SDO 002 - Selling Data Infrastructure - Mike Ebbers

What are your thoughts on sales?

I will never forget my first day picking up the phone to call potential customers pitching the AI solution my co-founders, and I were building— in short, it was very humbling. That day gave me tremendous respect for my Sales colleagues’ tenacity and insight into how they could make me a better data professional. Specifically, Sales professionals are typically the first contact points with customers our data solutions will impact. Thus, by staying in contact with Sales, I can listen to the pain points that are surfaced and use that information to determine how to position my data projects to best provide value.

Hear from Mike Ebbers, Account Executive at Monte Carlo:

Hear from "XYZ" highlights real-world use cases for all of us to learn best practices and upcoming trends within the DataOps space. Sales may not be your first thought of being a data role, but they are uniquely positioned to see the challenges data teams are facing across the industry through their discovery calls. I talked with Mike to learn more about what he is seeing in the market for data observability.

What pain points are you finding data teams are struggling with the most in your discovery calls?

Mike: “The biggest one that almost all pain points boil down to is data trust. The primary problem comes from the following symptoms: data issues are not caught before downstream consumers, whether it be analysts, data scientists, or actual paying customers of a data analytics product.

They're the ones slacking the data engineering or DataOps teams going, "hey, this data doesn't look right," or "we know this is wrong," or "this value can't be true," or whatever the case may be erodes the trust between the organizations. That issue continues to be pervasive in other ways because then people don't use the data as much. If they don't trust it, they're not gonna use it, right? So you can't be data driven if you don't have trustworthy, reliable data.

The second one is I think, just data engineering teams more specifically, tend to live in a more reactive mode. They're not actually doing the things they were hired to do, like building pipelines and do data modeling and some of the stuff that actually provides value to the business. They're being brought into these reactive kind of firefighting situations where they have to figure out why there was a null rate in a column where there shouldn't have been or why the data didn't show up from that third party data source.

So it really comes down to those two. It's the data trust and then also being reactive.”

Data practitioners are the user of data solutions, but they often need to “sell up” to their leadership to make purchases. How have you seen data teams successfully “sell up” in this situation?

Mike: “The first thing is awareness of the problem. People don't provide solutions, and they don't fund solutions, where they don't understand what the problem is. And the corollary to that is what's the impact to that problem? And it's gotta go beyond, ‘Hey, my job is being filled with a bunch of annoyances,’ it actually has to have some impact on the business. So that's the first thing. Awareness at the executive level of the actual problem that exists.

The next thing is what's the quantifiable impact? Right? And actually there's two buckets to this. There's hard dollars, ‘are we actually losing money on a product, for example?’ And then there's also soft costs, which is, ‘we hired 15 data engineers and 50% of their time on a weekly basis is being spent on various issues.’

So it takes both sides, it's soft and hard costs. But a key aspect that data buyers, particularly data technical folks, don't always pick up on is understanding how your company makes these decisions. Because, if I'm in a bank I'm probably gonna talk dollars and cents with a greater emphasis than I would if I'm an up and coming startup who's still trying to build things out culturally. So you have to understand how your leaders actually make decisions.

Last thing I'll mention is consensus. What about your peers? Who else does this impact? If it's data engineering, or data science, or finance, or some other department, some domain specifically that has this problem-- does that cascade to other departments as well? Is it cascading from other departments? Let's understand that a bit more and build consensus around what we need to go do. And, the answer is not the same for two companies. You have to assess that and kind of understand what people have a tolerance for versus not.

So those would be my answers, right? That's how, that's how people successfully kind of sell a direction, whichever they choose.”

Sales, at its best, helps companies solve their pressing problems. How can data teams best leverage individuals from Sales in solving problems that warrant an external vendor?

Mike: “The first part is coming prepared with ‘here's what the problem is and here's our general perspective on why we need to go solve it.’ Good sales people are really good at understanding from a big picture perspective how this all fits together: the business, the technical aspects of it, all the different resources that are necessary for assessing whether or not there's a fit, supporting them from a product perspective, from a business perspective, creating the white papers or business cases.

But sales people can't do that unless they understand what that problem is they're actually trying to solve and how it connects to the business. And I would argue that if there is no connection to the business, it might not be worth solving that problem.

So you have to come with that hypothesis and perspective, then just communicate openly. The worst thing is to be working towards two different things for two different reasons. Being as aligned and streamlined as possible towards that mutual goal of solving the problem is critical, and that's how you have a healthy relationship.”

Person Profile:

Mike Ebbers is an Account Executive at Monte Carlo, where he ensures data teams are the first to know about and solve data issues via data observability. Please connect with him on LinkedIn to learn how he can help you.

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