Help your business recognize they have a unicorn in the attic

A great way to develop motivation at the start of 2021 is to reflect on your team’s success to date – is it equivalent to a unicorn?

In this context, I’m using the term as US investment analysts do. That is referring to a privately held start-up company that is valued at over $1 billion. Famous examples include Google & Facebook before their Initial Public Offerings (IPOs), as well as Bytedance, SpaceX & Airbnb.

So, what have such tech success stories got to do with the analytics/data science function within your business? Well in this guest blog post, Harry Powell (Director of Data & Analytics for Jaguar Land Rover), shares how his team’s contribution could be valued in that ballpark.

Read on to see if you are undervaluing the financial contribution of your team. Over to Harry to share his approach…

Reflecting on your past success with Data & Analytics

Just before the Christmas break, the leadership group of my Data & Analytics team at Jaguar Land Rover were thinking about what we had achieved in the 3 years since we were formed. We thought about all the products we had developed, the stakeholder relationships we had built and the impact we had had on the business, all from a standing start. It is a quite amazing story when you put it all together.

But one achievement summed it all up, and somehow it surprised us all.

In each of the last 3 years, we made around £100 million in incremental profit. That’s £300 million in total, at a time when JLR and the automotive industry has faced unprecedented challenges. In one of those years our team made about half the profit before exceptionals of the whole company. It represents a 40x in-year ROI.

But this was not the surprise, although admittedly we had been so hard at work driving value that we hadn’t taken time to sit back and think about what an achievement it is.

Recognise consistent delivery & cumulative value generation

The insight was that it wasn’t a fluke; We had done it 3 years in a row. We had implemented processes that ensure that we can do it again and again, year after year. Even better, we can scale it, so that £100 million a year can grow to £500 million, given the right resources.

When a business has a sustainable cash flow, the market applies a value multiplier to that cash flow which reflects its risk and growth prospects. While car companies trade on low multiples of perhaps 4-5x, analytics businesses trade on much higher multiples of at least 10x. That would value my team’s contribution at £1,000 million.

So we have built a Unicorn, which if it were recognized in JLR’s valuation might be worth 20% of the whole firm! OK, so perhaps that stretches it a bit! But it points to the extraordinary amount of value that can be created by applying data analytics to big old companies like ours.

Reflecting your teams’ value in organizational structure

If data and analytics is going to represent say 20% of the value of a business, how should that be reflected in the structure of that business? In what sense will making better decisions based on evidence, which is what we do, be integrated formally into the way a business operates? How should it be represented at board level? What career paths should be available to this technical discipline?

I reached out to a number of my data and analytics contacts to see how it worked in their organizations. They reflected a very similar picture. In all of our businesses, senior management rather like the idea of the “free money” which they think analytics represents. But they haven’t really given much thought to what this means for their business, other than that they probably ought to have a team doing it.

The contrast with firms that “get data” is stark. Amazon may look like an online marketplace, but its core products are data and analytics products: recommendation and logistics. It is “data native”. Evidence-based decision-making is embedded at every level of the firm, from the board down.

What can we learn from the Amazon organisational approach?

You might say that Amazon is very different to companies in the old economy. I’m not so sure, and I’ll use the automotive sector as an example. It seems pretty obvious that the product of a car company is physical not data. It’s a 2-tonne lump of metal in our case. But when you look under the hood, the vast majority of the parts of the car are not manufactured by JLR. In fact, some of our cars are not even assembled in plants we own.

Like Amazon, our core products are really software: design, engineering, marketing & logistics are all primarily data. Even if they translate into a physical object down the line, they are data. The physical bit is no longer where the value is created.

The challenge of digitization is to face up to this fundamental change from hardware to software. Companies that rise to the challenge will have to orient their business towards the template set by firms like Amazon. More on this in a later blog.

Why do companies struggle to bring data & analytics in from the cold?

To understand why businesses struggle to bring data and analytics from the periphery to the core of value generation, I think we need to take a detour into economic theory.

Ronald Coase was a British-born economist at the University of Chicago who was awarded the Nobel Prize for his Theory of the Firm. He was wrestling with the problem of why firms exist at all:

• Why if markets are efficient?
• why do people need to incorporate into firms rather than remaining a “multitude of independent, self-employed people who contract with one another”?

His answer was that there are transaction costs in market trading that can be eliminated by internalizing the transactions. Businesses have historically thought of these transaction costs as mainly physical but increasingly they are informational.

There is a cost in not knowing how to:

• tailor your product to market demand exactly
• respond flexibly and quickly to changing demands
• get your product to market at the right cost and price, given your decentralized supply chain

Firms as information aggregators

These kinds of questions are becoming more important than the old questions of the average unit cost that drove old-world decision making.

The nature of the firm is therefore increasingly that of an information aggregator. It adds value by being at the centre of flows of information. But many companies still think of themselves as being about the flows of materials, parts and end product.

Companies that have their roots in physical production must become information aggregators if they are to continue to exist. Information is their product. Their ability to respond to the signals in that information is the key to their future prosperity.

Analytics is their core product

But it is hard to make the change. In particular, it is hard to think about the abstract world of information flows if you have many years of experience of thinking in physical terms.

In doing so companies will have to :

• bring data and analytics in from the periphery
• think of digitization as a core product
• structure their businesses around this concept
• convert £100 million to £500 million to £1 billion
• let the unicorn out of the attic!

Many thanks to Harry for sharing that post, which provides data & analytics leaders with a very timely boost & possibly a rallying cry. It builds nicely on Harry’s previous posts focussed on models for increasing stakeholder satisfaction & tackling the productivity puzzle.

This post is the first in a series to come from Harry, in which he will explore what the above means practically for firms. His goal is to get feedback and start a conversation. So, please let us know your thoughts either in the below comments box or via social media.