Like many global enterprises, Essity’s data challenges didn’t emerge overnight. Over time, growth into new markets, regional operating models, and acquisitions contributed to a fragmented data landscape.

“For us initially, no one owned data quality,” said Honaker. “And no one person or area of the business was tasked with measuring the ROI on our data.”

Despite multiple conversations about unreliable reporting and inconsistent insights, accountability for fixing the problem was unclear. That disconnect made it difficult to quantify the true business impact of poor data hygiene until the operational consequences became too painful to ignore.

While Essity consolidated multiple customer relationship management (CRM) systems into a single platform, doing so didn’t automatically create trust in the data. “Putting all the data into one system didn’t solve all the problems,” Honaker explained. “In some cases, it actually made them worse.”

Incomplete records, outdated information, and duplicate accounts persisted, sometimes surfacing at precisely the moments when teams needed reliable data most.

According to Honaker, one of the most visible costs of poor data hygiene showed up in sales productivity.

"If a salesperson creates an account in our CRM that already exists, it can delay how quickly our pricing teams can provide a quote,” he said. “Someone has to reconcile data from multiple systems just to confirm it’s the same business entity.”

Those delays did more than increase internal workload. They introduced friction into moments where speed and confidence matter most. When teams can’t immediately trust the data in front of them, processes tend to slow and credibility can be put at risk.

“If we don’t catch a duplicate, we could end up providing inaccurate information,” Honaker noted. “That creates more work for sales and damages credibility with the customer.”

The challenge became even more complex given Essity’s reliance on distributors. “We’re dependent on the data they provide back to us about who the actual end customer is,” he said. Without a reliable way to connect those records, different teams could unknowingly interact with the same customer under multiple identities.

Essity’s challenges are far from unique. Forrester Consulting research shows that one in four data leaders say they can’t fully trust their data sources, while the average enterprise now relies on more than 30 internal and 30 external data sources — an environment where maintaining data trust becomes increasingly difficult.

The consequences may extend well beyond reporting issues. Poor or insufficient data management has been linked to delayed business decisions, missed growth opportunities, regulatory exposure, and even brand damage. As organizations increase their reliance on analytics and automated decisioning, the cost of operating without a reliable foundation of data trust can grow exponentially.

Recognizing the scope of the issue, Essity reframed data hygiene as an ongoing discipline rather than a one-time cleanup effort. Honaker used a familiar analogy to help align stakeholders around the concept.

“Being a hygiene company, I like to equate it to handwashing,” he said. “It’s not something you do one time and never again. It’s a repetitive, trusted process you repeat regularly to achieve a desired outcome.”

For Essity, data hygiene meant ensuring a few core fundamentals were consistently correct: accurate business names, verified addresses, reliable segmentation, and — critically — unique business entities across systems.

But achieving that consistency at global scale required external validation. “The biggest decision we made was acknowledging we couldn’t fix everything on our own,” said Honaker. “We needed a partner.”

Establishing a Standard for Trust

That partner was Dun & Bradstreet, whose data and solutions Essity used to establish a common reference point for business entities.

“The Dun & Bradstreet D‑U‑N‑S^® Number gives us a visible indicator that we at least have the basics of data hygiene correct,” Honaker explained.

Essity initially measured progress using match rates against Dun & Bradstreet data. Over time, however, the company refined its approach, recognizing that overall match rates didn’t tell the full story.

“We developed a second KPI we called account quality,” said Honaker. “It measured the uniqueness of the D‑U‑N‑S Numbers in our system.”

That shift allowed Essity to focus not just on matching records, but on preventing duplicates from entering the system in the first place. The results were significant.

“The process pushed our match rate above 92% and reduced our duplicate rate to below 10%,” Honaker said. “Some countries had previously seen duplicate rates as high as 20%.”

For many organizations, early data quality initiatives focus on large, one‑off cleanup projects — often tied to major system migrations or transformation programs. While these efforts can provide short‑term relief, they may not address the underlying issue: Business data doesn’t stand still. New records are created daily, organizations expand into new markets, partners and distributors introduce fresh data sources, and existing relationships evolve over time.

Without a mechanism to continuously validate and standardize this inflow, even recently “cleaned” data can quickly deteriorate.

On a global scale, these challenges often multiply. Regional naming conventions, varying address formats, local operating models, and acquisition activity all increase the risk of fragmentation.

Without ongoing oversight and shared standards, data quality issues are likely to resurface in new forms — often downstream, where they are harder and more costly to correct.

From Reactive Cleanup to Proactive Data Stewardship

Reframing data hygiene as a continuous discipline usually needs a shift in mindset from reacting to errors after they occur to preventing them before they enter critical systems. This approach treats data quality as an operational responsibility embedded into everyday workflows, rather than a periodic back‑office exercise.

Proactive data stewardship also helps clarify accountability. Instead of data ownership being diffused or implied, shared rules and automated validation can help almost every function (from sales to supply chain) work from the same trusted baseline. Over time, this can reduce friction between teams and can increase confidence in how data was used to support decisions.

Rather than relying on downstream correction, Essity focused on introducing data validation and enrichment directly into the workflows where new records were created.

Crucially, Essity didn’t treat data hygiene as a back-office exercise. Instead, the company embedded it directly into operational workflows using Dun & Bradstreet solutions such as D&B Connect and D&B Optimizer.

“We started with batch processing through D&B Connect because it was the fastest way to get going,” said Honaker. “Once we proved the value, we integrated Optimizer directly into our CRM.”

That integration helped sales teams to create accounts that were already enriched and verified, eliminating downstream cleanup and accelerating time to value.

"With Optimizer, sales can work with the data immediately,” he said. “We want to get the data right the first time rather than fixing it after the fact.”

Essity also leveraged D&B Rev.Up^™ ABX to help improve data quality at digital entry points. “We were collecting contract data through web forms that had no segmentation info because we didn’t want to ask users to fill in more fields,” Honaker explained. “Form fill gives us that information without increasing friction.”

Forrester findings reinforce this preventative approach, showing that organizations embedding trusted data directly into operational workflows can cut time spent on reconciliation and data correction by up to 50%, allowing teams to reinvest that time into higher‑value, customer‑facing work rather than downstream cleanup.

As digital engagement increased, privacy considerations, especially in Europe, added another layer of complexity. Honaker noted that Essity adopted D&B Visitor Intelligence to better understand website traffic using a flexible approach that includes IP-based matching and tailored identifier solutions, helping support alignment with applicable cookie and data requirements.

“The information is about the business,” he said. “There’s no personally identifiable information involved, so it’s GDPR-compliant.”

That business-level visibility has helped open doors to future initiatives around content personalization and targeted engagement that can be grounded in trusted firmographic data.

As Essity matured its data foundations, the company began to see how consistent and trusted business data could support more advanced analytics and future AI initiatives.

“Curation is fundamental to trust,” Honaker said. “AI has tremendous potential, but bad data will send it down the wrong path very quickly.”

This concern is increasingly shared by data leaders across industries. According to Forrester, more than 40% of organizations report that poor data quality is already limiting their AI initiatives. In contrast, enterprises that establish clean, unified business data foundations are more likely to accelerate analytics maturity dramatically.

In a Forrester Total Economic Impact^™ study, organizations with trusted, standardized data advanced their AI adoption timelines by two to three years — demonstrating that data hygiene is not just preparatory work, but a competitive accelerator for analytics‑driven organizations.

By standardizing and validating data across functions, Essity has begun creating internal bridges between previously siloed teams — sales, finance, supply chain, IT, and master data management.

“These bridges are fundamental to developing the kinds of models we want to build in the future,” Honaker explained.

As organizations advance toward more sophisticated analytics and agentic AI‑driven decision‑making, the margin for data error tends to shrink dramatically. Unlike traditional reporting, which often allows for human interpretation and adjustment, AI systems consume data at scale and operationalize it automatically.

Inconsistent business identities, duplicate records, or incomplete attributes don’t simply create noise. They may skew models, amplify bias, and produce outcomes that are difficult to explain or defend.

In this environment, data quality is no longer a technical concern confined to IT teams. It becomes a strategic prerequisite for responsible automation. Without a trusted and standardized data foundation, even well‑designed AI agents and models risk reinforcing inaccuracies that compound over time.

The Hidden Risk of Fragmented Business Identity

One of the most common obstacles to analytics readiness is fragmented business identity. When the same customer, supplier, or partner exists under multiple representations across systems, analytics efforts struggle to produce meaningful insight. Forecasting becomes less reliable, segmentation is distorted, and predictive outputs are built on incomplete or conflicting inputs.

Building Explainable, Scalable Intelligence

Beyond performance, trusted data is essential for explainability and governance. These two concerns may become more pronounced as analytics and AI initiatives scale. Business leaders need to understand not just what a model recommends, but why. That transparency depends heavily on clear data lineage, standardized definitions, and confidence in the underlying inputs.

Today, Essity’s data hygiene efforts extend well beyond fixing names and addresses. The company has deployed machine learning models to improve sales pipeline quality and opportunity sizing — initiatives that depend significantly on trusted underlying data.

“Everything we’re trying to do is enabled by having verified, complete, and enriched customer data,” said Honaker.

Looking ahead, Essity plans to extend its data hygiene program into additional regions and continue building on the foundation it has established.

"We initially just wanted accurate names and addresses in our systems,” Honaker reflected. “We’ve come a long way.”