Recently, I had an introductory conversation with a new colleague about our Sales & Marketing (Data) solutions. During this conversation, the topic of CRM data quality came up – a point that still remains a stumbling block for many organizations. My colleague was surprised to learn that even renowned organizations still struggle to maintain the quality of their data. This got me thinking: why does this problem keep recurring? What makes so many companies, despite good intentions, continue to struggle with data quality? Is it the technical limitations, a lack of priority, or is there something else at play?
The answer is, of course, not straightforward, but several underlying causes almost always play a role. Here, I share my insights on the biggest challenges surrounding data quality and some concrete steps organizations can take to improve it.
Why does data quality remain a challenge?
1. Lack of ownership and responsibility:
Without a clearly designated owner for data quality, the issue remains spread across multiple departments. Departments such as sales, marketing, and customer service all use the same data, but don’t necessarily feel responsible for managing or improving it. This often leads to errors being ignored or passed along, causing data quality to gradually deteriorate. When no one is specifically accountable, there’s a lack of accountability. Data quality quickly becomes the "hot potato" that no one really wants to handle.
Interesting read: 3 reasons why almost no company has its data management in order
2. Insufficient priority from management:
When data quality doesn’t have a clear owner, it often remains underemphasized at the management level. Without priority or ownership, many organizations see no reason to invest in data quality. This leads to a lack of budget, time, and attention, while poor data quality has invisible but far-reaching consequences for business operations.
3. Short-term focus:
CRM systems are often primarily used to achieve immediate sales results. This means the focus is on quick data entry and processing, with little attention given to accuracy. Data is sometimes entered incomplete or carelessly in order to maintain speed, leading to errors that are difficult to correct later on.
4. Loss of local context in centralized data management:
For large organizations, it is often more efficient to organize data management centrally. However, when local knowledge and context are lost, this can lead to less relevant data for specific departments or markets. Without the nuances of local customers, market dynamics, and cultural differences, the data sometimes lacks the value it could have for local departments.
Solutions to improve CRM data quality
Although improving data quality is a complex challenge, there are indeed steps companies can take to make significant progress. Here are some practical solutions:
1. Clear ownership and shared responsibility:
The first step towards better data quality is to designate a responsible person – such as a Data Steward or Data Quality Manager – who will have ultimate accountability. Additionally, it helps to set up a cross-functional team consisting of representatives from sales, marketing, customer service, and IT. Together, they can develop and implement a strategy for improving data quality.
2. Start with a first step and learn along the way:
Instead of waiting for the perfect strategy, it is often more effective to just get started – even with a small project. Consider a data quality audit, a CRM data cleanup, or setting up a simple data quality check. By taking concrete steps and then evaluating the results, companies can learn and improve. This creates a culture of continuous improvement, where the experience gained along the way helps make increasingly focused decisions.
Interesting read: We have everything your CRM needs
3. Develop a culture of data-driven decision-making:
When employees realize that data quality directly impacts the quality of decision-making, the importance of accurate data naturally increases. By making more and more decisions based on data, a culture develops where data quality is central. This turns data quality into a shared responsibility that everyone is committed to, rather than an isolated problem that only falls on IT or data analysts.
4. Work towards a "Customer 360-view" as the end goal:
Aim for a complete customer view, where all relevant customer information is centralized and accessible to different departments. A "Customer 360-view" helps marketing, sales, and customer service obtain a more complete and consistent customer profile, leading to better insights and a more personalized customer approach. Such a 360-view directly highlights the importance of good data quality, as each department benefits from reliable and up-to-date data to perform their tasks optimally.
Interesting read: White paper: 360-Degree Customer View
In my current role, I enjoy speaking with clients about these challenges and solutions. While I know that we will never be "done" improving CRM data quality, I am convinced that every step in the right direction makes a difference. And while technology, such as AI, can help with cleaning and maintaining data, human insight and ownership will always play an essential role. It’s not about perfection, but progress.
