The differences between customer data platform (CDP) and data lake

Highlights

You’ll learn the differences between a CDP and a data lake and their use cases for data management.

• Customer data platform (CDP): Designed for marketers, it unifies customer data for real-time engagement and personalization
• Data lake: Built for data scientists, it stores diverse data formats for advanced analytics and big data

Opt for a CDP today to optimize real-time engagement and reach your customers with tailored content.

If you speak to business leaders, most will tell you they have no problems generating data. As businesses interact with customers, they get a steady stream of data available. Yet, they must find ways to manage and make sense of the data using the right tools, be it a data platform, data warehouse, data lake, etc.

However, data lakes and customer data platforms are two data tools that offer unique features for businesses to organize and make efficient use of their data. They also serve different purposes and cater to different audiences.

What is a customer data platform (CDP)?

A customer data platform is software that collects, organizes and unifies customer data into a comprehensive profile to support marketing decision-making. It allows businesses to activate customer data in real-time to deliver personalized marketing and enhanced customer experiences.

Core features of a CDP

At its core, a customer data platform provides specific functionalities that make it valuable to users, such as; 

• Real-time data activation: CDPs collect customer data in real time and transform it into actionable insights. It allows businesses access to real-time insights for flexible decision-making and adapting strategies. 
• Identity resolution: CDPs integrate data from multiple touchpoints to create unified customer profiles. That allows businesses to understand their customers and segment them into relevant audiences.
• Seamless integration: CDPs integrate with a wide range of marketing systems, making it easy for marketers to build on their existing tech stack.

Benefits of a customer data platform (CDP)

A CDP offers many benefits, but here are a few to consider.

• Enhanced customer engagement: By offering a single view of customer profiles, CDPs enable businesses to reach their audiences with tailored content at the right time and on their preferred platforms.

• Business growth and higher ROI: CDPs streamline how you manage data. That way, you can deliver more efficient marketing campaigns, reducing wasted spend and increasing ROI. 

• Operational efficiency: CDPs break down data silos, improving cross-team collaboration and supporting streamlined decision-making.

A point to note: CDPs thrive on quality data. So, if your data is incomplete, inaccurate or not up to date at the point of ingestion, the CDP cannot fix it. Prioritize data quality assurance when implementing a CDP.

What is a data lake?

A data lake is a centralized repository designed to store data, be it structured, semi-structured or unstructured data, in its raw, original format. It holds any data, not just customer data. It is flexible for data management operations, considering it does not require predefined schemas.

Data lakes support advanced analytics, artificial intelligence (AI), machine learning and big data applications.

Core features of a data lake

Data lakes offer core features that make them an essential data tool for organizations. This includes:

• Massive scalability: Data lakes can store massive volumes of data in various formats, from videos and logs to transactional databases. This allows a business to pivot or scale its data usage if required.
• Flexible schema: Data lakes employ a schema-on-read approach. This approach helps them adapt to future data use cases without much hassle.
• Advanced analytics support: They allow data scientists to run machine learning models and extract deep insights to drive innovation.

Benefits of a data lake

Businesses that use a data lake benefit from its superior ability to manage all forms of data. Aside from that, it also offers:

• Cost-effective: Data lakes offer a low-cost solution to store big data and other such extensive datasets, making them ideal for large enterprises with data-intensive needs.

• Support for predictive analytics: With data lakes in place, data scientists can build diagnostic and predictive models that enable businesses to be proactive with their strategies. 

• Versatile data use: With the ability to store data in any format, data lakes offer an ideal platform for experimentation and deep insights. 

Key differences between CDP and data lake 

Understanding how these two systems differ can help you decide which solution best aligns with your goals.

In essence, CDPs prioritize real-time data activation, while data lakes focus on batch processing and large-scale data analysis. Also, if your goal is personalized marketing, a CDP caters to that need. For data science and advanced analytics, a data lake is more appropriate as it is built for big data. 

CDP vs. data lake: What is right for your organization?

To decide on the right tool to use, consider what kind of data you need to collect, what you need to do with the data and what resources are available. Examine existing systems for possible integration needs. Here are some pointers to consider.

Choosing between a CDP and a data lake: Factors to consider

Now that you are clear about what a CDP and data lake do, how do you choose between the two? Here are some factors to guide your decision.

• Your business goals: If you want to deliver personalized experiences and marketing insights, a CDP is better. For advanced data manipulation and machine learning, a data lake is more suitable.
• Data type: Identify the data you work with. This could be structured customer data, unstructured or mixed data. In practice, this could be CRM and billing data or more complex ones like geolocation, app behavior data, etc.
• Use cases: If focused on customer engagement, CRM, data integration and real-time updates, choose a CDP. For advanced and predictive analytics, cross-departmental insights or big data, a data lake is a better fit.
• Team expertise: Marketing teams benefit from a CDP’s simplicity, while data engineers need the flexibility of a data lake. Also, ensure the chosen solution aligns with your future data growth.
• Integration with existing tools: A CDP integrates easily with marketing and CRM tools, while a data lake requires custom integrations for analytics, machine learning and cross-departmental workflows.
• Long-term adaptability: A data lake scales for diverse, growing datasets and advanced analytics, while a CDP is ideal for evolving customer engagement but is limited to structured data.
• Compliance with regulations: Data privacy and regulatory compliance are mandatory, no matter what data tool you opt for. Ensure you have the right governance practices and that the chosen tools support compliance with data laws such as GDPR and CCPA. 
• Technical expertise: CDPs are user-friendly, making them ideal for business users. For data lakes, you would need more technical expertise, considering it is built for data scientists, data engineers, etc.

A careful look at the above factors will nudge you toward the right tool. However, there are cases where organizations may have more complex needs and decide to combine both tools.

Combining CDPs and data lakes 

Far from being mutually exclusive, CDPs and data lakes can complement each other for an integrated approach—a so-called hybrid model. Combine a data lake and a CDP for raw data storage in the data lake and filter actionable insights via the CDP for real-time activation.

This data management model is integrated. So, raw data flows into the data lake, where it is stored and prepared for advanced analytics. Then, the most relevant, actionable data feeds into the CDP for activation in real-time marketing campaigns.

Challenges in adopting CDPs and data lakes 

Despite their benefits, organizations should consider the following challenges before investing in either solution.

• Integration: Businesses often face difficulty integrating these systems into their existing IT infrastructure due to data silos. 

• Costs: Both can involve significant upfront costs. A well-thought-out strategy is critical to ensure ROI.

• Skills gap: Data lakes require expertise in machine learning, analytics and coding. CDPs require marketing teams to be adept at campaign optimization and activation. 

Despite how serious these issues can be, they can be managed with proper planning. For integration needs, APIs, middleware and a phased approach will reduce disruptions. Use pilot projects to validate ROI and scalability before a full investment. 

Cloud-based solutions offer a pay-as-you-use system that helps manage costs. Finally, considering long-term goals, it may be worth investing in staff training. Also, partner with service providers for technical support.

Case studies

Land O'Lakes

After years of struggle, Land O’Lakes chose Contentstack’s real-time customer data platform to harmonize its data, enabling it to tailor content to each customer segment. This led to a CTR growth of 29% and a conversion rate growth of 38%.

Steve Rude said. "Impressions are the metric of a failed marketing campaign…If you’re not getting the conversions, it means you weren’t hitting the right audience. You may not reach a million people, but we’re going to reach the right people with the right message. And that’s where Lytics helps us."

Read on to see how Land O’Lakes increased the conversion rate after CDP implementation.

Industry Dive

Industry Dive needed a data tool for customer profiling to track the individual customer journey and personalize experiences. Contentstack's real-time CDP enables them to personalize customer journeys and increase customer engagement by 40%.

"Our editors really see the value of Lytics. They’re using it every day to shape their editorial content." Robin Re said.

Read on to see how Industry Dive increased CTR by 40% after implementing a real-time CDP.

FAQs

What is the main purpose of a CDP?

A customer data platform (CDP) unifies and centralizes customer data from various sources to drive actionable insights and personalized marketing.

How does a data lake differ from a database?

A database stores structured data based on predefined schemas, while a data lake stores all data types in their raw form.

Can CDPs and data lakes work together effectively?

Yes, they perform different functions that can be complementary. A data lake can be used to store raw datasets for advanced analytics, while a CDP extracts customer-specific insights from it, enabling personalized marketing and actionable strategies.

Who benefits the most from using a CDP?

A customer data platform (CDP) suits a marketing team looking to improve customer engagement across all channels.

Learn more

An effective data management strategy aligns your data, business goals and data tools. Depending on your needs, size or industry, you can opt for a CDP, data lake or data warehouse or, in some cases, combine them to achieve more complex goals.

Businesses that aim to deliver real-time engagement and personalized experiences would benefit the most from a customer data platform.

Contentstack’s real-time CDP supports your marketing strategy with first-party customer data, enabling you to orchestrate customer journeys, segment your audiences and deliver personalized experiences. Talk to us to get started today.