Carla was preparing a dataset for a new analytics initiative when she noticed something odd.
The CRM contained hundreds of fields tied to customer records - some were clearly useful, like contract dates or product tiers. Others looked important at first glance but hadn’t been populated consistently in years.
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Meanwhile, marketing had its own set of attributes for customer segmentation. Sales tracked pipeline information differently. Finance maintained separate fields related to invoicing and payment history.
None of it was technically wrong, but no one could clearly answer which data actually mattered.
And when Carla, the Head of Business Intelligence, asked teams which fields they relied on most, answers varied.
Her company didn’t have a governance problem. It had a data stewardship problem, because nobody had ever stepped back to determine which data was truly relevant, reliable, revealing, and reusable or to organize it in a way that supported how teams actually worked
The result? Six months later, the analytics project launched; the executive dashboard showed churn trends based on outdated field definitions, causing panic and rushed decisions on bad data before anyone caught it.
Solve These Challenges
Every team has their own version of the same customer record
Unclear answers leave key data areas - from product and pricing to AI - without accountability.
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Your Data Governance Framework Lives In A Confluence Doc Nobody Reads
Formal programs move slowly and don't translate into day-to-day decisions.
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You've Cleaned The Data Twice, Yet It Keeps Getting Messy
Your processes seem to be holding steady - or getting worse, not better.​
Realize Gains Such As:
Measurable KPIs
10% reduction in data costs
15% time spent correcting data by users
25% reduction in data-related escalations
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Operational Wins
Clear ownership for critical data points, faster decision-making by all teams, and cleaner data handoffs
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Executive Wins
A data foundation that doesn't have to be rebuilt every two years - and supports AI initiatives that don't get derailed by poor data quality.
Trusted By:
Try This Now: Open Your CRM And Apply These Four Rs. Is It...
Relevant? Does a piece of data answer a particular business question?
Reliable? Do you find it to be complete, consistent, and trusted?
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Revealing? Does this data lead to patterns or trends you can do something with?
Reusable? Can it be useful again in the next 6-12 months?
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Any data that isn't a "yes" to three of the four Rs isn't helping. If fewer than 20% pass this test (which is typical!), you have a stewardship problem.
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Think Of Your Data In These Eight Distinct Categories:
Customer. Account details. "Who's our customer, and how do they use us today?"
Sales. Pipeline, cross-sell/upsell, contacts. Often has more data than necessary.
Quote. Broken out due to complexity.
Purchase. What they actually bought.
Invoice. Accounts payable, payment history, etc.
Service. All things customer service.
Marketing. Includes campaigns, reporting, etc.
Communications. Everything else. The Wild West of unstructured data.
Chief Innovation Officer Andy Boettcher takes you further into the Four Rs, why they matter, and how to get started.
We Work
Across Systems, Including:
"They became a true partner in helping us overcome our data challenges."
Wealth Enhancement Group
Know your data works for you, instead of creating work.
Most data quality issues aren't caused by your technology - it's lack of ownership and enforcement.
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We fix this. Not a framework, but with hands-on categorization and adoption work that applies governance throughout your business.
Backed by a Success Guarantee.
SUCCESS STORIES
Customers like you are on the right track
Let's Talk
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Schedule a time that works best for you, and we look forward to learning how we can help - and adding value right away.
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