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Finding the Next Unicorn: When Big Data Meets Venture Capital

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Venture capital (VC) has been growing rapidly in recent years. So far the screening and evaluation of potential startups as investment objects largely depends on the venture capitalist's personal experience, network and qualitative evaluations. In the era of big data, the advent of new data sources and analytic techniques enables a data-driven investment process. Grounded in systems theory and the theory of complementarity, this study reports the findings from an exploratory study of 13 VC firms that synthesize and use novel data sources. Our analysis shows that the data-driven approach, in particular, impacts the deal origination and screening stages of investment. It leads to informational and transactional benefits, which lower operational costs in the short term and enlarge the potential return on investment of a VC firm in the long term. We contribute to the literature by shedding light on how various data sources complementarily lead to additional business value.
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Finding the Next Unicorn: When Big Data Meets Venture Capital
Johannes Weibl
LMU Munich
weibl@bwl.lmu.de
Thomas Hess
LMU Munich
thess@bwl.lmu.de
Abstract
Venture capital (VC) has been growing rapidly in
recent years. So far the screening and evaluation of
potential startups as investment objects largely
depends on the venture capitalist’s personal
experience, network and qualitative evaluations. In
the era of big data, the advent of new data sources
and analytic techniques enables a data-driven
investment process. Grounded in systems theory and
the theory of complementarity, this study reports the
findings from an exploratory study of 13 VC firms
that synthesize and use novel data sources. Our
analysis shows that the data-driven approach, in
particular, impacts the deal origination and
screening stages of investment. It leads to
informational and transactional benefits, which
lower operational costs in the short term and enlarge
the potential return on investment of a VC firm in the
long term. We contribute to the literature by shedding
light on how various data sources complementarily
lead to additional business value.
1. Introduction
In the few last years, the economy in Europe and
the US has experienced a significant boom in venture
capital (VC). This phenomenon is mainly driven by
the “explosion of startups” and the maturity of the
entrepreneurial finance market [1]. In the US, for
instance, venture capitalists (VCist) invested $74.2
bn. in 5268 deals in 2017 according to PwC [2]. In
Germany in 2016, the top 100 startups received
funding in the amount of $5.9 bn. [3].
By offering mentoring and financial resources,
VC firms can receive high return on their investments
if their portfolio companies are being acquired by
another company or going the route of an initial
public offering (IPO). VC refers to the financing of
private companies and is a form of private equity,
medium to long-term finance provided in return for
an equity stake in potentially high growth companies
[4]. By being the biggest capital supplier, VC firms
support entrepreneurial firms to facilitate, for
example, their operations, production, or expansion
[1].
Up until now, the screening and assessment of
startups for investments often relied on the investor’s
experience, relationships and networks, as well as on
qualitative assessments of entrepreneurial firms [5].
However, in the era of big data, more accessible data
sources and data explosion as well as more
sophisticated analytic techniques can facilitate a more
data-driven decision-making and investment process
for VC firms.
Venture capital has been discussed variously in
the academic literature. Prior studies have
investigated the performance of VC investments
regarding value creation and innovation power [6, 7],
various investment strategies [8], as well as
recommendation systems in the domain of venture
finance [4]. Other studies shed light on data analytics
and data-driven models in the VC context, for
instance, to enhance the existing prediction model to
ensure better investment decisions [9].
However, it still remains unclear how the broad
availability of new data sources and analytic
technologies (often termed as big data [10, 11]) are
used by VCists during the investment process and
how additional business value is generated. Thus, this
paper addresses the following research question: How
does big data transform the investment process of VC
firms?
In order to answer this question, we build on the
literature of big data analytics, systems theory [12]
and the theory of complementarity as a theoretical
foundation. Methodologically, we conducted
interviews with 13 VCists in Germany with the
objective to reveal the impact of using various data
sources within their investment processes.
This research makes important contributions to
both theory and practice. First, we contribute to prior
research by providing a qualitative empirical study in
the field of systems theory and the theory of
complementarity. Second, our findings indicate that
VC firms synthesize their existing transactional IT
systems, e.g., a customer relationship management
(CRM) system, with external, web-based data
Proceedings of the 52nd Hawaii International Conference on System Sciences | 2019
URI: hps://hdl.handle.net/10125/59547
ISBN: 978-0-9981331-2-6
(CC BY-NC-ND 4.0) Page 1075
services for better insights and data-driven decision
making. The advent and usage of more information
transforms, in particular, deal origination and
screening, stages 1 and 2 respectively of the five-
stage investment process. It leads to an automation of
sourcing and screening of entrepreneurial firms and
substantially enhances the ability to identify
successful investment deals. In the short term, the
data-driven transformation of VC firms leads to
lower operational costs, and in the long term the
usage of data analytics leads to greater return on
investments.
Third, this study reveals the path of value
generation through data and data analytics in the era
of big data. For VC firms it provides clear guidance
how to utilize the additional value of their
investments in a data-driven environment by
synthesizing their existing data systems with external
web-based services.
This paper is organized as follows: In section 2,
we present the study’s theoretical background
(systems theory and investment process). We then
describe the research methodology (section 3) and
present and discuss our findings (section 4). The
paper concludes by highlighting the research and
practical contributions that our paper makes (section
5).
2. Theoretical background
2.1. Big data sources and analytics
Big data as a term is generating significant
attention in a range of companies worldwide. It refers
to the process of managing large amounts of data that
come from heterogeneous data sources (e.g., internal
and external, structured and unstructured) that can be
used for collecting and analyzing an enterprise’s data
for the purpose of augmenting its performance [13].
The phenomenon “big data” is often summarized by
the notion of three Vs: volume, velocity and variety.
It refers to the large volume of data that companies
store and analyze nowadays, generated from a greater
variety of data sources with multidimensional data
fields with a high frequency of data generation and
data delivery [14]. In this paper we follow Davenport
et al. [10] and Duan and Cao [11] who focus on the
variety of data sources within the big data context.
Furthermore Goes [15] and Malgonde and
Bhattacherjee [16] highlight the importance of data
from various data sources as the major driver for
additional business value in the era of big data. In
other words, big data and related analytics (BDA) is
about the extraction of unknown patterns,
correlations and information across multiple sources
of data to enrich the information depths and produce
new insights for decision makers [11].
Following Chen et al. [13], we identify three
groups of data sources for companies (see Table 1).
Driven by the diffusion of new technologies (such as
mobile devices and social media platforms),
advancements and new developments of storage and
integration possibilities (e.g., data lake),
organizations are able to synthesize internal company
data sources (e.g., across divisions) as well as
integrate external data sources.
Table 1. Groups of data sources [13]
Groups Description
Transactional
systems
Data from enterprise systems,
including standard workflow and
automation tools, such as CRM
systems.
Web-based
services
Data from online systems, such as e-
commerce platforms and social
media systems.
Mobile
systems
Data from sensors (e.g., location
data) generated from mobile devices.
2.2. Systems theory and business value
From a theoretical perspective, our study is based
on systems theory, which looks at systems as a whole
rather than at its individual parts [17]. It addresses the
interplay of resources in organizations that
collectively create greater value than any of the
resources in isolation [18]. A system’s individual
components are described as subsystems [19]. If
different subsystems (a system’s individual
components [19]) interact complementarily, the
overall value of a system can exceed the value of
each subsystem in isolation. Complementarity,
defined as the ability of one resource to reinforce the
impact of another resource, is often described as the
underlying activity leading to enhancing effects on
the relationships between resources and outcomes
and is derived from the economic theory of
complementarities [20].
Data sources can be understood as IT resources in
companies with the ability to share or aggregate
information [21]. According to systems theory, each
data source can be conceptualized as a data
subsystem in a firm. Data subsystems can be
complementary to each other [22] allowing actors
deeper insights from their combination [23].
Combined data and related data sources have a
greater value than the sum of its individual parts,
which facilitates decision makers to derive more
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insights and leading to a better decision-making
process [24]. When various data sources are
combined, data complementarily interact with each
other resulting in additional business value [22].
In general, the complementary interaction of
different IT systems generates a wide range of both
tangible and intangible business value [25]. It can be
realized and measured in three dimensions:
transactional, informational and strategic [26, 27].
Strategic and transactional benefits can be seen as
tangible benefits because they can often be measured
in e.g., productivity, savings, innovation or market
share [28]. Transactional benefits include
effectiveness, process efficiencies as well as
automation and are often associated with cost
reductions [26]. Strategic benefits include increased
revenue, superior customer experience, shorter time
to market, and development of new products and
services [27]. Informational benefits as intangible
benefits, such as enabling, improving, increasing,
etc., are more difficult to measure. Informational
benefits also include improved information for
managing, enhanced reporting and communication
with customers and suppliers. Furthermore, they
support the responsiveness and reliability of firms
and enable more effective decision making [26].
Moreover, informational benefits can lead to real-
time decisions and new pattern discovery [27].
2.3. Venture capital and the investment
process
Venture capital, defined as a form of private
equity invested in high growth companies in return
for an equity stake is often seen as an extreme form
of financing [4]. Previous studied in the finance and
corporate governance literature have analyzed the
role for VCs in resolving various agency problems,
which arise in multistage financial contracting
problems [29], identified economic and legal
determinants of VC investments and exits [30], and
studied the effects of various investment strategies
(e.g., pure-play versus more general investment
strategy to minimize portfolio risks) [31]. Yet,
regarding the usage of new data sources and
capturing related business value in VCs, little
research exist, although the impact of big data on
companies can be large [13].
With our research question in mind, we shed light
on an investment model that explains the investment
activities of VCists. Several researchers have
discussed various process models, amongst the most
cited is that of Tybjee and Bruno [5], who developed
a five-stage investment model (see Table 2). Tybjee
and Bruno’s model helps to analyze and structure our
findings regarding the impact of big data on the
investment process.
Table 2. Venture capital investment process
[5]
Stage Description of VC firm activity
Deal
origination
Becoming aware of potential
investment activities.
Screening Concentrating only on a manageable
set of potential deals.
Evaluation Assessing the potential return and
risk of a particular deal. If the
outcome of the evaluation is positive,
the VC firm enters into negotiations
with the potential investee.
Structuring Structuring the deal in terms of the
amount, form and price of the
investment.
Post-
investment
activities
Setting up controls to protect the
investment, providing consultation to
the fledgling management of the
venture and supporting the
orchestration of the acquisition.
3. Research method
The goal of this study is to explore how the usage
of data and novel data sources impacts the investment
process of VC firms. To elaborate the impact in
detail, we decided to use qualitative research with an
explorative design. The qualitative approach enabled
us to analyze in detail the impact and relationship
between different factors and to consider contextual
factors [32].
In IS research, exploratory qualitative study has
been a legitimate means of research. Because of the
complexity of our chosen subject area and our goal to
explore the investment process and potential impact
in detail, this research approach is appropriate.
Furthermore, it stimulates further research questions
for related work. In fact, the explorative approach
allowed us to analyze our research subject in areas in
which only limited knowledge exists [32, 33]. To
achieve our goals, we conducted semi-structured
interviews.
3.1. Data collection
Data collection took place in 13 different
organizations in the VC sector in Germany.
Analogous to Islam et al. [34], we focused on the
primary key informant at each firm as our research
subject. In total, we conducted 13 semi-structured
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interviews (telephone: 10; face-to-face: 3) with
technology and big data experts at VCs. The
interviewees were mostly on the partner level of the
firm or were employees in key decision making roles
(e.g., director, principal, investment manager). We
chose employees of both corporate and independent
VC firms with various investment foci to present an
industry-specific picture of the data-driven impact on
the firms. We brought new interview experts
continually into our study until we received a high
rate of duplications of responses. After our 13
interviews we reached this point of saturation. So, we
did not extend the interview process further but
started to analyze the interview data. Table 3
provides an overview of the data collection.
Table 3. Overview of data collection
# Position of
interviewee
Type of VC Investment
Focus
1 Associate Independent Digital health
firms
2 Director Independent Travel and
consumer goods
firms
3 Analyst Corporate Energy startups
4 Director Corporate Mobility and
energy startups
5 Partner Independent Technology
firms
6 Associate Independent Software-as-a-
service firms
7 Investment
Manager
Independent Digital startups
8 General
Partner
Corporate Software-as-a-
service startups
9 Analyst Independent Broad focus
10 Data
Scientist
Independent Digital startups
11 Principal Independent Technology
firms
12 Associate Corporate Energy startups
13 Management
Director
Independent Technology
focus
Prior to conducting the interviews, we prepared
an interview guideline following Schultze and
Avital’s guide [35]. The interview guideline was
based on literature and included three parts: 1)
general aspects of the VC firms (investment focus,
employees, etc.), 2) the firms’ usage of internal and
external data-driven IT systems and 3) their usage of
data-driven IT systems for each investment stage and
its potential business value. Depending on the
respective knowledge of the interviewees, we asked
additional questions. The interviews lasted between
45 and 80 minutes and were recorded, transcribed
and anonymized using the software ATLAS.ti. In
addition to our interviews, publicly available data
were used to triangulate our findings (press releases,
management reports, VC websites, etc.)
3.2. Data analysis
The goal of our data analysis was to glean
essential insights by abstracting a manageable
collection of interview data, while still reflecting the
interviews. This methodological technique is known
as content analysis [36]. Our coding was based on the
descriptive coding scheme derived from the stages
and impact of the investment process model and
underlying technologies. The coding was conducted
independently by two researchers to ensure quality
data analysis. Both researchers read all the transcripts
in preparation for the coding, then familiarized
themselves with the interview data guided by the
fundamental principles of the hermeneutic circle [37].
After completing the initial coding, we used a
consensual approach to reduce obvious differences
between the researchers and to refine the coding
scheme.
4. Insights
In this section, we describe our results and discuss
the insights from our qualitative research approach.
First we present our insights on data and relevant data
systems of VC firms, second, we show their impacts
on the investment process and third, we describe its
business value.
4.1. Venture capital-relevant data and related
systems
Our interviews reveal that VC firms use both
transactional IT systems and web-based data services
as groups of data sources to leverage their investment
activities. The transactional system, often in the form
of a CRM system, is the backbone of the VC IT
system. “[It is used] to track our deal flows and
contacts in the firm,” one interviewee stated. In the
VC context, it is also used to log meetings, maintain
client contact details and structure marketing and
sales leads. The web-based data services include
various types of external data and data-driven service
providers.
Data from both types of systems interact in two
ways: a) data from web-based services enrich
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existing, internal data from transactional systems and
b) various data within web-based services are
combined within the external platform for further
insights for the VCist. We interpret this finding as
two types of complementary interaction (see Figure
1).
Transactional System
Web-based Services
Data
Data
a)
b)
Figure 1. Two types of complementary
interaction
Based on our interviews, we found that VC firms
typically integrate social media data, such as contact
information and related activities, into their internal
VC CRM systems. This makes it possible to reach
people more easily and faster” according to many
interviewees and provides further insights about the
general opinions (e.g., political opinions) and
preferences of entrepreneurs, which, in turn, may
influence the investment decision. Many of the VC
firms also synthesize external market and customer
information from market intelligence platforms (e.g.,
Mattermark, Statista, Gartner) with their internal
market information. This sheds light on potential
consumer trends and new market opportunities,
which cannot be validated without external market
information. “By merging information from Statista
and Mattermark into our system we are able to assess
whether the market conditions for a specific startup
are rather favorable or not” (The director of VC firm
#2).
A further insight emerged from our interviews
when we investigated the usage of web-based
services: VCists typically have access to various
crowdfunding websites and investment networks.
Here the objective is not necessarily to integrate these
data sources into their internal systems, rather, VCists
analyze and combine data directly on the
crowdfunding sites or investment networks for
further insights. Analyzing data from crowdfunding
sources helps to keep track of new deals in the VC
sector and innovative products and services that are
being started or generating significant traction.
Additionally, investment network services allow for
insights on team structure, team expertise, money
raised up to now, the success or failure of previous
startups of the entrepreneurs, etc. Combining these
insights helps to further identify high potential
entrepreneurs and startups and reduces the risk of
investment failures from a VC perspective. One
interviewee mentioned that by synthesizing data from
Crunchbase and Pitchbook, they found out that an
investment deal with founders from various
universities was more likely to succeed than one
whose founders came from the same university.
Moreover, web-based benchmarking services allow
VCists to evaluate their portfolio companies against
relevant peer groups in e.g., benchmarking financial
performance.
As an interim result, Table 4 summarizes the data
and VC related systems.
Table 4. Overview of venture capital-
relevant web-based services
Groups of Data
Sources
Data
(excerpt)
a)
Synthesize
data with
trans-
actional
systems
Social media
platforms,
e.g., LinkedIn
Contact
information,
social graph,
founders’
opinions
Market intelligence
platform,
e.g., Mattermark,
Statista
Consumer
trends,
market
opportunities
b)
Combining
data within
web-based
service
Crowdfunding sites &
blogs,
e.g., Producthunt
New deals,
product and
company
launching
Investment networks,
e.g., Crunchbase,
Pitchbook
Team and
related
expertise,
money raised
Benchmarking
platforms,
e.g., Social Capital
Business
data from
market and
competitors
4.2. Impact on the investment process
Based on the insights from the previous section
that VC firms use primarily transactional data
systems and web-based data services, we show in this
section how data usage impacts each of the five
investment stages [5].
Deal origination: In the beginning stages of any
investment, VCists face the challenge of identifying
prospective investment deals. Our interviews reveal
that in the past, next to typical digital inbound
services (such as emails and company website
forms), various intermediaries (such as lawyers)
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played an important role in shedding light on
potential startup companies: Mostly it was a
combination of different sources of information,
personal networks, personal recommendations,
which then come, for example, also from other VCists
or attorneys” (Director of VC firm #4).
However, the deal origination process
fundamentally transforms from an inbound
orientation to an outbound orientation by means of
the usage of data. By accessing investment networks
such as Pitchbook or Crunchbase, VCists combine
company information with market research for a
better overview of newly founded startups, which are
potential investment deals. Typical crowdfunding
websites (e.g., Producthunt) further support the deal
origination of VCists by providing data about the
existence of new crowdfunded startups and
underlying products or service ideas. Nowadays,
VCists have access to market intelligence platforms,
e.g., Mattermark, for data-driven insights on market
dynamics and related participants. By combining
information from various sources, such as website
traffic, employee counts, time since last funding, co-
investors and total amount of funding, these services
allow VCists to predict the growth and success of a
startup. Some interviewees further stated that their
firms are “automatically informed about the existence
of a new company. We use customized data tools,
sometimes including self-learning algorithms to find
investments by synthesizing external data sources
(Associate of VC firm #1).
We conclude that the usage of data, especially
from web-based services, has a fundamental impact
on deal origination. It reduces the effort required to
search for new investment deals and simultaneously
provides a broader and more sophisticated basis for
decision making.
Screening: During the screening stage, VC firms
screen a large number of potential deals based on pre-
defined criteria, concerning, for instance, the
technology, product or market of the startup. In the
pre-big data era this step was less data-driven. As one
interviewee stated: “The screening stage was less
data-driven, rather, we focused on qualitative
assessments. Discussions and talks with the founders
were more crucial for us (Investment Manager, VC
firm #10).
Nevertheless, our interviews reveal that the
screening stage rapidly moves to a more data-driven
activity. Here the integration of market intelligence
data and social media data in the internal
transactional systems (CRM system) plays an
important role. Market growth, based on Mattermark
or Capital IQ for instance, allows for analyzing and
refining business cases as well as return- and risk
models of potential startups in more detail, including
a better validation of market growth. As one
interviewee stated: Everything that goes in the
direction of evaluation and business case building is
of course strongly data driven, actually all the parts
we look at there, the market, the competitive
situation, modeling, return and risk modeling, so this
is all as quantified as possible. And some of the data
is available internally, others we obtain from
external providers” (Director of VC firm #4).
Furthermore, the seamless integration of contact
and team information from social media, e.g.,
LinkedIn, helps VCists in the screening stage to keep
in contact with entrepreneurs more easily on various
digital channels. Further information provided by
social media platforms, such as former startup
experiences of entrepreneurs and their academic
background, combined with data from investment
networks (e.g., team structure, money raised) allows
a data-driven approach to determine the probability
of a startup’s success.
In sum, data usage plays an important role in the
screening stage. All the VCist we interviewed
mentioned in particular the importance of combining
data from external web-based services with the
internal CRM system.
Evaluation: Due to the paucity of, or rather non-
existence of, operating histories of startups, VC firms
have to rely on a subjective assessment based on the
business plan and related information delivered by
the venture’s management to determine the future
viability of the startup. Typically, this step in the
evaluation aims to subjectively assess the venture
using a multidimensional set of criteria, such as
management commitment and marketing skills [5].
We recognize that this step is almost always data
driven. However, the focus has been mainly on
qualitative data, e.g., expert interviews or personality
tests, to better rate a potential investment activity. As
one interviewee stated: In this process we are
extremely data driven, i.e., we talk to many industry
and technology experts in the respective area. We
take a very close look at the market, we examine the
business case very closely, and in some cases, we
also conduct certain personality tests with the
founders” (Director of VC firm #4).
Furthermore, this step varies substantially from
investment case to investment case and heavily relies
on the investment team’s subjective assessment of the
startup. Besides some internal quantitative data that is
used for the evaluation, our interviews show that all
VC firms use only qualitative information for the
final evaluation of a deal.
Structuring: During this step, the investment
deal is structured in detail to reach a mutual
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acceptable investment agreement between the
entrepreneur and the VC firm that includes the price
of the deal and further contract details (e.g.,
management salaries). Our interviews reveal that this
step is less data driven overall than manually
determined on a case by case basis. We do not use
any templates or any data-driven standardized
process. This may sound odd, but I think that this is
actually the case with many VCists, because every
deal is different” (Associate of VC firm #1).
Post-investment activities: Once an investment
deal between the VC firm and a startup has been
realized, the role of the VC firm transforms from
being a pure investor to a being collaborator.
Hereafter, the VC firm plays a new role as a formal
representative on the board of directors of the startup,
and it exchanges market and supplier information
informally with the startup.
In the pre-big data era, VCists typically gathered
internal data from various portfolio companies to
compare a startup performance vis-à-vis its peer-
group. However, what we are very good at
compared to the startup is benchmarks of how
similar companies have performed in a similar
business phase. For example, if you look at an app, it
would be relatively easy for us to ask what the
acquisition costs were like or the conversion rate
(Associate of VC firm #1).
Today, VCists access and use web-based
benchmarking tools to compare the competition and
peer group of the startup with a broader data base.
This helps to aggregate performance data for similar
companies and leads to more balanced business
decisions. Some VC firms are more progressive and
create their own online benchmarking platform
during the post-investment stage. The idea behind
this is that both portfolio and external startups can
upload their core business data for an in-depth
benchmark with competitors and derive valuable
insights for further growth. Performance data, such as
the number of customers, churn rate, conversion rate
and fixed costs, are then collected from portfolio
companies as well as external startups and
synthesized, leading to an enormous amount of
valuable data. “We are working on developing an
auto benchmarking service similar to social capital
in the USA. They offer such an auto benchmarking
service, they say upload your data and then we
benchmark that for you, the cool thing is of course
that they build up a huge data asset because
everything that gets uploaded, save it of course, so I
would also be super happy long term” (Partner at VC
firm #5).
Nevertheless, the data-driven benchmarking
activities are a sideline activity during the post-
investment stage; this step is still characterized by
manual and individual management tasks.
Table 5 sums up our findings about the usage of
data sources for each investment stage.
Table 5. Usage of data for each investment
stage
Investment
Stage
Data
Usage
Groups of Data Sources
Deal
origination High
Web-based services
- Crowdfunding data
- Investment network data
- Market intelligence data
Screening High
Web-based services
- Social media data
- Market intelligence data
- Investment network data
Transactional systems
- Customer and deal flow
data
Evaluation Low None
Structuring Low None
Post-inv.
activities Low Web-based services
- Benchmarking data
4.3. Generating value with data
The usage and combination of transactional and
web-based data systems generate a wide range of
both tangible and intangible business value for VC
firms. We see that the majority of these benefits can
be classified as informational or transactional
benefits, which especially impact the first two
investment stages, deal origination and screening (see
Table 6).
By adding further information regarding a
potential startup or its market situation from external,
web-based services, the VC firm is able to check a
larger number of potential startups on a broad data
basis in a shorter period of time. The usage of
external web-based services, in addition, enables a
broader discovery of business models currently
emerging and respective products and services in the
startup sector, This, in turn, allows for more fact-
based decisions about potential investments.
Typically, the data-driven approach during the deal
origination and screening stages leads to efficiency
gains in the short term and greater return on
investments in the long term. As one interviewee
stated: In the beginning it is mainly efficiency gains
and in the end it will lead to better returns through
better investment decisions” (Partner at VC firm #5).
The data-driven transformation from an inbound
orientation (e.g., receiving e-mails from
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entrepreneurs) to an outbound orientation (e.g.,
actively scanning and analyzing new companies
online) leads to an automation of the deal origination
and may significantly reduce the time of work needed
to identify promising startups. “Typically, there are
not enough resources in VC firms to do everything.
Algorithms are a sensible investment for VC firms to
improve this ratio of a lot of work and few resources
in the long term” (Associate of VC firm #1).
We see further that a faster, and especially an
automated, deal origination and screening process
facilitates a shorter time for a VC firm to build the
initial relationship with an entrepreneur (summarized
as ‘time to contact’). As one interviewee stated: “I
think speed is the main advantage, because in the
very competitive tech investment sector it is
important to talk to companies as early as possible
and to be the first to get the chance to establish a
relationship with the founders(Principal at VC firm
#11). We further determine that the informational and
transactional benefits are consecutive and sequential.
The usage of various data and data sources leads to
informational benefits, followed by a higher degree
of automation and efficiency in searching for
potential investments and contacting entrepreneurs
(transactional benefits).
In addition to the primary outcomes
(informational and transactional benefits), our
interviews also revealed a potential strategic benefit.
Some VC firms not only synthesize external data to
gain better insights and improve decision making but
also focus on building their own data platforms (e.g.,
benchmarking platform), which portfolio companies
and external startups can use to compare company
performance with peer groups. In the long term, this
new data source leads to valuable data assets with in-
depth information about the VC sector overall.
Table 6. Generating value with data in venture capital
Business Value Purpose Affected
investment stage
Expected performance
benefits
1) Informational
Discover emerging business models and
related products and services to invest in
respective products to invest in
Identify entrepreneurs with high potential
more quickly and more accurately
Screen potential deals in (nearly) real-
time
Deal origination
Screening
Lower costs (short
term)
Greater return on
investments (long
term)
2) Transactional
Automate deal origination process
Increase the volume of startups identified
and screened
Reduce time until initial contact with
entrepreneurs (time to contact)
Deal origination
Screening
Lower costs (short
term)
3) Strategic Provide new services (e.g., benchmarking
platform)
Post-investment
activities
Greater return on
investments (long
term)
5. Contribution, limitations and further
research
The key findings from this research involve the
observation that many VC firms synthesize their
existing transactional IT system, e.g., CRM system,
with external, web-based data services to gain better
insights and improve data-driven decision making.
Various data from web-based data services 1)
complement the explanatory information power within
the external platform and 2) interact complementarily
with internal data from transactional systems. The
insights derived from such synthesis and the usage of
more information transform, in particular, the first and
second investment stages (deal origination and
screening) of the five-stage investment process. They
lead to an automation of sourcing and screening of
entrepreneurial firms and significantly enhance the
likelihood of identifying successful investment deals.
In the short term, the data-driven transformation of VC
firms leads to lower operational costs, and in the long
term the usage of data analytics leads to a greater
return on investments.
5.1. Theoretical and practical contribution
From an academic perspective, we contribute to
prior research by providing a qualitative, empirical
Page 1082
study based on systems theory and the theory of
complementarity. In line with previous literature [e.g.,
38], we see that transactional IT systems are
implemented mainly to automate processes (e.g., VC
deal flow, selection process of new startups) while
web-based services as a type of informational system
are implemented to inform the same process. Both
types together facilitate more intelligent and efficient
investment processes, complementing and reinforcing
each other. Through this, VCists are able to accomplish
higher-order organizational tasks.
Our study also sheds light on the business value
emerged from data variety [15]. Several researchers
have proposed that value is created from synthesizing
various data sources [e.g., 16, 23] in the era of big data.
To the best of our knowledge, none of these studies
focus on the complementary interaction among various
data sources as a theoretical foundation. In our study
the complementary interaction helps to understand how
the usage and synthesis of various data assets lead to
additional business value in organizations.
Furthermore, taking the findings of Wixom et al.
[27], it can be argued that informational benefits play
an intermediating role in the complementary
mechanisms of value creation through data. Initially,
the interaction of various data leads to informational
benefits for VC firms followed by transactional
benefits (e.g., a higher degree of automation and
efficiency) and occasionally by strategic benefits (e.g.,
the creation of new data services). Our study extends
existing literature [e.g., 27] by providing an example of
sequential logic. Moreover, our study reveals ‘time to
contact’ as a new and additional transactional benefit
which has not, to the best of our knowledge, previously
received appropriate attention in the current literature
[e.g., 26, 27, 39]
In terms of practical contribution, our study shows
that advanced analytics and the usage of a wide range
of data sources pays off for VC firms. In the short
term, enormous gains in efficiency can be realized by
the data-driven automation of single investment stages.
Practitioners should primarily concentrate on the first
two stages (deal origination and screening), where a
faster and more detailed evaluation of potential
investments can easily be leveraged. The benefits
identified provide a solid decision-making basis for
triggering expensive data analytic investments within
VC firms. This study also shows that a more data-
driven way of working should not be a side project in
VC firms, but rather should be supported by the entire
organization (including top management commitment
[e.g., 40]). Establishing the role of a technical partner
or chief technology officer (CTO) would effectively
signal this.
5.2. Limitations and further research
We acknowledge that our study has some
limitations. First, our study was conducted using a
qualitative research approach resulting in a limited
generalizability of our results. Our insights rely on the
experience and opinions of a limited number of
interviewees. Although the number of VC firms we
engaged is comparable to other VC studies [e.g., 39],
we must be careful when generalizing the results. A
larger sample size or in-depths case studies of VC
firms would provide further insights. Future research
could involve more in-depth empirical approaches,
such as quantitative analysis of the interview data and
further research methods to deepen the insights
regarding the derived business value.
Second, we conducted our study with VC firms in
Germany. There may be other VC firms, especially in
the US and Israel, that are more progressive in
applying innovative data-driven projects. Future
research could investigate whether these companies
have put data and novel data sources to other uses, and
thereby shed light on the differences. Further research
could also extend the derived business value by in-
depth analysis of how VCs and their employees utilize
the data and related technologies.
Future empirical studies can take our insights as a
fruitful starting point for further research on how the
complementary interaction of various data sources
impacts business processes, such as the investment
process, and validate, as well as revise, our results. To
deepen the understanding, our paper should be
extended in further research by elaborating in detail the
characteristics of the used data sources (e.g., volumes,
types of data etc.), which VCs use.
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Page 1084
... Generally, the usage of BDA has a potential to increase an organization's process performance. Various organizational processes were found to be improved by BDA, for instance delivery processes (Grover et al., 2018), palletizing processes in the air cargo domain (Mazur et al., 2022), and investment processes in the venture capital sector (Weibl & Hess, 2019). More concretely, process efficiency was increased significantly. ...
... More concretely, process efficiency was increased significantly. Efficiency gains especially concern the temporal dimension of process execution (Eggers et al., 2021;Weibl & Hess, 2019). As stated by Du et al. (2020), the application of BDA shortens "the average production cycle from 9 to 5 days" (p. ...
... 130) in the case of Du et al. (2020). In this vein, crossorganizational data platforms also contribute to an increased data exchange (Weibl & Hess, 2019). ...
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