Customer Data Platform vs Data Warehouse: What’s the difference?

cdp-vs-data-warehouse-2019-boutAs anyone familiar with Scott Brinker’s annual Marketing Technology Landscape ‘supergraphic’ will know, it can be challenging to keep up with new technologies – particularly with the proliferation of buzzwords, acronyms, and with many vendors in different categories claiming to have similar functionality.

In 2014 ‘Big Data’ was a buzzword that caught the marketing world, but has faded away to nothing more than an ambiguous term that really only defines the storage of lots of structured and unstructured data.

The 'Big Data' hype saw a significant move for enterprise businesses to invest in a Data Warehouse solution to store data that the business could analyze to improve efficiencies and spot patterns in data that might help the company perform better.

For Marketing however, this did not provide a single 'Golden Record' that could be used in campaigns and did not help to integrate, in real-time, the data within their many marketing execution systems. Moreover, it meant that Marketing was still reliant on IT for access to their data. Any changes to the way the data is stored, new connectors that are needed or any changes to the data layer would require marketing to log a ticket!

The use cases for a Data Warehouse could range from giving marketing access to data with the goal of reducing the strain of data requests on IT, to giving HR a consolidated view of performance, holidays, illness and company revenue to look for ways to optimize staff performance that might be impacting profitability. Essentially, the Data Warehouse purchase is an unselfish purchase by IT to provide data insights to every area of the business.

The Customer Data Platform however, is a selfish purchase for marketing only, and the use cases are more focused on delivering more revenue from customers through improved personalization and segmentation, improved integration of marketing channels - as well as the reduction in data requests on IT. It’s fully focused on marketing improvements that will optimize the results and revenue from marketing initiatives (and HR will never have any access!)

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So if you’re an enterprise marketer looking to provide the justification for a Customer Data Platform investment, then here is a simple look at the Data Warehouse and why it’s different to a Customer Data Platform (CDP*).

* Apologies for throwing another acronym into the mix

Data Warehouse

What is a Data Warehouse?

A data warehouse (often referred to as an enterprise data warehouse, or EDW) is a central repository of large amounts of business information from multiple data sources. Data warehouses contain data from sales, marketing, purchasing, finance, and other business functions. They help organizations to store and manage data, making data easier to find, access and use.

What does a Data Warehouse do?

A data warehouse consolidates and standardizes data, structuring it in a way for use in analysis and business intelligence reporting.

How is a Data Warehouse different to a Customer Data Platform?

The main differences between an EDW and a CDP are the scale, the purpose and the treatment of the data. As data warehouses store all corporate data, this typically makes them large, expensive, IT-driven (and IT owned) projects designed to serve as a repository for analysis across the whole enterprise.

A CDP, as the name suggests, is interested only in customer data, built for the needs of marketers and operated by marketing without the need for complex data queries or untimely IT requests.

An EDW does not include cross channel identity resolution for the creation of a Single Customer View, nor does it support real-time updates when campaigns are sent, or access. This means marketers cannot extract and use the data they need as quickly as if they were using a CDP.

Additionally, EDWs do not transform, standardize or normalize the data specifically for marketing purposes. A retail business, for example, may store purchase and/or transactional data as codes (‘MX1294’ rather than ‘brown leather shoes’). The process of ‘Normalization’ in a CDP will transform the MX1294 code into something that is (a) meaningful to marketing, (b) meaningful to the customer and (c) usable in the personalization of campaigns. Even more useful, and commonly seen in multi-channel organizations, it can merge and normalize different product codes, from different systems and consolidate into ‘brown leather shoes’.

Being able to bring all this data together in a standardized and normalized format is extremely valuable for marketers to understand the buying behavior of their customers across systems and activate that data without the arduous task of data wrangling, writing scripts, waiting on IT or typing endless v-lookup formulae into spreadsheets.

The Conclusion

A Data Warehouse is a fantastic purchase for an enterprise business enabling them to use data to inform company-wide business decisions and find both efficiencies and opportunities that will make the business more profitable. It is an IT led project and can have profound effects on any business that is looking to move to becoming an insight-driven business.

The CDP, however, is built specifically for marketing, and provides far-reaching benefits that the Data Warehouse does not. For personalization, integration of execution channels, de-duplication and normalization of the data, marketers need their own data store, and the Customer Data Platform meets these needs perfectly. Better yet, if the business already has a Data Warehouse in place it can make the implementation of a Customer Data Platform easier, quicker (and therefore cheaper.)


Want to know more? Download the ‘Marketer’s Guide to Customer Data Platforms’ eBook

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This blog article is an extract from BlueVenn’s ‘A Marketer’s Guide to Customer Data Platforms’ eBook.

Download a copy of the full eBook for more CDP advice, how a CDP differs from other data management technologies, or the different types of CDPs and what to look out for when researching technology vendors.

Download now

Topics: data warehouse Customer Data Platform