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Blog / A Clean Data Stack: The Foundation of Data-Driven Marketing

A Clean Data Stack: The Foundation of Data-Driven Marketing

In today’s age of digital technology, social media and a growing variety of smart devices, users are leaving an ever-bigger trace of their activities online. They interact with brands through social networks, email newsletters, ads, websites and forums, among others, and they do so from a number of different devices - from smartphones and tablets to laptops and home computers. The result? Plenty of data.

Advertisers, in particular, have to sift through growing amounts of information before they can come to a coherent overview of their - or their clients’ - marketing activities.

And yet, while marketing data seems like a key concept, many advertisers are still lagging behind in their ability to collect it all in real time and analyze it in its entirety. In fact, a 2016 study on the State of Pipeline Marketing revealed that the majority of marketers polled were only “somewhat confident” in the quality and accuracy of their marketing data as opposed to a lot fewer being “highly confident” in it.

Given that data is all around us and it is the key to understanding marketing performance, such trends need to reversed - and in time, too.

Not all data is good data

Clean Data Stack marketing analytics ETL

The trouble with marketing data is that it is becoming increasingly big and is gathered from a variety of sources, which often leads to it being messy and coded in a number of different formats.

Naturally, step one of the process is to pull all data into the same data stack. While that may not be easy in itself - unless automated - the real challenge begins with having to turn this data stack into one coherent whole. Since the data rarely exists in one unified format, it needs to be cleaned and harmonized to make it easier to detect possible anomalies or irrelevancies.

It is crucial for marketers not to have any blind spots in their analysis as a result of missing or incoherent data, as well as failed data imports.

Creating a clean data stack

Data cleaned presented

When it comes to cleaning data, marketers often deal with fixing various errors, filling in missing information, or making sure duplicate entries have been eliminated. They can do this manually, but increasingly so, they have been using advanced marketing technologies to cope with the large amounts of data coming in.

At Adverity, we are aware of the challenges this constant data inflow poses to advertisers.

Marketing data can be overwhelming, scattered and often misleading. This is why we have developed a tool that automatically detects discrepancies in your data and provides you with an integrated overview of it at any time. Within this tool, data is stripped of its various formats, harmonized and ready for your analysis.

At the end of the day, marketing performance and clean data tend to go hand in hand. A clean data stack is the gateway to marketers making quick and informed decisions based on accurate, real-time information.

How do you clean and harmonize your marketing data? We'd be interested to hear about your experiences of working with multiple streams of data. If you've got something you'd like to share, please drop us a comment. 

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