Data Challenges Facing Marketing Leaders Today

June 12, 2023

There is a clear expectation that today’s marketing leaders lead their teams and build their strategy based, in part, on data. That’s not to say that boards and CEOs do not place a tremendous amount of value on skills like product marketing and brand. But as a leader, you are hard pressed to get buy in and alignment without backing your decisions in data.

Let’s face it though… marketing data is complex!

Many of the leading data scientists out there work on marketing problems for the largest tech companies. But if you are an early or growth stage marketing leader without a data science team behind you, where do you even start. In this post, we will review some of the leading data challenges that marketers face and then discuss what you can do about it.

Marketing Data is Complex

As a modern marketing leader, you must span an incredible breadth of skills, including brand, creative, product positioning, messaging, pricing, content, advertising, events, PR, analyst relations, and so much more. And now, you are expected to be a leader in the company in terms of data proficiency, leveraging analytics to explain what is happening and inform the strategy for growth.

In a recent appearance on the Exit Five podcast, Dave Kellogg said: 

“You should be able to fill in for the CFO on an investor relations call if you had to." Suffice it to say that the data expectations for marketing leaders are high.

But if you’ve spent time digging into the data, you know that actually delivering on this is difficult. There are three core challenges that most teams face when it comes to marketing data: Lack of Clean Consistent Data, Ownership of Data Operations, and Deriving Insights from Data.

Lack of Clean, Consistent Data

The topic of data-driven marketing organizations is hot. Whether you are scrolling through LinkedIn, chatting with peers, or engaging with the board, you are almost certain to touch on the topic in one form or another. Doing the work to get the underlying data right, however, rarely shows up in these conversations.

Data cleanliness and reliability isn’t exactly a sexy topic. You don’t tend to encounter business influencers talking about the “Top 5 Ways to Clean Your Marketing Data.” But without clean, consistent data, you risk making less than optimal decisions (or worse, making the wrong decision).

If you’ve worked in a marketing org before, you’ve likely faced one of these situations:

  • A core business metric is not consistent across various tools (E.g., Marketo and Salesforce report a different number of MQLs for the quarter, and no one knows why!)
  • Metrics from ad platforms and other channels do not reconcile with what you are actually seeing enter the funnel
  • There is a disconnect between top of funnel activities and closed won revenue - you know that you are driving MQLs, but you don’t know which channels are performing best through the funnel.

Without accurate data that is consistent throughout all core tools, you cannot reliably use data to drive marketing strategy and decision making.

Ownership of Data Operations

Getting clean, consistent data requires data operations work to integrate tools and move custom data from one place to another. Staffing this can be a challenge. Today’s data operations responsibilities include a mix of skills from engineering, RevOps, marketing, and analytics. 

There is no clear owner of Data Operations responsibilities in many organizations.

Too often, DataOps efforts are not managed cross functionally, resulting in inefficient or failed efforts. When placed in engineering, DataOps is often missing the contextual knowledge and the speed needed to serve a marketing department. An understanding of how the various marketing and GTM tools work together is required for a successful DataOps function. When this function is staffed in marketing, however, it often misses the engineering best practices needed to ensure that the data is reliable. As a marketer, figuring out the path to get clean, reliable data is difficult.

Deriving Insights from Data

Even after the first two hurdles have been overcome and a team has clean, reliable data, it can still be difficult to derive insights from the data. As you think through web metrics, pipeline data, email opens and clicks, campaign reports and more, you may feel like you are drowning in data yet lacking any clarity.

While much of analytics and data science is (as the name indicates) a science, there is an art to figuring out what the data means and what is truly happening within the marketing funnel. Not everyone has the experience thinking this way, and it takes practice to know how to navigate the massive amounts of data and actually pull valuable insights.

In light of these challenges, what is a marketing leader to do?

The Answer to Your Data Problems: Infrastructure 

We all want the benefits of a data-driven marketing function. But getting there, in my opinion, starts with an investment in the infrastructure. Once the right infrastructure is in place, answering questions about marketing performance becomes simple. But without it, there is a lot of repeated manual work, wasted cycles, and unreliable decisions. 

That’s not to say that you shouldn’t start building a data driven culture right away. Approaching decision making and strategy through a data driven framework is always a good idea, and it is beneficial to get the whole marketing team thinking through this lens. But to really build for the future, the infrastructure needs to be in place. 

So what does this look like? I’ll cover this in greater detail in my next post, but at its core, there are a few principles for foundational marketing data infrastructure: 

  • Centralize and Own Your Data:
  • Resolve Key Entities and Events Across Tools:
  • Define Logic Once (and Build Automated Testing):
  • Push Consistent Data Back to End User Tools:

I’ll cover these concepts, along with the underlying why, in greater depth in my next post. If you’re interested, be sure to follow along!