Bad data butchers strategy for dinner

You’ve heard of “culture eats strategy for breakfast”? I’ll beat that. The quality of data used to build your strategy determines whether you even have a strategy.

Written between June 14th, 2024 and August 28th 2024

TL:DR Your strategy is only as good as the underlying data that informs it.  Garbage in, garbage out, a good idea is not good enough to realize the potential of your company.  Skimping on investing into accurate and relevant information around which to build your strategy is costing you millions or billions of dollars.

To reiterate, bad data butchers strategy for dinner.

I ask you, as a business leader, does your data match your strategy?

In other words, do you have the correct data to identify, develop and track your company’s strategy?

Your corporate strategy is only as successful as the data you use to build it, deploy for execution and track for performance outcomes.  There is a reason why some of the greatest strategists in history put such an emphasis on gathering intelligence on their enemy and the terrain between and around them.  The modern, corporate equivalent of it is understanding your current and prospective customers better than any of your competition and tying it into your operations.

To make Sun Tzu’s teachings, contemporary, shift from “know thyself, know thy enemy” to  “know thyself, know thy market!”

Essentially, there is a difference between using opinion, conviction and reality in crafting your corporate strategy.  Only the last one can be grounded in high quality data.

A common theme I’ve encountered over the past 15+ years of my career is the relationship between companies that successfully hit their strategic goals, and the ones who don’t: it’s grounded in their access to and utilization of accurate data.

An example is a global technology company I was working with.  They believed that they had a massive global opportunity to continue to grow with ‘enterprise companies’ (large multinational corporations).  Why? According to their ‘data’ they only had a few thousand of these ‘enterprise’ companies as customers.  Furthermore, they felt that they were at the leading edge of the technology in their industry and had few restrictions on their expansion.

When they started struggling with growth and hitting targets, the leadership became confused.  Why? Because, based on the ‘data’ that they were using, they only had a few percentage points of market share and loads more companies to approach as part of their growth ambitions.

When working with them, it became quickly evident that their definition of ‘enterprise’ didn’t match reality and that they weren’t using accurate data to define the basis of their market.

Let me elaborate.

The company leadership was obsessed with published 3rd-party industry analyst figures for the global TAM (Total Addressable Market) which was in the $10s of billions.

For me, these TAM calculations are estimates at best, and never seem to provide a breakdown of how they got to that number but that is a piece for another day.  

Their definition of ‘enterprise’ applied to companies more than 5,000 employees, and they had their ‘internal reality’ of only a few thousand companies of that size that they had even sold to vs a belief that there were over 10,000 of these companies left to sell to in the world.

When we tried to find out where this ‘data that there was over 10,000 companies left to sell to’ came from to inform their calculations, the leadership team had no idea!  These published figures had always ‘been there’ and everyone was referring to them including stock analysts covering the sector.  It was one of those “we’re using these numbers because everyone else is using these numbers” scenarios.

<you’re allowed to facepalm yourself here>

The math made sense, right? $10s of billions of TAM, less than 10% market share, therefore the sky’s the limit! 

Wrong.

They never added up the number of companies they could sell to – they only referred to the published industry dollar figures.

You state a lie enough times it becomes the truth.

When you can’t trace the origin of your data it is as good as a lie.

One of the first things we did with helping the company was to determine how many companies in the world even had 5,000 employees that they could sell their product to. It ended up being only about 25% more than everyone they had already tried to sell to (most of whom were not interested in my client’s products).  What did this mean? 

Sound data and information provides the foundation for any successful business.  It is the central gravity around which all other aspects of the business need to operate.

As in the words of the legendary Eminem “snap goes reality, oh, there goes gravity” 

1:17 if you want to hear the above phrase in the original (or you can listen to the whole song because its good)

Our client’s sales penetration (companies they approached to sell to) of the category amongst their definition of enterprise customers was already 80%.  They thought it was 30%.  In reality, they had already won over 30% of the Enterprise companies they had sold to as customers while thinking their market share by number of companies was still in the low single digits. This error was corroborated by their calculation of their revenue/analyst TAM estimates also being single digits.   

This large company’s low quality data, that informed their strategy, had failed them at a crucial moment!  

So how did this happen and what can you learn from this?

The reason it took so long for the failure to hit them is because in the first decade or so of the company’s existence, the size of the market, whether number of companies, TAM, etc., was so big it might as well have been infinite relative to the company through the early stages of its growth.  They could get away without knowing the untapped market accurately because their share of cumulative interaction, or sales attempts, in it was a small fraction that was barely moving the needle.

They never bothered investing $75-95,000 per year for a proper database of large companies - a fraction of a percentage in annual revenue for a multi-billion-dollar company.  They could have gotten the data for the US, nearly half of global count for less than $1,000 from American NAICS data alone.

How did they get here?  Why did they miss something so basic, in hindsight?

As the company stopped being a minnow in the sea and grew into being one of the larger companies in its industry, the size of the untapped market became very material.  They had already attempted to sell or were in the process of selling to 80% of the market without knowing it.  This lack of knowledge explained the stall.  Worse, they didn’t maintain their sales, marketing and CRM systems well enough to track if who they were selling to were present customers, past customers, or past prospects.

This is a $0.5MM+ solution to responsibly manage, however, again, for company worth billions, this is rounding error.

Hence, they couldn’t even use their own internal data to cross reference why revenue growth and demand stalled so suddenly.  They had crossed the threshold over which the share of the market that they hadn’t sold to was so small.

Thus, having a false estimate of the size of the market further impaired them.

So you might ask, how much does this really matter?  The company is growing and seemingly successful, right?

Well, if you’re a shareholder… and the share price drops over 40% because the growth prospects aren’t there all of a sudden… and the $ value of that decline is several billion dollars…

…all of a sudden, investment into good and accurate data would be critical for corporate strategy and valuation, right?

This is just one of many examples I’ve experienced where the viability of a corporate strategy is destroyed by an insufficient investment into accurate and representative data.

Going back to Sun Tzu, if you don’t know where or how many you’re fighting, how do you know if you’re winning?

This begs an interesting question about what is a minimum necessary investment for a validated strategy?  This is crucial in companies that have a sustained pressure to be as cost efficient as possible.  Companies are putting themselves at risk if they can’t develop their strategy accurately and monitor its realization over time.  This is especially crucial as the data that informs strategy needs to be accessible to and translate across sales, product, marketing, customer services, partners, etc.  Without a data-informed north star strategy, even a well articulated strategy is at best an educated guess.

Essentially, the era of ‘good ideas lead to strategy’ is dead.

This is especially the case in a lot of the Private-Equity-Owned companies I’ve worked with that slash a lot of data purchasing/subscription and analytics budgets.  The impairment isn’t immediate.  Sometimes it takes 2-3 years to fully manifest and another 2-3 years to become an existential crisis.  If the Private Equity firm is lucky, they’ve already sold/IPOd and the new ownership/leadership team is left to clean up the mess.

Also having been part of the M&A + Divestitures side of the business world throughout my career (Mergers & Acquisitions & Sale of companies or their divisions) reinforces a fascinating point of consideration about the impact of data impairment on strategy.

Today, for the most part, companies are no longer bought for their assets (buildings, inventories, contracts, even their intellectual property).  

They are bought for the viability of their strategy continuing or improving under the new ownership (or with greater access to capital in the event of an IPO).

If a company cannot articulate and back up their strategy through sound data, their valuation is impaired severely.

Not only does bad data butcher strategy for dinner, it also eviscerates valuations.

On that note, I’ll pause as I’ve started another train of thought on how to think about valuation differently in 2024 and going forward.

Edgar

oh fine… mini-rant. Why do so many Investment Banks keep missing necessity in accurate data when advising companies? I’m seeing way too many companies guessing themselves to wrong conclusions that cost them time and money AFTER they’ve already spent time and money on transactions.