The data tipping point - part 1

The key to a sustainable data transformation lies in understanding change management and tipping points

After a couple of years, data transformations suffer from fatigue, and those running them start to ask if there is an end in sight. The answer is all to do with tipping points.

Tipping points are so important to data strategy that this is the first in a series of articles I'm writing on the topic and will be the first article series I write.

What is a tipping point, and does a data transformation ever finish?

Whilst a data transformation will never end, it needs to get to the point where the data transformation team or programme can become optional.

Take Amazon as an example. Amazon is often named as one of, if not the most, data-driven organisation in the world. And yet I've heard from someone who ran a data maturity assessment there that Amazon has a bigger backlog of ideas to improve their data maturity than anyone else. However, Amazon has no CDO. No centrally led data strategy. No central data transformation programme.

This is because Amazon is way beyond the data tipping point. The point at which an organisation will work to use data better and get more value from data of its own accord - without anyone leading the change programme.

I spent six years leading data strategy and transformation in a major automotive company. I loved the job. But I am not interested in cars. Not even a little. So I always saw the data tipping point as my personal objective. Before I left, I wanted to feel confident that data transformation would continue to gain momentum even if there was no longer a data transformation team.

For a more visual explanation of a data tipping point, recall the viral video explanation of a movement starting with a lone man dancing on a hillside (below if you've not seen it before).

The lone dancer begins by doing something wildly different. But, by himself, he is an oddity and not a leader. The first follower is the one that makes him a leader and creates a movement. Next, a few further followers decide to join in. Soon you get a succession of people joining. No one is joining at this point because they saw or wanted to join the original person. They're joining a movement. We’ve reached the tipping. That first dancer and even the first follower could stop, and still, the movement would grow.

This is the point that data transformation teams need to be aiming for. The point where change has become sustainable, and the organisation is set to get sustainable competitive advantage from data.

Why is a data tipping point so important?

No one has unlimited time or patience. Even the most passionate data evangelists will eventually run out of energy. If this happens before the organisation has reached the data tipping point, all the hard work of the data transformation can be lost.

When I started working on a data transformation, I heard cautionary tales of companies that were once seen as leading the charge on data, got all the attention at conferences and then, everything stopped when 2-3 original leaders moved on.

But it’s about more than longevity. It’s about getting to the point where data starts materially benefitting the company’s performance.

The data maturity model I always refer to is the DELTA model from Competing on Analytics. The tipping point is the difference between being stuck in what DELTA describes as the localised phase analytics (silos of data expertise dotted around the business) and analytical aspirations (processes are in place for data work to scale).

The International Institute for Analytics has measured companies against the DELTA maturity model for long enough to show that companies that have gone passed the tipping point have higher annual revenue growth (6% vs 3.2% over a 10 year period), get higher average ROI on investments (12.3% vs 8%) and are more likely to rank higher on industry rankings of innovative and admired brands. (Note - If you need evidence for investment in data, this research is worth using).

However, in a related research article, the IIA highlight how hard organisations find it to reach this tipping point. So many organisations have gotten stuck in the localised phase of analytics that they have described the phenomena as the purgatory of localised analytics.

Reaching the data tipping point is like reaching escape velocity. Here the inertia of an organisation’s outdated ways of working is the earth’s gravity. If a transformation fails to get the company to the data tipping point then, then the company will be pulled back to the dull realities of localised analytics. However, if you reach the tipping point then you will be on the route to sustainable competitive advantage from data.

Change management and tipping points

One reason that many organisations fail to reach the tipping point with data is that they approach the challenge as a data challenge. In reality, it is more of a change management challenge. Fortunately, change and tipping points are well-studied concepts and there is a lot that we, as data leaders, can learn from this area.

In the definitive book on the topic Malcolm Gladwell introduced tipping points by saying that "we are all, at heart, gradualists, our expectations set by the steady passage of time. But the world of the Tipping Point is a place where the unexpected becomes expected, where radical change is more than possibility. It is - contrary to all our expectations - a certainty".

So what are the unexpected lessons? From studying major moments of change in society and points at which products went viral, Malcolm Gladwell concluded that tipping points are caused by a small number of very particular people, by the stickiness of the idea or product and by small changes in context and external environment.

In the next articles in this series, I will dive into these points and explain how they can help you to lead data transformations using examples from the successful data transformations that I have worked on and from others that I have come across.

---

Note to readers - this is now my 12th successive weekly article and newsletter. In a way this is my tipping point as a writer, a habit is formed and many more articles will be coming.

I’m really humbled by how fast the subscriber audience has grown. Thank you. I am looking for ways to start to differentiate and support subscribers.

Next week I will publish two articles. One will be resposted on LinkedIn (as usual) and the other will just be here, aimed at subscribers. I will use this to explain a little more about Datent and why I am writing and also announce how to sign up for a free subscriber only trial of a data transformation training product that I will be launching later this year.

If you know anyone who works in data transformations and would benefit from the newsletters and subscriber offers, then please share a link to this newsletter.

Or if you are reading this and are not yet a subscriber then please consider subscribing.

Thanks

Benny