Imagine this: You’re the CXO of a cool, upstart brand that’s growing. Growth is all about more—more sales, more products, more channels, more people. But it also means more expense lines on the profit and loss (P&L). So, while growth is great PR, CFOs staying up at night worrying about earnings before interest, taxes, depreciation, and amortization (EBITDA) know getting costs under control is of major concern.
What does this have to do with data analytics?
A lot, actually.
Again, growth is about more. And that usually means more data. It doesn’t just mean more places to collect it from though, but more frustration in ensuring all the numbers fit together, and in getting people to agree on “which numbers are right?” And if you can’t figure those out, planning and budgeting on those numbers is futile.
Believe it or not, many of these headaches stem from a single misstep that companies make when deciding on their data analytics strategy. In this article we’ll articulate that one mistake, and play out what happens if a company makes it.
One of my favourite anecdotes is from Tony Robbins. He recounts his experience of learning how to golf, where one day he’s crushing it on the course, and other days he feels like nothing is working in his favour. At first, his instructor simply says, “Don’t you love it? This is golf.”
An irate Robbins isn’t satisfied and demands his teacher explain why he’s receiving such inconsistent results.
His instructor replied with, “You’re only millimeters off.”
That’s it. Two millimeters too high, you shave the top of the ball and it just putters away. Two millimeters too low and you get a ton of loft without much distance. Hit the ball just right and you’re flying.
Robbins extrapolated that analogy to other areas of life, where a seemingly minor, two-millimeter difference today can have massive impacts in a few months or few years’ time.
So, what’s the two-millimeter difference in data analytics? It’s deciding which approach to use for your analytics infrastructure.
We’ve talked about choosing between Traditional and Minimalist approaches to business data analytics. The two-millimeter difference is which of these two approaches your mind crystallizes around before making the investment. It’s an important decision because, down the line, it will impact what your business looks like.
To be clear, one approach is not better than the other. They are both completely valid. It simply depends on which approach is the right fit for your company. A misalignment will result in higher expenses long term, and a huge barrier to exit if you ever want to switch.
We take you through an exercise to help you decide which approach is right for you. Click here to contact us.
As I mentioned above, picking and sticking with the wrong approach can lead to a huge barrier to exit. This barrier shows up in the form of ripping out an old system, implementing a new one, repositioning a multimillion-dollar team of people, and retraining your entire staff on new technology. This is what happens as a result of that two-millimeter difference. This is more relevant when switching from a Traditional Approach to a Minimalist one. At this point, the cement has dried and it’s extremely difficult to leave a Traditional investment.
Breadth vs. Depth. As you add more products, channels, and partners—more of everything that comes with growth—the business starts making trade-offs between the breadth of its analysis and its depth. Businesses must either settle for a very broad view of performance, or zoom in on one specific subject area. This has to do entirely with how their data architecture was set up (under the Traditional Approach). The only way to achieve both breadth and depth is to simply hire more people to build more dashboards and service more people in the company. This is the beginning of what we call Dashboard Hell.
Minimalism is like a stock option. The Minimalist Approach works far better for certain kinds of companies than others. But it’s more straightforward moving to a Traditional Approach from a Minimalist one, because it’s easier to add things in than to take things away. If you can make a strong business case to in-house your data engineering, then you can think of the Minimalist Approach as having an option to switch over to a Traditional Approach down the line.
CFOs understand that competition isn’t simply about strong positioning, superior products, or other market-facing factors. They understand that driving the leanest, meanest internal operation directly translates to higher free cash flow for reinvestment. The more efficiently a company can generate free cash flow, the more investments they can make, and the more experiments they can run.
As the cement continues to dry, your company’s ability to generate that free cash flow will deteriorate. The more flexibility you can maintain through your growth (i.e., keeping fixed costs down), the easier it will be to generate that free cash flow. Companies who can draw a clear, straight line between an internal data engineering team and higher free cash flow will benefit the most from a Traditional Approach to data analytics. The Minimalist Approach is more like a double-negative: it won’t directly add to your free cash flow, but it can remove expense lines that detract from it.
There are a number of reasons why companies continue to choose the wrong approach. A lack of experience, market misinformation, or just following what others are doing are all possible reasons.
But those are the easy answers.
The fair truth is that not many people know the Minimalist Approach is even an option. It’s still a relatively fresh approach. Think of when Amazon’s Web Services (AWS) appeared in 2007. Not many people knew the Cloud existed, let alone what it was. But today companies wouldn’t think twice about managing their servers externally. Maintaining on-premise servers are only done for very specific circumstances.
It’s the same for Minimalist vs. Traditional Approaches: both are perfectly valid but the one that’s right for you depends on your business model. And frankly not everyone knows that there’s more than one option. This is why we started TypeSift.
Do you know any CXOs of companies just starting their data analytics journey? Please share this resource with them so they’re informed before making any decisions.
Are you the CXO of a company just starting its analytics journey? Then reach out to us and we’ll get you started on the right path.
TypeSift is a Data Engineering & Design Minimalism Firm. Our expertise is decluttering information and solving problems in your data that are holding back your growth. We build software that corrals data and invokes ingenuity with the fewest moving parts.
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