Introduction

A well-crafted customer data strategy unlocks new capabilities for your organization and supercharges your existing processes. However, data must be managed under the guiding principles of a strong data governance strategy. This means your data needs to adhere to technical requirements, comply with privacy and data security standards, and be accessible to the right people within your organization to make effective use of it.

In this post, we’ll break down the difference between a good customer data strategy and a great one. A good strategy gets you off the ground, allowing you to start seeing things from a new perspective. It gives you the foundation to set future goals and to make progress toward them. But a great strategy? That’s future-proof. It not only helps your organization reach its destination with ease, but it also delivers fantastic experiences for your customers and provides a layer of optimization that keeps your processes running smoothly.

What is a customer data strategy?

A customer data strategy refers to the collection, management, and usage of customer information across various touchpoints and tools. It is essentially the combination of tools, processes, and use cases that empower your business to extract meaningful insights from your data. 

You work so hard to collect rich and relevant data, it only makes sense you want to get the most value out of all that work. A well-tuned customer strategy ensures this by unlocking a range of capabilities. From data-driven decision making that enhances customer experience and outcomes, to ensuring you can use that data safely and following the guidelines of privacy regulations like GDPR and CCPA.

Great data starts at the point of collection. Getting this part right allows you to confidently look at the customer data strategy maturity curve and plot a course forward. Progress will be made by focusing on ongoing optimizations, such as implementing schema frameworks and setting up integrations to services your team uses everyday.

Why Good is a Necessary Step on the Path to Great

You’ve got to start somewhere. The absence of a customer data strategy is a quantifiable point on the maturity curve, but it’s one you need to move beyond quickly. Knowing you need to improve is the critical first step, and many organizations are already at the “good” stage, working to architect their way to the next level.

Good, of course, is the stepping stone to great. It provides the foundation your business needs, enabling you to progress along the maturity curve. If at any point while reading this post you feel like “good” is somehow bad, let’s reframe that perspective. Good is simply a necessary step in the journey towards great. It sets you up to scale and optimize your customer data strategy, giving you the foundation to move confidently toward becoming truly data-driven and future-proof.

Good vs. great customer strategies

When we look at the customer data maturity curve, we get a clear picture of where we currently stand and where we have opportunities to grow and improve. In this section, we’ll break down the key aspects that differentiate a good customer data strategy from a great one. Below is a simple table that highlights these differences.

Central Control Plane

The central control plane is the system or platform that centrally manages all your data, acting as a unified control layer over your organization’s customer data infrastructure. It answers the key question: how do you manage all your data streams and sources? This is essential for consolidating your data and ensuring smooth operations across various systems.

Good

At the “good” stage, you’re just starting to introduce the concept of centralizing your data streams. While you begin to understand the importance of consolidating data, silos will still exist within your organization. This is the initial step in the journey toward building a centralized control plane.

Great

In the “great” stage, you’re realizing the full potential of centralization. At this point, Customer Data Platforms (CDPs) and Customer Data Infrastructures (CDIs) are fully in place to collect and manage data across the board. While you may still use point solutions like CRMs and marketing automation platforms (MAPs), there are no more data silos. Complete visibility and data governance now occur across all systems, ensuring seamless integration and management.

Total Control

Total control means having full access, visibility, and authority over all aspects of your customer data strategy. How you track your data is paramount here – reliance on third-party tags chips away at your control, and exposes you to risks you may not otherwise think about. Controlling your data collecting and routing fully insulates you from risks to customer privacy through third party vendors, but also future-proofs you in an environment where cookie deprecation is ongoing.

Good

At the “good” stage, you have visibility into your data, but it may be fragmented across different systems. Access to data is often manual and relies on ad hoc requests. For example, when you need specific data from a system, you either retrieve it manually or ask your IT team for assistance.

Great

At the “great” stage, you achieve complete control over your data. You can seamlessly access and manage all data sources, and data governance policies are automatically enforced across the board. Data flows smoothly throughout the organization, ensuring efficient operations and accurate insights.

Confidence in Compliance

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Is your data fully compliant with regulatory requirements like GDPR and CCPA? And how do you know? Could you provide this information at a moment’s notice if you were audited? Having the checks in place to ensure you’re working with compliant data is absolutely critical. Automating adherence to privacy regulations and managing compliance within your systems is essential.

Good

You have the basic frameworks in place to handle consent and compliance with regulations like GDPR and CCPA. Some manual processes are still in place, but you’re becoming aware of the increasing need for automation around your compliance requirements.

Great

Compliance is deeply embedded in your data collection and distribution processes at the point of data collection. You have real-time monitoring and proactive management of compliance and consent. Most importantly, enforcement happens at the point of data collection and distribution. When data is collected from customers, you know exactly what data is permissible to distribute to various endpoints and whether data needs to be anonymized or withheld from certain systems.

• Is your data fully compliant with regulatory requirements like GDPR and CCPA? How do you know? Can you provide this information at a moment's notice? What checks are in place to ensure you are working with compliant data? Are you reacting to compliance or do you have systems in place to automate this?
• Good - basic frameworks in place to handle compliance with regulations like GDPR and CCPA; some manual processes, and awareness of compliance requirements 
• Great - Compliance deeply embedded in data collection and distribution processes; real-time monitoring, proactive management, and enforcement at the point of data collection

Integration Capability

Your customer data strategy needs to support robust integrations. The data you collect should seamlessly connect with other platforms. For example, can your marketing team access the data they need for real-time personalization? Can you quickly understand friction in your checkout experience within the customer journey? A solid understanding of data ingestion, processing, and activation is essential for achieving integration capabilities.

Good

You have partial integration across tools and data flows. Platforms like your CDP, marketing automation, and other tools are somewhat connected, but silos still exist. Data doesn’t flow seamlessly between all platforms. At this point, it’s becoming clear how powerful connected data can be, and your organization is developing an appetite for deeper integrations to unlock more advanced capabilities.

Great

At the “great” stage, integration is fully realized across all platforms, with data flowing effortlessly between them. You have a unified customer profile that’s available in real-time across any system. Data governance policies, such as redaction or access restrictions, are in place and seamlessly integrated based on business requirements. Additionally, data is actively used across sales, marketing, and customer support, optimizing each department’s efforts.

Tech Agnosticism

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Lego is such a beloved toy because you can mix and match pieces from any set and let your imagination run wild. In tech, though, we often face something called vendor lock-in, which can stifle growth and limit flexibility. The ideal state for tech agnosticism is having a tech stack that integrates seamlessly, regardless of integration protocols, proprietary schemas, or existing data silos. You want to handle new technologies and data sources without taking on additional tech debt or locking yourself into one ecosystem of products. Server-side technologies allow you to manage your data while remaining free from the constraints of a single tech stack.

Good

At the “good” stage, there’s a reliance on specific vendors or platforms, meaning flexibility is limited. You may have some integrations in place, but they can be a little sticky, and vendor reliance is key to getting your customer data strategy off the ground. You need endpoints to collect and distribute data, but integrating multiple systems may already be showing signs of difficulty.

Great

At the “great” stage, you’ve achieved full tech agnosticism. Switching vendors is seamless from a data perspective. You can interlock different platforms in new patterns without disruption. There’s flexibility in adding new platforms and tools with minimal development effort or downtime, and data flows smoothly across those systems. When you bring on a new system, you can quickly integrate it with your unified customer profile, ensuring it’s up and running without issue.

Data Quality Assurance

Not all data is collected equally. High-quality data is key to unlocking the capabilities of your customer data strategy. You need your data to be accurate, complete, reliable and be able to obtain changes in the buyer profile in real time. This means having automated workflows in place for data quality checks, compliance, and accuracy. One aspect that’s often not discussed enough is trust in the data. It’s crucial that the various departments using the data believe it’s accurate. Setting up workflows and automations to ensure this trust is a key part of a strong customer data strategy.

Good

At the “good” stage, data quality measures are in place, but they tend to be manual or semi-automated. These processes are often handled within individual point solutions like your CDP or marketing automation platform. At this point, organizations recognize the importance of clean data and are taking steps to address it. For instance, cleaning up data in one system reveals that other systems might not align, creating the need for a unified customer profile that enforces consistent data quality across the board.

Great

At the “great” stage, there is a centralized, automated data quality assurance mechanism in place. This helps create a single source of truth for your data, ensuring data pipelines deliver clean, accurate data from a unified customer profile to various endpoints without loss of accuracy. The organization has a high level of trust in its data, knowing it’s reliable and can be used confidently across all departments.

Real-Time Data Access

When data is collected, how quickly can you use it to enhance your segmenting or timing of their next engagement? Real-time personalization means exactly that—milliseconds count in the fast-paced digital world, where enhancing your customer experience often requires real-time inputs. Having real-time data access is crucial because it enables you to send data to various systems and respond instantly. For certain use cases, like displaying location-based personalized advertising, this capability is essential for increasing conversion rates.

Good

At the “good” stage, you’re relying on batch processing, where data is sent from different systems, causing some delays. You’ve introduced structured data collection, which is a positive step, and you’re working toward making that data available to the necessary systems. However, there’s still a lag that can be frustrating, and you’re starting to see the potential of real-time data access and are preparing to unlock that capability.

Great

At the “great” stage, data availability is ubiquitous. All systems have real-time or near real-time access to data as it’s collected and can immediately use it for customer engagement, personalization, and operational purposes. The data flows seamlessly across different systems, unlocking powerful capabilities for boosting engagement and optimizing customer experiences.

• When data is collected, how quickly can you use it to enhance your activities? Real-time personalization means exactly that – milliseconds count in the fast-paced digital world
• Data needs to be accessible once its collected providing instant access
• Good - batch processing; relying on specific tools for access to data; structured data collection introduced and attempts at making data available to necessary systems in place
• Great - Data availability is ubiquitous; all systems have real-time access to data as its collected, and can use it in efforts like customer engagement, personalization, and operations

Scalability

Scalability in your customer data strategy is critical. Deploying your strategy on cloud-native architecture ensures that your systems can automatically scale to meet growing demands. Key questions to consider include: Can your systems handle surges in demand? Are you preparing for future growth? And what about integrating with new systems as your needs evolve? Being future-proofed for scalability challenges is essential for modern customer data strategies.

Good

At the “good” stage, your systems can meet current demands, but handling surges may require development on a case-by-case basis. These surges could lead to data loss or inconsistencies. While you have a solid understanding of your existing limitations, you also have clearly defined requirements for scaling in the future and know the path forward.

Great

At the “great” stage, dynamic growth is easily handled thanks to cloud-native, serverless, and auto-scaling architectures. Your data volumes are seamlessly managed, and new tools can be integrated without disrupting existing services. Surges in demand are handled effortlessly, ensuring that your systems continue to operate smoothly under any load.

Advanced Analytics

Your customer data strategy requires a parallel analytics and reporting strategy to ensure that you’re extracting valuable insights about the customer journey and experience. These insights should fuel your business decisions. Key questions to ask include: Does your data collection enable advanced techniques? Are you getting the deep insights you need? Are your reports and dashboards helping answer critical business questions? And is your data ready for machine learning (ML) and AI systems?

Good

At the “good” stage, you have standard reporting and analytics in place. You can clearly measure KPIs and metrics across the organization, and your teams have access to central reports and dashboards for key processes. The data fidelity is strong, but there’s room for improvement. You also have a solid understanding of what needs to be done to move to the great stage, especially to fully leverage ML and AI technologies.

Great

At the “great” stage, you have clean data sets that are ready for machine learning and AI-driven analytics. Your insights into the customer journey and experience are translating into actionable business insights, driving data-informed decisions. Optimization strategies are fully in place, and as you collect more data, your confidence in improving the customer journey grows. You’re seeing tangible benefits from your data-driven strategies, making continuous improvements along the way.

AI Utilization

Artificial intelligence is all the rage these days because of its ability to generously impact your data processes. Making sure your customer data strategy allows for AI to enhance data processes is a key component of a future-facing customer data strategy. You want to be able to use that data with AI tooling and in other systems for personalization, automation, and predictive analytics. These capabilities are important, but there’s also an element of preparing for the future because AI is rapidly changing.

Good

You’re currently using AI for basic, repetitive tasks like data cleaning or customer segmentation. These processes have gained efficiencies, and while they are helpful, you’re starting to realize the full potential AI has to offer and are interested in expanding those capabilities.

Great

At the “great” stage, AI is fully embedded into your systems and used to its full potential. It’s driving predictive analytics, personalization, and decision-making, all built on layers of data that allow for future expansion as AI continues to evolve. You’ve integrated AI deeply into your customer data infrastructure, and it’s delivering impactful results.

User Empowerment

Providing access to your data in alignment with your data governance strategy is a key component of your customer data strategy. The ability for non-technical users to access and use data in their day-to-day work without having to rely on technical teams is a major benchmark in any customer data strategy. Teams like marketing and sales will use this data regularly to enhance their efforts, such as real-time personalization and personalized outreach. The key here is self-service—ensuring that these teams can access data safely, securely, and on demand.

Good

At the “good” stage, data is accessible, but often requires assistance from technical teams to generate reports or handle more complex queries. You may have dashboards in place that provide insights, but they lack the flexibility that business users need for decision-making. This creates regular requests for more detailed data. There is a growing recognition of the need to improve data access, and your data governance policies are evolving to ensure that users have the appropriate access rights while aligning with security and privacy policies.

Great

At the “great” stage, your data access strategy empowers business users through self-service tools. Access to data is done safely and securely, adhering to data governance policies. You’ve right-sized access for users, ensuring customer data is accessed in compliance with privacy regulations. Additionally, there is a noticeable reduction in the need for IT support on routine tasks, which helps to eliminate bottlenecks and speeds up decision-making across the organization.

Privacy and Security

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Data privacy and security is paramount, especially in today’s regulatory environment where CCPA and GDPR are being fully enforced. This requires a robust data governance policy to ensure that your customer data is protected. You need safeguards in place to defend against unauthorized access, breaches, and misuse, while ensuring consent for data collection is obtained and enforced across all systems.

Good

At the “good” stage, security measures are in place, but there’s still a reactive component to your approach. Consent management is enforced across siloed systems, but you’re missing a central enforcement mechanism. While foundational security protocols are established, there may still be isolated or manual processes for identifying vulnerabilities. However, there’s a clear roadmap for understanding how to fully implement your data governance policies, with plans to set up the necessary tools and automate privacy and security components.

Great

At the “great” stage, privacy and security mechanisms are built into every layer of the data infrastructure. Data is protected through encryption, anonymization, and role-based access controls. Consent and compliance are enforced at the point of data collection, and a chain of custody follows the data throughout its lifecycle. Data governance policies are integrated into your tooling, with automation ensuring ongoing compliance. You are prepared to respond to an audit swiftly and with confidence, knowing that your privacy and security standards are rock solid.

Cross-Channel Cohesion

The promise of a customer data strategy for advertising, marketing and e-commerce teams is the ability to use a unified customer profile across all channels. Leveraging data from web, mobile apps, and in-store interactions represents the ultimate goal of first-party data collection. The key is breaking down silos and avoiding fragmented or inconsistent data, ensuring that the data used across channels is both consistent and reliable.

Good

At the “good” stage, data is managed within silos, with each system collecting data independently. While there are integrations in place that help manage certain stages of the customer journey, the full journey is still not fully unified. Solutions tend to be reactive, like using remarketing based on website interactions. You may not have a fully unified customer profile, but the need for one is becoming very clear.

Great

At the “great” stage, you have a unified customer profile that’s accessible and maintained across all systems in real-time. All teams can view the same customer information, creating a single source of truth. Cross-channel customer journeys are seamless, with consistent personalization across platforms like web, mobile, and offline touchpoints. Your strategy is fully aligned across departments, enabling coordinated improvements to the overall customer experience.

Adaptability

Business is constantly changing and evolving. New regulatory environments, market trends, and business requirements will put strain on your customer data strategy if it’s not flexible and adaptable. Adding new technologies to your platform and strategy is crucial. The rapid pace of change—whether in data collection capabilities, browser restrictions, or privacy regulations—can quickly impact your customer data strategy.

Good

At the “good” stage, adaptation is possible, but often requires significant development and manual efforts. When bringing in new tools or making updates to your data infrastructure, you need to involve your development team, which can turn into a sizable project. Your business is prepared for new regulations, but because you rely heavily on point solutions, aligning your data policies with those regulations can be challenging. You have a clear understanding of the future flexibility needed, but recognize the pressure this places on your IT teams.

Great

At the “great” stage, you have agility and adaptability built into your data infrastructure. You can respond to changes in the market or regulatory requirements with relative ease. The infrastructure can be tweaked with minimal effort to support new tools and workflows. Your business is fully equipped to meet shifting demands, seamlessly adapting to evolving data needs and external pressures without disruption.

The role of CDIs in Customer Data Strategy

Customer Data Infrastructure (CDI) tools serve as a command and control structure for your customer data strategy. They can become the linchpin of both your technological setup and data governance approach, helping you move from a good customer data strategy to a great one.

CDIs help by providing a unified data management system that enforces compliance at the moment of data collection. They automate compliance processes, making sure that the data you collect and distribute stays fully compliant with modern data and privacy regulations.

One of the significant advantages of CDIs is their ability to integrate with any platform, meaning you can send your data anywhere. You remain tech-agnostic because you control the data schemas and distribution endpoints. This gives you full control over what data you collect and where it’s distributed.

CDIs also allow you to improve data quality by putting data checks in place before sending the data to various platforms. You can clean up your data and append additional information to the payloads you send across systems.

CDIs make use of cloud-native technology, such as serverless architectures and auto-scaling features, eliminating the need for manual intervention when there’s a surge in demand. These tools, like MetaRouter, future-proof your customer data strategy by offering a tech-agnostic data collection engine that aligns with your data governance policies and allows for seamless data collection and distribution across any tool in your tech stack.

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