In this episode, Jason talks to Viktor Kazinec, Head of Analytics at Close Brothers, to discuss the value of working closely with external stakeholders on data science solutions.
Close Brothers is one of the UK’s leading merchant banks, adopting new technology to provide stakeholders with better insights and advice.
They discuss how to choose data projects that can prove and add value, the success drivers of a team, managing and gaining buy-in from internal and external stakeholders and creating high performing data science teams.
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Because the nature of data science can seem invasive, you need to build trust and rapport with the stakeholders beforehand.
[00:35] Viktor’s background in data and the role of Close Brothers in the finance sector
[08:12] How to pick and choose data projects that can prove the importance of data to stakeholders
[10:10] What signaled success for Viktor’s newly built data team
[14:30] How to convince stakeholders to invest in data
[20:30] Internal use cases vs. external use cases
[23:27] What excites Viktor the most about data science and his job and how to create a high-performing team in lockdown
It’s hard to build a team when the feasibility of data science is not widely known. Instead of trying to convince stakeholders to run risky experiments, run proof of concepts that can help demonstrate its use.
To help build trust and rapport, choose data projects to demonstrate the efficacy of data science within your organisation. Focus on projects that provide immediate value to the company without stretching too many resources.
Data has the potential to add a lot more value to a business. In competitive sectors, having the extra information data science brings is often the difference between standing out, or succumbing to your competition.
By constantly adding value to your customers you will reap the rewards of better performance, not just for the client but for your own business as well. For example in the insurance sector, if you can help your customer be 10% more successful, they have the potential to provide 10% more business back to you.
Data gathering is an invasive process, but it’s necessary to create meaningful results. Because of this, building trust with stakeholders before you pitch and start your data-gathering projects is incredibly important. If no trust is built between beforehand, it can become incredibly difficult getting the information needed to create datasets for analysis.
To start off with, begin projects with stakeholders you already have rapport with. Then you can use the case studies as proof for future data clients.
There is a time and a place for complicated data. However the best strategy is often the simplest one that can be delivered in the shortest amount of time. Try to find ways to get the biggest gains and shift your mindset from a ‘one and done’ to constantly improving.
Data at its core is about providing as much information as possible to help improve performance in a particular area. Look for problems that data can solve, rather than creating complex data models and trying to make the problem fit within it.
Lockdowns have had a major impact on the ability of businesses to create teams. What were once considered close-quartered, tightly-knit teams had to adapt to working remotely. But for many businesses, this has not posed any significant threat. Instead of becoming intimidated by a new way of working they have managed to adapt and evolved into a new way of flexible working.
Business has always been about people whether they are your customers or employees. Don’t be afraid to ask you employees about their day, if they need help or about their general wellbeing. If you treat your team members with compassion and humanity, distance doesn’t matter.
Data is all about helping people and businesses perform at their very best. However, sometimes it can be hard for a business to trust others with information – therefore building trust is essential. Even if you don’t have rapport with a stakeholder, you can help build it by communicating this value through case studies or proof of concepts.
When building data products, keeping things simple is often the best solution. Don’t try to force a round peg in a square hole.
For more on data products, take a look at our Data Product Management Webinar