You know what they say about too much of a good thing, right? It’s … well … not good. A glass of milk is a good thing; it’s full of calcium and other vitamins that every body needs. But drinking a gallon all at once? Not so much. (Don’t tempt yourself. The consequences ain’t pretty.)

When it comes to marketing, the same can be said for your attitude toward analytics. As a CMO, cultivating a data-driven marketing team can drive more insightful marketing decision-making and thus, better results. But an over-emphasis on metrics can actually lead to the opposite.

But wait … is this even a problem for CMOs and their marketing teams? If you look at all the data out there, it seems like the biggest problem that exists with the current state of marketing analytics stems from a lack of marketing analytics implementation, not an over-dependency.

Better Technology Means More Data

According to the latest CMO Survey, 65% of companies fail to leverage marketing analytics in their marketing projects. A recent IBM Global CMO Study also reported that more than 70% of CMOs feel they “lack true insights” and are underprepared to manage all the data at their fingertips. Furthermore, a 2011 CMO Council survey found that CMOs feel overwhelmed and under pressure to understand the influx of data available to them.

Given the rapid pace at which new technology and software is emerging for marketers — and as a result, how much more data is available to them — these statistics are totally understandable. In fact, Gartner predicts that CMOs will outspend CIOs by 2017.

And as we mentioned in our 2013 Marketing Trends and Predictions Guide, while Gartner also predicts big data will drive $232 billion in IT spending through 2016, so far, it has been for engineers — not marketers. In 2013, however, we expect to see a rise of startups that are dedicated to making big data more accessible to folks on the front end, such as salespeople, business development reps, and marketing professionals. Origami Logic, to name one, aims to give marketers access to big data in a way that is digestible and usable by them specifically.

And the future for the adoption of marketing analytics looks promising. According to the CMO Survey, while CMOs reported spending only 8% of their marketing budgets on marketing analytics, they expect to increase this level to 13.5% in the next three years.

With Great Data, Comes Great Responsibility

While technology has afforded marketers with a lot more available data, it’s not enough just to collect it. The power in data really boils down to your ability to effectively analyze and draw insights from it. And let’s face it: That’s a skill in itself.

The misinterpretation of data is a common thing. Mark Twain once said, “Figures don’t lie, but liars can figure.” That’s not to say that all marketers out there are purposely skewing and manipulating data in their favor. A lot of times, the misinterpretation of data happens accidentally, or as a result of misunderstanding — like thinking correlation is the same thing as causation. And believe me, it’s all too common. In fact, Smart Bear Software Founder Jason Cohen wrote a great article back in 2010 — “Avoiding Common Data-Interpretation Errors” — about this very problem. See how easy it is to unintentionally muck up the numbers? Data is some very sensitive stuff, and you can probably guess how this could affect your marketing decision-making … and not in a good way.

Don’t get me wrong. CMOs and their marketing teams should embrace data. It’s just important to be mindful of some of the dangers that come with a dependency on metrics.

There Is Such a Thing as Bad Data

Bad data is one of these dangers, and unfortunately, there is a lo-hot of bad data out there, especially when you start to consider and question how much of it is the victim of what we just discussed: misinterpretation. That being said, there is a host of other factors that contribute to bad data: out-of-date research, unscientific processes in data collection, unreliable sources, insufficient sample sizes, irrelevant or unqualified sample participants, incomplete data, lack of statistical significance, etc. Not to mention that data derived from surveys is often dangerous in itself. Just consider this funny, yet telling example from a survey conducted by Public P…, which just goes to show that people will tell you what they think you want to hear, or that they’re willing to lie in order to sound more knowledgeable.

In fact, in a recent Harvard Business Review article, “How to Repair Your Data,” author Thomas Redman writes, “Simply put, bad data make everything about Big Data — from discovering something truly novel, to building a product or service around that discovery, to monetizing the discovery — more difficult.”

“In business, bad data can be downright dangerous,” continues Redman, citing an example of financial companies in the mid-2000s that sliced and diced bad (actually, wrong) mortgage data into collateralized debt obligations (CDOs). This bad data came to an ugly head when the financial system almost collapsed.

When it comes to data-driven decision making, the quality of your data is of utmost importance. Heed our warning: Bad data can lead to bad decisions.

Some Metrics Are Actually False Proxies

I read a really poignant article from Seth Godin (ha — aren’t they all?) a few weeks ago about “Avoiding the false proxy trap.” It was actually the inspiration for this article (thanks, Seth!). His point was spot on:

“Sometimes, we can’t measure what we need, so we invent a proxy, something that’s much easier to measure and stands in as an approximation. … When we fall in love with a proxy, we spend our time improving the proxy instead of focusing on our original (more important) goal instead.”

It’s so true, right? Sometimes, that perfect metric to measure exactly what we’re trying to achieve in our marketing just doesn’t exist (ain’t it the truth, PR pros?). But as a great CMO who is trying to make data-driven decisions, you can’t just stand by and not try to measure its success, right?

Unfortunately, this leads to an unhealthy reliance on these so-called proxy metrics. Sometimes they take the form of vanity metrics like impressions, comments, and follower counts, which just don’t indicate success as it relates to your bottom line. And a lot of times, they even make us forget about the true goal of what we set out to do, leading us to implement tactics that help us juice those numbers, but don’t really align with the ultimate goal. Now that’s not really a good use of data, is it?

Metrics Can Give Your Marketing Team Tunnel Vision

Here at HubSpot, we love data, and our whole company is underpinned by an extremely data-driven culture. That’s why, on our marketing team, metrics keep us all accountable for and on track to hit our individual goals. It also keeps us liable for the service level agreement (SLA) we have with our sales team. And that’s a great thing … except for when it’s not.

When you over-emphasize the role of metrics, you increase the risk of a certain side effect we’ll call ‘metrics tunnel vision.’ Your team becomes so obsessed with their metrics that they lose sight of the bigger picture. Their decisions about which strategies and tactics to implement become so based on satisfying their particular metrics that they sometimes forget about the good of the marketing team, or even the company, as a whole. There is more than one way to skin a cat — or from a marketer’s perspective, achieve a goal — but that doesn’t mean every possible strategy or tactic is fair game. You could spam your marketing database over and over again with emails, and you may achieve your leads goal, but it won’t be without consequences.

It’s great to be working toward a goal, but sometimes you have to step back and question an individual strategy or tactic as it relates to your team’s overall goals, strategy, and marketing vision.

Metrics Can Become a Security Blanket

Similar to (and sometimes a result of) ‘metrics tunnel vision,’ an over-reliance on analytics can cause marketers to treat metrics like a security blanket. This leads to thinking like, “It doesn’t really matter if that negative thing happened as an off-shoot of the campaign I ran, because that campaign totally enabled me to nail my goal metrics this month!” Or, “I didn’t go the extra mile on that project because I knew it wouldn’t directly support my goals … (even if it would’ve supported the goals of another marketer on my team).” You can see how this doesn’t exactly promote internal team collaboration or support.

Metrics should motivate and keep your marketers accountable for their goals, but they shouldn’t excuse marketing that isn’t lovable … or cross-channel.

An Over-Reliance on Metrics Can Stifle Innovation

On the flip side of the security blanket dilemma, an extreme dependency on metrics can also end up suppressing marketers’ willingness to experiment, stifling any sort of innovation and truly creative, outside-the-box thinking. This usually stems from the fear that trying something new — since it’s not yet tried and true — might result in a failure to hit their goals, reflecting poorly on their performance as a marketer. It can also lead to marketers’ reluctance to experiment with innovative campaigns because of uncertainty about how they would measure success. Unfortunately, these fears can be huge blockers for innovation … and agility. Some of the greatest marketing ideas have come from taking big risks, and marketers will never feel truly comfortable with taking risks if they feel like they’re slaves to metrics.

This is all not meant to frighten you away from cultivating a data-driven marketing team, because there’s no denying the importance of data-driven decision-making. But remember, it’s important to tread lightly. Data is powerful, and with great power, comes great responsibility.

Too much of a good thing, you know?

Image Credit: Fields of View