The Impact of Data on Trends in the Automobile Industry

7

October

2020

5/5 (1)

According to McKinsey, big data’s influence on the automobile industry has the potential to generate $750 billion in revenue by 2030. While only a few years ago the idea of integrating internet connections and web applications into our everyday vehicles seemed revolutionary, big data has become a norm in the automobile industry. These innovations have provided more customized and safe driving experiences for car owners. Additionally, big data has also enabled car manufacturers to produce more efficiently and understand the preferences of their customers more accurately.

One of the most significant advantages that big data offers to automakers is improving the manufacturing process. It is estimated that data from a car’s engine has led to significantly decreased maintenance needs, which on average allow companies to save 10 to 20% percent in maintenance costs. According to a report from McKinsey on the monetization of data generated from vehicles, 73% of consumers globally are willing to pray for predictive maintenance services.

On the other hand, the digitalization of the automobile industry has also led to new products and services which enhance the efficiency of transportation. Apps such as Waze leverage data provided in real-time by app users and report information on traffic, accident, and other alarming news. The app’s platform encourages users to share their personal data, including their live location, in order to increase value for other users. This crowdsourcing model is just one example of how a business opportunity was created from connecting vehicle data with a practical application.

Due to the rapidly changing demands of consumers, players in the mobility industry such as alternative transportation providers, insurance companies, and vehicle manufacturers must continue to adapt to the megatrends and will play critical roles in providing complementary services to vehicle owners using data. In order to get a closer idea at the drivers of innovation, the visual representation below illustrates the cyclic nature of four disruptive technological trends. These trends will require incumbents to strategically position themselves in order to derive value from data retrieved from vehicles.

Screenshot 2020-10-07 230821

Lastly, the following approaches are, according to McKinsey, fundamental for companies looking to capture value from vehicle data monetization. First off, developing a compelling value proposition is key for incentivizing customers to share their data. Second, it is crucial that industry players define use cases where data will be leveraged in their business model. Thirdly, in order for these strategies to be implemented, the right technical enablers must be present. Fourthly, and finally, developing the vital partnerships along the value chain will be key in gaining the right capabilities for data monetization.

Although this is a mere glance at big data’s impact on the automobile industry, it’s important to consider the different ways in which industry players are leveraging your data and how!

 


References

McKinsey Article: Monetizing Car Data

https://www.mckinsey.com/~/media/McKinsey/Industries/Automotive%20and%20Assembly/Our%20Insights/Monetizing%20car%20data/Monetizing-car-data.ashx

On the Road Trends Article: Big Data and the Automotive Industry: The Future is Here

https://www.ontheroadtrends.com/big-data-automotive-industry-future/?lang=en

 

 

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From Data Analytics to Results on the Pitch

5

October

2020

5/5 (1)

On football shows such as Match of the Day, well-known pundits commonly let their sentiments be heard on the recent performances of certain players and clubs. After all, who doesn’t love to tune in on Jamie Carragher and Phil Neville arguing over Manchester United’s loss of form? While much of what is said about a player’s performance is an opinion, these accusations as well as glorifications are almost always supported by data, presented in the form of statistics. It is no surprise that data collected on a player’s total distance covered, shot conversion, and pass completion may be used to bolster these arguments, as this has been common throughout the past decade.

Recently, however, the value of data within the context of football has significantly risen, due to developments in deep learning and predictive analytics (Murray & Lacome, 2019). Adapted training sessions, player recruitment, and analysis of the opponent’s playing style are all ways in which clubs’ staff can improve their decision making by leveraging data.

Although from a fan’s perspective most of the football action takes place on game day, according to Murray and Lacome (2019), professional players train at least five days a week. Data is constantly collected on a variety of player metrics, such as running distance and number of accelerations, as well as force load distribution. Trackers that collect this data help prepare the intensity of certain drills. Analyzing the force load distribution, for example, allows coaches to examine which of a player’s muscle groups are weak, and therefore critical decisions can be made leading up to the day of the match.

Furthermore, data collected on a team and its opponents have proven to provide valuable insights. According to Burn-Murdoch (2018), football’s “analytics era” began in 2006, when London-based Opta Sports recorded the time and location of every pass, shot, tackle, and dribble. Today, about 2,000 data points are collected per match (Burn-Murdoch, 2018). This development in data collection has progressed to the point where Premier League shows such as Match of the Day now present viewers with the number of goals they can expect that weekend.

However, arguably the most impressive development in data-driven football, has come from sports scientists that have developed algorithms that predict the likeliness of certain in-game player decisions (Burn-Murdoch, 2018). As shown by the depiction below, machine learning programs are now able to determine player movements and the amount of space a player consequently creates by their positioning on the pitch. This technique, referred to as “ghosting”, has as a result uncovered an otherwise difficult-to-uncover aspect of a player’s skill set, namely creating space, which is an invaluable asset when considering buying a player.

Considering the impact data analytics has already had in the football world within the last decade, who knows which new technological developments will occur in the near future and how they will shape the way decisions are made!

References:

Murray, E. and Lacome, M., 2019. What Difference Can Data Make To A Football Team?. [online] Exasol. Available at: <https://www.exasol.com/en/what-difference-can-data-make-for-a-football-team/> [Accessed 5 October 2020].

Burn-Murdoch, J., 2018. How Data Analysis Helps Football Clubs Make Better Signings. [online] Financial Times. Available at: <https://www.ft.com/content/84aa8b5e-c1a9-11e8-84cd-9e601db069b8> [Accessed 5 October 2020].

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