Reading Notes on Transforming Corporate Finance

This weekend, I did some light reading on freeing up Corporate Finance Analysts from process work. I’m trying to find out what’s possible, gather lessons learned, and refine my point of view on the subject. Sharing some excerpts below.

Corporate Finance is awash in process work

In my first role in Financial Planning and Analysis, I was surprised just how much of my time was spent extracting data from disconnected data stores for processing in Excel. My experience covering for another analyst during quarter close made me realize that I had it easy – this other analyst had a process that took data from 10 different data sources just to get a usable dataset to help them understand their costs.

Reading Tableau’s case studies gave me more evidence that this isn’t an isolated incident.

How to use Tableau for financial planning to drive deeper business impact
“Many professionals can relate to the scenario of business partners relying heavily on Finance to review data and produce quick answers (and sometimes with undesirable deadlines). Honeywell is no different. In fact, our CFO’s tell Finance employees, “You didn’t go to school and become Finance professionals to develop manual, repetitive reports in Excel. And we don’t want you to. Instead, we want you to be focused on being better business partners, driving impactful analytics.” So, our adoption of Tableau has been key for me and others in Finance to stop the Excel gymnastics and capture, analyze and understand large amounts of data as we forge a stronger partnership with the business.”

4 Ways Finance Creates Value with Visual Analytics

“Every day millions of finance professionals extract data from variety of different platforms and often manually engineer reports with spreadsheets to find the answers they seek. As great as spreadsheets can be, the primary hardship with this ingrained manual routine is, of course, time.”

I’ve spoken with enough Financial Analysts to know that the one thing that they want to do more of is value added analyses – the kind of analyses that made them choose a career in Finance to begin with.

What could Corporate Finance look like?

Both analysts and those who lead analysts understand that things should be better, but they don’t know what’s possible. If only there was a way to hear from a company that’s already a few years into their transformation?

Modern Finance with Amy Hood and Christian Rast

If you’re starting your finance transformation, you’re already years behind Microsoft’s Finance team. Finance has traditionally been the “custodians of corporate data” and “data is our currency” but the function is ripe for transformation. In this video from Microsoft’s 2017 Envision conference, Amy Hood (CFO@MSFT) describes how Microsoft’s finance team was able to leverage internal talent and key partnerships to revolutionize their work and contribution to the company.

Data Informed Organizations

Amy Hood’s comment regarding Finance’s symbiotic relationship with data led me to look for other companies that are thinking about their data in new and interesting ways. The below HBR article argues for the need to spread data literacy throughout the organization, while AirBnB explains its’ philosophy towards data and how they applied that philosophy in their data infrastructure. While not every company should arrive at the same solution, it’s helpful to understand how corporate culture can be tied to its systems and processes, which can make or break the transformation effort.

The Democratization of Data Science

“Relegating all data knowledge to a handful of people within a company is problematic on many levels. Data scientists find it frustrating because it’s hard for them to communicate their findings to colleagues who lack basic data literacy. Business stakeholders are unhappy because data requests take too long to fulfill and often fail to answer the original questions. In some cases, that’s because the questioner failed to explain the question properly to the data scientist.”

How Airbnb Democratizes Data Science With Data University

“Another one of our fundamental beliefs is that every employee should be empowered to make data informed decisions. This applies to all parts of Airbnb’s organization — from deciding whether to launch a new product feature to analyzing how to provide the best possible employee experience. Our Data Science team firmly believes that part of our goal is to empower the company to understand and work with data. In order to inform every decision with data, it wouldn’t be possible to have a data scientist in every room — we needed to scale our skillset

Creating “citizen data scientists” is powerful — not only does it help ensure that decisions are grounded in data, but it enables people to make decisions autonomously. This is important because the person asking the question always has the best context on the question they are trying to answer, and it reduces the feedback loop to answering questions. This also has the side benefit of freeing up some of the Data Science Team’s time.”

Data Infrastructure at Airbnb

I love how AirBnB shares what they’re working on. Sharing intent is powerful – it informs the design and promotes greater understanding of the deliberate choices they made in their infrastructure implementation.

  • Look to the open source world: there are a lot of good resources for data infrastructure in the open source community and we try to adopt those systems. Furthermore, if we build something useful ourselves and it is feasible to give it back to the community, we reciprocate.
  • Prefer standard components and methods: There are times when it makes sense to invent a completely new piece of infrastructure, but often times this is not a good use of resources. Having intuition about when to build a unique solution and when to adopt an existing solution is important, and that intuition must properly account for the hidden costs of maintenance and support.
  • Make sure it can scale: we have found that data doesn’t grow linearly with the business, but grows superlinearly as technical employees begin building new products and logging new activities on top of the growth of the business.
  • Solve real problems by listening to your colleagues: empathizing with the data users around the company is an important part of informing our roadmap. In adherence with Henry Ford’s mantra, we must balance making faster horses vs. building the automobile — but listen to your customers first.
  • Leave some headroom: we oversubscribe resources to our clusters in order to foster a culture of unbounded exploration. It is easy for infrastructure teams to get wrapped up in the excitement of maximizing resources too early, but our hypothesis is that a single new business opportunity found in the warehouse will more than offset those extra machines.

At the end of the day, I’m still thinking about how data teams should be organized and how data-informed careers should look. I’m also thinking about what the function will look like in a few years if things go well, and if it will be enough to tackle the problems that we’ll be facing in the future. More to come!

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