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Blog / How to Create a Data Dictionary in 5 Steps

How to Create a Data Dictionary in 5 Steps

With the number of data sources available to marketers increasing, and data getting more and more granular, it’s easy to find yourself under a mountain of unstructured data. To make data-driven decisions that stretch your budget further, the first step is getting all this data in a format that lets you compare apples to apples. That’s why building and maintaining a data dictionary is so important.

A data dictionary is a list of ideal, front-facing field names that marketers use to unify or “data map” information from multiple data sources into a single analytics-ready data set.

To find out more about what a data dictionary is, and how data mapping can help marketers to make better decisions, check out our blog: What is Data Mapping and Why Should it Matter to Marketers?

In this blog, we’ll run you through the steps you’ll need to take to create and implement a data dictionary that will help drag you out from underneath that mountain of unstructured data.

 

Find out how you can build a data dictionary to standardize your dataset.
 

1. Know what data you have

Begin by understanding the data you have. Identify the fields that are relevant to your analysis and determine which ones you need to focus on. It's important to differentiate between similar fields and assign them meaningful names. For instance, if you have fields like "reach" and "daily reach," ensure they are clearly labeled to avoid confusion.

people making notes on laptop - knowing what data you have is crucial when building a data dictionary

Knowing what data you have is crucial when building a data dictionary
 
 

2. Understand the limitations of your data

Be mindful of data limitations and discrepancies - in particular, of non-aggregatable metrics. Verify that your fields are accurately named to prevent potential errors, such as unintentional merging or incorrect calculations. Pay close attention to field names to ensure data integrity.

3. Align across teams to decide what questions you need your data to answer

One of the key aspects of implementing a data dictionary is aligning across teams to determine the specific business questions your data should answer. Think about what you need from your final data set, who in your teams will be using and consuming this data, and get to grips with how your data will be utilized.

During this phase, it’s really important to get input from stakeholders from across departments and make sure your data dictionary is creating a single source of truth that meets everyone’s needs while working towards a unified set of goals.

Take the time to identify the unique goals and challenges faced by different employees. Consider the specific data perspectives needed by teams, regions, and roles to effectively democratize data in a way that meets broader business objectives.

child sorting blocks - Identify the unique goals and challenges faced by different teams

Identify the unique goals and challenges faced by different teams
 
 

4. Determine the final data set and format

Define the data you want to include in your final data set and decide on the desired format. It's important to avoid getting caught up in semantic debates. Instead, focus on selecting standardized names for fields that will make sense to any team at any stage, from data engineers to marketing and agency teams.

It is also useful to consider general rules of thumb to adopt. For example, for fields made up of more than one word, you might choose to replace spaces with underscores (“campaign_name”), or to use camelCase (campaignName). Ironing out these rules and discrepancies right at the beginning can save you a lot of time and hassle down the line, helping you to mitigate the risk of having multiple values for the same field in your data set.

5. Implement the data dictionary: workshop workflows to get your data into the format you need

Work together to design workflows that streamline the cleaning and combination of data into the format you need. This means creating the data schema that defines how you’ll transform your data into a standardized data set and using this as the blueprint for all your data mapping.  You’ll need to workshop this workflow at the beginning of the process and figure out what you can automate to streamline your data mapping.

Unless you have a third-party tool like Adverity that manages connector updates for you, then you’ll also need to keep an eye out for any maintenance that needs to be done around API updates — for example, if Google Analytics changes one of its field names or the way it calculates a certain metric.

Consider aspects such as data storage, processing and harmonization techniques, user-specific displays, and the need for training programs or technology change management.

Conclusion

In conclusion, a data dictionary is a valuable asset for marketers and organizations navigating the complexities of data analysis. By following the five steps outlined in this blog, you can effectively organize and unify your data, enabling better decision-making and collaboration across teams. 

A well-curated data dictionary enables accurate comparisons, prevents errors, and facilitates seamless data integration across multiple sources. With a data dictionary in place, you'll have a reliable resource that streamlines data analysis and supports your organization in achieving its business objectives.

Make insights-driven decisions faster and easier!

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