In Connected Intelligence Platform, Customer Analytics, Customer Intelligence & Insights, Digital Transformation
Customer Data Platforms (CDPs) and Data Lakes are better together, working in harmony for better business inssights

Customer Data Platforms and Data Lakes Complement Each Other for Better Business Insights

In the recent times, due to the ongoing COVID-19 global pandemic, most of us have been confined to our homes. Our behavior, consumption patterns, and spending habits are shifting dramatically across industries and geographies. This experience is similar for many of us as people, processes and technologies are becoming barriers to success for many companies rather than being catalyst for success.

The problem is the way many companies are collecting, storing and utilizing their customer data for their customer 360 initiatives. Data is becoming the lifeline for enterprise-wide digital transformation. Many companies have initiated or are passing through the various stages of digital transformation to improve their focus on their customers, especially by personalizing their experiences across channels and interactions. To accelerate this transformation, many organizations are turning towards customer data and analytics platforms, to help orchestrate their customer data and optimize the customer experiences they deliver.

This transformation has led to two competing schools of thought on how to solve their problem of unifying data, either using a data lake or a customer data platform (CDP). CDPs and data lakes both provide solutions to the problem, albeit in different ways.

During most of the CDP and data lake discussions, I see organizations still are not sure how these two technologies relate or differentiate.  There are substantial assumptions on the data lake’s ability to solve every problem while CDPs end up being heavily underestimated.

In my opinion, neither a data lake nor a CDP can be a replacement for each other. Instead, they are a perfect complement to each other.

Data lakes form a key source of data for the CDPs. They are typically geared to bring vast amounts of raw unstructured and structured data from the enterprise together, but it can prove difficult to work with for those outside IT as they are not tailored for building the last mile use cases for the digital, marketing and sales teams. Data lakes do not include core capabilities like identity resolution or audience management needed to address business growth or the customer experience. They are quite essential for analyzing disparate customer and prospect data and combining them into a unified view. This not to deny the power of data lakes. It is just that they are not tuned to the needs of teams focused on driving revenue, customer engagement, and business operations. If the purpose of unifying data is to enable real-time decisions that help orchestrate a unified customer experience, then data lakes fall short. There is no such concept of creating and easily acting upon a single unified view of each customer from a huge unstructured data repository.

That’s where CDPs come in. They help improve the quality and completeness of data in the data lake and primarily focus on appending digital and marketing specific data that may not be available in the data lake. CDPs unify the siloed data around a customer 360 view, yielding a persistent “golden record” of all knowable data about a business’s customers. Additionally, CDPs make that record easily accessible on demand so marketers can ensure personalized and highly relevant customer interactions at every touchpoint. As defined by the CDP Institute, a CDP system that creates a persistent, unified customer and prospect database accessible to other systems better serves revenue teams. It is a packaged platform that comes with prebuilt components and data models, enabling marketers and other business stakeholders to segment, analyze, and activate their data without significant IT involvement. In fact, marketing teams can easily share and activate data and change or add sources without any disruptions.


  CDP Data Lake
Definition Customer Data Platform (CDP) is a type of packaged software which creates a persistent, unified identifiable customer profile that is accessible to other systems. Data is pulled from multiple sources, anonymized, cleaned and combined with third party data, intent data etc. to create a unified profile of a customer. A data lake is a centralized repository that allows businesses to store their high volume structured and unstructured data as-is, without having to first structure the data. Data lakes provide ability to understand what data is in the lake through crawling, cataloging, and indexing of data in a secured manner.
­­Functionality CDP can enable real time activation of omni-channel experience and provide more personalized content delivered over web, mobile, Email, ABM, Ads etc. to support digital and marketing team needs for experience management, campaign management, marketing analysis and business intelligence. Data lake allows to run different types of analytics like simple SQL queries, big data analytics, text analytics, real-time analytics, and machine learning without the need to move data to a separate analytics system for generating different types of insights including reporting on historical data to guide better decisions.
Manageability CDP is always a hot storage meaning easily retrievable and live connected to the customer master. It does not need much technical skills to manage & operate. Data lakes have a combination of hot storage and cold storage (like over 3 years).  Data lakes need very technical resources to build and operate it as it does not offer integration with last mile solutions like MarTech solutions
User Persona Digital Marketing

Customer Experience

Sales (limited)

Data scientists, Data developers,

Business analysts (using curated data), IT, Sales, Finance, HR, Marketing, Digital

Data Sources – First Party Data sources
– Web Analytics
– Advertising Data
– Marketing Automation Data
– Second Party Data
– Third Party Data
– Marketing Lists
– Device data, Intent Data Etc.
-MDM systems
-Commercial Data
-Product Data
-IT and Data systems-other LOB Applications


As you can see, CDPs cannot be a replacement for data lakes, but rather a perfect complement to them. In many cases, enterprises already have or need both. In case they do not have both, they can start with a CDP as it can be built quickly and then they can focus on the data lake that can take years to build in a large organization.

Both data lakes and CDPs can host customer data in persistent stores implemented by the IT, but the similarities end here. These IT-managed Data Lakes can ingest enterprise-wide data without altering their structure. On the other hand, CDPs managed by the marketer can unite the first and third-party data and enable a real-time flow of data into and out of the system to help enterprises improve their targeting and customer experiences. There are easy integrations available for channels & other business tools, thus a CDP makes it easy to view, pull, and analyze data and arrive at audience insights paving the way for faster time to market and expanded customer reach for the business users. CDPs also enables the alignment between the revenue teams and IT when it comes to an enterprise’s data and technology ecosystem. While revenue teams can use the CDPs to capitalize on the data to drive business growth, IT teams can enjoy the benefits of CDPs and Data Lakes working in harmony to serve the business.

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