I’ve been using QuickBooks for my own business for almost 14 years as a user. I only consulted on it once when I helped my accountant with one of his clients. Lately, I’ve been thinking it’s time to move on. I want a database I can run reports from. And besides, shouldn’t the shoemaker get some shoes once in a while?
There’s an old story about a group of blind men who touch different parts of an elephant. They’re all convinced they understand what the elephant is. But, of course, their understanding differs depending on whether they touch the trunk, tail, tusk or leg.
Sometime I think I’ve become the elephant. When I speak to folks about what I do (and especially when they read my blog), they often latch onto a part that relates to their own experience. It’s really nice when someone’s interested in what you do. But, as someone given to distraction, I’m often led astray. I think, “Wow, someone’s interested in that. I should pursue it.”
Organizations sometimes ask me to help them clean up a mess. Messes such as, “The numbers aren’t trustworthy,” “The close takes too long,” and “The software is ancient.”
Whatever the type of mess, when cleaning it up we often encounter the same challenges:
- The folks you need to work with don’t trust you. People don’t like change. People don’t trust consultants in general. And most employees view the accounting department as a bunch of trolls.
- There’s a ton of stuff to do. Problems that took years to create can’t be solved immediately.
- Priorities are unclear. When there’s a ton of stuff to do, people think you’ve forgotten about what THEY think is most important—no matter where you start or how you proceed.
To address these challenges, I often create a one-page charter, or project overview, for new projects. The charter helps people grasp the key issues quickly, without having to flip through notes or talk to five different people.
In my previous post, I discussed the five key changes I made in 2014. As I wrote about these changes, I had to ask myself why it took me so long to make them. I’ve always known that my business, while profitable, was a “freelancer on steroids” model. We had the classic freelancer problem: get a great gig, work like heck on it, panic and start marketing like crazy when the “gig is up” (as it were).
Well, maybe it wasn’t that bad. Thankfully, we’re not dependent on one client—we have a base of about 10-15 recurring clients. But for most of the past six years, one big client has regularly “made our year.” And because we’ve been dependent on that one big client, we’d often get dragged into different technologies. Or at least that’s what use to happen until I hit an emotional and physical wall this year. Which proves what I’ve always said: people only change when they’re in extreme pain.
So, why didn’t I change my ways sooner?
Financially, 2014 was my best year ever. But it almost put me “over the edge” emotionally and physically.When the pain and exhaustion became too much, I finally got serious about narrowing the focus of the business.
Here are some of the changes my team and I’ve made over the past four months:
1. We cut the client list
I’ve talked a lot about focus (especially in this blog). But we really put that talk into action when we said goodbye to a client who gave us over $3 million of work over the last 10 years. Over the years, we did many major projects for these folks. During the last half of 2104—after a period of quiet—I was pulled back into a very active role. This role required not just my consulting expertise, but my detailed knowledge of everything that had happened in the financial system over the last seven years. This is knowledge I can’t leverage and can’t train someone else so. We helped them transition to another consultant.
You Don't Have Big Data
In previous posts, I wrote about how a financial data consultant differs from a regular data consultant and why it’s important not to oversell financial data projects.
In this post, I want to talk about big data. Because, contrary to what you might believe, you don’t have big data. Most financial executives work with regular data. As financial data consultants, we work with regular data. Even if you have $3 billion in sales and 100,000,000 transactions, it’s still regular data.
Let me explain how big data and regular data differ:
- Big data is unstructured. Regular data isn’t.
Let’s use an example. We often start projects with the general ledger. (For more on this topic, see Using the General Ledger as a Data Warehouse.) General ledger transactions have a standard structure. Every record has an accounting string, a posting date, and a currency amount (or maybe several). The consistency of this structure works perfectly with standard relational databases—tools that have been around (if evolving) since the 1980s. For our clients (and we’ve worked with companies with multiple billions in revenue), there’s no need for anything else.
Data is hot. So, as with any trend, folks begin thinking that data is the way to solve their problems. As financial data consultants, this is great.
But while we love the enthusiasm, we want to acknowledge that what we do is rarely transformative in itself. Don’t be oversold. As we like to say, having good data can keep you from being stupid. But being smart is a whole lot harder.
1. Past performance is no guarantee of future results.
We hear that disclaimer in mutual fund ads. It’s a cliché, but it’s absolutely true. Things change. For example, in the early 90s, my family’s chain of men’s clothing stores had a mediocre year. Why? Because sweaters, which had been a leading category for years, stopped selling. This impacted the entire clothing industry. Consumer desire changed and suddenly everyone had too many sweaters on hand.
Lately, I’ve been presenting on the topic “7 Keys to Cost Effective Financial Business Intelligence.” I’ll be covering this topic here in a series of blog posts too. But before I start, I want to use this post to discuss the title. What is “financial business intelligence” anyway? How does it differ from “regular” business intelligence? What do we mean at Red Three when we say our focus is data for finance and accounting?
To explain, let me start with a key belief (and something that has taken me years to acknowledge): There’s no such thing as a generic data consultant. Sure, it sounds cool to call yourself a data consultant, especially when “big data” regularly makes the front page of the newspaper. Suddenly, people who have no idea what you do, think they do know.
No one likes filling out time sheets, and I’m no exception. But if I wait until the end of the week (or the end of the billing period), my notes are, shockingly, not readable. I’ve been getting better at filling out my time sheets every day, and I’m enforcing this practice with my people too. And I’m not doing it just to be annoying.
Part of my insistence on this task is because I’ve found that successful people always know where they’re at in a project. They spend a few minutes at the beginning and end of each day figuring out what tasks are open, what questions to ask, and what issues to raise. Because they go through this “awareness” process, adding up their time each day is easy. It’s part of their process.
I haven’t been writing steadily for months. The good news is that we’ve been busy and more profitable than ever. The bad news is that I’m kind of spent. And while our pipeline isn’t bad, I’m a little nervous because I haven’t focused on keeping it full. But the worse news is that, despite my multiple posts on focus, not all of our client work has been in the area of “data for finance and accounting.” That’s right. I took on a HR/payroll project. While we understand this area, and the work is getting done, it hasn’t been profitable for several reasons:
1) I had to stretch to get the resources. While I have plenty of core folks who can work with finance and procurement data, I had to go to my “deeper bench” for this project. And while the requested reports weren’t all that challenging, when things didn’t go well I couldn’t rely on my team to figure it out. I had to jump in and fix things myself. Which isn’t good.