Interviewing the expert:\u00a0<\/strong><\/p>\n MIKE KORBA<\/strong><\/p>\n When it comes to data nomenclature, no one knows it better than Mike Korba<\/a>. <\/strong>As the Co-Founder of User.com<\/a>, 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.<\/p>\n We talk to Mike about the benefits of data nomenclature and how to boost data quality<\/a> to support marketing automation.<\/p>\n 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.<\/p>\n For example, in the contact name field you could end up with:<\/p>\n Matt Smith<\/p>\n<\/li>\n M. Smith<\/p>\n<\/li>\n Matthew smith<\/p>\n<\/li>\n matt p. Smith<\/p>\n<\/li>\n<\/ul>\n Setting specific rules around how data is entered will ensure that it remains uniform.<\/p>\n 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.<\/p>\n 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.<\/p>\n Here are some of the primary benefits of data nomenclature and standardizing data sets:<\/p>\n 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.<\/strong><\/p>\n 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.<\/p>\n 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\u2019s also important to implement consistent data cleansing for email marketing<\/a>, both to ensure that it\u2019s standardized and for data validations.<\/p>\n When there are no naming conventions in your contact lists, they become a breeding ground for duplicates.<\/strong> 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.<\/p>\n 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<\/a> can help to get your data ready to upload into your software and use naming conventions to keep it clean.<\/p>\n Get our ultimate bundle of checklists, workflows and swipe files to manage your customer database like a data pro<\/p>\n 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. <\/strong><\/p>\n To get an idea, here are the different forms of data that you can collect.<\/p>\n Email address<\/p>\n<\/li>\n Phone number<\/p>\n<\/li>\n Location<\/p>\n<\/li>\n Date of Birth<\/p>\n<\/li>\n Job Role<\/p>\n<\/li>\n<\/ul>\n Industry<\/p>\n<\/li>\n Number of employees<\/p>\n<\/li>\n Revenue<\/p>\n<\/li>\n Company address<\/p>\n<\/li>\n<\/ul>\n How much they use your software\/product<\/p>\n<\/li>\n Email open rate<\/p>\n<\/li>\n How many webinars they join<\/p>\n<\/li>\n Subscription plan details<\/p>\n<\/li>\n Lead magnet downloads<\/p>\n<\/li>\n<\/ul>\n 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\u2019s breakdown of some of the most useful file naming formats:<\/p>\n 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.<\/p>\n Integer format only allows numeric integers. You can set a specific range of characters to ensure that the data entered is accurate.<\/p>\n The float data type is numbers that are not integers, such as decimals or fractions.<\/p>\n The Boolean data type only allows a field to contain two possible values: true or false.<\/p>\n 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.<\/p>\n When you\u2019re 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:<\/p>\n 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.<\/strong> 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.<\/p>\n 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:<\/p>\n Casing (lowercase, camelCase, Proper Case, Sentence case, snake_case, etc.)<\/p>\n<\/li>\n Spacing (are you including spaces or not?)<\/p>\n<\/li>\n Order (in what order should the data appear)<\/p>\n<\/li>\n Format (how will you write names, addresses, job titles, dates? Will you use a prefix, abbreviations, or acronyms?)<\/p>\n<\/li>\n<\/ul>\n Ensure that the data entered into each attribute is coming from a consistent source.<\/strong> 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?<\/p>\n 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. <\/strong><\/p>\n 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.<\/p>\n 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.<\/p>\n 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.<\/p>\n 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.<\/p>\n You will still need to consistently clean your data using tools like tye<\/a> because human error will always be present in your data, even with internal procedures.<\/strong> Even with straightforward descriptions, you may still find that staff isn’t following them properly. Use data cleaning steps <\/a>and CRM data cleansing<\/a> tools to standardize your current database, and then implement naming conventions into your data management workflow going forward.<\/p>\n By implementing data nomenclature, you can effectively use your marketing automation tools and start running successful campaigns.<\/p>\nWhat is data nomenclature?<\/h2>\n
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The benefits of data nomenclature and standardizing data<\/h2>\n
1. Reduce error<\/h3>\n
2. Convenience\/helps you find what you’re looking for faster<\/h3>\n
3. Allows for data segmentation<\/h3>\n
4. Prevents duplicate data<\/h3>\n
5. Allows you to automate your marketing<\/h3>\n
Different types of data<\/h2>\n
![\"Graphic](\"https:\/\/www.tye.io\/en\/wp-content\/uploads\/sites\/3\/data-nomenclature-and-standarization.jpg\" "\\"data-nomenclature-and-standarization\\"")
<\/p>\n1. Contact data<\/h3>\n
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2. Company data<\/h3>\n
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3. Usage data<\/h3>\n
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Data nomenclature formatting<\/h2>\n
1. String<\/h3>\n
2. Integer<\/h3>\n
3. Float<\/h3>\n
4. Boolean<\/h3>\n
5. Date<\/h3>\n
How to establish effective data nomenclature naming standards<\/h2>\n
1. Choose what attributes you’re going to collect<\/h3>\n
2. Define the format of the data in each attribute<\/h3>\n
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3. Where are you sourcing the data?<\/h3>\n
4. Create predefined options for applicable attributes<\/h3>\n
5. Define how often each attribute needs updating<\/h3>\n
How do you know if your data is ready for marketing automation?<\/h2>\n