As one of the exhibiting sponsors, we found ourselves among some of the industry titans, like Qlik and Incorta.

We also heard from some of the most senior Data & Analytics leaders from organisations such as HSBC and BT Openreach.

The insights, ideas, and snippets of knowledge I soaked up over the 2 days are too many to fit into one blog post. Today, I decided to outline the insights and takeaways from the conference that really stood out for me. I hope you find them useful.

Let’s dive in!

Key Takeaways from The CDAO UK 2022

The importance of data is nothing new

Kate Platanova, the CDO of HSBC kicked off her presentation on data-led culture with a story about Vandeleur Molyneux Grayburn.

Vandeleur was the Chief Manager of one of HSBC’s earlier banks in Hong Kong. He had the unfortunate luck to be there during the WW2 Japanese occupation.

Tasked with the safety of his branch, teams, and assets, he had a choice to make – what should be saved?

He didn’t choose the cash, gold, or bonds…the actual tangible assets. He chose to save the books, the ledgers, and records.

Now, despite the practicalities behind his choice, it got me thinking. Vandeleur knew something we should all have the forefront of our minds.

Money can only be spent once. The value from data is permanent and can be extracted time and time again. Therefore, we must protect it and nurture it. We must learn how to use it and leverage its true value.

The importance of data is nothing new. We’ve been hearing the “knowledge is power” phrase since around 1668 (specifically in Leviathan by Thomas Hobbes) but it’s never been truer than today.

Data experts are struggling to prove the value of their own work

The strange truth about our profession has been evident to me for years.

At a fundamental level, we are tasked with quantifying and evaluating the business to extract value. Yet somehow, we are often terrible at quantifying and evaluating the value we ourselves extract for the business.

How can we be responsible for measuring the business, but be bad at measuring ourselves?

This is one of the key components of our platform – analytics on analytics.

I was fascinated to see that this issue isn’t the preserve of smaller or newer data teams. In fact, it is being felt at the centre of some the largest organisations in the country. Proving the value of what we do seems to be very hard for everyone.

Alwyn Thomas, the Head of Data Strategy at the Financial Times (The FT! A newspaper dedicated to the performance and activities of business), simply acknowledged that the company doesn’t do this very well at all. He referred to the process as “a work in progress”.

Peter Eckley, Head of Data from the Bank of England, spoke eloquently about how research and follow up required to write use cases around the value they have created is extremely difficult and laborious.

The input that really struck me however was from Paul Moxon, the SVP of Data Architecture and Chief Evangelist at Denodo. He said it was “impossible” to collate all the value from every project, and that it simply couldn’t be aggregated.

We disagree.

Proving the value of data projects is simply a matter of understanding what “value” means in the context of our work and collecting / aggregating that information to prove our impact. We have a separate post on this right here.

Organisations must focus on Data Enablement to leverage the true
value of the asset

My co-founder Alex and I embarked on the journey of building Brijj thinking that data enablement was going to have the largest impact on smaller organisations – those at the beginning of their data & analytics journey.

We simply thought that the already established, “mature” organisations must have surely had the means to extract value through systemised data team enablement. We thought they had tools and processes needed in place

And we were wrong.

What is now clear is that there is a wide variety of “maturity-levels” across all verticals and organisational demographics.

Pete Williams, the Director of Data at Penguin Random House, spoke about how for them it was all about Data Enablement. From a maturity perspective, they don’t “do data science” and are dominated by “Excel Jockeys”.

The first thing I have to say about that is that Excel is amazing and using it should never be seen as a negative or less advanced (in the right use cases of course).

The second thing is that it brings me perfectly onto my last point.

Don’t believe the hype

As I was building my career in Data & Analytics, learning how to lead and deliver value across various sectors, one of my greatest fears was my teams and I were somehow “behind the curve”.

I kept hearing about AI, MI, Data Science, Big Data and countless other technical and/or fad-like terms which did not correlate with the work we were doing.

We were performing well, we were adding huge value, but the tools, techniques and processes we were using were far from the ones receiving all the hype. No thousands of shares on LinkedIn, no keynote speaking slots at conferences.

If you believe the hype and buzzwords surrounding Data & Analytics, you must think that to be taken seriously you need to be barely distinguishable from the Google AI team or the hottest new start-ups. That’s simply not true.

Huge swathes of our industry are still deriving vast amounts of value from the techniques, processes, software, and systems we have been using for years!

The reason why it doesn’t feel like that is the case is because the teams and businesses that do get the hype are the absolute pinnacle of our profession. Often, the reason they can use these techniques is because they don’t work with businesses that use data…THEIR BUSINESS IS DATA.

Take my earlier example of Penguin Random House.

On day one of that business, they weren’t thinking of how to best to structure their databases to monetise their customers data like Google were from the first time Larry and Sergey turned on their computers in that fabled garage. Does that mean that Random House are not working to become as data driven as Google? No. Of course, they are. But they are at a level of maturity that works for them right now.

Why am I talking about this?

Because I think that believing the hype is dangerous.

Why is it dangerous?

Because chasing ML, AI and the next fad is a waste of time and resources which  could be be better applied to simpler, more attainable, and ultimately more impactful changes which can be implemented today, like setting up the right processes and systems to deliver value in the way that really matters.

Nearly every talk at the CDAO had a participant, or multiple participants, that outlined a variation of one of the following points:

“It’s not about the tech stack, it’s about asking the right question.”

“It’s the value you provide to your business customers that ultimately matters.”

If the data leaders of some of our most impactful businesses understand that the laser focus needs to be on the business question and how we work with our business customers, then it is incredibly dangerous to lose focus on these fundamentals to the next fad.

We do not need the hype; we need to do the basics well first.

Dhiraj Rajaram, the Founder & CEO of Mu Sigma, said:

“Data Science is dead. Decision Science is what we need”

However unprepared the industry might be to hear it, it is true.

The purpose of our discipline is not data wrangling, model building, visualisation, or dashboard testing. It’s empowering decisions.

The day-to-day activities we conduct are simply methods we use to get there. It’s why one day, when it’s able to facilitate decisions better than humans, AI will replace us all.

For now, the best decisions are made through collaboration between people which is informed by data. We all need to make sure we have everything in place to make that connection as optimised as possible.

So, I urge you, don’t believe the hype. Unless it’s about Brijj, of course. That hype’s 100% real.

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