Data Quality

Overcoming the Challenges of Customer Data

Data quality directly impacts business outcomes well beyond the customer experience. Learn how to overcome ever-changing customer data.

It’s a good problem to have, really – the business is expanding, customer data volumes are increasing, and strategies are becoming more data-driven.  

Yet acquiring more users and customers can make it challenging to simultaneously maintain customer retention and relationships. What’s more, increased entrance and collection points equal greater volume and variety in the kind of data you need to digest before you can make use of it.  

The right data management platform ensures your enterprise can identify, convert, and retain your customers at scale while avoiding the common barriers others face on their way to becoming a customer-centric enterprise.  

Challenges in Scaling Customer Data Management 

Businesses are increasingly taking on more integrations and relying on AI-powered automations to perform background tasks, adding to the complexity of managing large volumes of data. Common challenges preventing organizations from becoming truly data-driven, customer-centric enterprises include time-to-answers, disparate data and systems, and incomplete customer profiles. 

  1. Getting answers fast enough

Speed is a challenge for multiple reasons. Adding new customer data platforms and tools can take a long time to get fully onboarded, let alone until realizing value. Once they’re in place, processing speed can mean waiting hours or days for data. And the accessibility and intuitiveness of the platform has a major impact on speed when it comes to actually utilizing that data to extract insights. 

  1. Fractured Identities

Customers engage across multiple channels, online and offline, resulting in a different identifier each time. Without a common key to link these interactions, it’s impossible to be sure that they all apply to the same person. These unmatched and duplicate records are detrimental to your organization, preventing unified customer views and accurate segmentation, insights, and personalization.  

  1. Siloed Data

Not only is customer data fragmented, but the different bits are typically stored in their own systems — email, loyalty programs, digital engagements, on-site, etc. — and these systems don’t talk to one another. Manual attempts to connect them are notoriously time-consuming and fragile, often falling apart when changes are introduced. As companies add new customers and new channels, the problem only grows more complex. 

How to Maintain Large Volumes of Customer Data 

Enterprise customer data solutions focus on improving the customer experience with holistic customer profiles and relevant customer insights. That level of data transparency and control is only made possible by enabling increased access to data, along with data governance and data transformation controls for the business-user level. 

  1. Make your data accessible

It can be a challenge to collect the right data from diverse sources, and an even bigger challenge to do so at the speed your business demands. User-first data management platforms don’t just unify offerings like data quality, harmonization, or integration tools – they meet the user at their level with intuitive, accessible design. 

  1. Look for solutions capable of managing larger data sets

Whether on-site or in the cloud, look for data quality and other customer data tools that are built to support scaling massive data sets. Integrations with leaders in data management solutions like Snowflake, Spark, and Alteryx indicate a commitment to large-scale data and complex use cases. 

  1. Stop bad data before it enters your system

Scaling your customer data isn’t just about making more room for data but keeping that data clean and error-free. Best practices in customer identity management embed data quality checks at every step of the customer journey. AI/ML data quality functions remove friction and create a more seamless customer onboarding by verifying accounts at form submission. Validating addresses in real-time at checkout means reduced cart abandonment rates and greater ROI. These more robust, accurate, and valuable customer profiles drive a positive customer experience. 

  1. Remove unnecessary data prep

To simplify large-scale customer data management, automation of manual tasks is key. Data prep work like normalizing and standardizing customer data from various sources/schema will be seamless with a solution that integrates these steps effectively into the workflow. By removing as much of the inherent burden of managing customer data as possible, IT and data engineers have more time for other projects. 

Enterprise customer data management software 

Customer data management software needs to be as nimble as the strategy pulling in that data. When evaluating a potential customer data platform, use the five key points below to make sure they’re the right fit for you and your data. 

  1. Flexibility

 User-friendly data management software enables users to customize, save, and share complex workflows and simplify data transformation for structured and unstructured data. Easy-to-use data quality tools help business users generate the customer insights they need, quickly, no matter the user’s skillset. 

  1. Scale

Scalable customer data solutions obviously need to process billions of records quickly, no matter the source or shape of the data. But accessibility and user-friendly dashboards are also inherent to making sure your data strategy can scale with the organization. Enabling users to access and build unified customer profiles eases the workload from development teams and ensures long-term data quality. 

  1. Speed

Seamless customer experiences these days depend on speeding up the time-to-insights for both customers and the business user. Ingesting data and querying it on the spot requires real-time validation so customers are never left hanging. 

  1. Interoperability

Data management software should be source and target agnostic, meaning they’ll always work with your existing infrastructure and ensure you’ll never outgrow the platform. Data catalog and governance rules, for example, form a well-indexed, profiled database that enables a bidirectional flow of data between ERPs and other applications in the enterprise.  

  1. Quality

Above all, accurate and error-free data is the clear path to optimized customer relationships. Effective data cleansing should resolve fragmented customer data profiles across the business without excess strain on the IT or data engineering department.


Optimizing your customer data strategy can surely introduce its fair share of challenges. But the most powerful customer data solution doesn’t have to be the most complicated. Leveraging future-proof technologies means considering all the ways your workflow has evolved along with the customer data you manage – and considering that a different way is possible. 

The quality of data directly impacts business outcomes, efficiency, and agility well beyond the customer experience. Read the free resource, The Top 5 Reasons to Implement a Data Quality Solution, to learn more about how a unified data quality tool optimizes growth across the business. Ready to get started? Contact Syniti's team of experts today. 

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