What Are The Drivers For (Data) Change? (Part 1)
Word of warning: this article may not be what you want to hear.
The data market is booming.
According to the ever-increasing number of unsolicited emails that hit my inbox daily, that is. Each proclaiming their new bit of software will solve all my problems.
Happy days.
If only that were true!
Unfortunately, it is never that simple, no matter how good a salesperson is.
I can’t blame the software vendors, though; it’s not really their fault.
All they are trying to do is grab a piece of the data pie and make some money from clients.
This is business at the end of the day.
The real issue is that most companies don’t know how to leverage their data within their estate.
So, any help they can get will (more often than not) be greatly received. Even if it comes at a high cost.
Why?
Because data is valuable.
It is an asset (which if tapped) from which future economic benefits will flow.
Data is the proverbial secret sauce that will help companies unlock their revenue superpowers, turning them into unicorns or new additions to FAANG companies. Or at least giving a company competitive advantage.
I jest, but the essence is true.
For the last decade, companies have been told that unlocking the value held within their data is the key to success. Or rather greater success.
Many a McKinsey article or Gartner review has proclaimed as such. And these are serious titans of the consultancy world.
Bearing all that in mind, here is a question that has been bothering me:
“If companies acknowledge that data holds the key to serious competitive advantage, why are so many organisations reluctant to change?”
This is not a simple question to answer, but in attempting to do so, the starting point, I believe, is to understand their penchant for change.
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With this in mind, I believe there are three groups of companies: the Disruptors, the Herd and the Coerced.
The Disruptors
At the forefront of using data and on the left-hand side are the ‘Disruptors’. This group make up 15% of the market and are here to elicit change. Pure and simple.
They understand the power of data and use it to their advantage from the get-go.
This is their modus operandi: to cause waves in the industry, challenge the established companies and are prepared to fail fast in pursuit of success.
Data is core to their business. Innovation is what drives them.
Prime examples are Monzo, Titanbay, Netflix and FeverTree.
The Coerced
At the other end of the spectrum, we have the ‘Coerced’. This group make up about 5% of the market and are only changing because they have to.
This is likely to be through factors outside of their control being exerted upon them, such as regulatory penalties or sanctions. Perhaps the company was sailing too close to the wind on data privacy or had failed to disclose to a governing body appropriately.
Whatever the reason, the only reason they are embracing data change is because they have to.
Evolve or die. It’s a binary choice.
Examples include Wells Fargo, Clearview AI, Telecom Italia and H&M
The Herd
In the middle, we have the ‘Herd’. This group make up the remaining 80% of the market & is where most companies sit.
Companies in the herd are often the long-established profitable organisations comfortable in their market position. They know they need to do something with their data, but there is no rush to do so.
They are happy to flirt with data initiatives and try a few things but ultimately not at the expense of existing revenues or profit.
Ultimately, these companies can’t see the intangible benefits of leveraging their data, so change is slow or non-existent.
Examples include Natwest, Coca-Cola and most companies you can think of.
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Determining which category a company falls into helps understand whether they are serious about creating data change and associated drivers.
Market status, external factors and Executive mindsets contribute to the change appetite.
In much the same way, when it comes to losing weight, there is a big difference between knowing and doing, and the same can be said of data change.
In the next part, I will go into the relationship between each category and the factors contributing to driving data change.
Until then,
Cheers.
Please note, these views are my own and do not reflect those of my employer.
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