By Nickolay Penchev – SVP of Solutions Consulting and Support at Adverity

The good news, however, is there is a simple remedy for this headache. Implement a strategy to organize, clean, and harmonize your data, to a centralized location – i.e., a data warehouse or data lake.

What is a Data Warehouse?

Unlike a data lake (see more below), a data warehouse is a repository that stores only structured, processed data. This allows for quicker insights as the data has typically already been transformed and prepared to serve as the single source of truth.

This data can then be input into a variety of business intelligence solutions.

What is a Data Lake?

Data lakes are central repositories that allow you to store all your structured and unstructured data in volume. Data is usually stored in raw format and has not been processed or structured. It can then be transformed and optimized for dashboarding and reporting, analytics operations, or machine learning.

What is the Difference Between a Data Lake and a Data Warehouse?

It is very common for data lakes and data warehouses to be mistaken as opposites, but in fact, they are two sides of the same coin, and both solve the same problem – providing a safe location to store and access all my data.

However, they do have different structures and support different formats, which in turn, inform how and in what situation they are best used.

Data warehouses are specifically designed to hold structured, processed data, therefore, data analysis is much faster, this is also the case as they support SQL queries. This means that in most use cases a data warehouse contains critical data needed to answer important business questions and be used for quick efficient analysis.

However, there is a downside. Data warehouses are usually a lot less flexible than data lakes because they require all the data to be processed and structured before it can be stored.

A data lake on the other hand has no such requirements as it can hold both structured and unstructured data. But, this makes analysis a lot harder as not all the data will be uniformly structured.

So Why Should Marketers Care?

Storing data in either a data warehouse or a data lake will have an enormous impact on how a marketing team functions, what data they have access to, who has access to that data, how they access it and most significant what they can do with it, to help and improve their marketing performance.

Should I Have a Data Lake or Data Warehouse?

To answer this question, as a business, the first thing you need to do is have a full and clear understanding of the analytical maturity of the business, as well as the internal resources at your disposal.

The second aspect that you then need to understand, is what are the processes with the data down the line going to be. Both data warehouses and data lakes can support the task of centralizing data feeds and creating a single source of truth.

So ask yourself, will it be a business intelligence platform that will pick up the raw data? Are there plans to invest in machine learning initiatives? Who are/are there any internal stakeholders that can manage the relation and connection between data storage and the final data destination?

All these questions pose huge challenges for CMOs and marketers across the globe, but once answered provide a significant opportunity to become more data-driven. Additionally, there are now great third-party tools that enable marketers to access and analyze any data, regardless of whether it is stored in a data warehouse or data lake.

About Nickolay Penchev:

Nickolay Penchev is SVP of Solutions Consulting and Support at Adverity. In his role, he is responsible for leading the solutions consulting, post-sales consulting teams and post- sales support teams. Prior to joining Adverity, Nickolay was the founder of Virtus Quest, a non-profit organization promoting men’s health.

About Adverity:

Adverity is the fully-integrated data platform for automating the connectivity, transformation, governance, and utilization of data at scale.

The platform enables businesses to blend disparate datasets such as sales, finance, marketing, and advertising, to create a single source of truth over business performance. Through automated connectivity to hundreds of data sources and destinations, unrivaled data transformation options, and powerful data governance features, Adverity is the easiest way to get your data how you want it, where you want it, and when you need it.

Adverity was founded in 2015 and is headquartered in Vienna with offices in London and New York, and currently works with leading brands and agencies including Unilever, Bosch, IKEA, Forbes, GroupM, Publicis, and Dentsu.

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