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JOURNAL OF INTELLIGENT INFORMATICS AND SMART TECHNOLOGY, VOL. 8, OCTOBER 2022

Digital Transformation in the Use of
Organizational Assets
Yasuhiro Shuto1, JAIST, Naoshi Uchihira, JAIST
Abstract. Many companies have been operating for business systems for a long period of time and have therefore accumulated
a large amount of data. The data were originally accumulated on the premise that they would be used for business purposes, andthey can be said to have achieved their traditional purpose. By combining these data with various internal and external data, it
may become possible to create new value. For example, General Electric (GE) provides new value to its customers—such as
reducing operating costs and offering just-in-time maintenance—by using data that had been collected for the purpose of jet
engine maintenance and for flight route optimization. In order to utilize data as in the preceding examples, it is necessary to haveknowledge about data, to realize the value provided by combining it with existing data, and to understand the digital technologies
that make it possible to materialize. This research examines cases in which digital transformation has been successfully realized
by combining existing data with other data, considers the characteristics of these cases and the impact of tacit knowledge on them,and explores the effective use of organizational assets in digital transformation.
Keywords: Digital Transformation, Knowledge Management, Lean Development.

S

I. INTRODUCTION

ince the late 2010s, the importance of digitaltransformation has been touted by global companies. The
importance of digital transformation in global competitive
strategy is particularly significant, and Japan is promoting
digital transformation at a government initiative, while
Germany has been promoting Industry 4.0 since 2012. Other
countries are following the precedent cases.
The background of this topic is digital technology centeredon information and communication technology, specifically,
the Internet of Things (IoT), big data acquired through IoT,
and the development of Artificial Intelligence (AI). By
utilizing acquired data, companies have been able to take a
new approach that differs from their traditional businessapproach by combining efforts linked to technologicaladvances toward AI teacher data, future predictions, andindependence. GE is a pioneer in this area. This company
used information on the operation of an internal asset, the jetengine, to optimize flight routes and predict maintenance andservicing needs, and used it as a means to optimize theoperating and maintenance efficiency of its airlines. Thisinitiative, which began in 2011, continues to attract attention
today.
Building a profitable business utilizing data from existing
businesses cannot be achieved simply by introducing digitaltechnology. It is necessary to consider whether the data fromthe existing business are ready to be utilized and be effectivein digital transformation. Especially in terms of doing
something different from a conventional purpose, it isconsidered to be an unexpected use of the data. Bughin et al.
[1] pointed out that we should review our business portfolio
and, in some cases, even cannibalize existing businesses to
get ahead. Schumpeter [2] defined five types of innovation,and production innovation is a category that is closely related
to digital transformation. As a result of innovation,competition with existing businesses is considered to be
similar to the innovator's dilemma presented by Christensen
1
Yasuhiro Shuto is with Japan Advanced Institute of Science andTechnology, 1-1 Asahidai, Nomi, Ishikawa 923-1292 Japan, e-mail:yasuhiro.shuto@jaist.ac.jp.

et al. [3]. Ideally, new services should be incorporated into
the portfolios of existing businesses through digitaltransformation.
This study proposes effective ways to promote innovation
by examining examples of the effective utilization oforganizational assets, differentiation from existing domains,and synergistic effects.
A preliminary survey on the data inherent to organizations
and how they utilize them to realize digital transformation by
applying organizational assets obtained from previous
businesses. We will investigate how organizations use theknowledge that they have to leverage data that already they
possess.
II. LITERATURE REVIEW
Data are an organizational asset traditionally accumulated
for use in systems developed to improve internal work andoperational efficiency. As a result, the data within an
organization exist in a form that is considered business data.
These data in such systems are called “big data” and are used
to consider the business strategies of companies. According
to Mikalef et al. [4], there are 16 definitions of big data. And
Akter et al. [5]point out the importance of an organization
using different types of information for different purposes.
As a concrete example, Cao et al. [6] showed that acompany that changed from batch processing to real-timeprocessing of drill data at a drilling site and, as a result, wereable to correctly understand the conditions and course of thedrilling, which enabled them to optimize their operations.
This example shows how a drilling team streamlined its
workflow and saved engineering time by reducing the
traditional data acquisition interval and developing
applications to increase the value gained by reducing thisinterval.
However, the assumption is that these data mustthemselves be ready for transformation. The existence of thedata does not necessarily mean that it can be used for digitaltransformation. As a prerequisite for their use, Müller et al.

JOURNAL OF INTELLIGENT INFORMATICS AND SMART TECHNOLOGY, VOL. 8, OCTOBER 2022

[7] suggested the importance of data quality, which requires
some understanding of the business and operational flows
from which the data are generated, and how they can be usedin digital transformation. This includes the importance of data
cleansing, the quality of the cleansing, and the proper
management of the data in terms of quality.
In the case of Cao et al., it was a review of the data cycle,
not the data itself, so the fact that the data was in a format that
could be used beforehand made it effective for digitaltransformation. In its application to project risk management,
human interpretation of the prediction/estimation results ofAI models will enable us to recognize the influence of
unexpected factors on project failure and unknown
similarities between different projects. It will enable us tomake decisions about how to respond to ongoing projects
based on failure probabilities. Human experience andknowledge can be also reflected in the AI model tocontinuously improve its accuracy and the efficiency ofmodel-building work.
III. UTILIZING AN ORGANIZATION’S ASSETS
A. Study on how to utilize existing data
The realization of digital transformation lies in the use ofdigital technology to provide new value from existing
businesses and to generate additional revenue. When this isdone based on an affinity with the existing business, utilizing
the assets of an existing business is only a means to an end.
Launching a new business requires resources such as people
and funds. The example provided by Cao et al. is a good fit interms of data acquisition that has succeeded in capturing newvalue. The question behind this is, “What are the benefits ofchanging the timing of data synchronization?” [6] After
understanding the usefulness of existing data, it is required toconsider what can be done to create value, and examine
whether it is feasible. For data acquisition, we will examine
the feasibility of sensor technology used in cameras and the
IoT, due, in part, to the evolution of digital devices. Byutilizing the data acquired from digital devices, Porter [8] andothers have shown the process of value creation through data

Fig. 1 Capabilities of smart, connected products.
Porter, Michael E., and James E. Heppelmann. "How smart, connected products are transforming
competition." Harvard Business Review 92.11, 2014.

combination.
Control, optimization, and ultimately autonomy are
possible by utilizing accumulated data. It is important todetermine which data are effective for monitoring. The most

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important data are not necessarily the only data being
collected. In the case of GE’s jet engines, it was important tohave knowledge of the jet engine logs so that the possibilityof using the data could even be considered. In addition to theactual log data, it was also important to have the knowledge
to combine information about the flight itself, such as theweather and the number of passengers on the flight. The resultof combining the information held by the company with theinformation provided by the customer will enable thecompany to provide higher value to the customer.
B. Utilizing data and knowledge in an organization fromsuccessful digital transformation cases
The importance of intra-organizational data and knowledge
can be observed when examining several case studies. The
German automobile manufacturer Audi started using data inthe marketing area in 2013, demonstrating the fact that they
gradually expanded their use of data from marketing to enable
their use in the process of strategic decision-making.
Organizationally, the innovation hub has taken the lead in
expanding related departments and sharing knowledge, andeach department has succeeded in using data not only in themarketing area, but also in creating a process in which
members involved in data analysis participate in vehicle
development to improve performance. It was also noted that
Audi was able to improve its performance. For a case in point,
they used data from an online car configurator and existing
business sales data to acquire potential customer needs andshare the results with the design department to create aprocess for designing vehicles to better meet customer needs[9].
The American retail giant Best Buy, a major electronics
retailer, examined how the data could provide a new customer
experience by leveraging its physical stores, and how this newexperience could be applied digitally. The company began acomplete overhaul of its warehousing, software, and supply
chain, and implemented a data-driven strategy to break awayfrom price competition with e-commerce companies likeAmazon.com and re-examined the strengths of its stores. It
reinstated a special discount program for employees that had
been discontinued to train employees to be more
knowledgeable about the products. In an effort to provide a
better experience for its customers, the company rented out
some of its stores to manufacturers such as Apple andSamsung, successfully securing new revenue sources andadding new value to the stores for customers [10].
The Walmart Corporation was a pioneer in introducing thepoint-of-sale system and supply chain in 1968 and operates“Walmart’s Retail Link” [11]. The company has had great
success using this system and understands the importance ofdata. Based on their understanding of the effectiveness of theuse of these data, there is an organization called Walmart
Asset Protection that is aware of data intelligence and has
achieved digital transformation [12]. Data-driven withexternal data, these data are used in combination with 200
internal and external data, including event information nearstores, weather data, economic data, viewership data, social
media data, and gas prices. As a private cloud, it has theworld's largest dataset [13].
Honeywell Process Solutions (HPS), an IoT platform that
supports Honeywell’s industrial clients, is a service provider.
It collects, analyzes, and utilizes 2.5 billion gigabytes of data
every day and provides the results to its customers [14]. The

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JOURNAL OF INTELLIGENT INFORMATICS AND SMART TECHNOLOGY, VOL. 8, OCTOBER 2022

services Honeywell provides have been used extensively. Asa case study, the company started monitoring an oil and gas
export facility overseas in the Gulf of Mexico by providing
real-time predictive analytics to the customer to quickly
diagnose problems, improving their production efficiency andreducing downtime as well as saving $10 million annually. In
another case, Honeywell connected multiple plants for amining company in South America, improving reliability,
energy usage of monitored industrial equipment, and reduced
shutdowns, saving them $2 million per day in lost revenue.
Honeywell also offers the IoT as a platform that combines
multiple datasets. The analysis involves many experts from
Honeywell, such as the head of HPS, Andrew Hird. Hird, an
industrial plant expert for over 20 years, understands thecustomer’s need for optimal project management and isdriving the company’s digital transformation.
These four case studies summarize data and knowledge asassets within an organization. Their existence in a coreorganization is illustrated in Table 1.

generated from operations within the organization can be
combined.
To achieve digital transformation, it is desirable toimplement lean development as advocated by Ries [16] andbring it closer to the needs of customers. By repeating
development and verification in a short period of time, thevalue provided to customers can be refined through processes
such as optimizing the data used and revealing tacitknowledge. As a result, the time axis of Christensen’s S-curveis shortened, and a competitive service can be realized in ashort time. The same method is also used by startup
companies and is considered to have a high affinity with new
businesses. The method shown in Fig. 2 is considered to be aprocess of utilizing organizational assets and creating an
actual business.
Accumulated
data

Other
Organization

Table. 1 Matrix of accumulated data & knowledge, core organization
Company

Audi

Best buy

Accumulated
data
Marketing data,
Car Data

knowledge
Marketing,
Designing a new
car model

Sales data,
Logistics data,

Product

Location

information

information
Walmart’s Retail
Link operations.

knowledge

Additional
data

Develop
Application

organization
Digital innovation
hub
Renew Blue
(Enhanced
Project Team)
Walmart Asset
Protection

Walmart200 sources data

Honeywell

Data in multiple
Honeywell
IoT Sensing data disparate systems
Process Solutions
relations.

These cases show the use of accumulated data from the pastand the use of traditional knowledge. There is an environment
in which accumulated data can be utilized, and there is anorganization that can operate and evaluate the results. The
common point of these case studies is that data exist as aprerequisite, and the knowledge of how to combine and use
the data is important for the department promoting the project.
C. Innovation and acceleration of the utilization oforganizational assets
The essence of the task is the same as that of conventionalinnovation: to provide new value by utilizing organizational
assets. New business and existing business cannibalization
occurs in the course of disruptive innovation and daring tocreate different needs for existing profitable businesses. Whatis different about digital transformation is that compared to
product innovation, digital-driven innovation can be brought
to market in a relatively short period of time.
Dahlander et al. [15] pointed out that no single organization
can innovate in isolation. It is necessary to understand theoperations of the departments that require the data and theknowledge regarding the operations as well as the fact that it
is impossible to achieve digital transformation with systems
alone. Innovation through digital transformation is possible
through digitization, through which data and knowledge

Customer
New value

Intra-organizational
collaboration
AnalyticsCore

New service

Fit forsolutionsCustomerFeedback

Lean development

Fig. 2 A model for implementing digital transformation by leveraging data
and knowledge in the organization.

Knowledge in an organization exists as a precondition fordata. Based on the knowledge, we examine the feasible
options by combining new data and implementing thedevelopment process using lean-driven development. Whenthe performance of the service to be provided is confirmed, tosome extent, it is provided to the customer in the form of atrial, and the value is evaluated. By repeating this process, theservice can be refined.
D. Problems with digital transformation
There is certain effectiveness in digital transformation inleveraging the knowledge and data that exist within an
organization. In terms of failures, Davenport et al. [17]suggested that we must be persistent as there is uncertainty
about the investment and return on technology. In addition,according to a survey by Kurokawa et al. [18], the successrate of digital transformation is as low as 16%. A lack ofexperts within an organization has been identified as a reason
underlying this fact. Although there are personnel withspecific knowledge and information system departments,
there is a lack of data scientists and experts in AI for control
and autonomy that can connect the process of integrating theknowledge of both parties and turn existing data into
something valuable. Even if these people are hired fromoutside of the company or from a system supplier, they will
not be able to analyze data or develop AI based on theirunderstanding of the knowledge that the organization
possesses, which will greatly reduce efficiency andproductivity. Audi employed external consultants at the timeof their digital transformation, but changed to training theirown employees (Dremel et al. [9]). It has also been pointed
out that when managers do not see the performance of theirinitiatives, they dismiss them as costs. In Japan, the tendency
to seek short-term results is higher than in other countries,
and, in addition, the amount of investment is low. Even
ifyou have the knowledge and technology required, there are

JOURNAL OF INTELLIGENT INFORMATICS AND SMART TECHNOLOGY, VOL. 8, OCTOBER 2022

few resources for promoting it to a new business and newvalue creation. It is especially difficult to utilize data except
in cases, in which data can be collected in the short term.
Since it will be difficult to collect the data necessary for thegoal of Porter et al.’s step, there is a high possibility that thegoal will not be reached. Organizations must consider what
they should do about the challenge of having information
unique to their company but not being able to fully utilize it.
IV. CONCLUSION
We examined how to implement digital transformation
based on the premise of utilizing assets, mainly data, in an
organization from case studies. Utilizing the data in an
organization is important for digitizing and reflecting the tacitknowledge in the organization to make it effective in driving
digital transformation. An organization that supports the drivefor digital transformation, accelerates its use, and evaluates
its results is needed. For example, even if a data scientist or
an AI engineer is employed by the organization,
understanding tacit knowledge does not always go smoothly.
Therefore, the process of knowledge and knowledge transfer
to operations becomes more important. Understanding
operational knowledge is important for tackling digitaltransformation. Another approach is to bring processes thatenable knowledge transfer into the development process andapply them to the purpose of digital transformation. We
believe that it is important not only for one organization to
pursue digital transformation as a goal, but also for relatedorganizations to work collaboratively to drive digitaltransformation.
Uncertainty in business is always present, such as whensomething that was actually touted as a success story turns out
to be a failure. Therefore, a company should carefully monitor
successes in this regard and subsequent progress. Moreresearch is needed on how to drive and expand digitaltransformation to avoid the risk of failure. There is also thepotential for a business to expand the value proposition byconnecting with external ecosystems to drive digitaltransformation. Consideration should also be given to theextent to which company-specific data and tacit knowledge
should be disclosed to the outside world. In the future, it will
be necessary to continue research including those outside of
the organization.
REFERENCES
[1]
[2]
[3]
[4]
[5]
[6]
[7]

Bughin, Jacques, and Nicolas van Zeebroeck.,: 6 Digital Strategies, and
Why Some Work Better than Others., Harvard business review (2017)
Schumpeter, Joseph.,: Theorie der wirtschaftlichen Entwicklung. In:
Joseph Alois Schumpeter. Springer, Boston, MA, p. 5-59 (2003)
Christensen, Clayton M.,: The innovator's dilemma: when newtechnologies cause great firms to fail. Harvard Business Review Press.
(2013)
Mikalef, Patrick, et al.,: Big data analytics capabilities: a systematic
literature review and research agenda. Information Systems and eBusiness Management, 16.3: 547-578 (2018)
Akter, Shahriar, et al.,: How to improve firm performance using big
data analytics capability and business strategy alignment?. International
Journal of Production Economics 182, 113-131 (2016)
Cao, Dingzhou, et al.,: Digital Transformation Strategy Enables
Automated Real-Time Torque-and-Drag Modeling. IADC/SPE
International Drilling Conference and Exhibition. OnePetro. (2020)
Müller, H. & Freytag, J.,: Problems, Methods and Challenges inComprehensive Data Cleansing. Technical Report (HumboldtUniversitt zu Berlin, Institut ft Informatik Publication No. iroB-lB-164).
(2003)[8]

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Porter, Michael E, Heppelmann, James E.,: How smart, connected
products are transforming competition. Harvard business review,
92.11: 64-88. (2014)[9] Dremel, Christian, et al.,: How AUDI AG Established Big Data
Analytics in Its Digital Transformation. MIS Quarterly Executive, 16.2
(2017)[10] Mcgrath, Rita, Mcmanus, Ryan.,: Discovery-Driven. Harvard Business
Review, 98.3: 124-133. (2020)[11] Marr, B.,: Walmart: Big Data analytics at the world’s biggest retailer.,
BernardMarr&
Co.,
Available:
https://www.bernardmarr.com/default.asp?contentID=690 (2018)[12] D'Onofrio, T.,: The secrets to Walmart's success. The Digital
Transformation
People.,Available:
https://www.thedigitaltransformationpeople.com/channels/customerengagement/the-secrets-to-walmarts-success/. (2019).
[13] Shichor, S.,: Walmart’s Retail Link-Point-Of-Sale Data. 18Knowledge,
Eighteen Knowledge Group (EKG), March 16, 2017. Available:
https://18knowledge.com/blog/walmart-retail-link-talks-to-suppliersthrough-data/. (2017)[14] Aaron H.,: Honeywell Creates New Division Based on the Internet ofThings"
,May
2nd,
2016.
available:
https://www.automationworld.com/products/networks/news/13315376/honeywell-creates-new-division-based-on-the-internet-of-things.
(2016)[15] Dahlander, Linus; Gann, David M.,: How open is innovation?.
Research policy, 39.6: 699-709. (2010)[16] Ries, Eric.,: The lean startup: How today's entrepreneurs use continuous
innovation to create radically successful businesses. Currency, (2011)[17] Davenport, Thomas H.,:Westerman, George. Why so many highprofile digital transformations fail. Harvard Business Review, 9: 15.
(2018)[18] Kurokawa, M et al.,: Using digital transformation to thrive in Japan's
newnormal:Anurgentimperative.,
Available:
https://www.mckinsey.com/~/media/McKinsey/Locations/Asia/Japan/
Our%20Work/Digital/Using%20digital%20transformation%20to%20t
hrive%20in%20Japans%20new%20normal_an%20urgent%20imperat
ive_upd201223.pdf. (2021)

JOURNAL OF INTELLIGENT INFORMATICS AND SMART TECHNOLOGY, VOL. 8, OCTOBER 2022

Yasuhiro Shuto is currently a PhD
student at Japan Advanced Institute of
Science and Technology (JAIST). He
worked for Sony Group for 10 years, andwas involved in broadcasting equipment,
e-commerce, Internet video distribution
business, and electronic paymentbusiness. Currently, he is a business
manager at a data analysis and AI development company
focusing on the marketing domain. His current researchinterests include digital transformation, business ecosystem,and organizational evolution.
Naoshi Uchihira is Professor of Japan
Advanced Institute of Science andTechnology (JAIST). He received Dr.
Eng. degrees from Tokyo Institute ofTechnology, and Ph.D. (Knowledge
Science) from JAIST in 1997 and 2010,
respectively. He worked at the corporate
R&D center of Toshiba Corporation from1982 to 2013. His current research interests include project
management and digital innovation design.

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