Welcome to our Analytics Leadership Series of blogs, where we investigate key challenges for analytics leaders and how they can:
- Effectively demonstrate a return on investment (ROI) from data and analytics
- Make a practical move to an open-source analytical environment
- Master stakeholder relationships for a strong company data culture
- Implement a Best Practice approach
In this first blog of the Analytics Leadership Series, we look at what makes a great analytics leader and the core challenges they face.
The qualities and skills of a successful data science team leader
A great analytics leader will succeed in four key areas. With some flair, they will be able to:
Match the right technical skills to business requirements
It takes understanding of data science use cases and a change mindset to align the demands of data-driven business improvements to the technical capabilities of the team so that you achieve the required solution for the business.
Proactively identify opportunities for improvement
It’s one thing to respond to immediate business needs – for example addressing a fall in conversion rates. But a great analytics leader will also identify opportunities from experience and through deep domain knowledge to help improve business performance– for example learning hidden patterns in customer behaviour for personalising campaigns.
Create a culture of learning and development
A good analytics leader will manage hiring and team structuring. However, a great one will maintain high levels of job satisfaction amongst its data scientists and analysts by creating a culture of learning and development to constantly upskill their team.
Inspire and encourage innovation
In a role that touches multiple scientific disciplines, a great analytics leader will inspire innovation by encouraging team members to hypothesise and experiment.
Guide businesses through a data journey
It is the responsibility of an analytics leader to tease out the “unknown unknowns” in a business, e.g. understanding the importance of unconstrained demand and the appropriate KPIs that measure the success of demand forecast.
Core challenges facing today’s analytics leaders
As an organisation matures, the remit, challenges and priorities of the analytics leader will change. Over the course of this ever-evolving journey, the analytics leaders will face a series of core challenges.
Understanding WHY the business needs data
Pinpoint the business objectives. Understand and quantify what the organisation wants to improve on to achieve its business goals and work out which of these will benefit from using data.
Knowing WHAT to prioritise
There are plenty of prioritisation frameworks available to help leaders, such as value / complexity matrix, and RICE (reach, impact, confidence and effort) that will help leaders quickly estimate the value of a project and a path for up-scaling.
Managing WHO to hire/work with to build capability
The analytics leader has several options here. You can either:
Hire directly: importantly in a competitive market, this approach is good for retaining knowledge in the business.
Contract staff: this is a more straightforward way of directly aligning human resources to a business case for well-defined, short-term projects.
Use service firms: this approach usually involves a high initial investment but is great for kick-starting the data journey.
Take a hybrid team route: this involves augmenting teams to balance in-house know-how with outside expertise, which also helps build in-house knowledge in the long term.
The challenge of HOW
From a practical point of view, a key challenge for analytics leaders is how to get things done. For example, how to de-risk data projects, how to measure success, how to tie a project’s outcome to its value to the business, and how to make the analytics process a core part of the business so that it becomes a sustained business as usual (BAU) element.
Fundamentally, a great analytics leader needs to be a partner to the business, with a value-based approach to driving initiatives. This is a leader who will be trusted to make decisions when tackling these tough core challenges.
Are there any aspects of your analytics journey that we can help with?
About the Author, Xinye Li, Head of Data Science
Heading up our Data Science team at Ascent, Xinye’s technical expertise compliments the team’s skills with vast data led solutions. With degrees in physics/astronomy and economics Xinye started his working career in the marketing effectiveness world at a consultancy. From there on he’s worked at both agency and in-house data teams across a myriad of industries. Enjoying the hands-on aspects of project delivery, he also likes getting his hands dirty and solving problems with structure and logic. He joins the Ascent team with innovative experience gained from half of his professional career based in consultancy and client-led senior data roles.