What is a Data Dictionary? Definition and Examples

What is a Data Dictionary? Definition and Examples

October 21, 2021

What is a Data Dictionary?

A Data Dictionary is a central source of knowledge for your business that describes data- what each data point mean, its relationship with other data points, business impact, usage and context of why it exists in the first place. Sometimes, datapoints are automatically updated through a formula or a workflow- in that case it should also mention the name, version and/or the formula used to update the field.

Let’s face it, implementing a new system in an organization is quite a daunting task! Especially, in high growth small to medium sized businesses where things change drastically almost everyday- constant alignment between changes in systems and teams becomes even more challenging.

I have been a part of implementing systems such as Salesforce, Netsuite, Marketo, Tally and one thing I’ve always wished I had is a way to get the entire organization to speak the same language when it comes to addressing fields, processes and data. Let’s find out how we can make things easy!

A data dictionary is basically a shared resource or a set of documents to help your organization stay on the same page while addressing everything related to systems, integrations, data or processes.

Every business has its own unique ‘language‘ in which they communicate while referring to data and systems. Usually, it follows top-down from the C-suite. It is time consuming to keep a track of every change in the system and effectively communicate the changes to the entire organization.

Data dictionary becomes the go-to tool for several teams in-charge of operations teams (Sales Ops/ Marketing OPs, RevOps etc.) to have a visual log of all the changes made to the fields and processes on the system.

Why should you have a Data Dictionary?

1. Consistent Reporting and Analytics

Sales Manager: “Hey team, I am trying to put a report together to show the monthly trend of revenue in 2021, but I can’t find anything called “revenue” in the system. Can someone help?”

Sales Ops: “Hmm.. Oh! We recently added a new Annual Recurring Revenue field in the system. Try looking for that”

–2 hours later–

Sales Manager: “Oh! but, I now see five new fields called “ARR”, “Annual Recurring Revenue_new(ARR)”, “Total ARR”, “ARR (Annual Recurring Revenue), ARR (New)”- which one is the correct one?”

Does this sound like an everyday occurrence?

Don’t worry- it’s perfectly normal for companies to have different ways of defining a single term. However, the real challenge is in reporting and analytics because traditional systems don’t leverage data dictionaries to help users speak the same language while analysing data.

Knowing that ‘ARR’ means ‘Annual Recurring Revenue’ may not be super obvious to a newly joined sales rep- that’s why leveraging data dictionary is essential to maintain accuracy and consistency when it comes to analytics.

We understand that every team in a business may address fields differently while naturally communicating with one another. Since Conbi uses naturally asked questions to query databases- it is essential for us to address this challenge and make it easy for business teams to literally ‘speak’ the same language to your data. Learn How!

Conbi is business intelligence made simple for modern teams.

You can simply add synonyms and a description to your already existing fields and help your team make data-driven decisions more efficiently.

This is how we’re addressing this challenge:

Data Dictionary in BI tools

2. Business Continuity

A common challenge in startups – Imagine you have a systems admin who has taken care of all your fields, processes and workflows on your CRM instance for 5 years and suddenly he decides to pursue a new job opportunity.

He was the only one who has an entire grasp on what means what on your CRM instance and now your team has to figure out all on their own and guess the context of the fields and processes existing in your system. This is where a simple data-dictionary helps you save time and well, eventually money!

Not only your team, but the new systems admin you hire needs to just have a look at the data dictionary to get all the context he/she needs!

Startups usually work with leaner teams but the rate of changes occurring within systems is very high. It’s challenging to keep up with the changes without having a data-dictionary.

How to make a Simple Data Dictionary?

A data dictionary is critical to making your research more reproducible because it allows others to understand your data. The purpose of a data dictionary is to explain what all the names and values in your datasource really mean.

Some data-dictionaries can be quite extensive consisting of every minute detail, while some may just have the bare minimum. Every company has a different style of making their data dictionary.

Below, I’ve listed down the six most essential components of a simple data dictionary:

  1. Field Names: This first column should consist of the name of the field as it is. If you use CRM system like salesforce- it offers you the ability to export all the fields that exist in your datatable along with their meta descriptions (if any).
  2. Measurement Units: For numeric fields, It should be clear how the field is measured. For example- if the time is measured in minutes, seconds or hours.
  3. Data Type: The data dictionary should consist of the data-type of each field- whether they are boolean, integer, currency etc.
  4. Description: This is the most important component of a dictionary- Simply put, it should describe the intent of the specific field.

    For Example: ARR – this field is used to measure the Annual Recurring Revenue of closed won business
  5. Synonyms- How does your team address this field when talking to one another. Do they say “ARR” in place of “Annual Recurring Revenue” or simply “Revenue”. This is a good opportunity to standardize the language in which your team talks about data.
  6. Last Modified Date- As the organization grows, dictionaries are frequently updated with new details to account for all the systems changes- therefore, it is important to timestamp the update of when the most recent change was made to the field. This helps greatly in understanding the relevance of the field.

Protip: Modern tools help in making data dictionaries digital. You should check out this tool if you want to have a salesforce specific digital data-dictionary.

Data Dictionary Template

I have put together a template which can get you started with creating and managing your first data-dictionary.

Sign-up below to request a sample and I’ll send it over to your email!

1 Comment
  1. Stephanie
    October 21, 2021

    Very true - You don't know how many times I have wished that something like this was passed down from previous employees who are no longer with the company. It's also great to update the data dictionary advising if a field is outdated. As your example shows, we might have many fields for ARR...ARR 1, ARR 2, ARR - New, ARR - Old. This might mean something to you....but does it mean something to the next person looking at your information? It is monumental to be able to know which field should be used for which intended purposes (or even at all).
    Thank you, Hoshang!

    Reply Reply

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