We just came across a July 2013 HBR Blog Network article by Thomas C. Redman, “Are You Data Driven? Take a Hard Look in the Mirror.”  In the past, we have written a lot about data-driven marketing but Redman raises one important point that we haven’t addressed – data quality…

…the data-driven [individuals or groups] place high demands on their data and data sources. They know that their decisions are no better than the data on which they are based, so they invest in quality data and cultivate data sources they can trust. As a result, when a time-sensitive issue comes up, they are prepared. High-quality data makes it easier to understand variation and reduces uncertainty. Success is measured in execution, and high-quality data makes it easier for others to follow the decision-makers logic and align to the decision.

We completely agree but this is not an easy problem in the digital marketing world. As we build our analytics solution that will help digital marketers break down data silos (web analytics, social media, advertising, email marketing, etc.), we spend a lot of time integrating data from various marketing services and we are constantly faced with data that is not handled consistently across services. Here are just a couple of the many areas where we see the problem:

Time – The inconsistent representation of time is a big issue when trying to integrate data from various marketing services.  There are many reasons for this.  Here are a few: 

  • Different services define the start of their “day” at different times so the definition of “today” varies.
  • Some services store time based on GMT (Greenwich Mean Time) and some don’t.
  • When there is a transaction that involves multiple parties (an advertiser and a publisher, for example), there is no standard that defines which party’s time zone will be the baseline for a transaction.

Geography – Geography is another area where complications arise. Here are a couple reasons why:

  • “Regions” are categorized in different ways. In the United States, for example, some services may use zip code to geographically categorize users and some may use designated market areas (DMA).
  • The hierarchy of countries are organized and defined differently. Some countries have states and some don’t, as an example. And there are multiple standards for referencing city, state, country, and continent names.

Data inconsistencies, like the examples described above, lead to poor data quality, which is a big problem when trying to make decisions that involve data from multiple sources.  And all of this makes it harder for organizations to be data driven since there is uncertainty about decisions that are made, as Redman explained in his article.

How does your organization handle data quality in the digital marketing world?  Do you try to tackle some of these issues head-on, or do you avoid the issue by not integrating data yet?  Let us know your thoughts by adding a comment below.