Data decentralization. While the industry understands this as a simple term, in reality, it is the sole reason for many organizations' headaches throughout their ever-increasing need to become digitally transformed.
There is however a cure to this headache and it's the implementation of a strategy to organize, clean, and harmonize this data. This is why companies, especially in the martech and marketing sector, have started looking into solutions such as data warehouses, data lakes, and storage that are the solution to this problem - the centralization of data.
In this post, we explain the difference between a data lake and a data warehouse, why these are solutions to data centralization, and most importantly - why any of this matters to marketers.
What is a data lake?
Data Lakes are central repositories that allow you to store all your data – structured and unstructured – in volume. Data typically is stored in a raw format without first being processed or structured. From there, it can be transformed and optimized for dashboarding and reporting, analytics operations, or machine learning.
What is a data warehouse?
While data lakes store can store either structured or unstructured data, data warehouses are repositories that store only structured, processed data allowing for faster insights as the data is typically transformed and prepared to serve as the single source of truth. Like data lakes, data from the warehouse can then be ingested into a variety of business intelligence solutions.
Data warehouses are repositories that store only structured, processed data
What is the difference between a data lake and a data warehouse?
While data lakes and data warehouses are often mistaken as opposites, essentially they solve the same problem - that is; where do I store all my data in a way that is safe and accessible? Nonetheless, they do have different structures and support different formats that, in turn, inform how and in what scenario they are best used.
Because data warehouses are specifically designed to hold structured, processed data, data analysis tends to be much faster. They also support SQL queries which, again, means faster analysis. As such, in most scenarios, a data warehouse would contain the critical information needed to answer key business questions and be used for fast efficient analysis.
However, data warehouses tend to be less flexible than data lakes. This is because they demand that all the data be processed and structured before it can be stored there. A data lake, on the other hand, can be more flexible since it can accommodate both structured and unstructured data. But, precisely because it is not necessarily all structured uniformly, this makes analysis much more difficult since not all the data will necessarily be consistently structured.
Not sure what ETL is? Don't know your data lake from your data warehouse?
Well, don’t fret - explore our collection of What is...? guides to learn all the different marketing analytics terms you need to know!
Why Does This Matter to Marketers?
Modern data-driven marketers are faced with the challenge of using a growing variety of data feeds that come in from multiple different sources. And, storing this data is key for multiple reasons from reporting, to historical data analysis, to data ownership and security.
The decision of where to store this data - a data warehouse or data lake - as well as a lot of other decisions around their marketing data stack, will have an enormous impact on how a marketing team operates - what data they have access to, who has access to it, how they access it, and, most importantly, what they can do with it to help understand and improve their marketing performance.
Both data warehouses and data lakes can support the task of centralizing data feeds
Should I have a data lake or a data warehouse?
Choosing whether to use a data warehouse or a data lake is a business question that requires a clear understanding of both the internal resources available as well as the data and analytical maturity of the business.
While both warehouses and lakes can support the task of centralizing data feeds and creating a single source of truth for the marketer, it is essential to have an understanding of the further processes with the data down the line.
Would it be a business intelligence platform that shall pick up the raw data? Are there plans to invest in machine learning initiatives? Are there internal stakeholders that can manage the relation and connection between the data storage and the final destination of the data?
All these questions pose a challenge for the modern CMO and marketer, but also an opportunity to discover options and be more data-driven. Great third-party tools exist that can nowadays enable the marketer to access and analyze any data - regardless of whether it resides in a warehouse or a data lake.