I have had a varied career over 17 years across automotive, retail, gaming and fitness in the UK, Germany, Japan and South Asia. I have followed my passion for data and problem solving in every professional challenge I have taken so far. I describe myself as an insight leader with a commercial head.
I started my career with a techno-commercial focus while working for global automotive brands (Suzuki, Volvo). After my MBA, I stepped into insight consulting and led analytical delivery for the National Lottery UK. I later moved to Zeal (global leader in online lottery) and led the data transformation, which included democratising predictive models, revamping tech stack and influencing cultural change towards data and insights.
I was honoured there with an award for “inspiration in analytics” across Europe by SAP. Last year, I accepted a new challenge to build the insight function at PureGym (Europe’s second biggest gym operator). I focused heavily on laying a strong foundation for the team, creating a future roadmap and developing several analytical frameworks to reduce churn and improve the quality of new staff.
I am incredibly proud to have led data teams across industries and countries to influence decision making and transform business performance.
Influencing the culture of business towards data at Zeal. From the point of under-appreciating the value of data to craving data for both tactical and strategic decision making, the journey affected the DNA of the company. It started off with a data literacy programme and gradually moved towards demonstrating predictive modelling for effective targeting.
Over time, the data team completely transformed the way marketing was done by embedding machine learning into the operational rhythm. This has enabled marketers to think much more strategically. I still consider the data team at Zeal to be the best in the gaming sector in Europe.
I admire several data evangelists, but my favourite is Avinash Kaushik. The way he has mastered the art of simplifying perceived complexity around data is inspirational. I follow his blog (Occam’s Razor) and enjoy the wide range of frameworks he has developed to tackle challenges around data and technology.
After spending nine years with the best-in-class data team in an analytically mature gaming industry, I took a new challenge to build a data and insight function in relatively less mature fitness sector. This meant going back to the basics of laying strong data foundation and targeting low hanging fruit. The key challenge was to deliver actionable insights while the core data warehouse was still in development. Reflecting on 2019, I feel proud to have recruited top talent, developed robust processes, nurtured a collaborative culture and built a strong data team which will set a new benchmark in the fitness sector.
I expect increased realisation of data as a strategic asset and therefore better representation of data professionals on the board. I think personalisation and democratisation of data will continue to be on strategic roadmaps and will achieve the next level of sophistication. I am hoping to see a similar level of advancement to address the ethical issues around data. I think companies will find better ways to find insights which are actionable by asking better questions. For data practitioners, the focus on soft skills and storytelling will evolve and translate further into formal and informal development plans.
I still see a big gap between insights and actions. I still see good analytical models being developed and eventually unused. The classic issue of analysis-paralysis is still rampant across industries. I think realisation of insights is the biggest opportunity in data and the best way to address this is by people. I believe that every data professional has a responsibility to ask themselves a hard question of how insight is going to make any difference. Without focusing harder on delivering business impact, data teams will end up just being a support function rather than a key strategic asset.
The key challenge in digital transformation is around people. Firstly, finding the right data talent is a hard one. Universities have started to focus on analytics courses, but the trick is to find ‘T’ shaped professionals with a good balance of technical and soft skills. Secondly, organisational resistance to insights can be a tough one. It is often down to insufficient organisational alignment, a lack of data understanding and resistance to change.