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The Role of Data Analytics in Startup
Companies: Exploring Challenges and Barriers
Conference on e-Business, e-Services and e-Society I3E
2018: Challenges and Opportunities in the Digital Era
pp205-216
Vebjørn Berg[0000−0001−5611−964X], Jørgen Birkeland[0000−0003−3444−4075],
Ilias O. Pappas[0000−0001−7528−3488], and Letizia Jaccheri[0000−0002−5547−2270]
Department of Computer Science, Norwegian University of Science and Technology,
Sem Sælandsvei 9, 7491 Trondheim, Norway
vebjorbe@stud.ntnu.no jorgebi@stud.ntnu.no
ilpappas@ntnu.no letizia.jaccheri@ntnu.no
Abstract. The advancement in technology is transforming societies into
digital arenas and paves the way towards the achievement of digital trans-
formation. With every transaction in the digital world leading to the
generation of data, big data and their analytics have received major at-
tention in various fields and different contexts, examining how they may
benefit the different actors in the society. The present study aims to iden-
tify how startups that develop products with both software and hardware
parts can generate value from data analytics and what challenges they
face towards this direction. To this end, we performed a multiple-case
study with early-stage startups and employed qualitative analysis on a
dataset from 13 startups. Through semi-structured interviews, we ex-
amine how these companies use data analytics. The findings show that
although the benefits from data analytics are clear, multiple barriers and
challenges exist for the startups to be able to create value from them.
The major ones are about their resources, including human skills, eco-
nomical resources, as well as time management and privacy issues.
Keywords: Startups ·Big data ·Data analytics ·Empirical research
1 Introduction
In the digital era of the 21st century information and knowledge becomes readily
available to more and more people every day. Societies generate vast amounts
of data every moment from multiple sources, transforming them into landscapes
mediated by different digital media platforms, digital services, and technologies,
leading to the creation of big data and business analytics ecosystems [1]. The
different actors of the society (i.e., industry, public and private organizations,
entrepreneurs, academia, civil society) are increasingly realizing the potential
Cite this paper as:
Berg, V., Birkeland, J., Pappas, I. O., & Jaccheri, L. (2018, October). The Role of Data Analytics in Startup Companies:
Exploring Challenges and Barriers. In Conference on e-Business, e-Services and e-Society (pp. 205-216). Springer
DOI: https://doi.org/10.1007/978-3-030-02131-3_19
2 V. Berg et al.
of the generated data which can lead to value creation, business change, and
social change. To this end, many entrepreneurs and startups are actively trying
to harness the power of big data and create software and hardware with the
potential to increase value, gain a competitive advantage, and improve various
aspects of human life [2].
Startups are newly created companies producing cutting-edge technology,
having a major impact on the global economy [9]. In a context of extreme uncer-
tainty and restricted economical, human, and physical resources, startups have
unique challenges related to product development and innovation methods [10].
This results in a high number of failures, primarily due to self-destruction rather
than competition [10][11]. Operating in fast-changing, competitive high-risk en-
vironments, continuous experimentation is essential for learning and bringing
products fast-to-market [12].
There is increasing literature on how big data analytics can generate value
towards business or societal transformation [3][4], however further work is needed
in order to identify and overcome existing barriers that will allow practitioners to
generate value from big data and analytics [5]. Digitization and big data analyt-
ics have disrupted business models and can be essential tools to reduce increasing
failure rates of established companies [6]. Innovative startups profit on reduced
barriers for entering markets with technologies disrupting current distribution
channels, customer demands, and customer relationships [7]. Big data analytics
plays a crucial role in complementing and even substituting labor for machines,
especially in the context of value-creating managerial decisions [8]. Even if the
barriers to entry are lowered, startups operate in a context of restricted resources
and a lack of technical and managerial skills [13]. However, startups have some
characteristics (e.g., ability to quickly change and scale business model) enabling
them to compete with mature companies. The role and widespread of data ana-
lytics in startups is yet to be explored, even if utilization of such can be a major
success factor in the ever-increasing competitive business landscapes [4].
This study focuses on how hardware startups can benefit from big data and
seeks to identify the challenges they face which will allow them to make data-
driven decisions and generate value from big data analytics. To this end, this
paper will offer insight into software and hardware startup companies by an-
swering the following research questions:
RQ1 How can startups create value from (big) data and analytics?
RQ2 What are the barriers for working with (big) data analytics in hardware
startups?
To address these questions, this study performs a multiple-case study inves-
tigating early-stage European hardware startups, developing products of both
hardware and software parts [34]. Even if the potential of (big) data analytics
is huge, findings indicate that startups developing both hardware and software
do not take advantage of such. do not utilize data analytics for various reasons.
To this end, there are identified several challenges and barriers for working with
data analytics in such startups, including limited data variety and difficulty of
performing business experimentation.
The Role of Data Analytics in Startup Companies 3
The rest of this paper is organized as follows: Section 2 presents background
literature. Section 3 explains our research method, including case selections and
data analysis procedure. Section 4 presents the findings from the interviews.
Section 5 discusses the results, and highlights directions for future research.
2 Background
2.1 Product Development in Startups
The primary objective of startups is to speed up the product development in
the early-stages, streamlining the learning process [12]. Startups must respond
to fast-changing customer needs and requests [14], both by speeding up the
decision and design processes [15]. Startups typically do so by utilizing an evo-
lutionary prototyping approach, meaning that they iteratively refine an initial
prototype aiming at quickly validating the product/market fit. Customer feed-
back highlights new functionality and improvements. As long-term planning is
infeasible in the chaotic environment of startups, flexibility and reactiveness are
necessary [18].
Instead of utilizing repeatable and controlled processes, startups take advan-
tage of reactive, low-precision engineering practices with focus on the produc-
tivity and freedom of their teams [18]. Startups prefer ad-hoc development ap-
proaches customized to their own needs, limiting the administrative overhead.
In an experimental environment constantly compromising between speed and
quality, certain agile practices might not be beneficial (e.g., regular refactoring
and test-first), as excessive administrative overhead can inhibit business exper-
imentation [15]. To bring innovative products fast forward, startups depend on
team members and resources dedicated to all aspects of the development pro-
cess, and to be change-oriented and self-initiated. Startups capability to enter
new markets and disrupt current business models is largely associated with the
uniqueness of human capital and the different approaches they employ.
2.2 The Importance of Data Analytics
In the ever-increasing digital world, businesses need to develop and evolve their
(big) data analytics capabilities and competencies which are key to achieving
successful digital business [4][19]. The evolution of the digital economy and its
combination with (big) data analytics is challenging current business models
with many startups disrupting well-established companies [20]. Big data refers
to expansive collections of data (large volumes) that are updated quickly and
frequently (high velocity) and that exhibit a huge range of different formats
and content (wide variety) [21]. Yet, there is limited understanding of how en-
trepreneurs and startups need to change to embrace such technological innova-
tions and generate value in the digital economy. Indeed, they need to build upon
their main resources that include people, processes, and technology [22]. This is
very important, as it allows businesses and decision-makers to respond almost
4 V. Berg et al.
instantaneously to market needs, thus increasing their operational agility. An it-
erative and incremental approach combined with frequent releases is essential for
startups ability to quickly accommodate frequent change, and adapt prototyping
to business strategy [16].
Startups and the individuals working there have the opportunity to take ad-
vantage of the available data and create new products transforming a market or
an industry [3], and big data analytics may be viewed as resources in this process
that enable value creation and digital transformation. Many software startups
are using existing ecosystems (e.g., Apache Hadoop) to build value-added soft-
ware and solutions [23]. Nonetheless, since various challenges exist in improving
the value creation process, significant research is targeted on addressing these
challenges taking into account engineering issues related to specifications, design,
or requirements in software development [24]. However, a similar approach is not
that easy to be followed by startups that develop both hardware and software.
Availability of resources, as well as external and development dependencies, pose
restrictions to the implementation of hardware [17], thus influencing the ability
of these startups to utilize big data and analytics.
3 Research Method
To explore the research questions we performed semi-structured interviews on
13 early-stage European hardware startups. Semi-structured interviews are con-
sidered suitable for qualitative data analysis, and allowed for a discoverable
approach as interviewees could express themselves more freely and provide their
own perspectives on personal experiences related to the research topics [25]. The
rest of this chapter presents our research process, including case selections and
the collection and analysis of data.
3.1 Case and Subjects Selection
The units of analysis are people involved in product development in startup
companies that deliver products with mixed hardware and software parts. We
defined selection criteria as suggested by Runeson and H¨ost [26]. Table 1 presents
basic information about each case. The current stage in the table is adopted from
[27], however the first stage startup is replaced by concept to avoid misunder-
standings.
Startups were relevant for inclusion in the study if they met the following
criteria: (1) The startup develops both hardware and software parts. (2) The
startup has been active for at least six months. (3) The startup has a first running
prototype. (4) The startup’s ambition is to scale its business. People from the
relevant startups were eligible for participation if they had experience and/or
knowledge about software and/or hardware development. If the candidate met
the criteria, he/she was regarded as qualified for contributing to the research
study.
The Role of Data Analytics in Startup Companies 5
Table 1. Case Descriptions
Case Product Current Stage Founded Location # of employees
Startup 1 (S1) Smart gloves Concept 2016 Norway 18
Startup 2 (S2) Medtech biosensor Concept 2017 Norway 5
Startup 3 (S3) Physical exercise game Stabilization 2016 Norway 5
Startup 4 (S4) Unmanned aircraft system Concept 2016 Norway 7
Startup 5 (S5) Advanced noise cancellation Concept 2017 Norway 5
Startup 6 (S6) Medtech hydration monitoring Concept 2016 Norway 10
Startup 7 (S7) LPG management system Stabilization 2016 Norway 8
Startup 8 (S8) Cable cam system Stabilization 2016 Norway 10
Startup 9 (S9) Digital piggy bank Concept 2017 Norway 4
Startup 10 (S10) Collaborative camera Growth 2014 Norway 50
Startup 11 (S11) Interactive children’s toy Concept 2015 Netherlands 8
Startup 12 (S12) 3D-printer control board Growth 2009 Norway 1
Startup 13 (S13) Sensors for IoT Growth 2007 Italy 25
Fig. 1. Product illustration from the investigated startups
We used five different channels to find relevant startups: (1) Innovation Cen-
ter Gløshaugen, (2) NTNU Accel and FAKTRY, (3) our professional networks,
(4) OsloTech and StartupLab, and (5) The Hub. Figure 3.1 presents examples
of the products developed by the startups of this study.
3.2 Data Collection and Analysis Procedure
Data was collected using a semi-structured interview guideline between February
and April 2018. Author one and two attended all interviews to avoid one single
interpretation of the respondents’ perspectives and insights on topics. This first-
degree data collection approach allowed us to control what data was collected,
ensuring that all pre-defined interview questions were answered sufficiently, and
exploring new directions by asking follow-up questions [26]. All interviews were
recorded and transcribed shortly afterward. Before each interview, we looked
into the cases’ business background, either through their company websites or
other relevant incubator or accelerator websites. Additionally, participants were
encouraged to answer a simple questionnaire prior to interviews filling out basic
6 V. Berg et al.
information about themselves and the company. The following list presents the
main topics and interview questions of the interview guideline:
–Business background
– Describe your product and team.
– Name the three largest challenges you have encountered.
–Product development
– What development process do you use?
– How are internal/external factors influencing product development?
–Data analytics
– How do you collect customer data?
– Have you used data analytics for requirements elicitation?
– What are challenges related to data analytics?
The interviews were undertaken in the language preferred by the interviewee
(English or Norwegian). Several of the interviews were therefore undertaken in
Norwegian as this made the interviewees more comfortable. This allowed them to
express themselves more freely, and give more in-depth explanations. Because of
this, it was necessary to translate some of the interviews when transcribing. As
there often doesn’t exist a one-to-one relationship between language and meaning
[28], the translation of the transcribed interviews was ensured to ”express all
aspects of the meaning in a manner that is understandable” [29]. This implies
that not all parts of the interviews were directly translated word-for-word.
A total of 68 pages of interview transcripts were analyzed using thematic
coding analysis [30]. The transcripts were coded and analyzed using NVivo.
Firstly, all authors read through the transcribed interviews to generate initial
ideas. Secondly, descriptive coding was applied through an inductive coding ap-
proach to systematically identify concepts and topics of interest [32]. Related
codes were combined into themes to create patterns and a meaningful whole of
the unstructured codes [30]. Section 4 presents the findings from the analysis
process.
3.3 Validity Procedure
The validity must be addressed for all phases of the case study to enable repli-
cation of research [26] and to ensure findings are trustworthy [30]. To ensure
validity, we followed guidelines used in controlled empirical experiments in soft-
ware engineering [33].
Interviewees were either CEOs or engineers with insight into business- and
technical-related aspects. As the startups were mostly located in the same area,
mainly consisting of young, inexperienced entrepreneurs, generalization is lim-
ited to cases with similar characteristics (i.e., early-stage European startups).
To decrease the risk of biased interpretations, author one and two attended all
interviews. Some interviews were in Norwegian, hence transcripts were not al-
ways verbatim to preserve the actual meaning of respondents. Recordings were
transcribed shortly after each interview to mitigate bias. Since it is difficult to
The Role of Data Analytics in Startup Companies 7
understand a startup and its dimensions within a time-span of 30 minutes, we
collected data about the startups through incubator and company websites prior
to interviews.
4 Results
4.1 Utilization of data analytics
Among the investigated startups, the usage of data analytics methods was gen-
erally limited. Operating in early stages, they were often determined to rapidly
develop new features and perform customer validation. The startups in this study
mostly relied on qualitative measures (e.g., interviews and observations) to ob-
tain customer feedback. “We have not used data analytics, and do not collect
customer data.” When focusing on the short-term business goals, they minimized
any effort spent on data analytics, rather focusing on the core-delivered values
of their products to quickly release a minimum viable product to customers.
Improving data collection measures was considered as a rather time-consuming
activity. “Data analytics is not something we currently spend time on.”
Although the startups commonly spent little time on gathering or learning
from data analytics efforts, some had a clear perception of the possible business
opportunities and benefits from utilization of such. Even if so, data analytics was
usually outside their business scope. “We have looked at some future possibilities
of data analytics, but it is not something we currently focus on.” A brake-pad
in introducing greater focus towards data analytics was that the startups in this
study did not have large amounts of data at their disposal. The restricted access
to useful data inhibited potential value-adding activities from data analytics.
“It’s too early for us to get something valuable from data analytics.”
The capabilities of team members greatly influence the associated success of
startups. From the investigations, we saw an increased focus on data analytics
in startups with team members having experience or expertise within the field.
Despite for the general limited use of data analytics, possessing the required
knowledge and skills of such can have a positive impact on its widespread adop-
tion within a startup organization. “We work with data analytics and do most
of it ourselves [...] It requires that your company is able to get that expertise.”
Although some of the investigated startups were aware of opportunities and
benefits associated with utilizing data analytics for decision-making and require-
ments elicitation, they mainly focused on the core-delivered functionalities of
their products to speed-up development. The findings show, that value-adding
activities related to data analytics were considered as less important compared
to product development activities.
4.2 Barriers for obtaining deeper customer insight
Experimentation, testing, and assessment can be a challenge to startups de-
veloping products including both software and hardware components. Physical
8 V. Berg et al.
prototypes are more resource-intensive to develop, in contrast to pure software
products, thus limiting startups’ ability to test products with a larger customer
base. The testing ability of these startups will largely depend on their capacity
(i.e., third-party dependency, financial and human resources) to produce proto-
types: “There is a great number of people who want to test our product, however,
we do not have the capacity to produce enough prototypes. The main reason for
this is hardware production, which happens in China, and the manual assembly
we do ourselves.”
Findings from the investigated startups indicate that the amount of collected
data in early stages is limited in terms of volume, velocity, and variety, as the
data are generated mainly from one prototype used by a couple of users, thus
restricting data capture along with their ability to generate value from them.
This relates strongly to the early stages of a startup characterized by the ex-
istence of only a few customers, as well as to startups developing evolutionary
independent systems. Startups may be reluctant to invest in data analytics due
to the perceived limitations of the available data: “The data amount is still a
little too small to do any proper analysis of it, and we do not collect enough
personal info yet to perform the analysis.”
Acquiring people with the necessary knowledge and skills in data analytics is
one of the major challenges in generating value from (big) data. With startups
looking for team members with knowledge in a wide area of fields (boundary-
spanning knowledge), it is not easy to put a significant focus on data analytics
skills and knowledge. The investigated startups had limited expertise in per-
forming data analytics, and knowledge about available tools suited to address
startups’ concerns or requirements. The findings show that attracting knowl-
edgeable people is quite hard and with resources being severely restricted, hiring
specialized people only to work with data analytics is rarely an opportunity, not
to mention a priority of startups: “Finding talented people is hard. Since we are
a startup we cannot give very good salary [...] If we had more money we would
employ someone to analyze product and customer data [...] I see the value of it,
but for the time being, it is not a priority.”
The highly competitive environment of startups and severely limited re-
sources imply startups strict priorities. Data analytics efforts may exhaust the
already constrained financial and time resources. In addition, collecting the nec-
essary data may present an additional cost of components (e.g., sensors and IoT
technology) and human investments. This may be a priority startups are not
willing to take: “At the time this [data analytics] is not something we priori-
tize.”
Startups work with innovative technology and products for a wide area of
markets. Certain markets may pose specific restrictions and regulations for data
collection. This makes the customer testing an intricate process, involving a sig-
nificant amount of paperwork. Storing customer data for later analyses may be
illegal or too entangled, preventing the use of data analytics. Startups need guide-
lines for handling privacy (e.g., General Data Protection Regulation - GDPR)
The Role of Data Analytics in Startup Companies 9
and security issues to fully take advantage of the benefits of data analytics:
“When working with hospitals, data becomes more complicated due to privacy.”
The uncertain conditions and fast-changing environment of startups mean
long-term planning is not part of their business model, as this is not the way
they operate. Some of the investigated startups’ business managers lacked the
required knowledge to implement data analytics and the potential value in their
business plan: “I see data analyses as the next step for our business [...] Currently
we do not even know what our data can be used for.”
5 Discussion and Conclusions
This study examines how startups can generate value by employing data analyt-
ics methods. With the majority of the literature focusing on startups that create
software, here, we choose to investigate startups that develop both hardware and
software. This specific category of startups presents great interest due to specific
challenges that differentiate them from typical software startups. Indeed these
startups are more likely to face challenges such as limited availability of resources
or to be dependant on external factors linked with hardware development [17].
Such challenges are expected to affect their ability to use big data and analytics
in order to generate value.
The findings show that some of the startups are aware of the potential ben-
efits from using (big) data analytics, however, they face various barriers and
challenges which limit them from utilizing them in their business models and
business process. Table 2 presents the main barriers to working with (big) data
analytics as identified in this study. In detail, the startups face challenges related
with their prototyping capacity, as they are able to develop only limited amount
of hardware prototypes, thus limiting the number of users that can use them at
the same time. This is directly linked with the limited financial resources that
young startups have, as well as with the time-shortage that characterizes star-
tups, since they are forced to work on short deadlines and intensive processes.
The challenge with the limited prototyping capacity can indirectly affect data
availability. In detail, limited hardware and users lead to an impact to generated
data. However, such limitations could be overcome by better planning and more
focused testing of their products with their end-users. Furthermore, some of
the startups mention that they face specific security and privacy issues related
with the use of personal data, due to the nature of their business (e.g., medical
technology tested at hospitals). Nonetheless, such barriers can be overcome with
the collaboration of the different actors in the society (i.e., industry, government,
academia), and the recently directive from EU on data protection (i.e., GDPR)
is a step towards that direction. Finally, the startups indicate that generating
knowledge from data analytics is not a primary objective for them, thus it is
not included in their overall business strategies. This is also linked with the
other barriers, regarding prototyping capacity and resource availability, since
they believe that they are not able to achieve their short-term goals using data
analytics.
10 V. Berg et al.
Table 2. Barriers for working with (big) data analytics
Barrier Description
Prototyping capacity Physical prototypes are associated with individual
development costs and time (e.g., third-party dependency).
Limitations of data Data in early startup stages are characterized by low volume,
velocity, and variety.
Team capabilities
Startups have high demands for skillful teams with entrepreneurial
capabilities. Experience using data analytics will positively impact
its widespread organizational adoption.
Financial resources Hardware development includes production, manufacturing, and
logistics, which require more initial human and financial investments.
Time-shortage The uncertain high-risk environment forces startups to release
their products fast and to work under constant pressure.
Security & privacy issues Collecting customer and usage data for (big) data analytics
have associated privacy and security issues.
Integration with business strategy Data analytics activities are usually outside the
short-term business goals of startups.
Some business managers mention that they possess limited knowledge on
what additional value data analytics could provide to their decision-making and
design process. Increasing business managers’ awareness around the potential
knowledge and presenting them with practical information and knowledge will
increase the potential of including data analytics in their business models. This
can be achieved by offering to startups validated learning, through the use of
cohort metrics (e.g., actionable, accessible, and auditable metrics) and analy-
sis. As startups are characterized by short-term planning and frequent releases,
utilizing big data analytics will allow startups to make data-driven decisions,
which can be faster and with increased quality, thus being consistent with the
agile environment that most startups operate.
As with all empirical studies, this study has some limitations. Qualitative
data collection measures imply that results and implications are subject to bias.
To mitigate the risk of wrong interpretations, author one and two attended all in-
terviews, preferably face-to-face on-site. Recordings were transcribed shortly af-
terward to preserve respondents’ actual meanings. Furthermore, the study would
profit from a wider collection of data, both to discover more challenges and to
ensure credible conclusions. Also, employing quantitative methods would allow
for data triangulation.
This study provides initial knowledge on data analytics in startups, however,
future work should investigate more startups both to identify other challenges
and barriers, and for generalization of results to a larger startup population
(e.g., operating in different markets and lifecycle stages, and various geographi-
cal locations). Seeing that the widespread of data analytics is limited, startups
need specific methods for utilizing analysis tools in early startup stages. Startup
managers need guidance to understand how their data can generate revenues,
and what knowledge is required for their organization to thrive from data ana-
lytics. Startups need directions for how to implement a data analytics strategy
to benefit the company in the long run.
The Role of Data Analytics in Startup Companies 11
Acknowledgments
We would like to thank the startups that participated in this study. This project
has received funding from the European Unions Horizon 2020 research and in-
novation programme under the Marie Sklodowska-Curie grant agreement No
751550.
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