Datent - Why and what's next

The story behind the Datent newsletter, the launch of our first service

I’ve long puzzled over why so many data transformations fail, and the conclusion I’ve come to is the difference between success and failure often comes down to intent.

Consultancies’ whitepapers have shared statistics on failure rates for at least seven years. I know this because I first read those statistics seven years ago as I prepared to interview for a data transformation role. Today’s stat from both Mckinsey and Deloitte is that 70% of data transformations fail. An astonishingly high percentage, given the vast sums many organisations spend on their transformations.

Since succeeding in that interview, I’ve spent a lot of time thinking about why most transformations fail. I’ve read up on it. I’ve discussed it with peers at conferences. I’ve spent six years learning first-hand, testing ideas on a data transformation that succeeded. Now, with Datent, I’m launching a service that aims to reverse the odds and give at least a 70% chance of success.

Before I get to that, I want to share my thought process and start by dispelling some myths about so-called digital natives.

The mythical benefits of being a digital native

When I first started leading a data transformation, it was very vogue to talk about digital natives, companies that were founded in the digital era.

I was no different, I watched the video on how Spotify works, designed my team around tribes and guilds and took many other lessons from thought leadership that came from digital natives.

However, as some organisations failed to transform and apply lessons learned from digital natives, the narrative changed. The advantages that digital natives had were exaggerated (no technical debt and no data or digital gap) and used as a reason why many organisations were failing in their data transformations.

These advantages are a myth and the truth is that every company, digital native or otherwise, struggles with technical debt, legacy systems, data quality problems and digital and data skills gaps. I know this as I’ve spoken to prominent leading digital natives about their digital and data literacy challenges and pain points that come from the inappropriate use of spreadsheets for business-critical processes.

There are plenty of non-digital natives, like Disney, that are innovative and leading in data and adoption of new technology. Nintendo started in 1889, selling paper playing cards. Samsung started in 1938 as a grocery store.

The fact that an organisation is not a digital native cannot be used as an excuse for falling behind with data and tech or for failing data or digital transformation.

However, removing this excuse doesn’t explain why the transformation failure rate is so high and what we can do to lower it.

Finding patterns behind successful digital and data transformations

Enough old companies have transformed to become digital and data leaders so that we can spot the patterns, see what leads to success and create a repeatable, proven approach to data and digital transformation.

It isn’t just my gut telling me this, I’ve seen it.

A couple of years into JLR’s data transformation, when it started being held up as a case study for data champion programmes, I realised something. The presentations that I was giving on running a data champion programme weren’t dissimilar to the ones I’d seen, and that had inspired me two years earlier.

Patterns emerged.

Several years later, a good friend and I watched another presentation on a successful data transformation, and we came to the same conclusion. Their presentation was very similar to the ones we’d been giving midway through JLR’s transformation.

Patterns repeated.

I noticed the same thing again on data leadership forums. Those that were three or more years into a successful data transformation were all taking the same approach with similar or identical workstreams on their transformation programme.

Patterns confirmed.

However, at first, all I could do was clumsily list lots of the elements in the patterns (champions programs, career paths, transformation networks etc.) but couldn’t prove why they were essential to a successful transformation.

I worried I was at risk of seeing causation where there was only correlation - like the over-enthusiastic business leader who observes that the start-ups they’ve been to have bean bags in the office and then concludes that introducing bean bags to their offices would make their company more innovative. (Yes, I have actually seen people come to that conclusion).

Then when I was doing a course to recover from burnout, I realised the element common to successful transformations was a focus on systems. The Focus Course, looked at the systems that you need in place to have a focused life which is best explained in Sean Blanc’s Focus Flywheel.

Focus Flywheel from The Focus Course

A successful, focused life for Sean Blanc comes from identifying your vision, planning and building a system to make progress toward those goals with regular, intentional acts and celebrating success before reviewing progress and starting again.

Remove one of the four elements, and things fall apart.

• Fail to set a vision, and you engage in busywork and wheelspin.

• Fail to plan and build systems, and you spend your time on reactive work.

• Fail to act regularly, and you procrastinate and fall behind.

• Fail to celebrate, and you burn out.

The mistake I was making in my professional life was failing to celebrate enough. Vision, plan, act, vision, plan, act, vision, act… eventually collapse. This year I’ve worked really hard to build major and micro celebrations into my work life.

I see many data transformations failing to plan an effective roadmap and failing to build systems to maintain the transformation.

• Systems to ensure that friction points in data (from quality issues to data silos) are documented, prioritised and resolved.

• Systems to ensure that skills initiatives are not one-offs but part of a data talent management system.

• Systems to iteratively develop a modern data stack based on business needs rather than waterfall projects to deliver the business predicted requirements.

I could go on… Systems... Systems… Systems…

Without systems, most data transformations end up spending time on reactive work and heroic one-off efforts to move organisations forward. Major tech investments. Cloud migrations. Data quality remediation programmes. Reskilling initiatives. Discrete initiatives like these are as sustainable as New Year’s resolutions. People eventually lose interest, and in the absence of habits and systems, momentum is lost.

As James Clear of Atomic Habbit fame says.

You don’t rise to the level of your goals. You fall to the level of your systems.

Today most organisations have lofty and ambitious goals for what they want to achieve with data. They desire to achieve the promised benefits of a data transformation, but without comprehensive transformation plans and systems, they lack data intent.

Datent

This was the conclusion I came to almost a year ago.

Since then, I left my job, having got the original mission statement for JLR’s data transformation signed off “mission complete” by the person who’d originally recruited me and who is now CEO of JLR. I feel very fortunate to have co-founded a team that delivered all its objectives, including over £560 million of value.

More importantly for me, I became a Dad and have been lucky to have spent a lot of the last 12 months focused on just that. Now that he is, this week, starting nursery full time, I’m ready to progress Datent and create more successful data transformation successes.

The newsletter will keep coming, but this week I’m launching the first service with a Data Transformation Leadership course.

As a CDO I tried to find a data leadership course to support people with and without data backgrounds to develop data strategies and lead data transformations. I couldn’t find anything that met my needs, and so now I am going back to my teaching roots (I was a teacher before I worked in data) and am building the programme to fill this gap.

Courses should inspire and motivate and be immediately actionable. This course is, therefore, firmly focused on supporting participants to develop a data strategy and launch a transformation programme.

If you’d like to register interest in the first full course, then please complete the contact form here, and I will keep you posted on updates and be in touch to give you the opportunity to shape the course around your needs.

Final thought - I’ve been doing a lot of reading on startups, and a common piece of advice is working on a problem that solves a frustration you have. Why? Because it means you’re more likely to stay motivated long enough to succeed.

For me, data transformations are about so much more than the financial returns they can give organisations. Whilst that is important, I find most organisations incredibly frustrating places to work. They’re often built around a maze of labour-intensive admin and manual processes that make little sense, barely deliver value and, worst of all, prevent people from spending their time on areas that motivate them and make the most of their unique skills.

Effective data transformations are a large part of the solution to this mess. They automate lots of this manual drudgery. Give people time back and time to focus on higher value and more fulfilling work. And they result in a more innovative, dynamic, profitable and fulfilling place to work.

Whether you’re just interested in the newsletters or are registering for the courses, I look forward to seeing the Datent community grow and making data easier.