Has there been any phrase more overused in business lexicon over the last two decades than “data-driven”?
If it seems like this hyphenated compound word is popping up on every resume you read and every LinkedIn profile you scroll through, it’s probably because it is.
Everyone, from the real data scientists (who are actually data-driven) to entry-level marketers (who like looking at dashboards), claim that they are “data-driven.” 🙄
Google Trends shows a steady increase in search volume for the term over the last ~15 years.
Google Ngram Viewer analyzes word frequency in books and shows a quadrupling of the word “data driven” showing up in books since 2000.
The scary part for businesses, particularly on the revenue side, is that they are made up of sales and marketing people who consider themselves “data-driven” - but, in reality, are “data-reactive.”
Revenue leaders use at least 100 SaaS applications on average in their operations and go-to-market tech stack and then claim that because they have all these “great” tools to collect all this “great” data, that it somehow makes them a “data-driven” company.
Not quite...
There’s a common misconception of what it means to be data-driven...
The phrase “data-driven” implies that data comes first, and then what comes second is actions driven by the data.
In reality, most go-to-market teams are performance first, data second.
They operate for weeks and then the leadership team gathers for monthly or quarterly business review meetings to look at the top and bottom line numbers.
If the pipeline is light or bookings are short of their goal, only then do concerned revenue leaders seek out underlying data to explain why.
This reactive nature to finding data to explain performance shortfalls often leads to frustration.
Reactively searching is difficult because the data is often siloed in different systems or there are discrepancies that make the data they do find questionable.
That is not data-driven.
Most SaaS companies closely track bottom-of-funnel metrics to understand what closed during a period, what was won/lost, and changes in pipeline value. Pipeline inspection tools are commonly used to help the sales closers focus on immediate business opportunities.
Measuring pipeline performance is critical, but just looking at opportunities focuses attention on the end of the sales cycle which can only make a difference in the near-term.
It does nothing to ensure that your top-of-funnel investments are supporting the pipeline three quarters from now.
With a proactive and preemptive data environment, you can plan and operate the entire revenue engine with more certainty and make adjustments proactively so you can avoid being surprised by drops in performance.
Operating in react-mode has far reaching implications on your business.
Your data won’t uncover the underlying cracks in your go-to-market (GTM) process or strategies until there are significant discrepancies or performance issues. The impact of that delay means implementing changes in acquisition strategies will take longer.
The longer it takes to spot underperforming strategies or people, the more money you waste which could otherwise have been deployed into higher performing initiatives. These dynamics depress growth for startups and scale-ups.
Contrast that reactive approach to data with what is more accurately referred to as a “data-FIRST” approach.
Data-FIRST is the distinction between scrambling for data after the fact and actually having the data framework built-in at the beginning that informs proactive decision-making.
Instead of launching campaigns and then using your tech stack to measure its success, first start with determining what data needs to be measured, then configure your data model and tech stack to meet those needs, then launch the campaign.
Data-FIRST lets the data expose issues in real-time. Data-FIRST gives you time to change behaviors, alter course, and impact your outcomes before it’s too late.
The impact of just relying on data to track performance in retrospect usually means that you’re only able to focus on the outcome. Data is useful to show if the outcomes were successful, but if the data infrastructure is not properly configured, it rarely shows why an outcome happened.
Though it seems that it will take more time to determine the data metrics prior to launching strategies, once you determine what data you need to track on a granular level, you’ll gain efficiencies in being able to adapt quickly rather than reactively.
Cracks that a proper data infrastructure can uncover:
Adopting a data-driven mentality often requires a cultural shift in your company. It requires implementing a conscious data model and educating teams to put the data first before all strategies. The benefits of doing so, however, can pay off largely by eliminating friction from your GTM process and enabling your team to scale quickly.
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