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Adapting enterprise data lake architecture for marketing analytics
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Adapting enterprise data lake architecture for marketing analytics


Marketing data has been growing for the past 10 years. As mentioned in one of my  previous articles  , according to Toolbox , most marketing departments today have, on average, up to 16 MarTech platforms in their  stack  (and up to 20 on the B2B side). All these platforms generate data, even big data. 

Consequently, it becomes more and more difficult to manage the marketing data of these different platforms in silos. A better solution is to unify your data to get the most value out of it for the business. With this in mind, many marketers are beginning to consider creating their own data lake or warehouse, with the goal of centralizing and leveraging data in a single environment.

The challenge for these marketers is that very often their company already has a data lake or warehouse which, unfortunately, prioritizes sources of data other than those of marketing. These lakes or warehouses are usually managed by IT and business intelligence (BI) teams. In addition, marketing data is sometimes misunderstood by these teams, which accentuates the gap separating them from specialists in analytical marketing.

To deal with this problem, it is particularly possible to adapt the architecture of data lakes to give a better place to data in marketing. In the article I wrote for the  Journal of Applied Marketing Analytics , I also proposed a concrete architectural solution,

To obtain a copy of the article, simply complete the form below.


This article first appeared in  Applied Marketing Analytics  Vol. 6 No. 1 and is reproduced by permission of Henry Stewart Publications LLP, London, UK. For more details on the  Journal of Risk Management in Financial Institutions , see:  https://www.henrystewartpublications.com/ama .

Good reading!