EDITOR’ S QUESTION

The most advanced data analytics team found in most organisations is the centralised business intelligence( BI) team. This isn’ t necessarily inferior to having a specialist data science team – but the world’ s most successful BI teams do embrace data science principles. This isn’ t something that we see in all‘ classic BI teams’.

As analytics best practices continue to achieve cutthrough with BI practitioners, competitors that haven’ t adapted risk getting left behind. The charter and organisation of typical BI need to be set up correctly for analytics to address increasingly complicated challenges and drive transformational change across the whole business

Classic BI – no longer fit for purpose?

BI’ s primary focus is descriptive analytics – summarising what has happened and providing visualisation of data to establish trends and patterns. Visualisation is foundational in data analytics. The problem is that BI teams aren’ t working in a set-up that’ s aligned with worldclass best practices. It’ s often the case that BI teams are following an IT project model. They build specific reports and visualisations designed to be consumed by requested business departments. That’ s if they’ re consumed at all. It’ s often the case that such teams are mainly judged on how quickly they can produce data visualisation and how‘ nice’ this output looks.

The BI team that follows best practice data science principles has totally different aims. It’ s set up and empowered to explore data to uncover new insights and even change a business outcome.

The world’ s best BI teams have also evolved past following an IT project model. In practice, this means reporting to senior business leads rather than central IT teams and being emboldened with the authority to influence broader business strategy or transformation. The modus operandi of these teams is getting under the skin of the business and driving real change and Return on Investment( ROI). That’ s a stark contrast from‘ traditional BI’ which produces backwardlooking work that’ s siloed and disconnected from an organisation’ s core strategic objectives.

Becoming world-leading necessitates centralisation

How can organisations put themselves on the course of global analytics leaders and steer away from‘ traditional’ BI and its pitfalls? They should consider centralising data functions with a simple chain of command that feeds directly into the C-Suite. This aligns data science with the business’ s strategic direction, offering several advantages.

• Solving multi-domain problems

A compelling argument for centralising data science is the cross-functional nature of many analytical challenges. For example, an organisation might be trying to understand why its product is experiencing quality issues. The solution could involve exploring climatic conditions

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