Maturing Data and Analytics Organisation

Maturing your data organisation is a path many organisations are on and it requires a thorough and strategic approach. Thomas Bode, VP, Data and Analytics at Swarovski explained how this journey has evolved over years at his organisation.

Bode reveals practical insights into:

• Effective collaboration between business, data and IT
• Prioritising the right data efforts
• Setting up the right data team to meet your business needs

In order to be able to truly grasp the opportunities presented by data and analytics advancements today, organisations need to mature their data function. With the ever changing business landscape, this journey is arguably never ending, one that needs to constantly evolve and adapt.

A look into the set up of a mature data and analytics organisation 

Bode lets us know that it took Swarovski about eight years to reach its current level of maturity. Today, the organisation’s data and analytics department is divided into three main teams.

• Data engeneering team

  • This is the largest team in its department. Driven by a data-centric approach, the team bundles all data engineering into one – regardless of operating platform 

• Data governance team

  • A particularly good fit for analytics, as analytics sets the stage for data governance as well as leveraging it 

• Business intelligence team

  • A centralised team, it consolidates information, making it available to all users. It is itself divided into two teams:
    • BI Support Team, qualified to handle tools as well as content
    • BI Delivery Team, providing the right access to data and handling areas related to queries, reviews and standard reports 

Relationship between business, data and IT 

At Swarovski, data and analytics choose to work independently from IT in favour of focusing on the four enablers: people, processes, data, and technology.  

While IT is in charge of technology – including what has been leveraged for analytics – the data and analytics department owns the processes allowing to handle data as an asset. Thomas describes this approach as “a strong partnership with two different focuses”.

How mature companies prioritise data efforts

Being quite satisfied with Swarovski’s levels of maturity, Thomas feels ready for the next step: the rebuilding of the company’s data foundation.

The new analytics architecture will consist of different platforms, from data warehousing to capability, operating under one scalable and sustainable model. Within this infrastructure, users will be provided with the exact information they need.

Nevertheless, the pursuit of this ambitious project does not come at the expense of other short and mid-term objectives. Bode reassures us that Swarovski is committed to improving staff training as well as maintaining a strong relationship with users through continuous delivery of quality results.

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