How F1 and others are moving beyond descriptive analytics

Formula 1 and Merck Sharp & Dohme are using data analytics to better understand user behaviour and predict the future

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Data analytics has become a must-have for organisations across industries, and is being integrated into both operational and business sides of companies, experts said at the Chief Data & Analytics Officer Singapore conference last week.

While data analytics has been critical in optimising the performance of cars on a Formula 1 (F1) race track, the technology is now being used to help the business side of F1 shift from being a motor sports brand to an entertainment brand.

“In 2017 and before, F1 was using data, as drunk men using lamp posts, to support existing ideas vs for fresh insights,” said Max Métral, insight manager at Formula 1, in a keynote presentation.

Previously, only two reports related to the F1 business were produced on partner exposure and TV audience race data, which were reviewed solely by the CEO.

Now, analytics has an important role to play at F1, providing insights to enable better decision-making on F1 grand prix starting times, to understand user behaviour through footfall analytics at its races, and to drive website personalisation through a behavioural clustering model.

Analytics is also used to maximise TV viewership of its races, identify anonymous web users who are most likely to buy F1 TV subscriptions, as well as to prevent churn and improve customer retention for F1TV.

In a separate presentation, Roy Goh, director of data science, information and analysis, at Merck, Sharp & Dohme (MSD) said “the real value of data analytics is to gain foresight into the future”.

Descriptive analytics

While much of the analytics by MSD’s data science team is descriptive in nature, the team is trying to move beyond that to more sophisticated use of data.

“Dashboards always help to get people excited. As data scientists, we want to bring more value than just KPIs [key performance indicators] and reporting,” said Goh, adding that the team now builds predictive capabilities within dashboards to improve decision making and enable better outcomes.

The company’s six-year old data science team was also split into two in a bid to be more “future-ready”: a data science group that looks at enabling faster, more informed and accurate decision-making, and the artificial intelligence (AI) group.

Another priority is to inculcate a data-driven culture within MSD. “We focus on business questions and problems as fundamental starters for our solutions – which involve using analytics to increase revenue, reduce cost, improve compliance or cash flow,” Goh said.

To close any gaps in data literacy due to the size of the organisation or personnel changes, the data science team conducts in-house training to help employees ask the “right and sophisticated questions” and to avoid “making simple statistical conclusions from looking at charts”.

Read more about data analytics in APAC

  • Enterprises need to figure out the business problems they are trying to solve and foster a data-driven culture to benefit from data analytics.
  • ASEAN organisations need to develop an enterprise-wide approach to analytics and draw on customer insight if they are to maximise the business value of data.
  • Australian businesses need to change their attitudes towards data scientists if they are to unshackle the benefits of data.
  • Singapore is looking to shore up its expertise in data analytics as part of efforts to build strong digital capabilities in its economy.

Andy Goldin, director of data and analytics at PwC South East Asia Consulting, emphasised the importance of having standardised metrics and terminology surrounding data within an organisation.

“I don’t mind if the whole organisation is using data terms incorrectly, as long as it is used in the same way,” he said.

Goldin, who was previously head of analytics for Uber in Asia-Pacific, noted that many executives tend to have a limited perception of what analytics can do.

“Think beyond the obvious user cases. Move away from using analytics for upselling and cross selling,” said Goldin. “If you are ignoring the vast majority of use cases, you are probably not leveraging analytics very well in your organisation.”

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