What happens if everyone has access to data?
A month ago, Steven Sinofsky joked to a room full of product managers that before instrumenting analytics at Microsoft, they used to make decisions via “testosterone-based engineering” (read: arguing). The room filled with laughter.
But maybe it’s not so funny after all. In spite of the laughter, companies aren’t as data-driven as they purport to be. To level-set: 90% of enterprise mobile applications will be gathering analytics by the end of this year, according to Gartner. Fewer than 5% will be using their analytics effectively.
Are companies making decisions based off gut checks? Or just facing a bottleneck at the analytics team?
A combination of both, it turns out. But the solution isn’t to redouble your investment in headcount. The solution is to spread your analytics knowledge base broadly.
That’s what DocuSign realized after investing in product analytics. Spreading data throughout the organization, in engineering, marketing, and product, from the deeply analytical to the simply curious, makes a company stronger.
The analytics gap
A key reason enterprises aren’t using analytics effectively is because their resources constrain them. Data is one person or one team’s responsibility. Tools that can democratize data will do more than help a handful of stakeholders–they’ll enable the entire company to get data-driven.
Last week, we spoke to Ben Lorica, Chief Data Scientist at O’Reilly Media, who said that in 2017, simpler analytics tools would simplify and distribute tasks commonly limited to analytics professionals.
The analytics gap in the enterprise today has nothing to do with whether one wants to be data-driven or not. The tools just haven’t supported people’s curiosity with ease of use.
“As the tools that you need to build an end-to-end data pipeline become more accessible, many of the routine tasks might be possible for domain experts—the people right there making the decisions.” he continued. “And the line of business, front-line people need to be able to make decisions using data.”
The idea isn’t to cannibalize the work of the data specialists, either. It’s to allow them to do their best work by getting the lines of business to self-serve answers.
“In the past, you might have employed statisticians and machine learning people to do the modeling,” Ben said.“That probably will still be the case, but maybe your marketing analyst can do much more now.”
And that’s exactly what happened at DocuSign, when a marketing person started digging through product data.
At DocuSign, the company best-known for electronic contracts, product analytics started with the marketing team in 2013. They didn’t want to write SQL every time they wondered something as simple as, How many people are converting to paid users?
So, they adopted Mixpanel. But over time, project owner Drew Ashlock saw that a lot of what he would’ve traditionally handled with SQL he could do in Mixpanel. More importantly, a lot of tasks he’d traditionally done with SQL other people could perform in Mixpanel.
As stakeholders asked him more and more for answers, it became much easier to spin them up on Mixpanel than to perpetuate an inefficient back-and-forth.
Now a Senior Product Manager at DocuSign, Drew has found that having a tool like Mixpanel in place is more sustainable. It gets people to trust the data in the product and use it regularly to get instant answers.
“It’s tough to teach people SQL and the underlying data structures,” he said. “But when you can show them a user interface where they can with a few clicks get answers to their questions? It’s really saved me a lot of time.”
Four years after the initial implementation, the tool is being used by DocuSign in engineering, marketing, product, design, program management, and more. Now, domain experts are able to make better decisions with easy access to data.
“I’m not the primary consumer of Mixpanel,” Drew clarified, “but I want to get people access to the data I want to get them comfortable with it, using it, seeing value in it.”
It’s also a quality of life thing. Drew wanted to quickly assuage people’s fears of data, but he also to get his time back. And this is the second multiplier of getting everyone data-driven.
The direct effect that a democratizing tool has on teams may be obvious, but the indirect effects are just as important. Data teams will still rely on homegrown solution in some cases, but the time they get back from democratizing their data will be huge.
As with Drew, data scientist Chip Christensen isn’t the primary consumer of a tool like Mixpanel. And yet he’s still one of the key beneficiaries. Democratizing data has enabled him to focus on deeper analysis, not being a numbers runner.
“It’s been great for the people with a more advanced analytic background,” Chip said. “It moves a lot of the low-hanging fruit off our plates, so we can focus on work that’s more technical in nature.”
The queue standing in between decision-makers and analytics teams has encumbered both sides. With the emergence of simpler tools, this queue doesn’t need to be large, or in some cases, even exist. Lines of business can quickly self-serve the answers that matter to them, while analytics folks can work uninterrupted on the analyses that are most important.
“As a company, we want to move from hindsight to insight to foresight,” Chip Christensen, a data scientist at DocuSign, said.“And Mixpanel is speeding along that process.”
In a survey with Mixpanel’s 20,000+ customer base, it was shown that after adopting the product analytics solution, 90% felt their teams were more confident in decision-making. If you want to know whether you’re in the 5% of companies using analytics effectively, maybe it pays to look around and ask, “Are 90% of the teams around me confident in their decision-making?”
If the answer’s no, don’t recklessly hire a new data team. To deepen your company’s data proficiency, simply broaden it.