Data Management: When all the information is correct, but none of it fits together.

Sales views the customer as active. Marketing considers them inactive. Service sees a customer who has been waiting days for a solution. Who is right? Possibly all three. That is precisely the problem.

The meeting begins with a simple question: What is the status of our existing customers? Sales reports good prospects; Marketing notes declining response rates; Service points to a growing number of open inquiries. Three analyses, three different pictures. Consequently, the team must first clarify which figures are current, which contacts were included in the analysis, and what "active" actually means. The core question has to wait. If this sounds familiar, your company may not be lacking data; rather, it lacks a common foundation for interpreting that data correctly.

One customer, multiple truths

Differing perspectives are perfectly normal at first. Sales focuses on conversations and sales opportunities; Marketing looks at campaigns and responses; Service deals with inquiries and their resolution. Problems arise when these fragmented views are not consolidated. A customer might receive an invitation to a consultation while waiting for a solution to an urgent problem. Or they might be included in a re-engagement campaign just as Sales is negotiating a new contract. Each individual action makes sense based on the available information. Yet, from the customer's perspective, the company appears poorly coordinated.
The root causes often lie in a system landscape that has evolved over years. Contacts are stored in the CRM, orders in the ERP, consent records in the marketing tool, and open tickets in the service system. Then there are the lists created for specific tasks that have long since taken on a life of their own. Even when data is transferred between applications, a reliable, comprehensive picture does not automatically emerge. Do two data records belong to the same company? Has the new contact person been updated across all systems? And does “existing customer” mean an active contract, an order within the past year, or any purchase in the company’s history?

The cost of contradictory data in everyday life

This effort rarely appears as a distinct line item in cost accounting; instead, it is embedded in follow-up queries, manual reconciliations, and deferred decisions. Sales might review the recipient list one last time before a campaign; someone might merge multiple data exports for management reporting; or the current status might need to be pieced together from various applications before a client meeting. While this may work in isolated instances, as a permanent way of working, it consumes time and makes processes dependent on specific individuals knowing the missing context.
The situation becomes even more difficult when employees no longer trust the data in the system. Anyone who repeatedly encounters outdated contact details or incomplete customer histories will eventually create their own personal record. This is understandable, yet it creates yet another data source that requires maintenance. With every additional list, uncertainty grows regarding which version is the definitive one. Under these conditions, a CRM system may function flawlessly from a technical standpoint, yet still lose its relevance in day-to-day operations.

An interface alone does not create a unified view of the customer.

Connecting systems is an important step. However, it must first be clear where information originates, which system holds the authoritative data, and how changes are propagated. For instance, a new address might be maintained in the ERP system, while the sales team updates contact details in the CRM and the marketing team manages consent. This requires clear, transparent rules; otherwise, conflicting information is simply distributed between applications more rapidly.
Shared definitions are equally important. Sales and marketing do not necessarily have to evaluate a customer using the same criteria, but they should be able to understand the basis of each other's assessments. A contact might be relevant to the sales team yet lack consent for promotional communications. A company might place regular orders without ever responding to newsletters. When this information is accurately mapped and used in the appropriate context, the different perspectives complement one another. This is precisely where professional data management comes into play: ensuring the quality, integration, and reliable use of data across system boundaries.

Start with a specific question

You do not need to reorganize your entire data landscape right away to get started. It is often more helpful to begin with a specific question that your company currently struggles to answer without extensive coordination—for example: Which existing customers haven't placed an order in the last six months? Which open sales opportunities also have unresolved service cases? Which contacts are we permitted to reach out to for a specific campaign? Such questions quickly reveal missing information, mapping failures, and the need for specific rules. This allows data management efforts to be aligned with a task that delivers immediately recognizable day-to-day value.
This foundation remains crucial even when using AI. An automatically generated customer summary is only useful if the relevant information is available and correctly linked to the right customer. If current service cases are missing or outdated status information is being used, the summary may present an incomplete picture. It is therefore worth performing a simple check before utilizing further analyses and intelligent features: Can we trust the data underpinning these results?

Sales, marketing, and service teams do not need to share the exact same assessment at their next meeting; their differing perspectives are valuable. However, they should not have to waste time debating whether they are even discussing the same customer and the same set of data. That way, more time remains for the question that really matters: What do we do next?