Data-Driven Work Cultures: Verl Allen of Claravine On How To Effectively Leverage Data To Take Your Company To The Next Level
An Interview With Pierre Brunelle
The first, and most important, way to effectively leverage data to take your business to the next level, is to create a data-driven Culture. Data-driven culture starts at the top, with leadership that equates business strategy to data strategy. It spreads from there to empower a risk-friendly environment in which people are rewarded for bucking the status quo — and using data to help them do it.
As part of our series about “How To Effectively Leverage Data To Take Your Company To The Next Level”, I had the pleasure of interviewing Verl Allen.
Verl Allen is the CEO of Claravine, a data integrity company that provides data standards SaaS for global enterprises with complex, siloed data ecosystems. Over the past 20+ years, Verl has digitally transformed companies including Ancestry, Adobe, and Omniture, in addition to leading multiple data-driven tech companies to successful exits. He’s known for supercharging enterprise growth, scale, and revenue by deploying smart tech investments and pioneering data literacy, access, and usability.
Thank you so much for joining us in this interview series. Before we dive in, our readers would love to “get to know you” a bit better. Can you tell us a bit about your ‘backstory’ and how you got started?
I’ve always been a numbers person. My mom even tells me, instead of doodling funny characters as a kid I was “doodling” with numbers. Even recently, during COVID lockdowns, I found myself immersing myself in all the pandemic data going around, trying to make my own predictions. Data excites me.
I spent my early career in finance and investment banking, then business operations, and then to the most data-rich of all, marketing. I was at Ancestry.com and we had a data analyst leading marketing strategies. That was a huge transformation from the fluffy, emotional marketing I learned in college.
Our marketing at Ancestry was completely data-backed and that changed the company fundamentally — it scaled the business to be worth a billion dollars. Data, analytics, insight, and decisions. That’s what drove us.
So I was very much convinced that data paved the way to business value. And what solidified this even more was my experience with Omniture being acquired by Adobe because it realized that data analytics was no longer optional, but crucial.
Omniture of course knew the value of data for optimizing a website, but we pushed on the idea that data can be used to optimize the business and the customer experience more broadly. And that had traction. But the challenge remained that companies were using a bigger and bigger mesh of hundreds of SaaS platforms.
So all this great data was stuck in tools and teams and silos or stashed in cloud servers. My itch to unleash data for business decisions compelled me to find ways to integrate those silos not just at the workflow layer, but at the data layer.
Example: A global consulting firm unleashed AI/ML for decision-making, but realized it was a faulty investment because the data they could actually use — compared to the troves they had stored — became much too small of a set.
And with digital transformation and customers demanding a seamless and holistic digital experience, it was clear that every angle of the business needed better ways to leverage data.
In 2018 I met Craig Scribner, who created a tool called TrackingFirst. It solved the problem of baking in context — strategy, workflow, distribution, etc. — right into the data itself via tracking code taxonomy (metadata and naming conventions). It solved this problem of packaging data with the context required for someone unfamiliar to pluck it out of a data cloud — and actually understand it, use it.
I realized he had solved this huge data problem: capturing insight, strategy, campaign details, business value — or in other words, context — into the data itself, rather than letting it be relegated to silos, apps, and teams. And that could not only be huge for marketing, but huge for enterprise business decisions. And so we joined forces and became Claravine.
Can you share a story about the funniest mistake you made when you were first starting? Can you tell us what lessons or ‘take aways’ you learned from that?
Upon joining a previous company, I thought their technology was further along than it really was. It worked well for a lot of customers, I soon learned that it was almost falling over, unable to support new features and functions. This realization came after I made the call to invest in and accelerate our go-to-market strategy.
I learned an expensive lesson — two of them, actually. 1) I should have pulled the plug on GTM sooner and reinvested the funds in the product. 2) I needed to make decisions quicker, even if they’re hard, and be deliberate with them. Even if it meant letting go of a super bright person I had just hired to lead the rollout. I had to make hard decisions quickly, to avoid catastrophes later on.
I learned crucial lessons about what it means to lead. They were hard and painful lessons, but I’m a better leader because of them.
Is there a particular book, podcast, or film that made a significant impact on you? Can you share a story or explain why it resonated with you so much?
I love the Exponent podcast, by Ben Thompson of Stratechery and Harvard Business Review tech writer James Allworth. They discuss problems at the intersection of technology and business, and how decisions made by companies end up effecting the world we live in generally. I love that they think big, globally, on often industry-siloed problems to make grand insights.
They also have a unique balance of personality, altruism, and business sense to bring perspective to what’s happening with big players like Facebook or Apple, and how those lead to societal shifts. They take an ecosystem-view, and then dive back in to use that to inform micro-decisions. It’s truly opened my mind in how I approach and solve problems.
Are you working on any new, exciting projects now? How do you think that might help people?
My complete focus right now is on Claravine and how we’re using data standards to solve this massive measurement challenge in marketing. The loss of third party cookies, rise of privacy regulations, and other huge shifts in digital experience make the marketing data center so much more crucial, yet increasingly complex to navigate.
When global brands are losing visibility throughout their customer journeys and marketing strategies, we’re coming in to improve data integrity via standards that bring these tools, walled gardens, SaaS, agencies — everyone involved — together with a shared data language. It’s transforming marketing measurement and effectiveness, and it’s transforming businesses.
On top of that, we’re enabling more and more automation, so companies don’t worry about manually applying standards. When you have thousands of digital assets and campaigns, attempting to manually update taxonomies would be daunting to say the least. Ease of use through automation is a major focus for us right now.
Thank you for all that. Let’s now turn to the main focus of our discussion about empowering organizations to be more “data-driven.” My work centers on the value of data visualization and data collaboration at all levels of an organization. So I’m particularly interested in this topic. For the benefit of our readers, can you help explain what exactly it means to be data-driven? On a practical level, what does it look like to use data to make decisions?
It’s almost a foregone conclusion to be “data-driven” these days. But the most important element of being data-driven is actually the people.
You need the right people in place to ensure success when the data starts flowing. And you need to respect the balance between data and intuition (or institutional wisdom). Data-driven is not data-deciding — there’s a common misconception that data can suspend our assumptions, biases, preconceptions, and wisdom.
But that’s not the case. Don’t discount wisdom and gut feelings just because the data says something. Strike a balance and use data to explain assumptions or biases. Allow for experimentation, and then use one another to validate — answer the, “why?”
You can’t just become a data-driven company by inserting a bunch of data scientists into the organization. They need context.
Which companies can most benefit from tools that empower data collaboration?
Every company that puts data or data science into a silo. Especially as workforces become more remote, they have to be careful of pushing decision-making closest to its endpoint. Also, any company that is an established player — they’re most at risk for disruption.
Those big swingers in a market or a niche — think about the depth and breadth of knowledge they have on everything related to the industry. But those companies, mostly in the enterprise scale, are typically the slowest to make decisions.
They’re entrenched, but what they should be is data-accelerated, building a competitive moat around their business. They should be predicting industry trends, transforming quickly, and being the first — not second and definitely not last — to thrive in the next evolution.
But they need to disrupt internally. And it’s not even a more-vs-less data issue. It’s an issue of actually leveraging all the data you have across silos, to transform the way you serve your customers or go to market. Enterprises could have the inherent advantage here, but they have to tap into their data and ensure it retains its context.
We’ve seen so many startups just crush entrenched enterprises — even entire industries — because they’re small, nimble, quick — and because they use data holistically. Data can provide the confidence needed to reduce fear of innovation — we just have to get it in the right hands and with the right context.
We’d love to hear about your experiences using data to drive decisions. In your experience, how has data analytics and data collaboration helped improve operations, processes, and customer experiences? We’d love to hear some stories if possible.
I have a great story from when I was in digital marketing with Adobe. We of course analyzed what we spent to acquire a customer. The problem was, it stopped us from investing in certain channels, because we just couldn’t bring the average cost down to make sense.
So we started slicing the data thinner, to look at CAC (customer acquisition cost) by tactic and by channel and uncovered big variations from the average measurement. It also showed us that certain channels or customers had much higher lifetime values (CLV) than others, which would justify a larger spend to acquire them.
By better leveraging the data we already had, we fine-tuned our strategies to be more granular. And that transformed our business, across the enterprise. It all hinged on being able to slice data thinner and dive deeper into its context.
Has the shift towards becoming more data-driven been challenging for some teams or organizations from your vantage point? What are the challenges? How can organizations solve these challenges?
I think the biggest challenges sit right on the CEO’s desk: evolving to a data-centric culture and setting in motion a holistic strategy around how data is leveraged to make decisions that scale the business.
Culture and strategy has to start at the C-suite. But in most cases, the higher up you go in an organization, the less trust in data you find. Leadership has to guide the company on ensuring data can be trusted, especially as it moves higher up in the org or across silos. At its genesis, that data may never have intended to be so cross-functional, so leadership has to provide the tools and paths to bring it all together for amplified value.
They have to be able to discern which data is useful and valuable based on its context and the context of the decision.
So many leaders think, once you remove the people and insert the machines instead it will get so much easier. But there’s real issues with data quality and scale of data — you can’t feed your AI/ML machine just any old data.
Advanced data-decision engines need data that’s rich, integrated, contextual, and of high integrity. And then that poses another challenge: excessively investing in people and technology to clean and transform data to prep it. That not only tanks your ROI, but limits your speed, and worst of all, ends up reducing your valuable dataset so much that its depth and breadth is barely useful (or trustworthy) to the business.
Reactive approach after reactive approach just chokes the data-driven engine. The solution? Look at your data strategy and data culture. Be proactive, not reactive. Operate in the sense of data strategy = business strategy.
- Get senior-level commitments to utilizing data to make decisions, so they can drive the right investments, hires, and workflows.
- Clearly outline the problems you’re trying to solve with data before you even begin the data flow.
- Try to identify the limits of your data and set expectations — how far can data take us, and what are its limits? Where will we need to lean more heavily on people or wisdom?
Data will not be valuable to your organization unless it includes context. Data comes from applications that weren’t built to include cross-functional context or even be cross-functionally understood. That’s on you.
Prioritizing context in your data strategy means being able to map across different datasets, fields, sources, uses — everything that tells you which data is relevant to which business decisions. Data teams can’t be disconnected from business context.
You need a welcoming, well-traveled bridge between the quants, the creatives, and the decision-makers. Close the gap.
That’s the difference between saying ‘your Spring pickup truck marketing campaign was a success,’ and being able to say which source, business unit, channel, region, spend, et cetera contributed to that success — and how you can use that data-driven knowledge to replicate and improve.
Ok. Thank you. Here is the primary question of our discussion. Based on your experience and success, what are “Five Ways a Company Can Effectively Leverage Data to Take It To The Next Level”? Please share a story or an example for each.
Here are my “Five Ways a Company Can Effectively Leverage Data to Take It To The Next Level” — approach them in this order.
- Culture — The first, and most important, way to effectively leverage data to take your business to the next level, is to create a data-driven Culture. Data-driven culture starts at the top, with leadership that equates business strategy to data strategy. It spreads from there to empower a risk-friendly environment in which people are rewarded for bucking the status quo — and using data to help them do it.
That extends to creating a culture in which data access and collaboration is a given, not an afterthought. Data must be considered a cultural artifact, complete with context and understanding. (Note: Culture does not mean People. We’ll get to that.) - Strategy — Strategy drives People (that’s next, don’t worry!). If you don’t have a business strategy, how do you know who to hire? What you even need? Strategy drives People and Process, and now it needs to be integrated with data strategy. Without strategy and goals to measure towards, there’s no ability to create change in an organization via data.
And data can’t be relegated to second-class status; when it’s ingrained in corporate objectives, the likelihood of success goes way, way up. - People — The right People in the right place. People who are constantly thinking, “Why are we collecting this data? What will it accomplish? What other questions can I leverage it to answer? What are the business-data gaps? How do I increase the value of this data with some other data — and how do I do that? How is this applicable to our business — is it even?”
Those are the kind of people you want on your teams. You also want senior-level people with organizational power that are data-driven.
I have a good friend who helped a startup go from nothing to an IPO, but he walked out the door because they talked about being data-driven, but would not make the organizational commitments to bring it to fruition. They wouldn’t commit to the culture, strategy, people, or structure. They wouldn’t put their money where their mouth was. He knew it was set up for failure at that point. - Process — There’s this philosophy in data processes of, create data on the business side, hand it off to some data-silo-black-box, get back some decisions, then go implement. But that’s putting all the data responsibility — quality, strategy, completeness, ownership, collection — just with the “Data” silo. That’s putting all the onus on a team that’s not clued into what happened upstream and what needs to happen downstream.
So the way to really, successfully leverage data is with a proactive approach to Process. Think about data as a cradle-to-grave process.
Typically, what happens at data “birth” can never be truly fixed. You’re stuck with it, and it affects everything downstream. You have be at the headwaters of the data process to actually fix problems that appear later on (in rather expensive forms, I might add!).
It’s less expensive, less disruptive, and much more efficient to proactively avoid problems than reactively scramble to fix them. So the culture around data must extend well beyond the Chief Data Officer and their team — the data accountability needs to be embraced throughout the entire business, baked into its strategy. Approach data as an ecosystem, not as a silo. - Results — Finally, you have to focus on Results and ROI. And do that both using data and examining data’s efficacy and relevance. Some of this is a cost-benefit thing: you can only slice something so thin before it’s no longer valuable. You can get more and more granular, but at some point the ROI dries up.
I like to illustrate this with a very common scenario in enterprises today: it takes a village to get clean data. There’s a “throw it over the wall” approach to data-problems. But when one of the most finite and expensive resources to your business is data engineers and scientists, you need to take an honest look at where they’re spending their time.
Are you leveraging your most valuable resources effectively? Are you letting quants be analysts, or are you forcing quants to be data-janitors?
When you remove the data cleanup burden from data scientists, you’ll be amazed at what they can do for business results. So when you think about the village it takes to have clean data, think of it instead as your whole organization owning and bridging their data between teams and quants. Together, you can all find real solutions and proactive avoidance measures, rather than stopgap solutions.
The name of this series is “Data-Driven Work Cultures”. Changing a culture is hard. What would you suggest is needed to change a work culture to become more Data Driven?
Ditch the status quo and stop settling for “good enough” data. Your competitors will quickly sniff out that attitude and they will win the arms race.
When data is a given, and data-driven is the norm, that means tools to improve data quality, integrity, inputs, and outputs aren’t a “nice to have” they’re an absolute necessity to even stay afloat. Inaccurate, wasted, and merely directional data won’t cut it. Precision is the new directional.
Not perfection, I’ll point out, but definitely precision. “Perfect” can be just as stymying as “good enough,” but a data ecosystem focused on precision and context — that’s a recipe for success and scalability.
You also have to treat data as a company-wide asset, and data stewardship as a company-wide responsibility. And it extends beyond just the organization — think of your agencies and partners, clients or customers; you have to help them understand and be a positive impact.
Within this, you have to carefully avoid the challenge of “everyone owns data, so no one owns data” — that’s not the message here. The message is to contextualize data for every team, specifically. Example: A very bright woman was brought in to address enterprise data governance. But the executives’ eyes glazed over. Yet when she used the term “quality” instead of “governance” they paid attention.
Ditch historical definitions and ownerships so you can be thoughtful about the right, new areas of data ownership. Have the right people accountable for the right aspects of data strategy.
The future of work has recently become very fluid. Based on your experience, how do you think the needs for data will evolve and change over the next five years?
The most pressure is coming from customer experience. We’re well into the Data Era and I think the next Decade of Data will be incredibly transformative. CX is now about holistic, multi-touchpoint journeys instead of siloed activities.
Companies that know how to leverage data seamlessly, holistically, across experiences are charging ahead. And they’ll do that because they won’t rforce legacy structures into today’s business needs.
If productivity in the late ’90s improved with expensive suites of desktop software, and the 2000s thrived with a “there’s a SaaS for that” mentality, the next decade accelerates productivity from the data layer. Data is now the foundation of productivity, so it necessitates a shift from siloed apps to shared ecosystems.
Then the problem to overcome is data at scale. Enterprises have the money and people needed to attack it — small and mid-sized businesses may not. But all of them will still need to be proactive and thoughtful about their data strategies to be competitive.
A lot of data isn’t necessarily a lot of good, decisionable data. Strategy, not volume, yields results.
Does your organization have any exciting goals for the near future? What challenges will you need to tackle to reach them? How do you think data analytics can best help you?
Claravine is at an exciting stage because we’re starting to eat our own dog food — and I consider that a badge of honor.
We scaled carefully and now we’re serving almost a quarter of the Fortune 100: global organizations with billion-dollar valuations. And they expect us, the scrappy startup-turned-stalwart partner, to give them the same level of customer experience they get from Adobe, Apple, Microsoft, these enormous players sharing space in their tech stack with Claravine.
We’re constantly retooling the ways in which we leverage data, just as we help our clients do. We’re bringing on a dedicated data analyst to focus on our business, not our product or our customers’ data. It’s a senior-level role, just as I’ve preached, with direct responsibility — and a mandate, even — to leverage data analytics for company-wide decisions.
We’re investing heavily in our internal data structure. Any growth we pursue is ingrained with data strategy.
We’re fully embracing the cultural ecosystem in which we can extract value from resources across the organization. We’re looking at data not just for product or sales insights, but for holistic CX and process improvements.
How can our readers further follow your work?
Find me on LinkedIn and give a listen to some of the fantastically enlightening podcasts I’ve had the pleasure of joining at www.claravine.com/podcast-appearances/.
Thank you so much for sharing these important insights. We wish you continued success and good health!
