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The idea that data should serve as a fundamental building block for every company hasn’t been the accepted view for long. Companies of all sizes and across many industries are realizing only now that they’ve been sitting on top of goldmines of useful information for years.
We’ve studied and worked with some of the top performing growth companies in the world, like Stripe, Revolut and Airbnb. What we learned is that the #1 thing that separates the great teams from the good teams is their ability to proactively and strategically leverage data.
Truly data-driven teams use data insights across the whole company to identify, develop, and achieve their business objectives. Innovative companies use data to re-think how their employees operate on a daily basis. By understanding and leveraging the information at their disposal, companies can do a much better job at managing the “right now,” while at the same time charting a much clearer vision into the future.
In this article, we’ll 1) explore why building a data-driven team is important, 2) apply a framework to help you quickly identify growth opportunities, and 3) provide you with five practical steps that will help you instigate change in your team, and throughout your organization.
In short, being data-driven increases efficiency. And efficiency results in leverage - to grow faster and more effectively.
Being data-driven is the most effective way to boost every crucial metric in your organization. Sure, hard work is important, but if you’re working hard on the wrong initiatives, you won’t get far. Data points you in the right direction.
According to research by Forbes, data-driven organizations are 23 times more likely to acquire new customers, 6 times as likely to retain those customers, and 19 times as likely to be profitable as a result. Leverage data, increase revenues. It’s that simple.
A different study by McKinsey shows us that organizations that are using their consumer behavior insights strategically are outperforming their peers by 85% in sales growth margins, and by more than 25% in gross margins. Your salespeople might know their clients’ alma maters or the names of their clients’ kids, but do your sales people know how frequently your clients log into your product? Have they been using it or are they likely to churn soon? What prevents them from using it more?
The upside of being data-driven is enormous, and the opportunity cost of not being data-driven is becoming increasingly large.
Here’s a metaphor which shows the risk of under-utilizing data insights:
The best way to understand how and why data-centered organizations are increasingly successful is to look at the culture they’ve built around analytics. Does your company use data insights to guide decisions across all business units, or does your team only look at data retrospectively?
Have a look at the graphic below, and think about which describes your team’s current structure best:
Nearly every company fits into at least one of these “data culture” categories. Note that larger organizations (with 1,000+ employees) often have disparate business units with totally different appetites [and capabilities] for leveraging data insights.
Teams that fall into this category know their metrics well, and have a consistent reporting process set-up. This is absolutely better than nothing, and is an excellent starting point. However, as datasets and BI tools continue to get more sophisticated, the way you’re leveraging them has to evolve, too.
Metrics-driven teams tend to have limited data available, and are probably aware of the fact that there’s room to do more...but they’re risk averse, at least with respect to data analytics. This type of data culture isn’t conducive to exploring what’s possible, and rather, orgs in this category shy away from making changes or running experiments because “it’s working as is.”
Said otherwise, teams with this set-up tend to suffer from predictable, if not stale, data insights.
Another common drawback of a metrics-driven organization: analytical roles are fragmented, and tend to overlap between lines-of-business. This makes it difficult to find and align the right talent to the correct teams.
In the context of strategic use of data insights, metrics-driven teams tend to fall short of the mark of what’s possible.
The name might make this one sound better than it actually is! Teams that fall into this category tend to use data in a passive or retrospective way. The typical use case is when something bad happens within the company, which then triggers an internal investigation.
Example: “Our revenue from our high-value customers fell last week! OK....let’s look at the data to find out why…”
Organizations of this kind tend to suffer from a lack of actionable data, fragmentation of various data sources, and have mixed [or undefined] analytical roles. It often takes weeks to get useful reports together, which only serves to help pick up the pieces after something bad happens.
If this sounds like your team, don’t worry! Most companies today fall into this category.
Companies that have a “risk tolerant” data culture understand the true value of using data insights. While it’s possible that not every business unit is actively and strategically using data insights, these companies are more likely to proactively insert data resources into new functions, and often conduct experiments in an effort to uncover efficiency gains.
Example: “Can we automatically flag customers who haven’t logged into our service for more than a week? The product team should have that data, right?”
Companies in this category are also more likely to invest resources that improve how they work with data, whether it's in the form of hiring new analysts, scientists, and engineers; in new BI tools; or in up-skilling their current employees.
However, while this group definitely “gets it,” as it relates to the strategic use of data insights, they’ve not truly achieved full “data-centered” status. These companies usually have totally fragmented teams -- think sales analytics, marketing analytics, operations analytics -- that function entirely on their own, using different BI tools in some cases. This separation leads to inconsistencies, competing overlap of data, and can ultimately cause confusion.
Organizations that have a truly “data-centered” culture are metaphorical rocket ships. They apply data across all business units; they make proactive decisions quickly; they invest in data training for all of their employees, and constantly adapt their infrastructure before it breaks or becomes stale.
Not only are most (if not all) employees comfortable using SQL and their BI tools, they’re also convinced that by inserting them into their daily tasks, they’ll be more successful on an individual level. This is what it means to be a data-driven employee.
In a data-centered company, resources are dedicated to ensuring cohesion across business units. No siloed teams. All employees using the same tools. Constant collaboration and sharing of findings.
As a result, organizations that fall into this bucket benefit from their data the most. Because the entire operation is designed around the prioritization of data, they’re able to identify issues and opportunities early.
If growth is what you want for your company, this is where you want to be. If you’re thinking that this isn’t where your team is, again, don’t worry. There aren’t many companies that are truly “data-centered.” Later in this article, we share some practical, achievable steps to help you start to get there.
Software ate the world but it happened so fast that the majority of companies are still at early stages of data adoption. For their employees, data democratization hasn’t happened yet.
With that knowledge, be warned – not being able to use data creates a serious threat for your company. Younger, data-driven disruptors are quickly overtaking massive industries -- like finance services, insurance, and real-estate -- that once were dominated by a few household Fortune 100 behemoths. Here’s an excerpt from a recent article about the rise of neobanks.
“The problem with the old banks is they were built up at a time when data was very expensive to store and pass around,” explains Tom Merry, the managing director for banking at Accenture. “You had systems that captured as little data as you wanted. That legacy is what is holding these big incumbent banks back. Almost every single bank, even the ones considered to be leaders, are still built on very, very old structures.”
And as for data-driven disruptors…
“It gives those chief marketing officers a huge advantage,” explains Merry. “Neobanks are able to use data in a really advantageous way – whether that’s targeting new customers, driving loyalty in their existing customer base, cross selling, upselling or making experiences feel like they’re bespoke to the individual.”
“We should teach the students, as well as executives, how to conduct experiments, how to examine data, and how to use these tools to make better decisions.”- Dan Ariely, professor of psychology and behavioral economics at Duke University and a founding member of the Center for Advanced Hindsight.
Communicate clearly and repeat what is expected and what using data means. When writing specifications for new features or launching new products, explicitly communicate which metrics are important and how they should be collected.
Reward employees who follow through. Data exploration makes your company better. Cherish it, highlight it, incentivize it and your employees will continue to embrace it.
Test for curiosity, analytical thinking skills, and the ability to pick up tools and concepts quickly. For example, for non-technical roles take note of experience writing basic SQL requests or working with pivot tables. It’ll be easier for them to pick up more sophisticated skills if you invest in them.
When interviewing candidates, encourage them to show off their data curiosity. Ask them, for example - “what was the most unexpected discovery you made using data?”, or “give an example of a situation where you wished you had taken a closer look at a particular dataset?”
You don’t need to hire the “perfect” candidate, but if you can uncover which candidates have the fundamental skills and attitude to learn more, you’ll have a much easier time building out the data-centered culture you want.
Data infrastructure is important because it provides a gateway to insights for your team members. Based on how well your infrastructure is set up, your employees would either feel empowered with knowledge or depressed to death with .CSV exports and time consuming routine. Data democratization and ease of access are important here.
A good way to start is to discuss with all your employees - what tools do they need to ensure they can get access to the data they need for their work? What often works as a baseline is to store everything in SQL and provide your employees with read access. Then, add a BI tool (like Tableau, Mode, or Looker) to build charts and daily, weekly, or monthly reports. Start simple and expand it from there. If you want to explore more about proper data infrastructure - this article by Nate Kupp is a great start!
BI tools solve for a lot of pain by helping automate data collection and hiding analytics under the hood. But simply acquiring these tools won’t foster a culture of data-driven decision making any more than a paint-by-numbers kit will nurture artistic ability.
Investing in and providing employees with tools without proper introduction or onboarding seems illogical, right? Well, unfortunately that’s how most companies do it.
Train your employees to not only use the basics of the tools you provide, but also how they can be leveraged to improve their day to day role, and their work in general. Training, courses, data hackathons – invest in your team (but make sure you do it in the right way!) and it will be returned many times over.
There are many different data training options out there today. It’s important to choose the one that fits well into your company, and into your employees’ routine.
It's definitely an option to set up courses and workshops internally. But unless they are done right, there can be a lot of obvious costs (salaries of people you have to hire for this), and hidden costs (unnecessary time spent by everyone involved) that might not make it worthwhile.
And if you’re considering external providers, an ideal data training experience would be remote, customized, mindful of your employees' time, and focused on showing objective return on investment. We built the Enki platform with exactly this in mind.
Either way, one tip as a starting point is to think about how better data skills can improve the day to day work of each of your teams. This way, they’ll be much more motivated to learn, and the results will be much more tangible.
In our next article, we’ll go deeper into the various data tool training options. Stay tuned!
Enki enables any employee to become data literate, and for teams to improve their data culture. By combining always-on-hand remote mentors with an interactive online course, it’s simple and cost-effective.