Part 2: What goes wrong when business, finance, data, and technology worlds collide?

A 3-Part Series: Thriving Across the Worlds of Finance, Business, Data and Technology

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Welcome to the Data Score newsletter, your go-to source for insights into the world of data-driven decision-making. Whether you're an insight seeker, a unique data company, a software-as-a-service provider, or an investor, this newsletter is for you. I'm Jason DeRise, a seasoned expert in the field of alternative data insights. As one of the first 10 members of UBS Evidence Lab, I was at the forefront of pioneering new ways to generate actionable insights from data. Before that, I successfully built a sell-side equity research franchise based on proprietary data and non-consensus insights. Through my extensive experience as a purchaser and creator of data, I have gained a unique perspective that allows me to collaborate with end-users to generate meaningful insights.

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In part 1 we introduced this multi-part entry in The Data Score newsletter. We’re going to explore how to break down the silos between data, technology, and business to more easily get to the good part of a data insights practice: actionable, accurate decisions.

Part 1: Framing the problem: why silos are not viable anymore

1. Original silos and problems to be solved are mostly self-contained.

2. Problem complexity is accelerating.

Part 2: What goes wrong when the worlds collide?

1. Talking past each other

2. Smartest person in the room syndrome

3. Fear of failure

Part 3: How to break down the silos and align around outcomes

1. Build empathy.

2. Create proactive rituals.

3. Top-down culture

What I hope to offer here is a benchmark of the ideal for setting up the best possible outcomes when the worlds of business and finance come together with the worlds of technology and data.

I’ve seen the good, the bad, and the ugly in my experience working across all these worlds. But I want to be super clear on this: I don’t have all the answers. As I said in Part 1, I’m human, so I make mistakes too. I can’t promise that I’ve been perfect at following these ideals in the past. Nor can I promise that in the future there will not be times when communication will break down while I’m involved in the project.

The benchmark I put forward is to stimulate conversation. I’m keen to hear how everyone who reads this newsletter feels. Finance professionals, business leaders, data professionals, and technologists: how do you break down these silos to generate positive outcomes, even in difficult situations?

First, we diagnose the issues before building a better solution

Building bridges between the worlds of business, data, and technology to increase the overlap is critical to maximizing the return of a data and technology strategy for business decision makers. But first, let’s diagnose where the relationship goes wrong.

From my experience, the academic literature is right about organizations getting past 150 people and the pace of production slowing due to bureaucracy and organizational complexity. Even in organizations of 30 people or more, silos begin to form. And the majority of the Data Score readers work in or serve massive organizations with tens of thousands of employees who are trying to become more data- and tech-savvy in their approach to generating higher ROI.

Having one giant team is not possible, so we need to understand that functional and organizational silos are inherently going to happen in any organization that grows beyond the initial founding teams. Therefore, the challenge is in how the expertise from each silo comes together to achieve success.

The Analytics Power Hour podcast spoke about this subject of stakeholder buyer and creating alignment in the episode released on August 8th (shoutout to my friend Val Kroll, who joined the podcast as a regular host in this episode!).

The episode does really well to characterize what happens when business, data, and technology come together to make decisions that don’t go according to plan. They also offer suggestions to overcome it later in the episode, but I want to share a short section that highlights the struggle before I dig into how I would diagnose the underlying problems when experts across the different silos come together to problem-solve and make decisions.

Michael Helbling: I think that’s actually a really big one that it’s like the data is not gonna give you this perfect, definitive answer. I think it’s… I feel like it’s less if there is a definitive answer and it’s not what they wanna see, that they won’t accept it, I think just in general, that answers, the data provides always have a greater degree of uncertainty, they’ll look for a shred of uncertainty and say, well, but you’re saying you’re only pretty sure about this? Well, that’s my out to go do what I think because you need to give me the truth.

Tim Wilson: Yeah. Or the other side of it was sort of like, well, I was gonna do it anyway, so this is about the slimmest shred of evidence that I can find that gives me the justification for the thing I wanted.

Moe Kiss: Someone actually referred to it the other day as panning for metrics, and I was like, oh, that is a great term… I wanna be clear like this is obviously not every stakeholder.. these are intrinsic traits of being a human, of looking for information that you believe is true and things like that. It’s not like our stakeholders are real stupid or anything, I generally think they’re phenomenal people, but it’s like we’re trying to overcome the ways that our minds work, and that is even more difficult, I think, for someone that doesn’t specialize in data and understand the nuances with uncertainty and things like that.

Val Kroll: I think especially for more senior leaders who have been around the block and have some experience under their belt, they were making decisions long before they had access to this type of data, and so it’s a lot of unlearning of behaviors too. And so if they’re unable to model it, that’s like perpetuating that to their teams, and so it’s a lot of change management.

My view:

At the most basic level, when experts from business, finance, technology, and data come together around a single project with an expected outcome, there are inherent barriers to getting on the same page and aligning interests. I believe it’s the soft skills around communication, empathy, and psychological safety¹ that make or break projects. Soft skills are needed to reach a win-win outcome even with well-documented requirements, project request forms, and data contracts in place to govern the initial deliverables and continued maintenance.

I believe the three most common reasons projects break down are:

1. Talking past each other

2. Smartest person in the room syndrome

3. Fear of failure

Photo by Charles Forerunner on Unsplash

Talking past each other

Silos miscommunicate due to jargon and unstated motivations.

Jargon

The worlds of finance, tech, and data have an absurd amount of jargon. In the worlds of finance, data, and technology, the jargon used is helpful in distilling ideas down into a single word or phrase that captures a lot of meaning quickly. But it can also be a way that professionals can build barriers to entry by making their profession seem more complicated than it actually is.

Here are some abbreviations and jargon from both finance and data: P/E, DCF, LBO, SOTP, API, ETL, ELT, and RLHF². Why would a data professional know what an LBO is? Why would a financial professional know the difference between ETL and ELT?

In actuality, the jobs of finance, data, and tech professionals are quite basic in logic and easily explained. It’s like a magician revealing the secret behind the trick; the actual logic is usually quite basic.

“Then you first learn how a magic trick is done, there’s often a moment of disappointment where you say, oh, he just held it in the other hand.” - Teller from Penn & Teller’s Masterclass on Magic

Yes, Teller speaks during this Masterclass. https://coursereviewers.com/penn-and-teller-masterclass-review/

That’s why I’ve always included footnotes in The Data Score newsletter. Once the concepts are explained simply, none of them are hard to understand. But without a common language, it’s extremely hard to get on the same page.

Unstated motivations

The unstated motivations of each group become a blocker. The “how and why” each silo is making decisions often goes unstated because there’s an assumption that it’s already understood by the others. But that leaves the other side of the relationship without an understanding of the goals and blockers to success. Without that understanding, assumptions are made, which leads to misunderstandings about the priority and effort required.

Here’s a real example, without the details to protect those involved.