. Determining how to assess and act on data is one of the most challenging tasks for businesses as data points mount, and the need to account for information becomes crucial.
To facilitate collaboration and promote an understanding of the "why" and "how" of data utilization, it is necessary to have a vision. The key to unlocking and advancing this initiative is a data dictionary that acts as the basis for team collaboration within an organization.
What is a Data Dictionary?
A data dictionary is a collection of attributes and definitions for data objects and fields in a database. The purpose of a data dictionary is to provide your data models with relevant context so your wider teams can better understand the core business definitions inside your data warehouse. A data dictionary is a single document that provides detailed metadata and relevant information.
As data models become more complex and advanced, relying on multiple objects and fields, the difficulty in understanding these definitions also increases. If you have several customer definitions living in your warehouse, like LTV, MRR, churn rate, active workspaces, playlists, etc., a data dictionary will provide a short definition and overview for each of these models. The purpose of a data dictionary is to serve as a single source of truth for all of your business logic–reducing inefficiencies, increasing transparency, and aligning data and business teams.
Designing a Data Dictionary
Building and designing a functional data dictionary can be a daunting task, especially if you have large amounts of data, so laying the groundwork for a strong foundation is crucial to success. There are several key questions you’ll need to consider:
- Where does the data originate, and is it correct?
- How will the data be applied in theory and in practice by other teams and organizations?
- What data do you want to utilize, and how do you know if it’s useful?
Behavioral data can come from numerous touchpoints: websites, apps, ads, emails, social channels, etc. Enriching this behavioral data with additional context from your sales cycle is equally essential, so defining your sources shouldn’t be a one-time event. Instead, your focus should be on building a data pipeline that is continually updated and maintained. You likely don't need a data dictionary if your data isn’t flowing into your warehouse (e.g., your single source of truth).
Understanding how data will appear and be interpreted by the various teams analyzing this data is critical to promoting multi-application. Once this data is outlined and available for use, data governance should be prioritized to prevent redundancy and increase accessibility to promote efficiency.
The most important step in building anything that provides value is understanding the end-use case and forming best practices around it. Ultimately, you want to create an environment that drives value and supports the theories from data and analytics.
Building a Data Dictionary
Building a data dictionary can be challenging if you try to tackle everything at once. Breaking individual components into smaller sections will help you when it comes to actually undergoing this project.
The first step will be to collect a list of all the terminology or jargon your firm employs in databases, business intelligence, or marketing technology solutions. You should accomplish this by contacting colleagues and combining your knowledge from reports, dashboards, KPIs, and OKRs. This will benefit your organization in the long run and will be a significant step toward more successful cross-team communication.
Once you have gathered all the necessary terms, leveraging existing documentation to populate definitions for basic terms and meanings will help you avoid repetition. This will give you a good idea of which business sectors you should devote more resources to, as well as which teams can assist others in producing adequate documentation in the future. Make a note of any duplicate or ambiguous phrases since this will help with prioritizing in subsequent rounds.
Now that everyone's cards are on the table and the information is out, the following step is going through the terms that you believe to conflict with one another as a group. Make a list and schedule follow-up meetings with the appropriate teams to iron out these definitions. Building a scalable data dictionary now will eliminate these types of meetings in the future.
It’s important to have someone act as a project manager during this process so there is a mediator when teams disagree. One person should intake all of the feedback and help define these definitions, so there is consistency.
Make sure higher management has approved the approach and is aware of it. Without their approval, it will be challenging to maintain this new process, and you run the danger of teams creating their own data dictionaries, which would put you back where you started.
Once you’ve ironed out your key definitions, the final step is simply publishing your data dictionary in a central area and making it available to all the stakeholders within your organization.
Data dictionaries should be viewed as a living, breathing publication that constantly changes as your teams add new terms and definitions. As a best practice, make sure to decide on a procedure to apply these future changes cross-functionally across teams, with one group having the authority to approve a definition, one group to implement the change, and one group understanding how it affects reports and KPIs.
Example Use Case
A significant, durable goods and appliances manufacturer with a global footprint needed consulting on educating and aligning their consumer data and analytics teams on key business terms, calculations, and frameworks. This initiative was part of a larger task: adding a Customer Data Platform (CDP) to their marketing tech stack.
As part of Actable's consultative service offering, we came forward with the recommendation to include an analytics catalog, which is utilized as a reference for KPIs to define success on activations, to aid with implementing and onboarding an enterprise CDP (Customer Data Platform).
We built an analytics catalog to act as a reference for KPIs and define success for downstream Data Activation use cases. Once starting this project, we recognized noticeable misalignments within the data and knowledge of said data.
Thus, the Data Dictionary was born out of a lack of clarity for an analytics catalog that was initially in scope. This was necessary to align the team on what would inform the analytics and enable the use of the analytics catalog so that the KPI framework could be understood.
There are several advantages to building and implementing a data dictionary, but the primary value is in the transparency and clear documentation it establishes across cross-functional teams. Data dictionaries improve and optimize data navigation and search. They also help identify anomalies across projects and teams, eliminate unnecessary and redundant data, and resolve discrepancies.
This works in parallel to create an improved database structure and information architecture, resulting in increased confidence and database integrity. If you were going to summarize all of these sentences into one sentence, you could synthesize to “better data quality and more detailed data analysis.”
In Matt Greitzer's recent blog post, "Why Team Matters More than Tech: An Enterprise Framework for Customer Data Acceleration," he denotes key considerations often missed for a smooth transition when implementing a Customer Data Acceleration initiative that applies to many organizations.
Teams that look to drive innovation through adding technological improvements often ignore the impact that today’s changing workplace environment has had. Now, the success of any organization is more dependent on fostering effective and long-term collaboration within and across teams rather than their technical enhancements.
When applying this to modern marketing teams, the blurred landscape of technical, creative, brand, and retention marketing roles can present problems to companies unbeknownst to them. Inevitably, this necessitates that all team branches operate with a unified understanding of key definitions and metrics that implement success through strategic decisions.
Further, a lack of documentation for marketers who utilize data for effective personalization and first-party data audience activations can lead to confusion and disconnect among teams and overall strategy. The solution is to structure a modern data dictionary that will be a single source of truth for your team and empower cross-functional relationships that promote data-driven collaboration and strategies.
About the Author
Joe Dobyns is a Solutions Consultant at Actable, helping clients deploy 1st party customer data across their Martech & analytics ecosystems. Joe has deep client-side marketing & lifecycle marketing experience, including fashion retailer Charles Tyrwhitt and Equinox fitness centers. Joe has worked extensively with CDPs and customer data warehouses.