Challenges in Developing Data-based Value Creation

Jussi Myllärniemi

, Nina Helander

and Samuli Pekkola

Information and Knowledge Management Unit in Faculty of Management and Business, Tampere University, Finland

Keywords: Data-based Value Creation, Knowledge Management, Information Management Process, Case Study.

Abstract: Understanding data-based value creation helps organizations to enhance its decision-making and to renew

their business operations. However, organizations aiming to use modern data analytics face several severe

challenges that are not usually so evident or visible beforehand. In this paper we study a Finnish

manufacturing company’s data empowerment and information and knowledge management practices in order

to identify the potential challenges related to modern data-based value creation within industrial context. The

empirical data is consisted of group discussions, relevant data sets acquired from the case company’s

information systems, and lastly, 12 thematic interviews of the key actors in the company in relation to service

development. The study provides valuable insights for managing service development and decision-making

and creates understanding on data-based value creation. Achieved understanding provides meaningful

knowledge for organizations utilizing or having plans to utilize, for example, data analytic methods in their

businesses.

1 INTRODUCTION

Organizations seek ways to create value from data to

improve their decision-making capabilities and

productivity. Usually the amount of available data is

not an issue anymore (Chen et al., 2012). However,

the organizations need to distinguish what data is

relevant, how to refine it, how to share it within the

organization and if needed, to other stakeholders, and

how to use it in decision-making (Kaivo-oja et al.,

2015; Choo, 1998), and furthermore, in creating value

for themselves, their customers and/or other

stakeholders. This is referred to as data-based value

creation (Xie et al., 2016).

Understanding data-based value creation helps

organizations to enhance decision-making and renew

business operations. However, most research focuses

either on knowledge and its management (Hislop,

2013, Dalkir, 2013), or data and information quality

issues (Hazen et al., 2014). Quite rarely the value

chain from data to knowledge and its utilization are

illustrated.

In this paper we study the information and

knowledge management chain in an organization, and

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the challenges faced in creating data-based value. Our

case study consists a Finnish manufacturing company

that seeks ways for better use of data in their service

development and related decision-making. They

believe that integrating different data sources and

using their aggregate in decision-making would bring

competitive advantage and value for them. However,

similarly to many organizations using modern data

analytics (Ransbotham et al., 2016) or knowledge

management initiatives in general (Carlucci and

Schiuma, 2006), also our case organization faced

several challenges that were not evident or visible

beforehand.

In this paper, we try to understand the case

company’s value chain from data empowerment to

information and knowledge. We thus answer to

question “what kind of challenges the case

organization faces in data-based value creation”? Our

study illustrates practical challenges in relation to

research literature from several disciplines increasing

our awareness of intertwined nature of issues and path

dependency between details.

The structure of the paper is as following. Firstly,

the theoretical premises of the study are presented,

370

Myllärniemi, J., Helander, N. and Pekkola, S.

Challenges in Developing Data-based Value Creation.

DOI: 10.5220/0008366003700376

In Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019), pages 370-376

ISBN: 978-989-758-382-7

focusing on the process model of information

management that acts as the analytical lenses for the

empirical study. Before opening up the findings from

the empirical study, the methodological choices and

the research setting is discussed. The paper ends with

conclusions and identification of future research

avenues.

2 THEORETICAL SETTING

Knowledge-based approaches, like data-based value

creation, aim to understand and explaine how

organizations internal and external knowledge

resources contribute to organizations’ competitive

advantage (e.g. Grant 1996; Myllärniemi et al., 2012).

In this context, knowledge refers to the outcome of

human action that takes place in decision-making

situations. Knowledge, furthermore, is based on

information and the actor’s interpretations on it

according to their experiences and to a certain

context. Information, in turn, is processed from data

by adding some meaning to it (Choo, 2002). Data, on

the other hand, is unstructured facts that have the least

impact on the managers (Thierauf, 2001). Knowledge

is the most valuable for decision-makers (Thierauf,

2001).

This chain from data to information to knowledge

emphasizes its connectiviness. Knowledge does not

emerge from nowhere but from data and information.

This means that in order to make good, knowledge-

based decisions, information and data needs to of

good quality, and available in decision-making

situations.

Knowledge processes should be tightly connected

to service provision and value creation (Myllärniemi

et al., 2012). Value in business context is generally

regarded as the trade-off between benefits and

sacrifices (Helander and Vuori, 2017, Walter et al.,

2001). In data-driven value creation, the focus is on

analyzing the monetary and non-monetary benefits

and sacrifices related to data, information and

knowledge. Thus, value refers to the individuals’

enhanced decision-making capabilities and improved

productivity or performance (cf. Pirttimäki, 2007;

Grönroos and Helle, 2010). In order to exploit the

organization’s value creation and its full potential, the

company needs to focus on its capabilities to provide

products and services that are of high quality,

available when needed and produced cost effectively

(cf. Nordgren, 2009; Lönnqvist and Laihonen, 2012).

Consequently, the fluency of knowledge processes

and practices is a critical success factor and driver for

value creation. (cf. Kianto et al., 2014).

Knowledge management considers the processes

and activities supporting the utilization of knowledge

resources (Wiig, 1997), and further, information and

data. One means to structure information processing

is the process model of information management

(Choo, 2002). The process model is a framework of

deriving knowledge and insights from data and

information, but it leaves out the knowledge

management layer and the connection to strategic

level. In this study, we utilize Choo’s model as a

foundation when analysing case organization’s

knowledge processes, but we leverage the model

according to Jääskeläinen et al., (2019) in order to

include the whole value chain from data to knwoledge

in the analysis (see Figure 1).

Figure 1: Framework of information and knowledge

management (Jääskeläinen et al., 2019; Choo, 2002).

This information and knowledge management

framework includes the more technical side of

information handling and the softer side of humans

related to knowledge, but it also takes into account

both the viewpoints of the employees and the

organization (Jääskeläinen et al., 2019). As Lake and

Erwee (2005) have stated, information and

knowledge management is about of finding,

selecting, organising, distilling, and presenting

information in a way that improves an employee’s

understanding within the work context. Furthermore,

it also enhances organizations to gain insight and

understanding from its own experience and data

sources, and support utilization of knowledge in

problem solving, decision making and strategic

planning. (Lake & Erwee, 2005)

Within the framework there is the process of

information management (Choo, 2002). The process

starts with specification of information needs. The

needs are first defined so that they can be later

satisfied as well and efficiently as possible. Based on

this definition, information is then acquired and

Challenges in Developing Data-based Value Creation

371

gathered both from external sources, such as

competitors and customers, and from internal sources,

such as operational databases and information systems.

The collected information is stored in organization’s

repositories. This means the phase of information

organization and storage where the aim is to create an

organizational memory. This facilitates not only latter

phases such as information analysis for systematic and

advanced information products/services, but also the

phases of information sharing and information use.

Information gets its final meaning when it is utilized

for instance in decision-making, and changes in the

organizational activities take place. By utilizing and

adjusting organizational operations, the cycle starts

over. It should be noted that the process is an iterative

process and that the fluctuation between stages is not

always straight-forward (Gilad and Gilad, 1985; Choo,

2002; Vitt et al., 2002).

3 RESEARCH METHODS AND

EMPIRICAL SETTING

We have conducted a case study (Yin 1994) of a

globally operating manufacturing company, located in

Finland. The company has approximately 500

employees in 15 countries. The company exports

approximately 90 per cent of its products. It is

established in 2006.

The study was carried out in 2016, with a focus on their

service development. Our empirical data is consisted

of group discussions and workshops, different data sets

from the company’s information systems, and 12

thematic interviews of the key actors in their service

development. The list of interviewees is presented in

Table 1.

Open-ended interview focused on different themes

related to their service development and information

usage. The themes included information needs,

managerial practices, knowledge concepts,

information technology and information systems, and

knowledge and network dynamics. The interviews

were conducted face to face in the company premises.

They lasted for 30 to 90 minutes.

Kianto et al. (2014) say that “management

mechanisms should be analysed to understand the key

factors that impact firms’ ability to create value based

on knowledge”. We analysed the interviews, group

discussions and workshops, and data sets by using

Choo’s (2002) process model of information

management as a lens for analysis. By this analysis, we

were able to point out challenges related to information

needs, information acquisition, refining, sharing and

utilization, i.e. all phases of Choo’s model. Afterwards

we analysed what are the requirements for data and

information empowerment, and summarize our lessons

learned how to create value and actionable knowledge

from data.

4 DATA-BASED VALUE

CREATION IN THE CASE

ORGANIZATION

Choo’s (2002) process model is a tool for

organizational development. Table 2 summarizes our

findings related to the model, and categorized the

challenges in related to its main components. Through

these challenges, the role of knowledge management

in the case organization could be understood (cf.

Valkokari and Helander, 2007).

Table 1: The List of Interviewees, Their Titles and Main Work Activities.

Title

Main work activities

Business development manager

Market intelligence

Chief mechanical engineer

Product testing, quality measuring etc.

Condition monitoring engineer

Customer contacts, service data analytics

Customer service engineer

Operation planning: information management, service reports

etc.

Development engineer

CRM administrator, ERP main user, sales and operation planning

Global product manager

Team manager of service products, data management

Manager conceptual design & analysis

Part of management group

PLM manager

Only inner support of PLM system

Quality engineer

Product development from quality point of view

Sales manager, Northern Europe

Head of service sales

Unit manager, Finland service

Head of field services in Finland

Vice president of product management

Head of global product management

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Our case organization has significant challenges

in its knowledge management. The analysis illustrates

the organization’s own perceptions: their data quality

is poor in general, data is distributed throughout the

organization and its information systems, and they

have not enough resources to analyze it. This

evidently creates problems especially in the

management of product information and, further, data

analysis.

Above mentioned challenges are quite general by

nature. However, the problems and their root-causes

are more profound and complex, and are only

revealed after careful examination. For example,

there is friction between different in-company

interfaces, i.e. between departments, between

information systems, and even between people. One

example of friction is in customer relationship

management. According one of interviewees “it is

sale’s duty to collect feedback from customers”.

However, our studies reveled the organization has

many customer contact points, official as well as

unofficial, across the organization to collect the

information and no guidelines how to collect that

information.

“It would be easy to have the conversation with

customers if the poor quality of products or delays in

service don’t come up”, said one participant in group

discussions. However, during the interviews, few

interviewees were happy with the quality.

Communication problems occurred because of

unawareness of information collecting and sharing

processes. Communication problems occurred even

during our group discussions. One participant claim

they had made decisions based on information

collecting and coordination in a meeting day before

our group discussion. The meeting’s other

participants were confused of this claim.

This unawareness causes cooperation break-

downs between sales, operations and research and

development departments. For example, the

information needs about the customers are neither

communicated through the organization nor

unambiguously defined. The organization does not

really understand the customers. One interviewee

wondered “When we are developing data analytics,

who is our customer? Do we develop business or are

we serving one man’s passion?”

The previous quote emphasizes the organization’s

state of knowledge management. The organization

seeks ways to better utilize their data in service

development. Yet they do not comprehensively

understand the meaning of knowledge and for whom

they are creating value. The organization, for

example, used 148 emails to find out single product’s

product number. Like said previously, data and

information is incoherent and splattered throughout

Table 2: Knowledge management challenges in the case organization.

Aspect

Challenges recognized

Information needs

 Communications break-down between sales, operative and development functions

 Poor understanding concerning customer requirements and their crucial meaning

for other units

 Wrong questions to define information needs

Information acquisition

 Data and information are incoherent and splattered into different information

systems and silos

 Data logging is insufficient

 Information is not easy to use

 Not enough ambition to store information to systems, e.g. sales information

Information refinement

 Not enough resources to refine data into information

 Current ways of data refinement do not serve decision-making

 Lacks in information analytics, like forecasting

Information sharing

 Communication with sales insufficient despite of weekly conversations

 Insufficiency in communication leads overlapping in data refinement

 Based on customer feedback, information sharing takes time

Information utilizing

 Unrealistic value propositions e.g. in sales

 Information systems are not used comprehensively and systematically

 Lack of tacit knowledge utilization and sharing.

Measures

 Measurement is not strongly linked to knowledge practices and processes

Governance and

organisation

 Ownership of data and information is missing

 Culture and policies are built based on products and systems

Strategy & vision

 Impulsive and non-knowledge-based decision-making

 There is no will to develop a culture of knowledge management

Challenges in Developing Data-based Value Creation

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the organization and its information systems. Just

integrating different data sources and conducting

analysis on poor quality data does not bring value for

them. The organization has challenges in data

acquisition and refinement as well as information

sharing. Based on interviews, customers have said

that information sharing takes time.

We perceive this is mainly as a management

problem. The organization has persons responsible

of, e.g., CRM and PLM systems but owner of data-

based value creation is missing. Like vice president

of product management said: “We have done great

things in our own personal sandboxes, now we need

a mandate for someone to make changes.”

Besides this single quote, organization seems not

to have willingness to develop a culture that support

data-based value creation. Current situation leads to

ad hoc, impulsive, and non-knowledge-based

decision-making and unrealistic value propositions.

Information utilizing is not as effective as it could be.

However, the organization has recognised the

problems and has started to discuss these issues. In

the next chapter we discuss these problems more

general and give some recommendations of data-

based value creation to the organization as well as for

more broader audience.

In overall, we can conclude the main challenges

faced in the case organization as following:1) data is

of poor quality and scattered across multiple systems,

2) there are friction especially between internal

interfaces, 3) lack of understanding of importance of

data and information, and 4) there is no will to

develop a culture of knowledge management.

5 DISCUSSION

Knowledge-based organization’s performance

differences based on firms ability to utilize its

knowledge resources and knowledge management

processes. Development of knowledge processes

should be started by focusing on the decision-makers’

and the organizations’ knowledge needs (cf. Choo,

2002), and by fostering open knowledge-sharing

culture and supportive processes despite of

organizational boundaries (cf. Laihonen, 2012). Our

analysis shows that knowledge processes must be

integrated to other processes within the organization,

as otherwise mundane daily operations, high quality

information, and information products do not bring

value to the decision-makers. The purpose should be

on producing insights, visions and knowledge for

them.

The case organization has some major challenges

related to its knowledge practices: data is poor-quality

and information systems are scattered, there are

increasing friction of communication between

different units, and knowledge is not the prime

resource. In order to get competitive advantage and

value from data the case organization must change

their attitude towards knowledge and take following

recommendations into account. These

recommendations are general in manner and, hence,

are beneficial for other organizations as well.

First, knowledge management must be

organization-wide. Data and information must flow

through the whole organization in order to serve its

business. For example, data governance is approach

that provides a more systematic way for managing

information as a resource (Vilminko-Heikkinen and

Pekkola, 2017). The quality of essential information

has to be taken care of. This means looking after data

integrity, validity, availability and accordance as well

as data management issues.

Second, data-based value creation, i.e. data

analytics, is possible to achieve by changing the

mindset and attitude towards data. The organization

acquired data warehouses and tried to integrated

information systems. However, they forgot to discuss

the meaning of knowledge in their decision-making

and in service provision. Data was not their primary

resource, after all.

Thirdly, it is all about management and

leadership. Data, information and knowledge

resources are key factors determining organizations

value creation potential but equal factor is the

management (Kianto et al., 2014). It must have a

person who is responsible of it. Data needs owner like

organization’s other key resources have. Kianto et al.

(2014) continue by saying that poor management

could damage value creation although organization

has the best workforce and working ICT-systems.

As a summary, the data-based value creation is the

issue of the whole organization. The major problems

are related to clarification of the role of knowledge

and to poor management. This lead to challenges like

undefined knowledge needs, insufficient knowledge

practices, communication break-downs and

inefficient decision-making.

6 CONCLUSIONS

This paper includes a case study of Finnish globally

operating manufacturing company that find out

competitive advantage and value by using integrated

data sources and data empowerment. In this paper we

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study what kind of challenges the case organization

faces and what are the lessons learned from

advancing such endeavour. We analysed the case

organization’s knowledge practices and processes by

utilizing Jääskeläinen et al.’s (2019) framework of

information and knowledge management. With this

analysis we understand better the key factors that

impact organizations’ ability to create value based on

their knowledge.

The organization has significant challenges in its

knowledge management. The data is not quality

enough, data is distributed throughout the

organization, and they are lacking resources to refine

it. The reasons behind these challenges are mainly

result from poor management and lack of

communication. To achieve the potential of

knowledge management organization requires

organization-wide conversations in where knowledge

must is highlighted as one of the most important

assets. Data-based value creation necessitates high

quality data. In order for information systems are

working correctly and data is acquired properly, it is

crucial to define external and internal customers’

information needs properly.

Most of the earlier research focuses either on

knowledge and its management, or data and

information quality issues. Quite rarely the value

chain from data to knowledge and its utilization are

illustrated. In this paper we present concrete

challenges and solutions the the case organization

faces and what are the key lessons for creating data-

base value creation. Consequently, our study

illustrates practical challenges in relation to research

literature from several disciplines increasing our

awareness of intertwined nature of issues and path

dependency between details. This understanding and

lessons learned also open up new research avenues.

The approach provides valuable insights for

managing service development and decision-making

and creates understanding on data-based value

creation. Achieved understanding provides

meaningful knowledge for organizations utilizing or

having plans to utilize, for example, data analytic

methods in their businesses. This understanding and

lessons learned also open up new research avenues.

By analysing and modelling business critical

processes, i.e. product manufacturing or sales and

marketing, problematics of data utilization could be

highlighted.

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