Business Question

Each student is required to study and present the findings of the attached research paper. Thus, I need a ppt presentation with 10 minutes script to be read and present.

No.
Criteria
1
Organization (25%)
Does not meet expectations
•
•
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Inadequate or illogical flow
Evidence to support assertions is
unclear or incorrect
Lacks basic understanding of topic
Meets expectations
•
•
•
2
Content (25%)
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3
Delivery (35%)
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The content is inaccurate or
accomplishes the purpose of
presentation only partially.
Little or no supporting data; or
data provided is not relevant,
specific, or accurate.
•
Exhibits low level of enthusiasm
and confidence appearing anxious
or uncomfortable
Reads notes
Ignores listeners
Does not answer questions
effectively
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4
Use of Supporting Media
(15%)
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Does not use media or media used
is unclear, or unattractive, or
contains many errors
•
Exceeds expectations
Some incidences of inadequate or
illogical flow
Evidence to support assertions is
mostly clear and correct with
some issues
Some issues with understanding
of topic are evident
The content is generally accurate
and reasonably complete.
Major topics covered but
supporting details occasionally
lack specificity, accuracy, or
relevance.
•
•
Exhibits satisfactory levels of
enthusiasm and confidence
appearing relaxed and
comfortable generally
Does not read notes
Listeners are generally recognized
and understood
Answers some questions more
effectively than others
Media uses distracting slide
design or contains a few errors
•
•
•
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Logical flow
Evidence to support assertions is
clear and correct
Demonstrates thorough
understanding of topic
The content accomplishes the
purpose of presentation
accurately and comprehensively.
All major topics covered
thoroughly; supported by specific,
accurate, and relevant data.
Exhibits high levels of enthusiasm
and confidence appearing
completely relaxed and
comfortable
Does not read notes
Interacts effectively with listeners
to generate their interest
Answers all questions effectively
Media is clear and professional
and reinforces the presentation
•
Most visuals do not clarify,
simplify, or emphasize numerical
data or main points
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Uses visuals reasonably to clarify
simplify, or emphasize numerical
data or main point
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Uses visuals effectively to clarify
simplify, or emphasize numerical
data or main point
Australasian Journal of Information Systems
2024, Vol 28, Research Article
Xu et al.
Using Analytical Information for Digital Business Transformation
Using Analytical Information for Digital Business
Transformation through DataOps: A Review and
Conceptual Framework
Jia Xu
University of Melbourne, Australia
jx3@student.unimelb.edu.au
Humza Naseer
RMIT University, Melbourne, Australia
Sean Maynard
University of Melbourne, Australia
Justin Filippou
University of Melbourne, Australia
Abstract
Organisations are increasingly practising business analytics to generate actionable insights
that can guide their digital business transformation. Transforming business digitally using
business analytics is an ongoing process that requires an integrated and disciplined approach
to leveraging analytics and promoting collaboration. An emerging business analytics practice,
Data Operations (DataOps), provides a disciplined approach for organisations to collaborate
using analytical information for digital business transformation. We propose a conceptual
framework by reviewing the literature on business analytics, DataOps and organisational
information processing theory (OIPT). This conceptual framework explains how organisations
can employ DataOps as an integrated and disciplined approach for developing the analytical
information processing capability and facilitating boundary-spanning activities required for
digital business transformation. This research (a) extends current knowledge on digital
transformation by linking it with business analytics from the perspective of OIPT and
boundary-spanning activities, and (b) presents DataOps as a novel approach for using
analytical information for digital business transformation.
Keywords: Digital business transformation, Business analytics, Analytical information
processing capability, Boundary spanning, DataOps.
1 Introduction
Digital business transformation is a strategic imperative for many organisations. Gartner
(2021) reports that 58% of boards flagged digital tech initiatives as the single biggest strategic
business priority. Organisations invest in digital technologies such as the Internet of Things
(IoT), Cloud Computing, Big Data Analytics, and Artificial Intelligence to transform their
business and thereby improve customers’ experience, achieve operational efficiency, deliver
new products or services, and innovate with new business models (Bonnet & Westerman,
2021; Dwivedi et al., 2021). While these disruptive digital technologies are valuable to
organisations, Warner and Wäger (2019) suggest that they may also create uncertainties in the
business environment. Hence, organisations need to adjust their business strategies to thrive
in the dynamic digital environment (Sia et al., 2016).
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Australasian Journal of Information Systems
2024, Vol 28, Research Article
Xu et al.
Using Analytical Information for Digital Business Transformation
Digital business transformation refers to how organisations can alter their value creation and
change the scope of their business by using digital technologies (Hess et al., 2016). In this
digital age, the ability to manage and exploit data for value creation and business
transformation is becoming a vital competency in organisations’ transformation journeys
(Dremel et al., 2017). Hence, organisations are increasingly practising business analytics to
generate actionable insights that can guide their digital transformation initiatives (Pappas et
al., 2018; Sebastian et al., 2017). Business analytics is defined as the extensive use of data,
statistical and quantitative analysis, and explanatory or predictive models to drive decisions
and actions (Davenport & Harris, 2017). Through business analytics, organisations generate
analytical information that refers to the insights (quantitative or qualitative) used by business
executives in their analysis and decision-making (Naseer et al., 2021). For instance, predicted
ATM usage and customer withdrawal patterns can be identified by analysing transactional
data, which a bank could then use to optimise its business (Sia et al., 2016). The capacity to
generate analytical information for business transformation in organisations that have high
volumes and wide varieties of data requires a mature business analytics capability (Vidgen et
al., 2017).
Although existing research has explored how organisations could gain value from business
analytics (e.g., Wixom et al., 2013; Vidgen et al., 2017;Grover et al., 2018; and Wee et al., 2022),
there is a scarcity of research that links the value of business analytics with digital business
transformation. In the context of digital business transformation, business analytics needs to
not only support decision-making, but more importantly, it needs to support organisations to
drive change in the business and generate a transformative impact on the business across the
whole organisation. Using business analytics for digital business transformation requires
organisations to develop enterprise-wide analytical capabilities and promote collaboration
activities (Karippur & Balaramachandran, 2022; Setia et al., 2014).
DataOps is a business analytics practice that aims to better manage data, provide high-quality
insights, and promote collaboration through analytics in a dynamic business environment
(Ereth, 2018; Heudecker et al., 2020). The majority of digital transformation literature focuses
on digital transformation strategy (e.g., Hess et al., 2016; Ismail et al., 2017; and the
organisational and societal impact of digital technologies (e.g., Nambisan et al., 2019).
However, digital business transformation is an ongoing, long-term process (Davenport &
Westerman, 2018), and organisations need a disciplined approach to using analytical
information for their transformation. Although DataOps has the potential to be such an
approach, there is little research linking DataOps with digital business transformation.
Therefore, we employ organisational information processing theory (OIPT) (Galbraith, 1974;
Premkumar et al., 2005; Tushman & Nadler, 1978) and a boundary spanning activities
perspective (Aldrich & Herker, 1977; Ancona & Caldwell, 1990; Someh & Shanks, 2013) to link
DataOps with digital business transformation. Subsequently, this research aims to answer the
following research question:
How do organisations use analytical information for digital business transformation through DataOps?
To address this research question, we propose a conceptual framework by reviewing the
diverse areas of literature on business analytics, DataOps, and digital business transformation
through the lens of OIPT. The details of the framework explain how organisations can employ
DataOps to develop an analytical information processing capability and facilitate boundary
spanning activities for digital business transformation.
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Australasian Journal of Information Systems
2024, Vol 28, Research Article
Xu et al.
Using Analytical Information for Digital Business Transformation
In the next section, we provide the theoretical background and present the research method
for our literature review. Then we synthesize the business analytics, DataOps, OIPT and
digital business transformation literature to develop the conceptual framework. We also
develop a set of propositions that explain the utility of our framework. Finally, we conclude
the paper by describing the contributions and limitations of our work.
2 Theoretical Background
In this section, we review the literature around the intersection of business analytics, DataOps,
OIPT, and digital business transformation. In addition, we also link the key concepts in our
study (i.e., analytical information, analytical information processing capability, boundary
spanning activities, and DataOps) to the context of digital business transformation.
2.1 Using Analytical Information for Digital Business Transformation
Existing research views digital transformation as the evolution of digitization and
digitalization (Verhoef et al., 2021). Digitization refers to the encoding of analogue information
into digital format (Yoo, 2010). Digitalization is about how IT or digital technologies can be
used to alter existing business processes for not only cost savings but also process
improvements (Verhoef et al., 2021). Compared with digitization and digitalization, digital
transformation is a broader concept. It refers to an enterprise-wide phenomenon with broad
organisational implications in which, most notably, the core business model of the firm is
subject to change by using digital technologies (Hess et al., 2016; Verhoef et al., 2021).
Digitization, digitalization, and digital transformation can be viewed as three phases of the
transformation journey, in which organisations “may start with minor changes (e.g.,
digitization or digitalization) to gradually transform their traditional business into a digital
one” (Verhoef et al., 2021, p. 892). This research focuses on how organisations transform their
business in the digital era using analytical information.
An organisation can be viewed as an information processing system facing complexity and
uncertainty (Tushman & Nadler, 1978). In OIPT, uncertainty refers to the absence of
information (Daft & Lengel, 1986). When organisations first start their transformation
journeys, they are very likely to encounter a high degree of uncertainty (Matt et al., 2015; Vial,
2019). For instance, given the changing diffusion of technologies and customers’ expectations,
organisations may lack information that can guide them regarding how to transform their
products or services using disruptive technologies to improve customer satisfaction (Ismail et
al., 2017; Matt et al., 2015). Organisations may need rich analytical information about their
current operations assessment to identify bottlenecks that need to be transformed by digital
technologies (Sia et al., 2016). Analytical information about customers’ demands is also needed
to sense an opportunity for new business models (Loebbecke & Picot, 2015; Setia et al., 2014).
Indeed, digitization and digitalization have fostered the generation of big data, which
emphasizes the volume, velocity, variety, veracity, variability, and value of data (Conboy et
al., 2020). Big data provides huge opportunities for organisations to get rich analytical
information (Dwivedi et al., 2021; Grover et al., 2018; Ranjan & Foropon, 2021). By applying
statistical and quantitative analysis, and explanatory and predictive models to analyse big
data, organisations can get useful analytical information that affords them opportunities for
their transformations (e.g., improving the current business models or creating new business
models) (Bonnet & Westerman, 2021). Table 1 lists examples of how analytical information is
obtained from big data and used for digital business transformation in different industries.
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Australasian Journal of Information Systems
2024, Vol 28, Research Article
Xu et al.
Using Analytical Information for Digital Business Transformation
Digital Business
Reference
Transformation
Segment the market and
(Frank et al., 2019)
Product-related
Patterns of product usage
identify opportunities for
Manufacturing
extracted from product
data from
new product development
embedded sensors usage
and new services
Data generated by Best practices and
Improve customers
(Conboy et al.,
Telecommunicatio interactions with methods for marketing
satisfaction and position
2020)
ns
users on social
approaches by applying itself as the largest service
media
social media analytics
provider in the market
Real-time patterns and
(Müller et al.,
trends of customers’
2016)
Customers’
Achieve efficiency gains
Technology
needs by monitoring the
service requests
and improve customer
provider
feedback of thousands of
and feedback
service
customers with text
analytics
Transforming the supply
Predictive information
chain management by
Business data such (e.g., future sales) and
proactively preparing for (Papanagnou et
Retailer
as orders and
prescriptive information
emergency situations and al., 2022)
delivery schedule (e.g., price
preventing and mitigating
recommendations
risks
Insights (e.g., fail rate and
User behavioural
user’s preference) that
data, public
Shifting towards a new
Entertainment
(Tim et al., 2020)
help to optimize the
feedback, and
business model
game design and
comments
maximize user experience
Revolutionising the
Predictive demand of
operational processes
Sensors’ data and
(Neirotti et al.,
Electronic
electricity to match the
through which electricity is
satellite images
2021)
supply and demand
generated, transported,
distributed, and sold.
Industry
Data
Analytical Information
Table 1. Examples of Using Analytical Information for Digital Business Transformation in Different
Industries
2.2 The Need for Analytical Information Processing Capability and Boundary
Spanning Activities for Digital Business Transformation
Modern digital technologies provide two notable business value drivers: the expansive role of
data, and digital ecosystems (Subramaniam, 2021). We argue that analytical information
processing capability and boundary spanning activities are needed to realize these business
value drivers. Analytical information processing capability enables organisations to transform
expansive data into useful analytical information that solves uncertainties in the
transformation journey (Cao et al., 2019). Boundary spanning activities enable organisations
to share their analytical information (Karippur & Balaramachandran, 2022; Someh & Shanks,
2013) and foster the digital ecosystem for digital business transformation (Tan et al., 2020).
Below, we explain why these two aspects are necessary for organisations’ transformation
journeys.
Based on Galbraith (1974, p. 28), “the greater the uncertainty is, the greater the amount of
information is needed to be processed among decision-makers to achieve a given level of
performance.” To get the needed information, organisations need to develop the information
processing capability: the ability to gather, transform, interpret, synthesize, analyse, store, and
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Australasian Journal of Information Systems
2024, Vol 28, Research Article
Xu et al.
Using Analytical Information for Digital Business Transformation
communicate data, information, and knowledge to cope with the variety, uncertainty and
unclear environment (Naseer et al., 2021; Premkumar et al., 2005; Tushman & Nadler, 1978).
According to Li et al. (2021), the information processing capability enabled by digital
technologies can positively impact business transformation by enhancing market agility. We
argue that analytical information processing capability, a specialised form of information
processing capability enabled by business analytics, is needed to generate analytical
information for digital business transformation. Analytical information processing capability
is the ability to use business analytics technologies or applications that analyse critical business
data to better understand business and market and make timely business decisions (Anand et
al., 2020; Cao et al., 2019; Naseer et al., 2021; Saldanha et al., 2017).
From the boundary spanning perspective, a digital business transformation has a boundaryspanning nature. Boundary spanning refers to the activities or practices that transcend
functional division and increase communication and coordination between different
stakeholders (Schotter et al., 2017). From the internal boundary perspective, digital business
transformation needs cross-functional efforts and requires alignment between different
functions (Matt et al., 2015). For example, Dremel et al. (2017) emphasize the important role of
collaboration among the sales and marketing departments, digital innovation hub, and IT
department in leveraging business analytics to transform their business. Treating digital
business transformation in functional silos would not maximise cross-fertilization
opportunities (Verhoef et al., 2021). From the external boundary perspective, the digital
business transformation will impact the interactions that take place across firm borders with
their clients, competitors, partners, and suppliers (Hess et al., 2016). Sia et al. (2016) give an
example of how an organisation collaborated with an external research institute to develop its
analytics capabilities to better understand its customers and thus provide more personalized
interactions. Pappas et al. (2018) also propose a big data and business analytics ecosystem
where data, information and knowledge are shared and transferred among stakeholders,
enabling collaboration among multiple actors, including private and public organisations,
academia, and individuals/entrepreneurs. This results in new business opportunities, the
development of digital data-based designs, and the transformation of current business models
(Pappas et al., 2018). The blurring boundaries of entities resulting from digital business
transformation exemplify the need for boundary spanning activities to enable organisations to
use analytical information effectively.
Existing research has identified a wide range of boundary spanning activities such as buffering
and representing activities, coordination of task performance, information searching, and
guarding (Ancona & Caldwell, 1990; Marrone, 2010). In our research, we focus more on the
activities that enable organisations to share and exchange information (Fleischer & Carstens,
2021), bridge the knowledge gap (Someh & Shanks, 2013), and facilitate communication,
interactions and cooperation (Schotter et al., 2017) across different domains (e.g., business
departments, suppliers, partners, and customers) involved in the transformation journey.
Table 2 summarizes boundary spanning activities that seamlessly enable a team/organisation
to interact with its external actors and meet the overall goals of digital business transformation.
These boundary spanning activities can complement analytical information processing
capability by disseminating the information delivered by the analytics process, thus
facilitating the cross-boundary collaboration required for digital business transformation.
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Australasian Journal of Information Systems
2024, Vol 28, Research Article
Activity
Explanation
Types
Organisational Arranging business functions and
structure
resources to promote the crossdesign
boundary collaboration
Task
coordinating
Scouting
Embedding
Influencing
Adopting
boundary
spanning
objects
Xu et al.
Using Analytical Information for Digital Business Transformation
Examples
Reference
Decentralized governance
architecture; having crossfunctional work groups
(Singh et al., 2020)
Interactions with interdependent
Formal or informal meetings to
entities to solve coordinating issues, discuss data ownership
synchronize work efforts, exchange, problems, obtain feedback,
and combine work outcomes in the coordinate, and negotiate with
transformation journey,
outsiders.
(Ancona &
Caldwell, 1990;
Dremel et al., 2017;
Levina & Vaast,
2014; Marrone,
2010)
Searching and accessing information Sensing and exploiting new
or expertise from different domains; opportunities to implement
learning different organisational
analytics-enabled transformation (Someh & Shanks,
initiatives in other functional
systems, and developing a shared
2013; Yang et al.,
areas
language to interact with them
2021)
Developing social ties and
establishing relationships with
Creating a data-driven culture
another based on familiarity, trust, and embedding analytics in the (Someh & Shanks,
and commitment to connect
organisation’s process and
2013)
resources, collaborate, share
routines.
knowledge
Influencing or forcing other
The successful adoption of
functional areas or organisations to analytical initiatives in one
(Someh & Shanks,
conform to values, norms or
functional area may influence
2013)
traditions, and social expectations in other functional areas to change
an institutional environment
their values and norms
Developing protocols,
Adopting and having entities that
repositories, standardized
enhance the capacity of an idea,
documentation, models,
(Beckett, 2021; Fox,
theory, or practice to translate
information systems and
2011; Vilvovsky,
across culturally defined
collaboration tools to facilitate
2009)
boundaries, for example, between
the shared understanding and
communities of knowledge or
collaboration work of different
practice.
actors
Table 2. Types of Boundary Spanning Activities
Digital business transformation is a long-term process and journey. As emphasized by Warner
and Wäger (2019, p. 327), “the genuine digital transformations are an ongoing process of using
digital technologies in everyday organisational life”. The long-term nature of digital business
transformation requires not only extensive investment and time but also a holistic and
integrated approach to make steady progress toward the right end state (Davenport &
Westerman, 2018; Sia et al., 2016). According to OIPT, organisations should develop their
information processing capabilities to fulfil their information needs (Flynn & Flynn, 1999;
Galbraith, 1974; Premkumar et al., 2005). This means that there should be a disciplined
approach that supports organisations (a) to develop their analytical information processing
capability to constantly fulfil their analytical information needs, and (b) to facilitate boundary
spanning activities to better share analytical information throughout the transformation
journey. We argue DataOps as such a disciplined approach and explain its potential role in the
digital business transformation below.
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2024, Vol 28, Research Article
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Using Analytical Information for Digital Business Transformation
2.3 Linking DataOps with Digital Business Transformation
With the high volume and wide variety of data being generated each day, getting and using
analytical information from analytical information processing capability and boundary
spanning activities is challenging. Wells (2019) and Zahid et al. (2018) identify several
challenges, including rapidly increasing data volumes, more sources of data, more data use
cases, more stakeholders in modern data ecosystems, and poor data management. These
challenges increase the risk of conflicting and erroneous data, the establishment of data silos,
and delays in getting needed information, which subsequently inhibits organisations from
extracting analytical information as required for digital business transformation. DataOps, as
an emerging business analytics practice, aims to overcome such challenges in modern
analytical ecosystems.
Popularised by Palmer (2015), DataOps acknowledges the interconnected nature of data
engineering, integration, quality, security and privacy to help organisations accelerate
analytics and enable previously impossible analytics tasks. DataOps applies to analytics the
best principles and practices from diverse methods (i.e., Agile, DevOps, Lean, and Total
Quality Management) used in the software engineering and manufacturing areas. It enables
organisations to streamline the process of analytics, maximize the business value of data as
well as improve business analytics user satisfaction (Eckerson, 2019a; Ereth & Eckerson, 2018).
Since DataOps was coined, it has been most closely associated with the quality and efficiency
in the delivery of analytical information (Aslett, 2020; Capizzi et al., 2020). From the
perspective of quality, DataOps helps to promote communication between, and integration of,
formerly siloed data, teams, and systems (Thusoo & Sarma, 2017), which reduces the risks of
conflicting and erroneous results from analytics and thus ensures the quality of analytical
information. From the perspective of efficiency, DataOps represents a culture change that
focuses on improving collaboration and accelerating analytical delivery by adopting lean or
iterative practices where appropriate (Heudecker et al., 2020). Moreover, teamwork and
collaboration are the key themes in DataOps (Atwal, 2020). Transforming data into insights
usually requires collaboration between different stakeholders such as data architects,
engineers, analysts, and scientists, as well as IT operations and business users (Ereth &
Eckerson, 2018). The goal of DataOps is to foster greater collaboration and trust among
development, test, operations, and business teams and thus improve both the speed and
quality of analytical information delivery (Ereth & Eckerson, 2018).
Therefore, in our research, DataOps is viewed as a disciplined approach to improving
collaboration and building analytical capabilities in a way that continuously accelerates output
and enhances the quality of analytical information (Ereth & Eckerson, 2018; Naseer et al., 2020).
Given the potential of DataOps, organisations can adopt DataOps to develop their analytical
information processing capability and facilitate the boundary spanning activities to get their
required analytical information that will improve the likelihood of optimising digital business
transformation.
3 Research Method
We conducted an integrative literature review to combine the diverse literature on digital
business transformation, business analytics, and DataOps, following the guidelines outlined
in Snyder (2019). The aim of the integrative literature review is to assess, critique, and
synthesize this diverse literature in a way that “enables new theoretical frameworks and
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2024, Vol 28, Research Article
Xu et al.
Using Analytical Information for Digital Business Transformation
perspectives to emerge” (Snyder, 2019, p. 335). Accordingly, the aim of our integrative
literature review is to build a novel conceptualisation linking DataOps with digital business
transformation through the lens of OIPT (Snyder, 2019).
3.1 Data Collection
The purpose of our data collection is to combine perspectives from different fields rather than
cover all published articles on a topic, which helps expand on the theoretical foundation of a
specific topic (Snyder, 2019). We first searched literature from popular literature databases,
including Science Direct, AIS Electronic Library, Scopus, and Google Scholar by using a
combination of key words such as digital business transformation, analytical information
processing capability, and DataOps. Our search terms are listed in Appendix A. We selected a
timeframe to include literature published between 2015 and 2023, with 2015 being used as the
earliest date, given that the DataOps term was only popularised by Palmer (2015) in that year.
The search results for the different search terms across the databases are listed in Appendix B.
As the aim of this integrative review is not to comprehensively cover as many publications as
possible, we sorted the search results by relevance and went through the first 200 results for
each search to focus on the most relevant works in the field (vom Brocke et al., 2015). We
examined the title and abstract of the articles to exclude papers that were duplicates or deemed
not relevant to our research question (i.e., How do organisations use analytical information for
digital business transformation through DataOps?). We also excluded papers published in
journals that are not ranked in either Australian Business Deans Council Journal quality list or
Scimago. This screening process resulted in the selection of 301 articles from the literature
databases for the full-text eligibility assessment. Notably, as DataOps emerged from practice,
we also searched 178 articles from practitioner research and advisory firms such as Gartner,
DataKitchen and The Eckerson Group to identify articles that provided us with insights into
how DataOps is used in practice in the context of using analytical information for digital
business transformation. In an effort to broaden the search, we noted cited works of potential
interest in the articles reviewed and identified 30 articles as per Webster and Watson (2002)
for eligibility assessment. As a result, there were 509 articles in total for full-text eligibility
assessment.
In the eligibility assessment stage, we read the full text of each article and omitted articles for
the following reasons: a) papers focused on the technical side of analytics and DataOps rather
than the usage of analytical information for digital business transformation; b) papers that
mentioned digital transformation in passing, such as growing trend type papers; c) papers in
which data operation(s) refer to the general operation of data rather than the DataOps method
that learns from DevOps, Agile, Total Quality Management, and Lean method. The eligibility
assessment resulted in a total of 87 articles for in-depth review. The data collection process
and the exclusion criteria for the screening and eligibility process (in the dashed boxes) are
shown in Figure 1.
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Xu et al.
Using Analytical Information for Digital Business Transformation
Figure 1. Data Collection (Literature Search) Process – Adapted from Page et al. (2021)
3.2 Data Analysis
We synthesized, analysed and integrated the literature using OIPT by following Watson and
Webster (2020)’s concept-centric approach. Specifically, we first reviewed the literature to get
an in-depth understanding of the key concepts (e.g., DataOps, analytical information
processing capability, boundary spanning, and digital business transformation) included in
the study to lay the foundation of the framework building.
Next, guided by OIPT, we analysed the literature to identify the relationship between these
concepts. We first identified the uncertainties in digital transformation and what analytical
information can be provided by analytical information processing capabilities for digital
business transformation. Then we focused on the boundary spanning activities needed in
digital business transformation. We linked DataOps research with digital transformation by
focusing on the principles and practices from DataOps that can help in using the analytical
information processing capability and facilitate the boundary spanning activities for digital
business transformation. We classified the literature and integrated the review into the paths
shown in Figure 2. The list of 87 articles that were reviewed for this study is provided in
Appendix D.
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Using Analytical Information for Digital Business Transformation
4 Digital Business Transformation
Conceptual Framework
through
DataOps:
A
Based on our synthesis, analysis, and integration of the literature, we develop a conceptual
framework that links digital business transformation and DataOps through the lens of OIPT
(see Figure 2). The dynamic digital environment brings uncertainties to organisations’ digital
business transformations. DataOps provides organisations with an integrated and disciplined
approach to developing their analytical information processing capabilities and facilitating
boundary spanning activities to address the uncertainties in digital business transformation.
Boundary spanning activities mediate the relationship between analytical information
processing capability and digital business transformation by enabling the analytical
information to be shared and exchanged across the whole enterprise for digital business
transformation. Table 3 gives definitions of the concepts in this framework. Below, we explain
the framework by deriving propositions about how analytical information can be leveraged
for digital business transformation using DataOps.
Figure 2. Using Analytical Information for Digital Business Transformation through DataOps
Concept
Definition
Dynamic Business
Environment
A dynamic environment consisting of disruptive digital technologies,
new competitors, and changing customers’ expectations.
DataOps
An integrated and disciplined approach to developing analytical
information processing capability and facilitating boundary spanning
activities for digital business transformation.
Exemplar
References
(Vial, 2019;
Warner &
Wäger, 2019)
(Heudecker et
al., 2020; Naseer
et al., 2020)
The ability to use business analytics technologies or applications that
analyse critical business data to better understand business and market
and make timely business decisions.
(Cao et al., 2019;
Saldanha et al.,
2017)
Analytical
Information
Processing
Capability
Boundary
Spanning
Activities
Digital Business
Transformation
The activities or practices to increase the communication and
coordination between different stakeholders involved in digital
business transformation.
An ongoing process where an organisation strategically transforms its
business dimensions (e.g., value propositions, critical business
operations, organisational routines and structures, and management) to
radically improve the business enabled by digital technologies.
(Schotter et al.,
2017)
(Vial, 2019;
Warner &
Wäger, 2019)
Table 3. Definitions of Key Concepts in the Proposed Conceptual Framework
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Using Analytical Information for Digital Business Transformation
4.1 Dynamic Business Environment
The business environment in this digital age is becoming more dynamic than before. Table 4
summarizes the dynamics of the business environment. Organisations operate in a dynamic
digital environment with opportunities and uncertainties. On the one hand, opportunities to
transform a business exist within the digital environment, given the rapidly evolving nature
of digital technologies. Artificial Intelligence is one example where organisations can use it to
engage customers and employees and deliver new products and services (Borges et al., 2021).
3D printing is another example of where customized products can be produced and
manufacturing a range of goods using the same resources can be made possible (Frank et al.,
2019). On the other hand, the environmental dynamism is intensified given the advancement
and disruption of novel digital technologies (Gupta & Bose, 2022), thereby increasing
uncertainty, characterized by the entrance of new competitors and changing expectations of
customers (Warner & Wäger, 2019).
Taking a market competition perspective, disruptive technologies impact the existing market
by creating a new market in which entrants would always win because of the established
firms’ delay in making the strategic commitment to enter the emerging market (Christensen,
2013). Facing the threat of new competitors, organisations have an imperative to start their
digital business transformation journeys to better compete in the market. Further, disruptive
digital technologies such as smartphones also play decisive roles in shaping and mediating all
dimensions of people’s lived experiences (Yoo, 2010), creating change in customers’
expectations and requirements (Schallmo et al., 2017; Vial, 2019). Facing uncertainties,
organisations require analytical information to understand the market and their competitors
as well as customers (Sia et al., 2016). According to OIPT (Daft & Lengel, 1986; Flynn & Flynn,
1999), organisations should develop their analytical information processing capability to
match their information needs for digital business transformation. Thus, we propose the
following:
P1: A dynamic and uncertain business environment triggers the need to develop analytical information
processing capability.
Dynamics of the business
environment
Description
Exemplar References
Disruptive digital technologies
Disruptive digital technologies are
creating far-reaching effects on business
(Ashrafi et al., 2019; Matt et al.,
2015; Verhoef et al., 2021; Vial,
2019; Warner & Wäger, 2019)
Changing customers’
requirements and expectations
Competitive Landscape
Digital technologies (e.g., mobile, and
social media) have a profound impact
on customers’ behaviour and thus
influence their expectations and
requirements of services and products
Digital technologies facilitate the
generation of new forms of digital
offerings and new disruptive
competitors, which moves the
competition from a physical plane to a
virtual plane
(Ashrafi et al., 2019; Ismail et al.,
2017; Schallmo et al., 2017; Sia et
al., 2016; Vial, 2019; Warner &
Wäger, 2019)
(Ismail et al., 2017; Sia et al.,
2016; Verhoef et al., 2021; Vial,
2019; Warner & Wäger, 2019)
Table 4. Dynamics of the Business Environment
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The uncertain environment encourages team members to broaden their attempts to collect
information from outside of the team and so turn their focus externally, scanning for
information (Ancona & Caldwell, 1990; Gupta & Bose, 2022). Boundary spanning activities
allow boundary spanners to search, acquire, and convey relevant information about the
environment to the internal organisation to reduce uncertainty (Xue et al., 2022). According to
Leifer and Delbecq (1978), when the perceived environmental uncertainty is high and the
information need is unanticipated and irregular, the boundary spanning actives will be
nonregulated and nonroutine. Therefore, with the increasing uncertainties in the dynamic
business environment, organisations need to have more nonregulated and nonroutine
boundary spanning activities as an important mechanism to cope with the complexity of the
dynamic business environment (Ancona & Caldwell, 1990). Accordingly, the following
proposition is developed:
P2: A dynamic and uncertain digital business environment triggers the need to have boundary
spanning activities.
4.2 Employing DataOps for Digital Business Transformation
Organisations can leverage analytical information for digital business transformation,
however, unpacking the value of such information requires an integrated approach. DataOps
can provide this to help organisations unite stakeholders, govern the flow of data, and ensure
that the insights from analytics can satisfy the digital business transformation needs in time.
Just as DevOps contributes to building collaboration between software development and IT
operations (Munappy, Mattos, et al., 2020), DataOps unites data stakeholders such as data
engineers, data analysts, IT operations, and business users, around business requirements to
achieve digital business transformation (Ereth & Eckerson, 2018; Munappy, Bosch, et al., 2020).
DataOps verifies that results at each intermediate step in the production of analytics match
business requirements, borrowing the process from Total Quality Management in using
statistical measurement to monitor and control manufacturing processes (Bergh et al., 2019).
To identify waste and manage the flow of data, lean thinking can be used to deliver analytical
solutions as quickly as possible (Atwal, 2020). Moreover, DataOps can discover common
ground where various stakeholders and technologies can act in concert (e.g., orchestration
tools that enable automatic combinations of various technologies) (Ereth & Eckerson, 2018;
Richardson, 2020). Last but not least, Guinan et al. (2019)’s research showed that organisations
navigated digital business transformation through the Agile method to improve the speed of
adapting and responding to the dynamic business environment. By adopting Agile and
welcoming changing requirements, DataOps embraces changes and enables organisations to
cope with the dynamic digital environment when using analytics (Atwal, 2020). Overall,
learning from these mature methods of software engineering and manufacturing, DataOps
makes it possible for organisations to sense internal and external changes, improve decisionmaking effectiveness, and reorganise the resources to remain competitive (Ereth, 2018; Gur et
al., 2022). DataOps is not only a method for analytical product development but also a
discipline that integrates people, processes, technology, and data for digital business
transformation. Therefore, we propose:
P3: DataOps provides an integrated and disciplined approach for organisations to leverage analytical
information for digital business transformation.
The analytical information processing capability in our framework refers to the ability to use
analytical practices and applications to process data – that is, to capture, store, transform,
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analyse, and visualise it, and generate insights for digital business transformation (Cao et al.,
2019; Saldanha et al., 2017). OIPT posits that organisations should design their structures,
mechanisms, and business processes in such a manner that facilitates information processing
and thereby enables informed decision-making and improved organisational performance
(Flynn & Flynn, 1999; Galbraith, 1974; Premkumar et al., 2005). Therefore, to build analytical
information processing capability, organisations need to leverage tools, techniques, people,
and processes in analytics to meet the above goal (Srinivasan & Swink, 2018). During the
literature review process, we identified the relevant DataOps principles and practices that
enable the development of analytical information processing capabilities by integrating the
analytical resources such as people, processes, tools, and techniques (Aslett, 2020; Ereth, 2018;
Naseer et al., 2020).
From a people’s perspective, DataOps champions the business value mindset that data is not
an end in itself but should deliver insights that add value to the business (Atwal, 2020; Bahaa
et al., 2023; Ereth & Eckerson, 2018). By applying this mindset, each stakeholder involved
views data quality as a top priority to deliver analytical insights that satisfy the needs of digital
business information. Moreover, teams in DataOps are expected to self-organized to meet
goals (Atwal, 2020). Self-organized teams can help to create self-contained tasks which help to
reduce the amount of analytical information processed and thereby reduce the uncertainty
(Galbraith, 1974).
From a processes’ perspective, emphasizing the importance of testing and monitoring at every
stage in data processing is key to performing DataOps, which ensures that the analytical
information delivered satisfies the needs of digital business transformation. Issues can be
identified quickly through testing before they are delivered to the business and become hard
to fix (Mainali et al., 2021). Monitoring the performance of data movement processes is also
important as it can detect unexpected patterns and problems (Ereth & Eckerson, 2018; Sahoo
& Premchand, 2019). Moreover, a key pillar in DataOps is to continuously improve (Eckerson,
2019a), where teams learn from their mistakes and review processes continuously to adapt to
the changing environment (Heudecker et al., 2020; Rodriguez et al., 2020). Such an iterative
process with incremental improvements ensures that the analytical information that
organisations extract is high-quality, thus promoting digital business transformation in a
sustainable manner.
From a tools and technologies perspective, wherever possible, DataOps uses technologies to
automate (Ereth, 2018), which can improve the reliability of the analytical information
processing capability easing reliance on human intervention and thus reducing the time
required for data processing. With appropriate levels of governance and metadata, the
automated analytical information delivery also helps to improve the use and value of
analytical information in a dynamic environment (Heudecker et al., 2020).
Overall, DataOps can be a catalyst for organisational and cultural shifts (Wells, 2019), as it
emphasizes the notion that processing data is about more than simply the technologies.
Instead, all resources, including people, technologies, and processes, need to be combined and
orchestrated (Friedman & Heudecker, 2019; Wells, 2019) for data processing capabilities and
delivering the needed analytical information for digital business transformation. Accordingly,
we propose:
P4: DataOps enables the integration of analytical resources such as people, processes, tools, and
technologies needed to develop the analytical information processing capability.
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DataOps also helps facilitate the boundary spanning activities when organisations leverage
business analytics for digital business transformation in the following ways:
Leveraging boundary spanning objects: DataOps provides boundary spanning objects, such
as version control systems and code repositories, fostering parallel development and code
reuse for analytics (Ereth & Eckerson, 2018). Like the Agile method, DataOps is a highly
iterative approach in which prototypes of analytics products/solutions (as boundary spanning
objects) are provided to gather feedback across different domains (Ereth & Eckerson, 2018).
The adoption and usage of these boundary spanning tools enables stakeholders to better align
and integrate their knowledge domains (Beckett, 2021; Pershina et al., 2019).
Promoting cross-functional coordination: Based on Beckett (2021), a multidisciplinary agile
team with a clear collaboration and knowledge-sharing mindset facilitates boundary
spanning. Learning from the Agile and DevOps method, DataOps requires a cross-functional
team consisting of people from the IT team, data team, and business team (Ereth & Eckerson,
2018; Walsh, 2023). Also, DataOps values cross-functional ownership over siloed
responsibility (Bergh et al., 2019). Teams in DataOps should be organized around shared datacentric goals to eliminate barriers (Atwal, 2020). Rearranging business functions by creating
such a cross-functional team with shared goals brings everyone involved together, helping
stakeholders to interact and coordinate more efficiently (Eckerson, 2019b), thereby promoting
boundary spanning across different domains.
Enriching communication and collaboration mechanisms: DataOps provides both informal
and formal mechanisms for cross-functional teams’ communications and collaboration. For
example, communities of interest can be established to share knowledge, tools, and practices
(Atwal, 2020). Teams can also be linked by a formal hub and spoke model through a central
team responsible for harmonizing best practices and consistency across other teams (Atwal,
2020). These formal and informal mechanisms enable knowledge sharing and improve
communications within and between teams (Ereth & Eckerson, 2018), which facilitates both
the scouting and task coordinating boundary spanning activities.
Encouraging continuous learning: DataOps encourages continuous learning through
iterations (Heudecker et al., 2020). Each iteration generates feedback, which better informs the
next iteration, creating a loop that is critical to improving results. It also helps to publicize the
early benefits of adopting DataOps and thus creates curiosity and excitement among other
teams (Atwal, 2020). In this sense, the adoption of DataOps in one team can influence other
business teams to learn and change their ways of using business analytics for digital business
transformation, thereby creating a standardized ways of using business analytics for digital
business transformation across an organisation. Therefore, we propose:
P5: DataOps enables boundary spanning activities for digital business transformation by leveraging
boundary spanning activities, promoting cross functional coordination, enriching communication,
and collaboration mechanisms, and encouraging continuous learning.
4.3 Analytical Information Processing Capability, Boundary Spanning
Activities, and their Interaction for Digital Business Transformation
The existing literature conceptualised, explained, or gave examples of the analytical
information processing capability, analytical information, and boundary spanning activities
in the context of digital business transformation differently (summarised in Appendix C).
Below, we explain in detail how analytical information processing capability and boundary
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spanning activities play a role in digital business transformation by synthesizing and
analysing the literature.
In a dynamic business environment, organisations may lack information for digital business
transformation. For example, since the diffusion of digital technologies can change swiftly,
organisations may not have enough information to make reliable assumptions within their
organisational digital business transformation strategies (Matt et al., 2015). Further, it can be
challenging to obtain sufficient information to predict how the market will change in line with
customers’ varying expectations and requirements, especially given the rate at which borndigital firms enter the market (Ismail et al., 2017). Moreover, many complexities exist in
internal operations, such as the tension between exploiting the existing business while also
exploring new digital initiatives that are compatible with the pact dependencies of the past
(Warner & Wäger, 2019). It is within these types of complexities that organisations may not
have adequate information to cope effectively, impacting the digital business transformation
process.
From a OIPT perspective, when the information process capability satisfies the information
needs, it brings the effectiveness in the tasks for organisations (Tushman & Nadler, 1978).
Facing uncertainties in digital business transformation requires extensive useful information,
including market trends, customers’ expectations, the performance of business operations, and
stakeholders’ sentiments on the cultural shift towards digital business transformation (Ismail
et al., 2017; Loebbecke & Picot, 2015). To significantly improve the amount and richness of
information used in digital business transformation, analytical information processing
capabilities can be utilised to generate actionable insights to satisfy the analytical information
needs (Ashrafi et al., 2019; Dremel et al., 2017; Vidgen et al., 2017; Wee et al., 2022). For
example, organisations can get analytical information about new customer-centric trends that
are hard for strategic planners to predict (Warner & Wäger, 2019). Conboy et al. (2020) and
Sebastian et al. (2017) also found that organisations can extract rich analytical information,
such as the sentiment of customers and staff and predicted defect rates, to understand
customers and competitors and identify internal inefficiencies that need to be addressed
through transformation.
Hence, the following proposition is derived:
P6: Analytical information processing capability provides the analytical information needed to address
uncertainties and guide digital business transformation.
Analytical information processing capabilities also play a role in facilitating boundary
spanning activities. First, analytical information processing capabilities help to measure and
monitor boundary spanning activities. For instance, in Schwade (2021)’s research, a dashboard
monitoring a boundary spanning network is developed to facilitate and interpret boundary
spanning activities. Second, given the explosive growth in data generated in this digital age,
organisations may face information overload when managing, exploiting, and disseminating
information across organisational boundaries (Aldrich & Herker, 1977; Fleischer & Carstens,
2021). Analytical information processing capabilities can help to reduce information overload
for boundary spanning, as evidenced in Song et al. (2021)’s research on the usage of analyticsembedded digital platforms to analyse relevant data, which enabled decision makers to find
the targeted information, make targeted decisions, and effectively organize business activities.
Third, analytical information processing capabilities may reshape the organisational
boundaries. Internally, the existing boundary setting will be reshaped based on data and
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analytics accessibility (Jonsson et al., 2009). Externally, analytical information processing
capabilities deliver analytical information that alters organisations’ understanding of its
external environment, which facilitates new boundary spanning forms that can interact with
the environment (Conboy et al., 2020). For instance, supported by analytics-enabled
capabilities to monitor and analyse internal and external data, a firm can sense threats and
trends in the environment and adapt as necessary. Further, the organisation can provide
aggregated information to their customers and other companies under an alliance, which
creates new boundary spanning forms in which the organisation interacts with their external
stakeholders (Conboy et al., 2020). Taken together, the following proposition is developed:
P7: Analytical information processing capabilities facilitate boundary spanning activities by
monitoring the boundary spanning network, reducing information overload, and reshaping
boundaries.
As a strategic imperative for organisations, digital business transformation requires boundary
spanning activities to extend the benefits beyond the focal team itself to the performance of
other parties and to the achievement of higher-order organisational and cross-organisational
goals (Marrone, 2010). Boundary spanning activities improve coordination effectiveness, scale
analytics applications across the organisation, promote innovation, and foster a digital
ecosystem for digital business transformation. Below, we explain how boundary spanning
activities play a key role in digital business transformation.
Improve coordination effectiveness: Increased collaboration, both internally and externally,
is integral to organisations’ digital business transformation (Dremel et al., 2017). According to
Azzouz and Papadonikolaki (2020), boundary spanning activities at all frequencies (e.g.,
project, iteration, and ad-hoc), their associated boundary spanning artefacts, and a coordinator
role increase explicit coordination effectiveness. For example, Singh et al. (2020) explain the
importance of the Chief Digital Officer (CDO) and its coordinator role in spanning the vertical
boundary and horizontal boundary through formal (e.g., board meetings) and informal
coordination mechanisms (e.g., webinars). These mechanisms performed by CDO facilitate
effective cross-unit relationship-building and achieve efficient coordination across an
organisation. Dremel et al. (2017) explained that solving the data ownership issues and data
transparency issues (as a task coordinating activity) helps to embrace operational transparency
and grant access to the data, which fosters a data sharing culture and improves the
coordination of business functions across the firm when leveraging operational data for digital
business transformation.
Scale analytics applications across the organisation: Someh and Shanks (2013) explain how
the success of analytics initiatives in one functional area of an organisation may influence other
functional areas to change their values and norms and therefore adopt analytics. This assists
with scaling analytics-enabled digital transformation initiatives across the entire organisation.
Lighthouse projects and proof-of-concept are valid devices for familiarizing the business with
the opportunities arising from digitization and operational business data (Dremel et al., 2017).
Moreover, the embeddedness as a boundary spanning activity helps to establish social ties
across the organisation based on the analytics-driven culture (Someh & Shanks, 2013). Dremel
et al. (2017) exemplify how embedding analytics as an internal competence, cutting across
individual IT units and other organisational entities, enables the firm to leverage analytics as
a service and evidence-based decision-making, thus assisting digital business transformation.
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Promote innovation: Most research on digital business transformation acknowledges the need
for firms to engage with other parties to generate digital innovation (Vial, 2019), which always
occurs in the context of a community of cooperatives (Harter & Krone, 2001). According to
Glaser et al. (2015), a potential key driver of exploratory innovation is boundary spanning,
with activities such as scouting helping to reduce knowledge gaps and promote digital
innovation for digital business transformation. Searching for new knowledge across
organisational and technical boundaries brings in novel and heterogeneous knowledge to
accelerate technological advancements (Yang et al., 2021). By creatively combining a diverse
set of knowledge, digital business transformation occurs by prompting the emergence of novel
ideas, services and products (Pershina et al., 2019).
Foster a digital ecosystem: The collaboration and interaction of a firm with its stakeholders
(e.g., customers, suppliers, and partners) in the digital transformation journey result in a
digital ecosystem (Pappas et al., 2018). Such ecosystems provide opportunities for
organisations to transform their business and create value by engaging with their customers
and partners (Suseno et al., 2018). The analytics embeddedness boundary spanning activity
can help to establish a digital ecosystem necessary for digital business transformation. For
example, by embedding analytics into operations, a firm can better interact with its customers
and co-create value with customers (Gupta et al., 2020).
Accordingly, we formulate the following proposition:
P8: Boundary spanning activities enable digital business transformation by improving coordination
effectiveness, scaling the analytics across the whole organisation, promoting innovation, and
fostering a digital ecosystem.
Although analytical information processing capability generates the needed analytical
information that supports organisations’ digital business transformations, boundary spanning
activities elevate the analytical information usage and enable organisations to integrate,
exchange, and share the analytical information for digital business transformation. By
integrating, exchanging, and sharing the analytical information across the internal and
external organisational boundaries, a network effect can be created to improve communication
and coordination in the transformation (Khuntia et al., 2022; Papanagnou et al., 2022). Based
on Aldrich and Herker (1977), information needs to be summarized and directed to the
organisational unit that needs it. It is important for boundary spanning roles to summarize
and interpret information and mediate the flow of information between relevant actors in a
focal organisational unit and its task environment (Aldrich & Herker, 1977), making each
relevant unit get its needed analytical information. According to Beckett (2021), boundary
spanning activities facilitate the flow of information across different kinds of boundaries,
including geographical, cultural, and organisational. By having boundary spanning activities
such as embedding analytical experts into a hybrid team and making analytics reports
available to all employees, stakeholders across boundaries can better share and exchange their
knowledge and analytical information. This enables the entire organisation to embrace
analytics as the key driver for the transformation (Tim et al., 2020). Therefore, we propose:
P9: Boundary spanning activities foster analytical information usage and sharing and thereby mediate
the relationship between analytical information processing capability and digital business
transformation.
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5 Discussion
The purpose of this study is to answer the following research question: How do organisations
use analytical information for digital business transformation through DataOps? By analysing and
synthesizing the literature through the lens of OIPT, we develop a conceptual framework that
explains how DataOps helps to enable the analytical information processing capability and
facilitate boundary spanning activities required for digital business transformation. Below, we
discuss the implications of our study for research and practice.
5.1 Implications for research
By integrating the diverse literature on digital business transformation, DataOps and Business
analytics through OIPT, our study formulates a new topic of investigation (Cronin & George,
2023; Torraco, 2016), that is, using analytical information for digital business transformation
through DataOps, and thereby contributes to research on data-driven digital business
transformation. Our framework links business analytics and DataOps with digital business
transformation, which extends the current knowledge on digital business transformation by
arguing DataOps as a disciplined approach to enabling organisations to use analytical
information to achieve such transformation. Organisations need to develop their capabilities
to realize the benefit of business analytics on digital business transformation (Setia et al., 2014).
Our research contributes to this by proposing analytical information processing capability as
the requirement and introducing DataOps as the discipline to build the analytical information
processing capability. According to Ranjan and Foropon (2021), business analytics is
considered a vital information processing mechanism to reduce uncertainty and equivocality
in decision-making processes. Other analytical information processing capabilities have been
identified by Anand et al. (2020), such as Online Analytic Processing (OLAP), automated and
ad-hoc reporting, social media analytics, and sentiment analytics. Our research extends these
studies by explaining the importance of analytical information processing capabilities for
digital business transformation and proposes a disciplined approach to develop such
capabilities.
One of the key features of digital business transformation is its broader scope. Digital business
transformation needs to include various business functions and customers across all valueadded chain segments (Schallmo et al., 2017), and as such, digital transformation can be
significant and have implications beyond the organisation’s immediate value network (Vial,
2019). Our research extends these studies by emphasizing the importance of boundary
spanning activities in digital business transformation. Our study explains what boundary
spanning activities are needed and how these boundary spanning activities contribute to
digital business transformation. Boundary spanning activities such as task coordinating,
scouting, embedding, and influencing help to improve coordination effectiveness, scale
analytics applications, promote innovations, and foster a digital ecosystem for digital business
transformation. Further, boundary spanning activities allow the analytical information to be
shared and exchanged across the organisation, enhancing the value of business analytics for
digital business transformation.
This paper also contributes to research on DataOps by linking it with digital business
transformation through OIPT and boundary spanning activities perspective. Based on
Heudecker et al. (2020), the real benefit of DataOps is as a lever for organisational change. Our
research aligns with this view of DataOps and indicates that DataOps enables organisations
to develop their analytical information processing capabilities for digital business
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transformation. The principles and practices of DataOps, such as testing and monitoring,
automation, continuous improvement, and business value mindset, help to align analytical
people, processes, and technologies to generate high-quality analytical information needed in
the digital business transformation. DataOps also helps to foster boundary spanning activities
for digital business transformation by providing the boundary spanning objects (e.g., code
repositories) and communication and collaboration mechanisms (e.g., communities of
interests). DataOps is not strictly a technical competency (Heudecker et al., 2020). Instead,
DataOps is a discipline that helps organisations leverage business analytics and maximizes the
value of business analytics for digital business transformation.
5.2 Implications for practice
First, this research highlights the important role of analytical information in digital business
transformation. By using analytics practices and applications, the numerous data generated in
the digital world can be transformed into useful analytical information, solving the
uncertainties in digital business transformation. Therefore, organisations need to realize the
importance and necessity of analytical information and use it throughout their digital business
transformation journey. More importantly, rather than viewing analytics simply as a
technology, organisations need to build analytical information processing capability that
aligns people, processes, and technologies for digital business transformation.
Second, this research provides organisations with ways to manage external and internal
boundaries (e.g., the boundary of different business functions) and improve collaboration for
digital business transformation. For example, using DataOps, a cross-functional team across
the business, IT, and analytics can be established. People in this cross-functional team can act
as boundary spanners who connect their original teams with other teams across the
organisation. As the joint use of assets or combining resources in a company is valueenhancing (Cao et al., 2019), organisations can adopt the practices from DataOps to foster the
joint use of analytical resources across the organisation to drive digital business
transformation.
Third, given the potential role of DataOps in fostering the boundary spanning activities for
digital business transformation, business analytics vendors need to consider the different
needs of users and develop DataOps tools accordingly. In other words, DataOps tools or
platforms need to provide a common language that can be understood by people from
different backgrounds. DataOps tools should allow different users to work collaboratively to
improve the efficiency of teamwork in digital business transformation with business analytics.
6 Conclusions, Limitations, and Future Research
An increasing number of organisations are embarking on their digital business transformation
journeys. However, transforming a business digitally and getting value from it is challenging
(Davenport & Westerman, 2018). In this study, we develop a conceptual framework that
explains how organisations can transform their business by leveraging analytical information
through DataOps. Specifically, DataOps provides principles and practices that help
organisations develop their analytical information processing capabilities and facilitate
boundary spanning activities to share and exchange analytical information for digital business
transformation.
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Although our research extends the current knowledge on digital transformation by bringing
in business analytics and DataOps for digital business transformation through the lens of
OIPT, this research has a few limitations that offer opportunities for future research. First, we
did not explore the specific analytical information processing capabilities needed for digital
business transformation. Future research can explore the role of different types of analytical
information processing capabilities, such as descriptive, diagnostic, predictive, and
prescriptive analytics (Lepenioti et al., 2020). Second, boundary spanning activities usually
include both the activities that enable cross-boundary collaboration and activities that guard
the boundary to control the release of information (Ancona & Caldwell, 1990). Our framework
focuses on the boundary spanning activities that enable cross-functional collaboration and
encourage the sharing and exchanging the analytical information across the organisation.
Future research can explore the boundary spanning activities that protect the key analytical
information to keep a firm competitive in the market. Third, our framework does not include
details on the implementation of DataOps for digital business transformation. Future research
can explore the aspects (e.g., technologies, processes, and people) needed to implement
DataOps for digital business transformation. In addition, in this study, we only discuss the
relevant DataOps principles and practices that play an integral role in the development of
analytical information processing capabilities. Future research can focus on building a
comprehensive list of DataOps principles and practices in the context of digital business
transformation. Finally, we propose a conceptual framework using an integrative literature
review. Future research can further develop, refine, and test the proposed framework through
in-depth case studies and validate it by conducting a large-scale survey.
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