Insights from #MarTechChat on Dirty Data


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TweetChatDirtyData_WrapUp_v0.1I think it’s safe to say the second MarTech Tweet Chat was a success.

All in all, this particular #MarTechChat – focusing on the importance of prospect data quality and the prevention of dirty data – has generated 349 tweets that reached over 200K people and resulted in over 3.4M timeline impressions. 

While this is terrific, I’m still going to complain…because I’m the lucky individual who had the privilege of diving into all those tweets to glean the conversation’s big insights – and there were many. 

So, Matt Heinz, to answer the question you posed to me the other day: Yes, you did in fact add work to my plate. But it’s welcomed work, which definitely paid off with the following learnings:

1. The biggest obstacles to prospect data quality are complexity of sources and silos.

I’ve said it many times: marketing is evolving rapidly, and with this transformation comes much specialization and the inevitable silos. It was nice to see that I’m not alone in my views; there seemed to be wide-held concern for the compartmentalization of data and the need for standardization to raze growing barriers between and within departments.

When asked about the biggest challenges to ensuring accurate data, almost all responses agreed with these tweets:




2. Compromised customer experience, sales-marketing misalignment and hindered analysis are the major consequences of dirty data

Without accurate data, marketers are handicapped in their pursuit to understand their audience, properly target personas and personalize messages. This not only has huge implications for conversion rates, but it has direct, negative effects on your relationships with prospects and customers.





Moreover, prospect data quality issues drive a wedge between marketing and sales efforts.




And of course, how are you supposed to optimize programs if the data you’re analyzing is inaccurate?


3. The industry needs more education on dirty data prevention – at all levels

Most participants agreed that not enough marketers are concerned with data quality. In some cases it’s an issue of neglect – marketers being too busy or too stuck in their old ways to tackle new problems associated with data-driven marketing. In other cases, it’s simply a case of ignorance. In both instances, education on the benefits of dirty data prevention is needed. And this education must start with CMO and other execs.





4. Everyone needs to play a part in ensuring data quality

Though there’s some debate on where primary ownership should lie – some say CIO (IT) should own, others believe all customer data should remain the responsibility of marketing – everyone agrees that ensuring data quality should be a concern of all.






5. Hiring analytical-minded marketers and properly educating them are key steps to ensuring prospect data quality

I must admit, I was rather surprised to see so many people jump right into the importance of hiring the right kind of marketers as the key to quality data. This may be further indication of negligence, and the problems of teaching old marketing dogs new tricks of the trade.




Yet, it does little good to simply hire capable individuals; developing a culture in which continual education of data issues and problem solving will becoming increasingly important.

Sameer Khan went on to specify how organizations should invest in teaching employees of one group on the tools and processes used by adjacent departments, such as social media experts learning to use the CRM or customer experience learning analytics tools. Doing this allows organizations to create cross-functional subject matter experts that are better equipped to breakdown the siloes creating data quality issues.

6. Marketing automation success is dependent on data cleanliness

This particular topic garnered a strong, consistent reaction. Every respondent agreed that the repercussions of poor quality data are amplified by marketing automation. Really think about this. The benefits of marketing automation platforms such as Marketo, Eloqua and Pardot have cemented this technology into the industry for good reason – they’re not going anywhere. But just think of how much more value marketing tech systems would provide if they weren’t being injected with inaccurate, duplicate or incomplete prospect data 40% of the time.




It’s probably hard to imagine, but the above insights aren’t even an exhaustive account of the conversation. Data velocity, ensuring vendor alignment and quality mindset and properly integrating all marketing tools garnered sizeable discussions as well.

Furthermore, Jon Burg posed a great question regarding whether anyone had ever heard of an organization-wide “dirty data prevention plan.” The concept was praised by a number of participants, and Matt Heinz and I are set to discuss possibly undertaking a white paper on this topic shortly. More to come soon.

To see the entire discussion, type #MarTechChat into the Twitter search bar…and then scroll down for five minutes to the beginning. Believe me…it’s worth it.

We’ll definitely be holding another #MarTechChat next quarter. But for now, I’d like to once again thank our headlining participants for their time, knowledge and ability to tweet incredibly fast.

Republished with author's permission from original post.

David Crane
David Crane is Strategic Development Manager at Integrate and an ardent student of marketing technology that borders on nerdy obsession. Fortunately, he uses this psychological abnormality to support the development and communication of solutions to customer-specific marketing-process inefficiencies.


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