## Automated data cleansing isn't a nice-to-have. It's a revenue lever. One-time data cleanup projects buy you six months before the problem returns. Openprise runs continuous cleansing and standardization jobs that catch bad data at the point of entry, enforce your field standards automatically, and keep your CRM and MAP clean without your team spending their week fixing what reps entered wrong. ### Clean data before it lands Openprise standardizes and validates records in transit, from list imports, form fills, and system syncs, before they land in your CRM or MAP. Your downstream systems get clean data from the start, not a cleanup project six months later. ### Standards enforced across every system Company name, phone formats, country codes, job titles, lead sources, product names — whatever your data standards are, Openprise enforces them consistently across every record in your database, every time a record is created or updated. ### AI cleans what rules miss Badly abbreviated titles, ambiguous geography, unstructured note fields — a rule-based approach can't handle the lowest-quality data in your database. Openprise's AI-assisted standardization fills that gap, cleaning and classifying the records your rule sets leave behind. ## See automated data cleansing and standardization in action Watch how Openprise normalizes field values, enforces your data standards, and cleanses records across your entire GTM database — automatically and continuously. ### Built for Ops teams tired of being data janitors Data cleansing should not be a manual job. Your Ops team did not build their careers on fixing phone number formats and normalizing country fields. Openprise automates the cleansing and standardization work that used to eat your team's week, so they can spend that time on the GTM architecture problems that actually move pipeline. **Read what Ops leaders say about Openprise** “Implementation of Openprise is in four stages. The first stage led us to the standardization and normalization of data. The criteria we set for field normalization led us to 7.5 million fields being standardized. The second stage led us to the cleansing of our existing data. We were able to clean 4,200 (duplicated) records.” **Rupal Shah** Data Systems Manager, MNTN ### Replace your Ops stack You have 30 tools. None of them agrees. Replace the point-solution sprawl with a single platform built for every GTM data workflow your team runs. #### Point Tools - **Limited to single use cases** Setup time: Weeks Workflow complexity: Basic logic only Integration coverage: Limited Maintenance burden: High Coding required: Yes #### Openprise - **Built for RevOps workflows** Setup time: Days Workflow complexity: Advanced logic included Integration coverage: Extensive Maintenance burden: Minimal No coding required ## Questions ### What types of data does Openprise cleanse and standardize? Openprise handles the full range of GTM data fields: company names and industry codes, job titles and job functions, phone number formats by country, state and country values, lead source and campaign attribution fields, product names and software versions, and custom fields specific to your business. It also handles unstructured data — extracting clean, structured values from form fill notes, survey responses, and free-text fields that standard cleansing tools can't parse. ### How does Openprise handle data that's too messy for keyword-based rules? This is where Openprise's AI-assisted standardization capability adds the most value. Keyword-based rules work well for predictable variations — "US" and "United States" can both map to a clean value. They break on low-quality, ambiguous, or abbreviated data: a job title entered as "VP Sales, N.A. SLED" that should read "VP Sales, North America State, Local and Education," or a geography field that contains something a rule set was never written to anticipate. Openprise's AI standardization layer handles these edge cases — cleaning and classifying the records that fall through your rule-based logic. You get coverage across your full database, not just the well-formatted records. ### Does Openprise cleanse data before it enters our CRM or MAP, or only after it's already there? Both. Openprise runs cleansing at the point of ingestion — applying your standardization rules to records from list imports, form fills, and system syncs before they land in your destination system. It also runs continuous cleansing jobs across your existing database, catching records that were created before your standards were defined or that entered through a source Openprise wasn't monitoring at the time. ### How does data cleansing connect to the rest of our GTM workflows? Every GTM workflow depends on clean, standardized data to function correctly. Your routing logic routes based on territory, segment, and rep assignment, all of which rely on clean account and contact data. Your scoring model scores based on job title, company size, and industry — all of which need to be standardized before the logic runs. Your segmentation assigns personas based on job function — which only works if titles are normalized. The downstream impact of cleansing is not limited to the fields you cleaned. ### We already have some cleansing logic in Marketo and Salesforce. Why would we move it to Openprise? Marketo smart campaigns and Salesforce workflow rules are good at reacting to individual record changes. They are not designed to run bulk cleansing across millions of records. Openprise runs cleansing outside your CRM and MAP: processing records in bulk, applying your logic, and writing back clean values. ### What is data cleansing and standardization in a CRM like Salesforce? Data cleansing in Salesforce means identifying and correcting inaccurate, incomplete, or inconsistently formatted records. Data standardization means enforcing a consistent format across all records so your downstream processes can run against reliable field values. Openprise runs continuous cleansing and standardization jobs that detect and correct non-standard values on a schedule. ### How does data standardization improve marketing automation performance in Marketo, Eloqua, or HubSpot? Your MAP's segmentation, scoring, and campaign logic is only as accurate as the field values it reads. Openprise standardizes data upstream of your MAP — resolving field variations and enforcing picklist values — before records enter Marketo, Eloqua, or HubSpot. ### How does data cleansing in Openprise connect to Snowflake or other data warehouse environments? Openprise connects to Snowflake as both a data source and a write-back destination. Openprise standardizes and cleanses records before they reach your data warehouse. ### What is the difference between data cleansing and data enrichment? Data cleansing fixes what's already in your database: correcting formatting errors, standardizing field values, removing invalid entries, and resolving inconsistencies across records. Data enrichment adds what's missing. Openprise handles both in a connected workflow. ### How does automated data standardization support AI readiness across the GTM stack? AI models are only as reliable as the data they're trained and run on. Openprise automates the standardization layer that prepares your GTM data for AI — enforcing field standards and resolving value inconsistencies.