It’s a good problem to have, really – the business is expanding, customer volumes are increasing, interaction and engagement are thriving, and revenue grows. 

But along with the positives of taking on more and more customers are certain growing pains. More collections points – eCommerce, email, social media, apps, and more – equals greater volume and variety in the kind of data you need to digest before you can make use of it. 

As the business scales and you take on increasingly more customer data, don’t get blindsided by some of the common challenges faced when data grows.

Top 3 Challenges in Scaling Customer Data Management

Consider this: if you could process large volumes of data in minutes instead of hours or days, what would need to change? The frequency in which you investigate? The speed at which decisions can be made? The questions being asked?

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 are 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.

2. 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. That means no unified customer view, which in turn means mistakes in segmentation, insights, and personalization. This is detrimental to the customer experience and as a result, bad for business.

4. 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

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.

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.

2. 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.

3. Stop bad data before it enters your system

Scaling with your customer data isn’t just about making more room for data, but keeping that data clean and error-free. That means embedding data quality checks in every step of the customer journey, like AI-powered data quality functions that verify accounts at form submission or validating addresses at checkout. So the data you have is exactly the data you need.

4. Remove unnecessary data prep

One step to simplifying large-scale customer data management is removing unnecessary tasks wherever possible, and automating anything that can be. Data prep work like normalizing and standardizing customer data from various sources/schema will be seamless with a solution which 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.

To recap: what goes into a truly scalable customer data solution?

Optimizing your customer data strategy can surely introduce its fair share of challenges. Contact data solutions need to be as nimble as the strategy pulling in that data.

But sometimes the best 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.

Keep it simple – when you pull back focus, there’s really only 5 key points that you need when it comes to customer data solutions. Save this as a checklist next time you’re evaluating a potential partner to make sure they’re the right fit for you and your data.

1. Flexibility

Customization available at every step to work with your existing infrastructure and ensure you’ll never outgrow the platform.

2. Scale

Able to process billions of records quickly, no matter the source, so no data goes unused.

3. Speed

Ingesting data and querying it on the spot, with real-time validation so customers are never left hanging.

4. Interoperability

Fast, easy, comprehensive ways to get data in from anywhere and out to any system, so you’re never held hostage by one product or suite of tools.

5. Quality

Above all, accurate and error-free data is the goal. Effective data cleansing can make your database really trustworthy, but done incorrectly will be detrimental to your working day, your corporate and personal goals, and your customers.