Knowledge Management as a Service

When Big Data Meets Knowledge Management

Thomas Ochs

and Ute Riemann

Villeroy & Boch, P.O.Box 11 20, 66688 Mettlach, Germany

SAP Deutschland SE & Co. KG, Hasso Plattner Ring 7, 69190 Walldorf, Germany

Keywords: Big Data, Big Data Analytics, Knowledge Management, Cloud Services, KMaaS.

Abstract: Purpose – Nowadays the world is getting more technological savvy. The collection of data is becoming a hype

which is a phenomenon is called “big data”. Companies seeking for these data collections and data analytics

assuming valuable insights. As for now, these valuable insights are perishable to a high degree - perishable

because the insights are only valuable if you can detect and act on them (The Forrester Wave, Q3 2014, p2).

In our article, we propose to take advantage of big data analytics while introducing a service-oriented

knowledge management discipline that will allow gaining the full value of big data. Herein, we focus on the

benefit aspect of big data linked to the service approach of knowledge management, which may increase the

value of big data.

Findings –In fact, big data analytics offer value and the use of big data has the potential to transform business

in itself. However, there are greater opportunities beyond big data analytics once we turn data from

information into a knowledge linked to business strategy, easy accessible and consume. With the introduction

of knowledge management-as-a-service to the concept of big data, we provide justification for bringing

proven knowledge management strategies and tools into the cloud sphere to bear on big data and business

analytics. With the introduction of pre-defined service to knowledge management, we open the ability for

increased competitiveness as a final consequence (Thuraisingham and Parikh, 2008) and the value of any

company (Bertino et al., 2006).

Originality/Value – Our article outlines the previously underestimated strong link of big data and knowledge

management and how the delivery of data-driven intelligence is supported with the appliance of a cloud-based

service model. When big data and cloud-based knowledge management are combined are able to not only

uncover a new revenue stream but also create a true competitive advantage.

1 INTRODUCTION

With the success of big data and the remarkably

collection, storage, processing and analysis of data

volumes never seen before (Bughin et al, 2010) an

increasing volume, velocity, and variety at nearly

exponential rates available goes alongside and are

mined for useful information (Rajpathak and

Narsingpurkat, 2013). The excitement of big data is

has arguably been generated primarily from the

technical possibilities in doing so but the question is

if it is it appropriate to purely have a focus greater

data volume, faster and ease to capture data? In our

article, we would like to propose to take the next step

and focus on the generation and management of

knowledge that big data generates while using the

paradigm of cloud-based services.

Knowledge Management in general emerged

some decades ago (Drucker, 1969) and goes beyond

a mere collection of data or information, including

expertise based on some degree of reflection. It

focuses on knowledge as a valuable intellectual

capital that needs to be managed adequately

(Erickson and Rothberg, 2011). As of today, nobody

argues about the importance of managing knowledge

for continuous business success. The success rely on

non-value precursors like data and information that

needs to be turned into valuable knowledge assets. In

establishing the conceptual foundation of big data as

an additional valuable knowledge asset (or at least a

valuable asset closely related to knowledge due to big

data analytics), we can begin to make a case for

applying knowledge management services to data

assets.

Ochs, T. and Riemann, U.

Knowledge Management as a Service - When Big Data Meets Knowledge Management.

DOI: 10.5220/0005851703150323

In Proceedings of the International Conference on Internet of Things and Big Data (IoTBD 2016), pages 315-323

ISBN: 978-989-758-183-0

315

This will help us to bring big data and big data

analytics into the knowledge management field,

creating a new and easy accessible “knowledge

experience”. Here, we can cite Yogesh Malhotra: “In

knowledge management <…> the most limited

resource is no longer information. It has become

human attention – the ability to deal effectively with

the growing volume and speed of information.” In our

opinion, the pure provisioning of data and

information with the advantage of big data

technologies is not enough. Therefore, we have to

think of a new knowledge experience, to create a

consistent and easy to handle experience. With the

foundation of a cloud-based service approach in

mind, a paradigm such as knowledge management-

as-a-service can be discussed to increase the value of

knowledge even further.

2 KNOWLEDGE MANAGEMENT

Before taking a deeper look into our proposed

approach, we need to first understand the core

elements of knowledge management and the

distinction between data, information and knowledge.

2.1 Data, Information and Knowledge

 Data: is a set of discrete and objective facts about

events. Information is a message, usually in the

form of a document or audio-visual

communication. As with any message, it has a

sender and a receiver.

 Information: changes the way the receiver

perceives something, and to affect their judgment

and behaviour.

 Knowledge: is broader, deeper, and richer than

data or information. Within managerial theories

formulated by e.g. Schumpeter (1934), Drucker

(1991) and Nelson and Winter (1982), knowledge

has been defined as a source of competitive

advantage in managerial theory. Knowledge can

be categorized in the two types of knowledge:

 Tacit knowledge is highly personal and

subjective and focuses on learning and

experiences (Beijerse, 2000).

 Explicit knowledge which is formal,

systematic and system-bound (Beijerse, 2000)

and can be further divided into “know what”,

which is knowledge about facts and “know

why” which refers to scientific knowledge of

rules.

If we try to categorize knowledge based on the

dimension of usage, we differentiate between.

 Organization knowledge deals with management

in the organization such as policy, culture,

personnel, career planning, internal processes, cut

backs, alliances and teamwork.

 Marketing knowledge is about the external

environment such as competition, suppliers,

customers, markets, target groups, consumers,

clients, users, interested parties, trade and

distribution and relation management.

 Technological knowledge is knowledge of

products, research and development, core

competencies, technological development,

information and communication technology and

product development (Beijerse, 2000).

To sum up, data are a set of facts about events,

information is a processed set of facts that are

meaningful and knowledge is broader, deeper and

richer as both data and information. (Hota et al,

2015). Having stated that, knowledge management

can be considered as a process that comprises the

creation, organization, sharing and usage of tactic and

explicit knowledge (Wong and Aspinwall, 2004). In

this regard, knowledge management systems are IT

based systems developed to support the knowledge

management processes (Alavi and Leidner, 2001) and

based on the (big) data generated from big data

(analytic) tools within a dynamic process from the

perspective of business and technology (Khoshnevis

and Rabeifar, 2012).

3 BIG DATA

For companies real-time business insights that big

data technologies are offering becomes the main

source of information on top of which companies

decide and build their strategy. The value that

companies aiming to get out of big data is

tremendous, and becomes a necessary asset to survive

in a highly competitive world. Real-time insights

allow organizations, for example, to build better

products, predict what future business outcomes

might be, detect early data signals or better manage

their inventory.

3.1 Key Characteristics of Big Data

All deﬁnitions towards big data make at least one of

the following assertions (Ward and Barker, 2013):

 Size: the volume of the datasets is a critical factor.

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316

 Complexity: the structure, behaviour and

permutations of the datasets is a critical factor.

 Technologies: the tools and techniques which are

used to process a sizable or complex dataset is a

critical factor.

Manyika et al., (2011) considers that big data refers

to datasets whose size goes beyond typical databases

that can be created, stored, managed and analysed by

existing tools; they also consider the need of the use

of new technologies for managing big data. However,

Fisher et al., (2012) considered that most often big

data refer to the conception that the volume of data

cannot be treated, processed and analysed in a

simplified way, requiring much more robust

technologies, techniques and people with new skills

for managing these large data sets.

Overall, big data refers to the idea that a vast

amount of data cannot be treated, processed and

analysed in a simplified way. One of the most cited

deﬁnitions is included in the Gartner report from 2001

they proposed a deﬁnition encompassing the “three

Vs”. Volume, Velocity, Variety. (This idea is

supported the NIST deﬁnition which states that big

data is data which: “exceed(s) the capacity or

capability of current or conventional methods and

systems”, see Intel Peer Research, 2013)

 Volume: Refers to the amount of data, which is

higher. Gartner first described it back in 2001: the

Big Data volume definition will continue to

remain a moving target and it is a matter of fact

that Big Data sizes were ranging from a few dozen

terabytes to many petabytes of data in a single

dataset.

 Velocity: Refers to the speed of data collection.

This means how frequently the data is generated.

Typically, we can identify three main categories:

occasional, frequent, and real-time (Zaslavsky et

al., 2013) and often time-sensitive (Lévai, 2013).

 Variety: Refers to the range of data types, sources,

and languages. In addition, difference sources will

produce big data such as sensors, devices, social

networks, the web, mobile phones, etc. For

example, data could be web logs, RFID sensor

readings, unstructured social networking data,

streamed video and audio (Zaslavsky et al., 2013).

These 3Vs allow a perfect context-independent

definition of the key characteristics of big data. What

is missing here is the “V” that answers the question

why to deal with big data. Reducing big data to the

3Vs we merely focus on no more than collection,

simple analytics, and reporting for purposes of

understanding and optimizing on process or decision

and assessing the improvement of the existing

effectiveness of one’s company. So address even

more value from these data we need to consider a “4th

V – the value” as the context-sensitive dimension to

overcome the “flashlight in the dark” where the

companies are today. Therefore, we have to consider

mainly three issues:

 High Complexity – getting data out of various

platforms is different and the data we get is

different – if they are available at all.

 Low Insight – because there is no apples to apples

data comparison, measuring processes across the

company or even beyond becomes virtually

impossible.

 Low Business Support Value – essentially a result

of the first two, means it is hard to measure the

success of the company and with a lack of

knowledge curve to improve even faster.

4 SENSING KNOWLEDGE

MANAGEMENT AS A BIG

DATA SUPPORTED SERVICE

MODEL

Cloud computing consists of a wide array of new

business models, the most prominent of which are

Software as a Service (SaaS), Platform as a Service

(PaaS), and Infrastructure as a Service (IaaS).

According to various documentations (e.g. TBR

Cloud Program, 2013).

4.1 Cloud Services

 Infrastructure as a Service (IaaS): IaaS is the basis

of the cloud architecture; and constitutes the

dynamic provisioning of computing, storage, and

network resources. IaaS users, in particular

system administrators, IT architects, and

developers (the latter for testing purposes), can

access these infrastructure resources as required.

IaaS provides linkage between different types of

services which in turn leads to efficiency

improvement and time reduction in business

processes (Chang, 2013). With IaaS, the cloud

offers platform virtualization to the customer.

Instead of buying servers and other network

equipment, users just rent these resources. In

addition, whereas public cloud services are

dominated by SaaS, “outsourced Private Cloud”

services (managed/ hosted) are dominated by

IaaS. For knowledge management it is necessary

using a cloud infrastructure as a service for the

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317

storage of aggregated data and knowledge to have

the required computational capacity and the

processing power.

 Platform as a Service (PaaS): PaaS is on top of the

IaaS architecture and comprises the middleware

and/or development platform, that enables PaaS

users, in particular application developers and IT

designs, to develop applications within the Cloud

and/or operate them. PaaS is the offering of a

computing platform as a service. Users are able to

deploy their applications on such a platform. The

platform offers auxiliary functionality such as a

web server, databases, load balancing and more.

For knowledge management this reflects to the

ability to design own applications based on the

provided infrastructure.

 Software as a Service (SaaS): SaaS contains the

uppermost layer of the Cloud architecture, the

actual business application: e.g. CRM, ERP,

collaboration, etc. SaaS users are generally

“traditional” end-users within business units.

SaaS is the basic cloud service models are well

known (Chandramouli and Mell, 2010). It is a

model in which software is offered as a service to

the user. The software is hosted on a server and

users access the software by using a web browser.

For knowledge management it is important to

have the ability to gather knowledge from

different – structured and non-structured sources.

Providing everything as a service is model that

emerged with cloud computing. Garter defines cloud

computing as a style of computing in which

massively scalable IT-related capabilities are

provided “as a service” using Internet technologies to

multiple external customers (Patidar et al., 2012).

Cloud computing realizes the idea of everything is a

service (XaaS) (Riemann, 2015) and can be

differentiated in three basic categories: Software-as-

a-service (SaaS), Platform-as-a-Service (PaaS),

Infrastructure-as-a-service (IaaS) (Villegas et al.,

2012).

Overall, cloud services have the following key

characteristics that are relevant for this article as there

are self-service on demand and network access to

cover the ability of accessing a broad range of data

sources. Cloud services as a paradigm for convenient,

on-demand access to a shared pool of configurable

resources. As guidance toward the cloud market two

dimensions shall be used to define the cloud service

market segments (Forrester, 2010): “What resources

are shared?” and “With whom resources are shared?”

For this article, we will use the slightly simplifying

assumption that clouds are commonly classified into

Public Clouds, Private Clouds and Hybrid Clouds

(Chang et al., 2014).

 Private Clouds: The cloud infrastructure is

provisioned for exclusive use by a single

organization comprising multiple consumers

(e.g., business units). It may be owned, managed,

and operated by the organization, a third party, or

some combination of them, and it may exist on or

off premises (Mell and Grance, 2011).

 Public Clouds: The cloud infrastructure is

provisioned for open use by the general public. It

may be owned, managed, and operated by a

business, academic, or government organization,

or some combination of them. It exists on the

premises of the cloud provider (Mell and Grance,

2011).

 Hybrid Clouds: The cloud infrastructure is a

composition of two or more distinct cloud

infrastructures (private, community, or public)

that remain unique entities, but are bound together

by standardized or proprietary technology that

enables data and application portability (e.g.

cloud bursting for load balancing between clouds)

(Mell and Grance, 2011).

5 KNOWLEDGE MANAGEMENT

AS A SERVICE (KMaaS)

Considering the need of companies of having more

valuable, data-driven insights and having already

understood that knowledge management has to do

with identifying and managing knowledge assets

effectively in order to gain this competitive advantage

and about effectively managing these assets, through

combination, sharing, and other methods leading to

their growth (Zack, 1999a; Grant, 1996) we propose

to shift the available concept to the paradigm of

cloud-based services to capture knowledge (Nonaka

and Takeuchi, 1996; Polanyi, 1967), lower

complexity, and stickiness (McEvily and

Chakravarthy, 2002; Zander and Kogut, 1995; Kogut

and Zander, 1992) and to employ a tool and to even

better manage the knowledge (Choi and Lee, 2003;

Schulz and Jobe, 2001; Boisot, 1995).

We believe in the two following main statements:

 With the use of big data and the benefit of cloud

computing we have the ability to provide a major

shift in how to create knowledge leading to a new

reality of knowledge management

 With the shift from a centralized knowledge

management information solution and asset to a

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flexible and based on pre-defined services-based

solution

If we emerge from the basic types of cloud service

models, Knowledge Management-as-a-Service adds

a context-sensitive layer to the service models

available aiming to provide the most valuable

knowledge, in the appropriate context and the easiest

way possible. Therefore and according to our

understanding Knowledge Management-as-a-Service

is a layer on top of the basic service types that

consumes the already available and aggregated data

from the big data tools and its aggregation by big data

analytics.

Knowledge management-as-a-service is a service

model based on analytic data and consisting of

aggregate data such as lessons learned, studies to

leverage knowledge from anywhere, anything, and

anyone in a distributed computing model. With the

introduction of the cloud-based paradigm to

knowledge management, any knowledge will be

easier to access and even more important we can

address on already mentioned issue – the will created

a unique and easy-to-access knowledge experience.

To embark the new service model we have to

understand three main things:

 The technology fit to allow the data capturing

 The analytic fit to aggregate the data to

information and the low the first step from data

gathering to knowledge generation

 The fit of access of knowledge

With the concept of Knowledge Management-as-a-

Service we will be able to move forward from purely

data collection to knowledge generation that delivers

the desired knowledge asset and competitive

advantage. Knowledge Management-as-a-Service

lets us access knowledge and experience structured in

a way that we can find it, understand it and use it

instantly.

Let us assume that Knowledge Management-as-a-

Service is a productized and cloud-based service

model offering a set of pre-defined knowledge assets

that allows the execution data analytics, information

aggregation and knowledge provisioning. Although

such a service harbours an economic potential and

proposes value towards data and information,

questions that need to be answered are: can an

individual and company-specific need for knowledge

supported by a service? Additionally we need to

understand how to manage the way from operational

data, information towards valuable knowledge.

5.1 Big Data Impact towards

Knowledge

Management-as-a-Service

Big data and big data analytics can be seen as the first

step in our journey of more meaningful analytics,

from quantitative to substantial qualitative analysis.

Gradually it is necessary to combine quantitative as

well as qualitative analytical methods within the big

data approach to enable more and more actionable

insight-driven decisions. This will be one mail pillar

to support. The identification of the right data and

definition of right aggregation of data plays a

fundamental role when thinking about a meaningful

integrated experience for information leading to

knowledge. To gain this competitive advantage we

have to make sure that the workflow that distils low-

value data, transforming them into high-value data

reaches the level of knowledge to support the

business adequately. The ability to generate

knowledge from a large amount of data with different

structures is part of what can determine big data

management. It is a common understanding, that big

data as a technology is a key enabler to success. Even

though we have already seen the clear distinction

between data, information and knowledge there is a

strong relationship since the potential for data and

information to turn into knowledge (Rothberg and

Erickson, 2014). Once we look beyond big data and

its enabling technology, Brynjolfsson and McAfee

(2012) suggest that organizations have to consider on

knowledge management, as the era of big data means

not just more data.

It seems that big data has its main effect on

explicit knowledge since the generation of any

analysis is inherent in big data analytics but provides

the ability to have a broader set of raw data for these

analytics to increase the quality of explicit

knowledge. However, the effect of big data on tacit

knowledge shall not be underestimated: due to the

ability to analyses masses of data and simulate

scenarios the support on an individualized learning

experience can be significant.

5.2 Cloud Services Impact towards

Knowledge

Management-as-a-Service

Since we have already proposed an answer how to

provide a new knowledge experience with the benefit

of cloud services. However, the question cannot be

divorced from a thorough understanding of the access

of data and information. For explicit knowledge

private clouds has the most limited the access to any

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319

knowledge as it ends at the companies’ boundaries.

This access to knowledge increases in other types of

clouds and has its maximum in public clouds. On the

contrary, for tacit knowledge, the type of cloud has

only little effects or it is more likely to assume, that

the private cloud has a positive influence towards

tacit knowledge since it relies more on the users itself,

their personal minds.

Therefore, access to tacit and explicit knowledge

is neither as limited as in private clouds, nor is it as

open as in public clouds, however, tacit knowledge is

more accessible in private clouds compared to the

explicit knowledge, because of the mentioned reason

stated for public clouds.

Table 1: Benefit for knowledge types in different cloud

deployment models.

Explicit Knowledge Tacit Knowledge

Private cloud Low High

Public cloud High Low

Hybrid cloud High – medium Medium – low

If we now add the context of usage, we will also

find highly differentiated benefits due to the cloud

deployment models. The organisational knowledge is

driven by mainly company internal driven knowledge

that is ti a certain degree focused on the tacit

knowledge type since e.g. career planning and

personnel data are certainly more focused on a

personnel view , learning behaviour and subjective.

Having this in mind the benefit of a private cloud is

somewhat higher compared to marketing and

technology drive knowledge aspects. Especially the

marketing knowledge dimension relies on the benefit

of a public accessible knowledge. While being mainly

explicit driven the benefit of a broad knowledge base

and network is very high – much higher compared to

the technological dimension. Even though technology

has a strong benefit from information and

communication a lot of knowledge lies within the

company and in personal experiences.

Table 2: Benefit for knowledge dimensions in different

cloud deployment models.

Organizational

Knowl.

Marketing

Knowl.

Technological

Knowl.

Private

cloud

High Low High-Medium

Public

cloud

Low High Medium

Hybrid

cloud

Low-Medium High – Medium Medium

6 CONCLUSION

Knowledge and knowledge management are largely

connected to the idea of an IT-based infrastructure.

For a successful knowledge management it is

therefore essential to handle many aspects through

and with technology: data gathering, data analytics

that ultimately leads to a better enablement of

decision-making.

While decision-making, reflects the need to

maximize cross-functional cooperation between

people who manage the data and the people who use

them we have to consider knowledge as a

fundamental object to improve competitive

advantage and decision-making. In the information

era, and more precisely in the era of digital

information, this smart asset becomes increasingly

necessary for business survival.

It is obvious that big data are much more than just

a hype as this allows us to collect and crawl almost

every data, which gives us more knowledge transfers

opportunities. On the other side cloud services have

the potential to transform the business and with the

promise of a rapid scalability, the adoption cloud

services will increase. Briefly, cloud services deliver

the dynamic and flexible infrastructure that is needed

for today’s business requirements that are mainly

driven by the factor of knowledge and the competitive

advantage that is generated out of it. Therefore,

knowledge management is in the focus of many

companies. With big data, we have now a technology

in place that is described by the 3 Vs and that provides

opportunities for data generation and data analytics

that mainly has a positive impact on explicit

knowledge. Considering the 4

V – the value of big

data it becomes obvious that it is important for

companies not to stick solely in the big data

technology nor in the big data analytics but invest

time upfront to carefully identify what is the

knowledge you would like to generate out of these

data and make this accessible to the desired audience.

Here the application of cloud services to knowledge

management allows benefiting from a flexible

infrastructure provided in different dimensions

depending on the cloud service model. The degree of

benefit often results from the type of cloud that is

used. Therefore, it is necessary to analyse the

knowledge types and dimensions prior to the alliance

of big data in a dedicated cloud environment.

The key to success lays in the definition of

knowledge management service. The idea is to define

services.

That allow an easy adoption of (new) knowledge.

Having the knowledge is unfortunately not enough.

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Therefore, the service concept will allow the

distribution of on-demand knowledge improving

knowledge sharing and business connectivity.

Knowledge Management-as-a-service (KMaaS)

is actually digital knowledge and experience

packages accessible at any time, any place and any

device. With the big data technology, it is of course

much easier to get the right data – even in real time

within a broad network of data sources that are

accessible using digital sources of knowledge,

independent of if the person that is providing the

knowledge is available or not.

Knowledge Management-as-a-service depends on

the type and dimension of knowledge. It is a context-

sensitive layer in addition to the already existing

service types. The emergence of KMaaS and the

delivery of knowledge on demand will enable new

knowledge generating and sharing models especially

within explicit knowledge and in the dimension of

marketing and technological knowledge.

Organizations can more or less immediately benefit

from reduced upfront costs and obtain increased reach

based on a scalable and dynamic platform with the

use of an agile big data technology.

These “knowledge-based-services” prioritize and

guide of what data are needed and how these data

need to be presented to be aggregated to information

and experienced as valuable knowledge. Establishing

knowledge services is therefore a continuous task. It

helps reducing information overload within times of

delivering big data at high volume, quality, and

accuracy in a timely fashion is not possible without a

streamlined approach towards managing the data to

become information.

With the idea to introduce big data and big data

analytics to the context of knowledge management.

With the provisioning of a comprehensive set of

already analysed data, enhanced these already

available meaningful and comparable data with

cloud-service-based knowledge management tools to

we are able to “turnonthelight”andletusaddress

thepainpoints:

 Lower the complexity by having introduced big

data analytics for getting data and doing the

operational analytics

 Greater insight through consistent, comparable

data, e.g.

 Insight into customer buying behaviour can

drive key improvements in the sales channel,

as is already happening

 Analysis quality issues within manufacturing

across the entire supply chain may improve the

entire production process

 Closed loop transparency – being able to

measure the success and drive greater

efficiencies

Aligned with big data and with the cloud paradigm of

a service oriented architecture might shift the

knowledge management, but the impact big data has

will affect knowledge management significantly in

dedicated areas. With KMaaS based on big data

technology and analytics, knowledge management

will be shifted while providing a way to quickly

address target group focused. At the same time, much

of what we call knowledge today will be automated

like physical labour and transactional tasks have been

widely automated over the last three decades.

Knowledge management is the cross discipline that

will help to accomplish this task of an increased

competitiveness by providing knowledge to strategy

and clarity to content. Since KMaaS is context-

sensitive we will have the ability that contextual

connect the data sources versus just focusing on one

thing at a time. It is about the ability to connect

process, people and technology in a way that

analysing a business problem, defining a solution and

realizing success can happen seamlessly and in an

integrated fashion. Knowledge Management is

nothing overly complicated or esoteric. Companies

are complex, human beings are as well, and

sometimes even technology still is. As such

knowledge management as a discipline aiming at

connecting and integrating all of the above often

failed to deliver what was promised in the first place.

However, knowledge management is simply about

finding simple, but nevertheless reliable and

sustainable solutions for concrete business problems.

For the same reason, knowledge management is never

a goal in itself, if it is done right is always about

enabling and bettering concrete business decisions

Of course only people create competitive

advantages, not hard- or software alone. But in times

of big data and cloud services it is not only impossible

but not necessary to do it without carefully designed,

integrated Knowledge Management that link

corporate strategy to a structured yet people-focused

approach to knowledge. We believe that the success

of knowledge management depends on putting all the

parts together: the right data, bid data and adequate

analytics, and a flexible service-based platform to

enable and valuing knowledge a culture to create a

real “knowledge experience leading to a sustainable

competitive advantage.

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