Instruction to QBUS6860 Assignment 2 We strongly suggest you consider Assignment 2 as a research project and expect you finally present a research paper alike report. You can refer to many research papers on their pattern or style and understand what information is expected in such a report. For your convenience and our expectation, I am attaching a sample report from 2021S2 QBUS6860. Although the topic is different from ours for this semester, but it does serve as a good example that you can follow. Basically your report shall contain the following information: (a) A title (b) Background story (telling your audience or readers what it is about) (c) Your question to be answered or your hypothesis to be verified in the project and its meaning and importance (that is about why and your motivation for the project). It is important for you to choose a topic first, e.g., who is the leading country in AI/Machine Learning research, how other countries can improve etc. (d) Your methodology (that is how you solve it to get answers/insights/conclusions) and toolsets etc (e) Data description (what facts you rely on and their formats, or how you change the data for your purpose.) (f) Results (could be presented visually with explanation) (g) Your insights for Machine Learning community (this is not about a business) (h) What can be further improved and your suggestions if any (i) References
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QBUS6860 Visual Data Analytics
(2022S1)
Individual Assignment 2 Due date: Monday 23 May 2022 Student ID: ______________________________________________
Note: The University new policy asks only your ID for all written assessments. Please
insert your ID in the header area.
Contents: 1. Background ………………………………………………………………………………………….2 2. Business Question and Justification ……………………………………………………….2 3. Methodology, Hypothesis and Justification of Selected Analytical Tools ……….3 4. The Data ……………………………………………………………………………………………….6 5. The result ……………………………………………………………………………………………...7 6. Insights for Business ……………………………………………………………………………………….9 7. Limitations ……………………………………………………………………………………………10 8. References …………………………………………………………………………………………….10
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The Impact of Big Data Analytics on Innovation in Formula 1


1. Background

The challenges and opportunities posed by Big Data have attracted the attention of both
scholars and industry practitioners (Akhtar et al., 2017; Marr, 2015; Reinmoeller and Ansari,
2016). Academic research acknowledges the opportunities offered by Big Data when
information (data) is translated into decision making strategies and improved innovation and
performance (Chen, Chiang, and Storey, 2012; McAfee and Brynjolfsson, 2012; McKinsey
Global Institute, 2011). Current debate also points to the competences companies need to
deal with advanced technology and Big Data (Akhtar et al., 2017; Reinmoeller and Ansari,
2016). Although the benefits and challenges identified in the management literature are
numerous, the link between Big Data and innovation remains largely anecdotal due to lack of
empirical work on how large datasets can influence business outcomes (Kache and Seuring,
2017; Sen, Ozturk, and Vayvay, 2016). Several scholars have proposed frameworks for how
Big Data applications can be exploited to generate value, relying mainly on a case-based
research methodology (Matthias et al., 2017). However, the generalizability of these findings
to a wider population of companies is difficult. The lessons learnt may be unique to the in-situ
performance at a particular time. A systematic review conducted by Frizzo-Barker et al. (2016),
of Big Data papers published between 2009 and 2014, acknowledges the lack of empirical
work and shows that this stream of research is ominated by conceptual papers.

2. Business Question and Question Justification

This report addresses the following business question: what is the impact of Big Data
analytics on innovation in Formula 1? This question is not only applicable to the Formula 1
industry, but also to other analytically dense industries. Yet, F1 constitutes an ideal setting to
investigate the use of Big Data (Aversa, Cbantous, and Haefliger, 2016; George, Haas, and
Pentland, 2014). First, in the F1 context, major innovations are distinctly and precisely
measured (Gino and Pisano, 2011). Furthermore, unlike many industries (where
products/services are heterogeneous), all F1 teams produce homogeneous output (final
standing in the race ranking), which allows us to compare team performance directly and
more precisely (Goodall and Pogrebna, 2015). Second, F1 is a highly analytically dependent
and data-dense industry and has seen a transition from systematic manual processing of data
to predictive analytics and, more recently, evolution to a mature stage in the Big Data
revolution. Their performance depends on how the teams respond to these data, which makes
a good testbed for information processing theory (Rogers, Miller, and Judge, 1999; Tushman
and Nadler, 1978) operationalized in the Big Data operations management domain. Third, in
most high technology industries, the strictness of safety and regulatory standards increases
over time as more information is revealed. Examples are the Registration, Evaluation,
Authorisation and Restriction of Chemicals (REACH) and Restriction of Hazardous Substances
(RoHS) regulation, which is aimed at tighter control of EU supply chains by monitoring
substances used in products (Westervelt, 2012). This tighter regulatory control involves
increasingly more complex data collected from manufacturers. The F1 industry has
experienced the imposition of many regulations over time (Marino et al., 2015) and how
teams respond and adapt to process ‘future data’ into existing systems before making an
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informed decision about the design of cars and race strategies provide important lessons for
other industries.

Scholars have recognized the distinctive characteristics of F1, for example, Jenkins (2010)
suggests that F1 firms possess sustaining capabilities - munificent resource configurations that
extend the time available for firms to adapt to technological changes – thereby allowing them
to remain competitive across discontinuities. In this study, I focus on the Big Data structure
(BDS) of F1 teams from the perspectives of information integration and information
accessibility. Building on an information processing perspective, which emphasizes the need
to process information by considering external demand (Rogers et al., 1999; Tushman and
Nadler, 1978) and the technological environment in which firms operate (Hughes et al., 2014;
Hughes, Hughes, and Morgan, 2007), I examine how different strategies for Big Data
information processing lead to different BDS configurations. I show how different BDS
structures influence team performance in terms of output (achievement of podium positions)
as well as innovation production.

3. Methodology, Hypothesis and Justification of Selected Analytical Tools

F1 is recognized as a unique setting to investigate the role of Big Data and business analytics,
since it relies heavily on sophisticated applications of real-time information systems to
support informed decision making processes during a race (Aversa et al., 2016; Aversa,
Furnari, and Haefliger, 2015; George et al., 2014; Goodall and Pogrebna, 2015; Marino et al.,
2015). F1 is estimated to be worth approximately $6 billion annually (Sylt and Reid, 2011).
Constructor teams' profits come from advertising and TV. A higher finishing position, primarily
a podium position (1st to 3rd), generates more sponsorship and TV income. Increasingly,
modern teams are raising money from the development of F1 technologies that spill over into
other industries. For example, Williams and McLaren (Applied Technologies) have associate
companies. It is an interesting industry intellectually because it is subject to a great deal of
regulatory turbulence. The Fédération Internationale de l'Automobile (FIA), the F1 industry
governing body, imposes strict conditions, which are revised annually, on all aspects of F1 (the
teams, technology, resources, track, tyres, drivers, etc.). The link between regulation and
innovation has been well documented (Stewart, 2010). T is embodied in F1; regulation is
unambiguously associated with innovation and performance (Jenkins, 2004, 2010; Jenkins,
Pasternak, and West, 2007; Khanna, Kartik, and Lane, 2003; Marino et al., 2015) and
regulatory compliance results in a level playing field for all competing teams. Indeed,
sometimes rule changes are made with the specific intention of curtailing the dominance of
one team, for example, Ferrari and Michael Schumacher in 2003 (Hoisl, Gruber, and Conti,
2017).

F1 is an extremely data-dense industry with sophisticated data analytics. All contemporary F1
cars are using sophisticated telemetry systems to obtain, transmit, process, and analyse
information. According to NASA, the term telemetry originates from the Greek “tele” which
means “remote” and “metron” which refers to “measure” and depicts a process of
automatized communications and transmissions allowing to obtain data from remote or
poorly accessible points for monitoring (SAO/NASA Astrophysics Data System, 1987). In the
F1 context, telemetry is implemented through a large number of sensors and electronic
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devices inclusive of the Electronic Control Unit (ECU) which communicates data to the pit wall,
pit garage or another remote site (e.g., Toet, 2013).

Each Formula 1 car is equipped with 150-300 sensors (dependent on the racetrack, weather
conditions, and other factors). For the reasons explained below, F1 telemetry is a one-way
transmission system: the data is sent from the car to the engineering and strategy team, but
the team does not have an opportunity to send the data to the car. According to various
technical forums1, the data from the car is transmitted wirelessly using 1,000-2,000 encrypted
telemetry channels using either 1.5 GHz frequency or another locally allowed frequency.
While the delay between the data collected and received at the team boxes varies, on average,
it is around 2 milliseconds. Since the received data is compressed, the number of actual
gigabits of data received by teams may differ from race to race, although each team collects
approximately 1.5 billion samples of data from a single race and approximately 5 billion of
samples throughout the race weekend (this includes data from all training sessions). The
transmitted data on engine performance, suspension state, gearbox performance, fuel status,
temperature readings including tires temperature, g-forces and actuation of controls by the
driver is analysed by the engineering and strategy team and results of this complex live
analytics is communicated to the driver in the form of racing strategy advice. Since telemetry
is the major source of (live) Big Data for the F1 teams, in my analysis I use the telemetry
development stages in the F1 industry as a “natural” proxy of (Big) data analytics evolution.
Since 1950s, an F1 team performance was highly dependent on this team’s ability to collect,
process, and analyse large amounts of data. Historically, we can distinguish between 6 phases
in the F1 data analytics progress using the development of telemetry as a proxy for
determining the boundaries between different stages. The Big Data analytics history of the F1
industry proxied through the evolution of the use of telemetry was drawn from Jenkins (2010),
the data collected by the McLaren F1 team2 as well as from the Formula 1 Dictionary Technical
Forum.3

I distinguish between the following phases in F1 data analytics (depicted in Table 1). The
evolutionary phase-based approach summarised in Table 1 allows us: (i) to identify the
pretelemetry period (Phase 1); (ii) to identify phases of significant heterogeneity between F1
teams in terms of their access to telemetric technology (Phase 2 and Phase 4); as well as (iii)
to understand when F1 teams had similar or standardized telemetric technology (Phases 3, 5,
and 6). Using the identified phases, I can now use F1 performance data to understand the
differences between phases and explore whether performance of industry as a whole as well
as performance of individual players changed from one phase to the other.

To develop my hypothesis about innovation in Formula 1, I use a combination of Linden and
Fenn (2003) and Fenn and LeHong (2011) Gartner Hype Cycle framework. I assume that
analytics phases shown in Table 1 determine the boundaries of Gartner Hype Cycle stages of
innovation development: Technology Trigger, Peak of Inflated Expectations; Through of
Disillusionment; Slope of Enlightenment; and Plateau. Therefore, my main hypothesis is that
the lifecycle of major innovations in Formula 1 follow the Gartner Hype Cycle shape where
each stage is determined by the data analytics phase from Table 1. Specifically, I hypothesise
that the correspondence between analytics and innovation will follow the pattern depicted
on Figure 1. Specifically, Phase 1 (Driver as a Sensor) pre-dates telemetry analytics and,
therefore, represents a preliminary stage. This stage leads to Phase 2 (Telemetry
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Development) where the development of telemetry technology is triggered which causes as
increase in the number of innovations. Phase 3 (Early Telemetry Phase) represents the peak
of inflated expectations where data potential is uncovered by the industry and positive “hype”
is created allowing to reach the peak of innovation. Phase 4 (Turbulence) captures a “through
of disillusionment” where drawbacks of the telemetry technology start to significantly
outweigh the benefits and number of innovations rapidly decrease. Phase 5 (Mature
Telemetry) phase is equivalent to the slope of enlightenment where new capabilities of
technology provide a new positive boost to innovation. This boost flattens or even disappears
in Phase 6 (Big Data Telemetry) when technology reaches its plateau or even post plateau
stage.
Table 1: Phases of Data Analytics in F1 Industry

Phase Time period Major milestones
1. Driver as a
Sensor
1950-1974 The majority of teams used drivers as sensors who
fed back the information about the car performance
to the teams after the race.
2. Telemetry
Development
1975-1988 1975 - McLaren started to experiment with
telemetry first deployed 14 sensors on IndyCar.
Until late 1980s – F1 teams started to experiment
with
telemetry.
3. Early
Telemetry
1989-2001 By 1989 - F1 teams used “patched” telemetry
transmitting data when cars came close to pits Early
1990s - F1 teams had high rate live information but it
had blind spots (especially on tracks with dense trees
or high buildings like Monza, Monaco, etc.). So
information was incomplete.
1998 - Plextek4 became a major supplier for
telemetry systems
2000 - Incomplete information problem was fixed
4. Turbulence 2002-2004 2002 - Two-way telemetry was allowed (teams could
not only receive but also send information to cars
remotely)
2003 - FIA banned two-way telemetry
5. Mature
Telemetry
2005-2012 2005 - Electronic Control Unit (ECU) TAG-310B SECU
by McLaren Electronic Systems and Microsoft is
developed
2008 - FIA standardized ECU for all F1 cars
2008 - FIA makes Advanced Telemetry Linked
Analysis System (ATLAS) Express produced by
McLaren Electronic Systems standard
6. Big Data
Telemetry
2013-2020 2013 - Volumes of data become very high requiring
major system upgrades
2013 - Upgraded standard ECU to the new TAG-320
SECU
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2017 - Data Viewer developed by McLaren Applied
Technologies5
Why wouldn’t Big Data telemetry provide an extra boost to the innovation lifecycle in Formula
1 instead leading to a plateau? Information Processing Theory (IPT) provides a theoretical
basis for this (Rogers et al., 1999; Tushman and Nadler, 1978). Early research on IPT (Daft and
Lengel, 1986; Daft and Weick, 1984) maintains that information gaps can be reduced by
gathering more data. A large part of IPT discusses the reduction of uncertainty by facilitating
decision makers’ access to the right information at the appropriate time (Sakka, Barki, and
Côté, 2016). However, obtaining more data is today not a major concern since most electronic
devices and transactions generate abundant data. At the same time, greater access to data is
linked to higher levels of misinformation and misinterpretation of those data. Although all F1
teams have access to Big Data, this does not mean that all of them will necessarily benefit
from the data. In fact, greater volumes of information can be very difficult to process so only
few market players may be capable of coping with the industry’s increasing data supply.


Figure 1: The Major Innovations Lifecycle in the Formula 1 Industry Predicted Based on the
Phases of Data Analytics Evolution
In my analysis, I am planning to capture the major innovations’ lifecycle using bar chart and
provide several F1 teams use case illustrations using line chart. Instead of a single line chart, I
am planning to use a chart with multiple lines, because it is more appropriate to demonstrate
the differences between teams. Though bar chart is a good way to demonstrate life cycle, I
will also use area chart in order to capture the dynamic shape better.

4. The Data

Data used in this analysis spans 68 years from 1950 to 2017. The data includes 976 F1 races
that took place in that period, with 22,083 car records forming our dataset. For each car
record, I have data on: the starting and final positions of the cars that participated in each
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race, that is, team performance; constructor teams; their leaders' names, personal
information and background; drivers' personal information and background; and information
on each race circuit (weather conditions on the day, length of the circuit, etc.). The data were
compiled from several sources (please, see my dataset attached as a separate file). For
information on car entries, circuit, constructor, drivers and other detailed Grand Prix race
information, I used the provided dataset. Records in the starting dataset were cross-checked
and augmented by other sources of data such as Wikipedia, Grand Prix Encyclopaedia, and
other websites.6 Data on F1 regulations were compiled from the website of the F1 regulatory
body, Federation Nationale d’Automobile, https://www.fia.com/regulations.

Main dependent variable

My dependent variable is radical innovation. Radical innovation variable captures significant
changes in technology which led to serious changes in the F1 industry (innovations associated
with telemetry are excluded). In order to construct this variable, I used FIA regulations which
were scanned for restrictions on or bans of major innovations complemented by data from a
broad variety of technical online forums.7 My dataset also includes the performance variable
(final standings at the end of the race) for every race car in every Grand Prix season since the
first year of the Formula 1 industry existence. I use the final race standings ranking as a
determinant of performance for each car with 0 identifying the race winner; 1 – 2nd place; 2
– 3rd place; etc., to demonstrate the impact of Big Data on performance in a case study
illustration.

Key Independent variable

My key independent variable is a nominal variable which identifies 6 phases of analytics
development in the Formula 1 industry: Driver as a Sensor = 1; Telemetry Development = 2;
Early Telemetry = 3; Turbulence = 4; Mature Telemetry = 5; and Big Data Telemetry =6.

5. Results

In the dataset, phases cover different number of years (see Table 2): specifically, while Phase
1 includes 25 years, Phase 4 has only 3 years. Yet, despite these differences the number of
races per year increased in later years, meaning that there are at least 51 races in each phase
providing sufficient amounts of data for our analysis.

I test my hypothesis by considering whether radical innovation in the F1 industry follows the
Gartner Hype Cycle (see Figure 1). Since our data on radical innovation is annual data per
team, we calculate the sum of all radical innovations per phase from all teams taking part in
F1 Grand Prix competitions in that phase and then dividing the sum by the number of years in
a given phase. Results of my calculations are presented in the 5th column of Table 2 as well as
on Figure 2 (a) and (b). I show that innovations’ lifecycle indeed follows the Gartner Hype
cycle, where stages of the cycle are determined by the phases of data analytics development
in the Formula 1 industry. This confirms my hypothesis.




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Table 2: Performance and Innovations by Phases of Analytics Development

Phase Time Period Number of
years
Number
of
races
Average
annual
radical
innovations
Normalised
performance
change
1. Driver as
a Sensor
1950-1974 25 250 71 -0.10
2. Telemetry
Developmen
t
1975-1988 14 218 137 0.03
3. Early
Telemetry
1989-2001 13 212 188 -0.09
4.
Turbulence
2002-2004 3 51 24 -0.18
5. Mature
Telemetry
2005-2012 8 147 45 0.18
6. Big Data
Telemetry
2013-2017 5 98 19 -0.13

My results suggest that analytical peak in terms on innovation in Formula 1 was during the
Data Telemetry phase. And currently we are living through the data analytics plateau.



(a) Innovation and Data Analytics in F1 by Phase (b) The Dynamics of Innovation in F1 by Phase
Figure 2: Gartner Hype Cycle for Radical Innovations in the F1 Industry
To illustrate my results, I will consider an example. I will consider the following well-known F1
teams: Ferrari, McLaren, Williams, Red Bull, Sauber, and Mercedes. On Figure 3 I show the
historical normalised performance corrected for the number of major innovations of each of
these 10 teams for all years where these teams took part in the F1 competitions. The value
0.5 on the vertical axis depicts average normalised performance so all values below 0.5 refer
to poor performance relative to competitors while values above 0.5 capture successful
performance relative to competitors.
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Figure 3 clearly shows team heterogeneity with regard to their ability to handle large amounts
of data. The Ferrari team (one of two teams present in all 6 stages in the industry) exhibited a
steady performance growth from Phase 1 to Phase 4 and became one of the top beneficiaries
of the two-way telemetry8. It then suffered a slight decline in performance when the majority
of teams gained access to telemetry in Phase 5 (the Mature Telemetry stage) and then
managed to improve its performance through the use of Big Data telemetry in Phase 6. A
completely different pattern is exhibited by McLaren who did not take full advantage of the
two-way telemetry but then improved their performance in the mature telemetry stage. Yet,
despite the strong analytics capability, McLaren seems to suffer from data overload in the Big
Data phase where the performance of the team is significantly reduced. Interestingly, 4 teams
saw their performance decline from Phase 5 to Phase 6: McLaren, Red Bull, Renault, and
Sauber; one team did not significantly change its performance (Toro Rosso); while Mercedes,
Ferrari, Force India, and Williams took advantage of Big Data. Haas team was only present in
Phase 6 so it is not possible to make a comparison of performance between phases for this
team. The biggest winner from the Big Data Telemetry appears to be Mercedes team whose
performance received a significant boost from Phase 5 to Phase 6.


Figure 3 Historical Performance of F1 Teams By Analytics Phase Corrected
for Major Innovations
6. Insights for Business

This report set out to assess the impact of data analytics in general and Big Data analytics in
particular on F1 teams’ innovation as well as to provide empirical evidence to add to our
understanding of current debate on the challenges and opportunities enabled by Big Data.
Using real time telemetry evolution as a proxy of data analytics history in the F1 industry, I
identified 6 phases of data analytics development. Using these phases, I showed that (1) data
analytics phases as defined by telemetry technology evolution shape radical innovations in
the F1 industry which follow the Gartner Hype Cycle; (2) performance in F1 follows a lagged
Gartner Hype Cycle where performance suffers a delay compared to innovation; and (3) F1
teams exhibit significant heterogeneity in their ability to handle large datasets.

The findings from this study have important implications for managers working in an
environment characterized by complex information and where the ability to process this
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information represents a distinctive competence which leads to better performance. I show
that more data does not necessarily lead to more innovation or better performance. Rather,
players who are capable of quickly adapting to changing data environment tend to win (e.g.,
Mercedes) while others seem to suffer from the data overflow (e.g., McLaren). This suggests
that instead of collecting all possible data, the F1 teams should concentrate on collecting only
relevant data and concentrate on development of effective predictive models.

7. Limitations

My project has some limitations which represent opportunities for future investigations. First,
the empirical setting analysed is highly dynamic and more traditional industries may not
experience the same opportunities or capacity to deal with similar amounts of information
and types of Big Data. However, my results could be useful for conventional industries that
eventually will be confronted by Industry 4.0 Big Data revolution; it could help them to
anticipate what to expect from the new regulation and the consequences of introducing
radical innovation in a competitive market.
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Appendix:

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