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Innovating with data is an attractive proposition for many businesses, but becoming innovative takes time and skills not always readily available inside the organisation. We’ve discussed the practical steps to becoming more innovative with Daryl D’Cruz, Former Head of Data Innovation for Samsung’s European region:

  • Keeping stakeholders engaged
  • Organising data science team for success
  • Scaling data innovation

Delivering business value

Working as part of Consumer Market Insight (CMI) team D’Cruz was tasked with helping the business increase their data-driven maturity. Whilst Samsung is a fairly mature organisation when it comes to data, his roles was focused on finding the pockets where improvements can be made to accelerate business growth or improve efficiencies with data.

D’Cruz talked us through the Smart BI initiative, that over the course of a couple of years helped to eliminate the use of excel spreadsheets to store and analyse data. By the end of this award winning project, D’Cruz and his team had automated 2000 reports and saved 1000 days of efforts, allowing to repurpose the time towards more insight-driven work. In order to achieve it, CMI and digital teams partnered to create a DevOps function consisting of data scientists, analysts, engineers, data architects, scrum leads that helped to bring the ecosystem together and break down the existing silos.

D’Cruz stressed the importance of focusing on showing the business value over time, as you go along: “It wasn’t just one business case, it was a series of business cases over time – building something, demonstrating ROI, adding to it, demonstrating ROI again”. Quick wins help to get traction with the stakeholders, however, data and analytics leaders must keep the end goal in mind to ensure they are always moving to the right direction.

Executing data innovation

In order for companies to become more innovative with data, they must focus in on the culture first. D’Cruz advocates for setting the expectations with the data team right at the start in order to build the foundation that gives people comfort to experiment. 

It’s important to consider the wider business culture as well, that might not be prepared for fail-fast approach. In large organisations culture may vary from one business unit to another too. 

When it comes to data innovation at a larger scale, moving fast is not always easy. “Data science is such a complex arena and there are so many aspects of it that culturally for an organisation to go to this direction and for it to be impactful data innovation has to be a consistent conversation that includes everybody”, says D’Cruz. 

Scaling data science

What type of person is most suited to lead data innovation? Organisations need someone who can see a bigger picture and influence the c-suite, but it’s equally as important to understand engage and lead the data science team. Leaders tasked with data innovation must be able to hire the right talent, have robust conversations with the team and motivate them.

Raising the right questions for the data science team to answer, and providing the team with enough freedom to do their work and use their skills are critical for a well functioning team. And when it comes to hiring, “attracting data scientists is about attracting them to your brand of analytics”, says D’Cruz. Their Smart BI project was the European version of analytics at Samsung, and branding it helped communicate the focus on data innovation when hiring.

Finally, as you come to scale your data science efforts, D’Cruz argues it’s critical to look at the organisational structures. As the platforms emerge allowing to scale exponentially, the most critical question every data and analytics leaders will have to answer is where their data scientists are best placed – in the data team, or within the business function. 

Data Leaders members can access the full interview and further discuss it with their peers here. Interested in becoming a member? Get in touch.

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Laura Bineviciute


Make better informed decisions by assessing your data and analytics capability and use community intelligence to close the gap between strategy and execution.


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