A 60‑day plan to clean, govern, and trust GTM data in Salesforce.
If your forecast is wobbly and your dashboards spark more debate than decisions, you likely have a data health problem - not a dashboard problem.
In life sciences GTM, the stakes are higher: regulated workflows, multi-stakeholder buying committees, and milestone-driven timelines amplify the cost of bad data. A 60-day plan can stabilize quality fast without boiling the ocean.
Start with governance. Appoint a data owner (in RevOps) and a small governance council with Sales, Marketing, and Delivery. Draft a one-page data charter that defines your critical objects, required fields, and quality standards for each stage of the funnel.
Clarify roles: who creates, who edits, who approves, and who audits. Standardize naming conventions for accounts and opportunities (e.g., Sponsor – Study – Solution – Region), and lock down picklists for therapeutic area, company type (Sponsor, CRO, eClinical), and buying role.
Establish a simple data lifecycle: create, enrich, convert, win/lose, archive.
Set measurable targets for the first 60 days - raise ICP field completeness to 95%, cut duplicate rate by 50%, and reduce opportunities missing milestone tags to under 10%.
Make it real through systems. Add just enough structure to prevent garbage-in: required fields on lead conversion and opportunity stage progression; context-sensitive help text; and quick actions for common updates.
Introduce milestone tags on opportunities (protocol status, InfoSec review, budget release) so commercial reality is captured consistently. For conceptual backing, review Salesforce’s guidance on building data quality programs in this primer.
With ownership, standards, and a right-sized operating model, you’ll see immediate lift in routing accuracy, forecast trust, and handoffs across Marketing, Sales, and Delivery.
With governance in place, clean what matters most first. Focus on the records that drive pipeline, forecast, and delivery: Accounts, Contacts, Leads, Opportunities, and Activities.
Start with profiling - measure field completeness for ICP-critical data (company type, therapeutic area, trial phase relevance, buying role), standardize picklists, and identify duplicates.
Set conservative, automated merges for obvious duplicates (exact domain or email matches) and route edge cases to a weekly data clinic led by RevOps.
Create validation rules to block stage movement when required fields are empty or inconsistent - for example, you cannot convert a Lead to an Account without company type, size, and region; you cannot mark an Opportunity as Commit without economic buyer identified and milestone tags present.
Build lightweight enrichment to reduce manual entry: company and contact enrichment for firmographics, technology, and trials footprint.
Use flows to auto-assign territories based on region and segment, and to write derived fields (e.g., “Life Sciences Segment” based on company keywords).
Then, establish a monthly cleansing cadence. Pull exception reports that show records missing mandatory fields, high-duplicate-risk leads, and stale accounts with no activity in 90 days. Rotate clean-up sprints by segment to avoid overwhelming teams.
For practical, vendor-agnostic guidance, borrow from Salesforce’s own resources on data quality programs: see this overview and a hands-on module for improvement planning in this Trailhead module.
Tactically, communities like SalesforceBen offer actionable checklists you can adapt quickly; a useful starting point is SalesforceBen’s guide. Document each change, measure impact, and keep scope tight to hit tangible wins inside the 60-day window.
Data quality deteriorates unless it’s wired into daily behaviors and incentives. Start by making quality visible. Build three dashboards:
Review these weekly in sales and RevOps stand-ups. Tie compensation accelerators or SPIFs to hygiene, for example, paying on qualified pipeline requires required fields complete and opportunity milestones tagged. For managers, include a hygiene KPI in QBRs so coaching addresses both selling and system discipline.
Train for the job reps actually do. Provide 30-minute micro-trainings on why each required field matters (e.g., how therapeutic area drives territory assignment and content), and embed help text and picklist examples in the UI.
Simplify layouts: less clutter equals fewer errors. Protect your progress with guardrails: regular backups, audit trails on critical fields, and a quarterly validation rule and picklist review to avoid brittle processes.
For broader governance context - especially in regulated environments - review life sciences-specific guidance, like this NCBI chapter on governance frameworks and FAIR principles in NCBI’s resource and pragmatic commentary from IQVIA in this article.
Close the loop every month. Compare conversion rates and forecast accuracy before and after hygiene initiatives; publish a short, visual update to leadership. When reps see faster routing, fewer reassignments, and less rework, adoption sticks.
Over 60 days, these habits transform CRM from a reporting burden into a trusted revenue platform that supports reliable forecasting and smoother clinical-commercial execution.
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