After completing a Computer Science Engineering and a master’s in business management, you would think your first job would be that of a software engineer or a consultant, but life is not so predictable. My first role was as a tax officer for HMRC. I then joined HBOS as a business analyst before developing risk scorecards for Lloyds TSB.
After that I found myself working in the sub-prime sector as a senior marketing analyst; I had suddenly found my mojo, with my head in data and my heart in marketing.
From there, my career very much turbo charged to drive marketing outcomes through clever use of data. I moved on to senior data-led marketing role in GoCompare.
I then switched agency side, joining Kin + Carta as director of data science at Edit. While there, I developed data science functions and teams, won awards, co-authored books on predictive modelling, and served as a non-executive director in various organisations. But life has come full circle and I am now back working in the public sector for The Royal Mint, leading its data and digital transformation programme.
There are hosts of proud achievements, including winning awards, becoming an international author, getting articles published in Oxford University Journals, changing business cultures, influencing decision making at senior levels, de-risking business strategy, identifying market gaps through the clever use of data etc.
But the proudest ones relate to data leadership, when some of the people I have recruited, mentored, and coached over the years have gone on to become great data leaders, some starting their own firms. I feel very proud of how I have developed people, inspired them and given them the confidence to excel and succeed in their careers.
Silly, I know, but my role model is my four-year-old son, Shaaz, because unknowingly he has taught me more concepts and inspired me more than anyone else. The way he learns has taught me a lot about designing machine learning systems. The way he tries to use tools for things they are not meant for, has taught me how to think differently.
Nothing comes out as you expected so I have stopped expecting. Apart from every bit of craziness going on in the UK and the world, 2019 was very different to what I expected. I never imagined I would end the year leading data transformation for a public sector body for a start.
I didn’t think there would be so many AI and ML start-ups in one year; I didn’t think SCV and DW would still be common terms last year. However, one thing that came out as expected was that I still can’t explain to my parents what I actually do.
- Companies will still not know what AI and ML actually mean for them
- We will try our best to make data scientists commercial but will not succeed
- There will be plethora of data courses introduced in universities
- Most companies will embark on a data and digital transformation but will still not know where to start
- The proliferation of data tools will leave everyone more confused
- ‘Good enough’ will be the new ‘Perfect’
- Success will come from simplification not sophistication
- Creatives will work in the data teams to visualise complexity to make it easier to grasp
- Data literacy will continue to scare business leaders
- Organisations will still struggle to scale analytics
The biggest emerging opportunity is saving lives. Data and technology can truly help medics treat patients better by more proactive diagnosis of ailments. From a patient’s perspective, tracking data and trends and knowing key health parameters can help them to lead healthier lives. Clever technology can effectively utilise data and use nudge theory through push notifications to make you change habits. Like my Fitbit showing that my wife is walking faster than me inspires me to get fitter, society will benefit from this as data becomes critical to healthcare. And, of course, data and analytics will decide elections.
A leadership challenge will be about showing the return on investment on tech in a digital transformation project. Convincing stakeholders about how the tech will enable change and putting an ROI number against tech in the business case will remain a challenge. Similarly, change management with regards to tech will continue to be a challenge.
One of major challenges will be the tech keeping up with the fragmented world where the customer journey, their habits, and the media landscape are all becoming more and more fragmented, and the tech will need to capture the key data points from this fragmented landscape.