Data Leaders Who's Who: Kathryn Gulifa

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Kathryn Gulifa

Head of Data & Analytics, Catch

A true analytics champion, Kathryn has spent more than a decade in financial services, government and retail industries, combining analytics, innovation and customer-first thinking in a variety of analytics leadership roles. Specialising in transformation, new builds and growth agendas, Kathryn has experience setting a data strategy, modernising analytics architecture, building and leading teams, engaging broad stakeholder bases, and delivering on analytics programmes to create business value.

Kathryn is Vice-Chair of the IAPA Advisory Committee and brings to IAPA the same drive, enthusiasm and uber-cool nerdiness she has injected into industry roles for the past 15 years. In this article she shares on the essential pillars for great data strategy and leadership, plus the key ingredients to deliver on the promise of data and AI.

During this interview, Kathryn shares a wealth of information in answering the following questions:

How do you devise a data strategy? What sets apart the good from the bad?

The most important element of a good data strategy is that it is anchored to your Corporate Strategy and it makes choices about what to do, and what not to do. There are so many opportunities so it’s important to be disciplined at focusing what are usually scant, expensive resources towards the most critical business priorities. It will help your team understand how their efforts contribute to the organisational goals, simplifies prioritisation of new demand, and makes it easier to justify the value your team is creating. By contrast, a poor data strategy is one that isn’t well connected to business strategy. It chases the ‘cool tech’ and can’t be linked to business value.

Kathryn also shared her thoughts on:

• How do you structure metrics to create effective delivery of projects/ products?

• What are the essential qualities of a data leader?

• What work are you most proud of?

• What do you wish senior leadership knew or understood?

• What have you found to be the key ingredients to make data analytics capability create an impact on business outcomes?

• How do you determine which projects to use AI for and how do you prioritise your projects?

• What is the best way to structure your data and analytics teams? What processes and methodologies are key to underpinning analytics project success?

• What legacy do you hope to leave behind you at your organisation?

"Generally centralised teams work best when trying to build capability, so best practices can be established and knowledge sharing accelerated"

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