Welcome to part two of our series on automated data onboarding. If you missed the introduction, you can find it here.
This post covers the first two major phases of the automated onboarding process: setting up your input, and completing your data preparation tasks. These steps apply to list loading and to any other data-driven process you automate. Getting them right is what separates a clean, reliable database from one that silently corrupts your lead scoring, routing, and segmentation.
Step 1: Set up the input
The first step in the automated data onboarding process is to standardize how data enters the process. You need a standard file format, a shared folder, and a master template.
File format
Choose from CSV, Microsoft Excel, or Google Sheets. The decision is largely a matter of organizational preference. We recommend Google Sheets for two reasons:
It is easy to convert CSV and Excel files to Google Sheets. Just drop the original file into Google Drive and it takes one click to convert. Google Sheets handles non-English text well. Exporting Excel to CSV can corrupt non-English characters if the Excel configuration is not correct.
Shared folder
Pick a location where list uploaders deposit files – a Google Drive folder or a shared network location. The automated process monitors this folder and triggers when a new file appears.
Master template
Design a master template with a standard set of fields for every file that comes through the process. This template should include a superset of the usual fields, including but not limited to: First Name, Last Name, Email, Company Name, Street, City, State/Province, Postal Code, Country, Phone Number, Job Title, and any custom fields relevant to your business.
The template also handles pre-processing tasks before the automated steps begin:
Adjusting any non-compliant input fields to the master format — for example, breaking a full name column into separate first name and last name columns. Putting the file in the right folder in the right format.
Step 2: Data preparation tasks
Automated tasks for list loading — or any data-driven process — generally fall into three categories: data preparation, decision-making, and action execution. This post covers data preparation. The next post in the series covers decision-making and action execution.
A.1 Clean
Any process you automate should start with data cleansing first. Typical cleansing tasks include:
- Fix bad email syntax — jdoe@acme..com → jdoe@acme.com
- Fix invalid email suffix — jdoe@acme.con → jdoe@acme.com
- Fix name capitalization — John DOE → John Doe
- Clean and truncate company name — Acme, Inc. (NYSE ACM) → Acme
- Fix and truncate website URL — https://www.acme.com/478khghf7q65tr → www.acme.com
Modern platforms like Openprise can run these cleansing rules automatically on every incoming record with no manual review. The platform's rules engine is designed so that a single rule handles an entire category of problems — for example, one rule normalizes all country name variants rather than requiring a separate rule for each spelling. Zendesk's marketing operations team reported a 25% improvement in data cleansing efficiency after automating these steps, eliminating the delays that had previously caused leads to grow cold before sales follow-up.
A.2 Normalize
Normalize data to your standards so you don't end up with 1,000 country variants or 250 state variants for the United States. Typical normalization tasks include:
- Normalize Country — US, U.S.A, USA → United States
- Normalize State/Province — Calif, CA, Cal → California
- Normalize Phone Number — 408.555.1234 → +1 (408) 555-1234
To be efficient at normalization, the technology you use needs fuzzy matching or equivalent capabilities to catch all the spellings of Massachusetts — Massachusettes, Masachusetts, Masachusetes, Masssachusett. The technology should be easy to set up and maintain. For example, to normalize the Country field, how many rules do you need to set up? If the answer is 200+, you have the wrong technology. The answer should be one rule. This is why CRM and marketing automation platforms do such a poor job at data cleansing tasks on their own.
A.3 Standardize
Standardization is closely related to normalization but focuses on field formats and value consistency. Common examples:
- Standardize Job Title format and capitalization
- Standardize company name abbreviations and legal suffixes
- Standardize date formats across sources
A.4 Enrich with open data
Before spending money on third-party data providers, use open data sources to fill gaps and validate what you have. Examples include:
- Using a service like Google Maps or Foursquare to validate and parse address data
- Deriving state, city, and country from a postal code
- Validating email domain formats and MX records
With GDPR and other data privacy regulations in force, this step is also valuable for identifying whether a contact qualifies as an EU data subject before you enrich further.
Openprise connects to the Openprise Data Marketplace, which provides direct access to leading third-party data providers for enrichment — including D&B, Clearbit, and others — from within the same platform you use to cleanse and normalize. Rather than switching between tools or managing separate API connections, enrichment is orchestrated as a waterfall: Openprise tries your primary provider first, then falls back to secondary sources to maximize match rates and fill data gaps without manual intervention.
A.5 Enrich and validate with third-party data providers
Once data is as complete and standardized as possible using open data, enrich and validate it with third-party databases. Examples include:
- Validating and appending firmographic data from Dun & Bradstreet or similar providers
- Validating and appending contact data from providers like ZoomInfo or Cognism
- Validating email deliverability
A.6 Handle international data
If you market and sell globally, you will deal with non-English data, which creates specific challenges:
- Non-English characters that survive poorly through CSV export and import
- Address formats in Asia and South America that use different field sequences than US and European conventions — meaning even a complete and accurate address can end up in the wrong fields if your mapping assumes a Western format
- Using a service like Google Maps to correctly parse address components across different regional conventions
A.7 Align geography to your sales organization
A CRM system's data does not have to be geographically accurate — it has to support your sales organization. Common examples:
Is Puerto Rico a state or a country? If it falls under your US sales territory, populate it as a state. If it falls under Latin America, populate it as a country. The UK has no concept of a state/province, so many companies populate county data into the state field. Are Taiwan and Hong Kong independent countries or part of China? How your sales territories are structured determines the answer — not a map.
These alignment decisions should be built into your normalization rules once and applied automatically to every record, so your database reflects your sales model rather than a political geography.
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