Insights
Customer Data: Why Organizations Still Struggle.
Organizations are investing heavily in AI, personalization, and digital customer experiences. Yet one critical prerequisite is often overlooked: the quality of the customer data that powers these initiatives. Without reliable, complete, and consistent data, it becomes difficult to truly understand customer behavior, automate processes, acquire reliable statistics, or unlock the full potential of AI.
This article was developed in collaboration with Stibo Systems
Quick navigation:
- Trust In Customer Data
- The Impact Of Poor Customer Data
- The Real Challenge
- Start With The Foundation
- From Insight To Action
Trustworthy Customer Data: The Key To A Complete Customer Profile.
Although virtually every organization collects customer data, confidence in that data remains surprisingly low. According to the research, 69% of organizations do not fully trust their customer data. In addition, 82% report that employees need to gather information from multiple systems to create a complete customer view, while only 10% say their data sources are well integrated.
This article draws on findings from the research article “How organizations manage customer data today – in numbers” , conducted by Stibo Systems.
Spreadsheets also continue to play a significant role. More than three-quarters of organizations still rely on them alongside their core systems to manage customer information. This increases the risk of duplicate records, conflicting versions of data, data leaks, and errors throughout business processes.
As a result, employees spend more time searching for, validating, and correcting information, while customers receive a less consistent experience.
The Impact Of Poor Customer Data.
The consequences of fragmented and unreliable customer data extend far beyond operational inefficiencies. Organizations miss opportunities to improve sales, customer service, and overall customer satisfaction.
When different departments work with different datasets, they inevitably develop different views of the customer. Marketing relies on other information than sales and customer service uses different insights than e-commerce. AI applications can amplify these inconsistencies when they rely on incomplete, outdated, or fragmented data.
The research shows that 55% of organizations lose revenue as a result. Decision-making, reporting, and analytics also become less reliable. As organizations increasingly adopt AI for personalization, forecasting, and automation, these issues only become more visible.
After all, AI generates unreliable insights when the underlying data is inaccurate.
AI Starts With Strong Customer Data.
Many organizations currently focus their efforts on implementing new AI solutions. While understandable, the biggest challenge often lies one level deeper.
Successful AI starts with a strong data foundation. This means customer data should be:
- Complete and up-to-date;
- Consistently managed across systems;
- Governed with clear ownership and responsibilities;
- Consolidated as a single, trusted source of truth when using for analytics and statistics.
Without this foundation, existing customer data issues are not resolved, they are simply amplified. AI magnifies data quality issues, exposing them faster, more broadly, and more visibly than human processes. If your data foundation is weak, your AI initiatives will be too.
Customer MDM: Start With The Foundation.
A consolidated Customer Master Data Management (CMDM) approach provides a strong foundation for creating a single, reliable view of the customer. By bringing customer data from different systems together in one governed environment, organizations can improve data quality, establish clear ownership, and ensure that departments and applications work with consistent customer information.
Rather than managing customer data separately across multiple systems, a consolidated CMDM solution creates a centralized source of trusted customer data. This provides the consistency and governance needed to support AI initiatives, analytics, and customer-centric processes at scale.
From Insight To Action.
The research shows that many organizations face the same challenges. The question is no longer whether customer data needs improvement, but where to start.
At Squadra, we help organizations build a future-proof data foundation. From assessing the current state of your data and establishing governance to developing a Master Data Management (MDM) strategy and implementing the right solutions, we help create a reliable foundation for better customer processes, data-driven decision-making, and successful AI initiatives.
Ready to improve the quality of your customer data?
Exceptional customer experiences and successful AI initiatives start with trusted data. Our experts help you assess your data maturity, identify improvement opportunities, and develop a roadmap tailored to your organization’s goals.
Get in touch with us today and discover how to build a future-ready data foundation.
