The powers that be have finally given you the go-ahead to invest in marketing automation software. You have tons of contacts to market to, from various sources, but up until now you've had no real strategy around database management.

As you start compiling your various contact lists you're faced with the reality that your database has no structure. Birthdays, addresses, and job titles are in entirely different formats. There's no way you can segment it in this state. Before you can even think about using marketing automation software, you need to get this data under control.

More importantly, you need to fix this problem for good by implementing data nomenclature so that your lists never end up like this again.

Data nomenclature is easier with clean data. We clean it for you.  Request a demo today.

Interviewing the expert: 


When it comes to data nomenclature, no one knows it better than Mike Korba. As the Co-Founder of, he helps clients worldwide use marketing automation technology effectively. With over 17 years of experience in the industry, he's seen how using data nomenclature directly impacts the success of marketing automation.

We talk to Mike about the benefits of data nomenclature and how to boost data quality to support marketing automation.

What is data nomenclature?

Data nomenclature is the system of establishing naming conventions across a dataset. These rules will dictate how data is entered into your contact databases. Naming conventions are essential in keeping datasets organized and standardized. There are multiple ways that you can enter data, and unless you have specific rules in place then you will inevitably end up with different formats. 

For example, in the contact name field you could end up with:

  • Matt Smith

  • M. Smith

  • Matthew smith

  • matt p. Smith

Setting specific rules around how data is entered will ensure that it remains uniform. 

For your internal team, it will provide detailed guidelines that they can follow each time they add or update a contact. From the user side, these conventions will tell your users how to enter their details so that it is consistent with the rest of your data. 

The benefits of data nomenclature and standardizing data

It's unlikely that you'll find companies that establish naming conventions before they start data collection. But better late than never. Mike helps many established companies implement data nomenclature into their massive, unstandardized data sets.

Here are some of the primary benefits of data nomenclature and standardizing data sets:

1. Reduce error

Establishing naming conventions will help to reduce errors in your data sets. Human error is inevitable, but by making strict decisions around how data is entered and what data goes where you can minimize mistakes.

2. Convenience/helps you find what you're looking for faster

With a standardized database, you can easily sort your lists and find the data you're looking for. For example, if you want to send a special offer to subscribers who joined a year ago, you can sort by subscription date and feel confident the data is correct. When you need to update someone's contact details you can easily find their listing without having to spend time digging for it.

3. Allows for data segmentation 

Data segmentation is the key to successful marketing campaigns. Sending the exact same email campaign to all your contacts isn't effective. You need to personalize your campaigns by sending them to relevant contacts within your data sets. The only way to do this is by segmenting your lists, which is impossible to do without data nomenclature. It’s also important to implement consistent data cleansing for email marketing, both to ensure that it’s standardized and for data validations. 

4. Prevents duplicate data

When there are no naming conventions in your contact lists, they become a breeding ground for duplicates. For example, Matt Smith joins your email list using the contact name Matthew Smith. He also attends a seminar, signing in as M. Smith. Now you have a duplicate in your list without even realizing it. With strict naming conventions, you reduce the risk of this happening.  

5. Allows you to automate your marketing

If you have a database that isn't standardized, you won't be able to automate your marketing. Marketing automation software is useless without a clean list. Your emails will bounce, names will have typos, and contacts will receive irrelevant content. Data cleansing tools can help to get your data ready to upload into your software and use naming conventions to keep it clean. 

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Different types of data 

There's an endless amount of contact data that you can collect, but more doesn't necessarily mean better. Mike stresses the importance of choosing only data that is relevant to your marketing efforts and not clogging up your database with stuff that you can't use.

To get an idea, here are the different forms of data that you can collect.

Graphic showing example of different forms of data that you can collect to form nomenclature and standarization

1. Contact data

  • Email address

  • Phone number

  • Location

  • Date of Birth

  • Job Role 

2. Company data

  • Industry

  • Number of employees

  • Revenue 

  • Company address 

3. Usage data

  • How much they use your software/product

  • Email open rate

  • How many webinars they join

  • Subscription plan details

  • Lead magnet downloads

Data nomenclature formatting 

Once you know what data you want to collect, you need to make sure each field is formatted correctly. For example, if one field is a birthday, it shouldn't be formatted as a string. Here is Mike’s breakdown of some of the most useful file naming formats:

1. String

String is a text format that allows for alphabetic, numeric, and special characters (i.e., underscores and punctuation.) It's the most flexible format available. 

2. Integer

Integer format only allows numeric integers. You can set a specific range of characters to ensure that the data entered is accurate. 

3. Float

The float data type is numbers that are not integers, such as decimals or fractions.

4. Boolean

The Boolean data type only allows a field to contain two possible values: true or false. 

5. Date

Using the date format ensures that the only information entered into the field is a date. There are multiple date templates to choose from, including yyyymmdd, ddmmyyyy, mm/dd/yyyy, etc. 

How to establish effective data nomenclature naming standards

When you’re building a data management strategy you get to decide how you want to organize your data. You will need to make some decisions around data nomenclature. Mike has helped many businesses establish and implement effective naming conventions as part of their data management strategy. Here are some of his top tips:

1. Choose what attributes you're going to collect

As mentioned above, there's no point in collecting irrelevant pieces of data as it will only clog up your database. Choose the attributes that will actually be useful to you in your marketing campaigns. If your product is a travel booking platform, then a valuable piece of information might be your contact's top holiday destination. But if you're a project management software, then this information isn't useful. 

2. Define the format of the data in each attribute

Create a defined description for every attribute that you collect. Get incredibly specific. You want to be so descriptive that there is no doubt about what piece of information goes into each attribute and exactly how it should look. Include information such as:

  • Casing (lowercase, camelCase, Proper Case, Sentence case, snake_case, etc.)

  • Spacing (are you including spaces or not?)

  • Order (in what order should the data appear)

  • Format (how will you write names, addresses, job titles, dates? Will you use a prefix, abbreviations, or acronyms?) 

3. Where are you sourcing the data? 

Ensure that the data entered into each attribute is coming from a consistent source. Be specific about where you're sourcing information. For example, if you're inputting the yearly revenue for a company, where is that information coming from? Are you using Crunchbase, Clearbit, LinkedIn, or Owler? 

4. Create predefined options for applicable attributes 

Consider whether an attribute can have predefined options to help reduce error and keep your database organized. The more predefined data elements you can create, the less you will risk human error.

Create these attributes both in your chosen CRM and all the places you collect data (such as lead magnet downloads and subscription forms). These predefined options will be a part of your data dictionary, containing the most suitable options for your staff and contacts to choose from. 

For example, a contact's job role could be a text string, but it's better to have specific options to choose from, such as CEO, CMO, Head of Sales, Head of Marketing, etc. This will allow you to segment your data in a specific way. One job title can be written in multiple ways, such as the person responsible for marketing. It could be Head of Marketing, Marketing Manager, or Marketing Lead.

5. Define how often each attribute needs updating

For each attribute, define how often the data needs updating. Certain information, like names, birthdays, and source of acquisition, won't need to be updated. Others like job titles, software usage data, and the number of employees will need to be updated regularly.

Does looking at your disorganized #CRMdatabase make you anxious? #DataNomenclature is the key to standardizing your list, and essential for implementing #marketing automation. This is how to implement it:

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How do you know if your data is ready for marketing automation?

100% perfect data does not exist in a CRM, but it's possible to get it close. At the bare minimum you need an email address and a first name, but this base data must be clean and accurate. Once you have this data, you can start building your automation, but it's not enough to scale. The more attributes you collect, the more you can segment your lists and create more targeted and effective campaigns. 

You will still need to consistently clean your data using tools like tye because human error will always be present in your data, even with internal procedures. Even with straightforward descriptions, you may still find that staff isn't following them properly. Use data cleaning steps and CRM data cleansing tools to standardize your current database, and then implement naming conventions into your data management workflow going forward. 

By implementing data nomenclature, you can effectively use your marketing automation tools and start running successful campaigns. 

Data nomenclature is easier with clean data. We clean it for you.  Request a demo today.

Get our ultimate bundle of checklists, workflows and swipe files to manage your customer database like a data pro

Markus Beck

Markus Beck - February 2, 2021

CEO with a passion for data relationships. Markus is half Finnish, half Austrian & fully committed to helping businesses keep bad data from ruining great relationships. Process Engineer by training, with digital marketing & project management skills from previous jobs.