knowledge moment

Week 3

The Knowledge Management Cycle

• Progression from Data to Knowledge

The Data-Information-Knowledge-Wisdom (DIKW) hierarchy, or pyramid, relates data,

information, knowledge, and wisdom as four layers in a pyramid. Data is the foundation of the

pyramid, information is the next layer, then knowledge, and, finally, wisdom is the apex. DIKW

is a model or construct that has been used widely within Information Science and Knowledge

Management. Some theoreticians in library and information science have used DIKW to offer

an account of logico-conceptual constructions of interest to them, particularly concepts relating

to knowledge and epistemology. In a separate realm, managers of information in business

process settings have seen the DIKW model as having a role in the task meeting real world

practical challenges involving information.

Data is conceived of as symbols or signs, representing stimuli or signals. Information is

defined as data that are endowed with meaning and purpose. Knowledge is a fluid mix of

framed experience, values, contextual information, expert insight and grounded intuition that

provides an environment and framework for evaluating and incorporating new experiences and

information. It originates and is applied in the minds of knowers. In organizations it often

becomes embedded not only in documents and repositories but also in organizational routines,

processes, practices and norms. Wisdom is the ability to increase effectiveness. Wisdom adds

value, which requires the mental function that we call judgment. The ethical and aesthetic

values that this implies are inherent to the actor and are unique and personal.

Knowledge Pyramid, Wisdom Hierarchy and Information Hierarchy are some of the names

referring to the popular representation of the relationships between data, information,

knowledge and wisdom in the Data, Information, Knowledge, Wisdom (DIKW) Pyramid.

Like other hierarchy models, the Knowledge Pyramid has rigidly set building blocks – data

comes first, information is next, then knowledge follows and finally wisdom is on the top.

Each step up the pyramid answers questions about the initial data and adds value to it. The more

questions we answer, the higher we move up the pyramid. In other words, the more we enrich

our data with meaning and context, the more knowledge and insights we get out of it. At the top

of the pyramid, we have turned the knowledge and insights into a learning experience that

guides our actions.

Information is the next building block of the DIKW Pyramid. This is data that has been

“cleaned” of errors and further processed in a way that makes it easier to measure, visualize and

analyze for a specific purpose.

Depending on this purpose, data processing can involve different operations such as combining

different sets of data (aggregation), ensuring that the collected data is relevant and accurate

(validation), etc. For example, we can organize our data in a way that exposes relationships

between various seemingly disparate and disconnected data points. More specifically, we can

analyze the Dow Jones index performance by creating a graph of data points for a particular

period of time, based on the data at each day’s closing.

By asking relevant questions about ‘who’, ‘what’, ‘when’, ‘where’, etc., we can derive valuable

information from the data and make it more useful for us.

But when we get to the question of ‘how’, this is what makes the leap from information to

Knowledge

“How” is the information, derived from the collected data, relevant to our goals? “How” are the

pieces of this information connected to other pieces to add more meaning and value? And,

maybe most importantly, “how” can we apply the information to achieve our goal?

When we don’t just view information as a description of collected facts, but also understand

how to apply it to achieve our goals, we turn it into knowledge. This knowledge is often the

edge that enterprises have over their competitors. As we uncover relationships that are not

explicitly stated as information, we get deeper insights that take us higher up the DIKW

pyramid.

But only when we use the knowledge and insights gained from the information to take proactive

decisions, we can say that we have reached the final – ‘wisdom’ – step of the Knowledge

Pyramid.

Wisdom

Wisdom is the top of the DIKW hierarchy and to get there, we must answer questions such as

‘why do something’ and ‘what is best’. In other words, wisdom is knowledge applied in action.

We can also say that, if data and information are like a look back to the past, knowledge and

wisdom are associated with what we do now and what we want to achieve in the future.

Data

1. information, often in the form of facts or figures obtained from experiments or

surveys, used as a basis for making calculations or drawing conclusions

2. information, for example, numbers, text, images, and sounds, in a form that is

suitable for storage in or processing by a computer

Information

1. definite knowledge acquired or supplied about something or somebody

2. the collected facts and data about a particular subject

3. a telephone service that supplies telephone numbers to the public on request.

4. the communication of facts and knowledge

5. computer data that has been organized and presented in a systematic fashion to

clarify the underlying meaning

6. a formal accusation of a crime brought by a prosecutor, as opposed to an

indictment brought by a grand jury

Knowledge

1. general awareness or possession of information, facts, ideas, truths, or principles

2. clear awareness or explicit information, for example, of a situation or fact

3. all the information, facts, truths, and principles learned throughout time

4. familiarity or understanding gained through experience or study

Wisdom

1. the knowledge and experience needed to make sensible decisions and judgments,

or the good sense shown by the decisions and judgments made

2. accumulated knowledge of life or in a particular sphere of activity that has been

gained through experience

3. an opinion that almost everyone seems to share or express

4. ancient teachings or sayings

• Definition Data, Information and Knowledge

Data: Facts and figures which relay something specific, but which are not organized in any way

and which provide no further information regarding patterns, context, etc. The definition for

data presented by Thierauf (1999): "unstructured facts and figures that have the least impact on

the typical manager."

Information: For data to become information, it must be contextualized, categorized, calculated

and condensed (Davenport & Prusak 2000). Information thus paints a bigger picture; it is data

with relevance and purpose (Bali et al 2009). It may convey a trend in the environment, or

perhaps indicate a pattern of sales for a given period of time. Essentially information is found

"in answers to questions that begin with such words as who, what, where, when, and how many"

(Ackoff 1999).

IT is usually invaluable in the capacity of turning data into information, particularly in larger

firms that generate large amounts of data across multiple departments and functions. The human

brain is mainly needed to assist in contextualization.

Knowledge: Knowledge is closely linked to doing and implies know-how and understanding.

The knowledge possessed by each individual is a product of his experience, and encompasses

the norms by which he evaluates new inputs from his surroundings (Davenport & Prusak 2000).

The definition presented by Gamble and Blackwell (2001), based closely on a previous

definition by Davenport & Prusak:

"Knowledge is a fluid mix of framed experience, values, contextual information, expert insight,

and grounded intuition that provides an environment and framework for evaluating and

incorporating new experiences and information. It originates and is applied in the mind of the

knowers. In organizations it often becomes embedded not only in documents or repositories, but

also in organizational routines, practices and norms."

In order for KM to succeed, one needs a deep understanding of what constitutes knowledge.

Now that we have set clear boundaries between knowledge, information, and data, it is possible

to go one step further and look at the forms in which knowledge exists and the different ways

that it can be accessed, shared, and combined.

• What are the types and challenges of knowledge?

The Different Types of Knowledge

Understanding the different forms that knowledge can exist in, and thereby being able to

distinguish between various types of knowledge, is an essential step for knowledge management

(KM). For example, it should be fairly evident that the knowledge captured in a document

would need to be managed (i.e. stored, retrieved, shared, changed, etc.) in a totally different way

than that gathered over the years by an expert craftsman.

Over the centuries many attempts have been made to classify knowledge, and different fields

have focused on different dimensions. This has resulted in numerous classifications and

distinctions based in philosophy and even religion.

Within business and KM, two types of knowledge are usually defined, namely explicit and tacit

knowledge. The former refers to codified knowledge, such as that found in documents, while

the latter refers to non codified and often personal/experience-based knowledge.

KM and organisational learning theory almost always take root in the interaction and

relationship between these two types of knowledge. This concept has been introduced and

developed by Nonaka in the 90's (e.g. Nonaka 1994) and remains a theoretical cornerstone of

this discipline. Botha et al (2008) point out that tacit and explicit knowledge should be seen as a

spectrum rather than as definitive points. Therefore in practice, all knowledge is a mixture of

tacit and explicit elements rather than being one or the other. However, in order to understand

knowledge, it is important to define these theoretical opposites.

Some researchers make a further distinction and talk of embedded knowledge. This way, one

differentiates between knowledge embodied in people and that embedded in processes,

organizational culture, routines, etc. (Horvath 2000). Gamble and Blackwell (2001) use a scale

consisting of represented-embodied-embedded knowledge, where the first two closely match the

explicit-tacit.

Explicit Knowledge

This type of knowledge is formalized and codified, and is sometimes referred to as know-what

(Brown & Duguid 1998). It is therefore fairly easy to identify, store, and retrieve (Wellman

2009). This is the type of knowledge most easily handled by KMS, which are very effective at

facilitating the storage, retrieval, and modification of documents and texts.

From a managerial perspective, the greatest challenge with explicit knowledge is similar to

information. It involves ensuring that people have access to what they need; that important

knowledge is stored; and that the knowledge is reviewed, updated, or discarded.

Many theoreticians regard explicit knowledge as being less important (e.g. Brown & Duguid

1991, Cook & Brown 1999, Bukowitz & Williams 1999, etc.). It is considered simpler in nature

and cannot contain the rich experience based know-how that can generate lasting competitive

advantage.

Although this is changing to some limited degree, KM initiatives driven by technology have

often had the flaw of focusing almost exclusively on this type of knowledge. As discussed

previously, in fields such as IT there is often a lack of a more sophisticated definition. This has

therefore created many products labeled as KM systems, which in actual fact are/were nothing

more than information and explicit knowledge management software.

Explicit knowledge is found in: databases, memos, notes, documents, etc. (Botha et al. 2008)

Tacit Knowledge

This type of knowledge was originally defined by Polanyi in 1966. It is sometimes referred to as

know-how (Brown & Duguid 1998) and refers to intuitive, hard to define knowledge that is

largely experience based. Because of this, tacit knowledge is often context dependent and

personal in nature. It is hard to communicate and deeply rooted in action, commitment, and

involvement (Nonaka 1994).

Tacit knowledge is also regarded as being the most valuable source of knowledge, and the most

likely to lead to breakthroughs in the organization (Wellman 2009). Gamble & Blackwell

(2001) link the lack of focus on tacit knowledge directly to the reduced capability for innovation

and sustained competitiveness.

KMS have a very hard time handling this type of knowledge. An IT system relies on

codification, which is something that is difficult/impossible for the tacit knowledge holder.

Using a reference by Polanyi (1966), imagine trying to write an article that would accurately

convey how one reads facial expressions. It should be quite apparent that it would be near

impossible to convey our intuitive understanding gathered from years of experience and

practice. Virtually all practitioners rely on this type of knowledge. An IT specialist for example

will troubleshoot a problem based on his experience and intuition. It would be very difficult for

him to codify his knowledge into a document that could convey his know-how to a beginner.

This is one reason why experience in a particular field is so highly regarded in the job market.

The exact extent to which IT systems can aid in the transfer and enhancement of tacit

knowledge is a rather complicated discussion. For now, suffice it to say that successful KM

initiatives must place a very strong emphasis on the tacit dimension, focusing on the people and

processes involved, and using IT in a supporting role.

Tacit knowledge is found in: the minds of human stakeholders. It includes cultural beliefs,

values, attitudes, mental models, etc. as well as skills, capabilities and expertise (Botha et al

2008).

Embedded Knowledge

Embedded knowledge refers to the knowledge that is locked in processes, products, culture,

routines, artifacts, or structures (Horvath 2000, Gamble & Blackwell 2001). Knowledge is

embedded either formally, such as through a management initiative to formalize a certain

beneficial routine, or informally as the organization uses and applies the other two knowledge

types.

The challenges in managing embedded knowledge vary considerably and will often differ from

embodied tacit knowledge. Culture and routines can be both difficult to understand and hard to

change. Formalized routines on the other hand may be easier to implement and management can

actively try to embed the fruits of lessons learned directly into procedures, routines, and

products.

IT's role in this context is somewhat limited but it does have some useful applications. Broadly

speaking, IT can be used to help map organizational knowledge areas; as a tool in reverse

engineering of products (thus trying to uncover hidden embedded knowledge); or as a

supporting mechanism for processes and cultures. However, it has also been argued that IT can

have a disruptive influence on culture and processes, particularly if implemented improperly.

Due to the difficulty in effectively managing embedded knowledge, firms that succeed may

enjoy a significant competitive advantage.

Embedded knowledge is found in: rules, processes, manuals, organizational culture, codes of

conduct, ethics, products, etc. It is important to note, that while embedded knowledge can exist

in explicit sources (i.e. a rule can be written in a manual), the knowledge itself is not explicit,

i.e. it is not immediately apparent why doing something this way is beneficial to the

organization.

Challenges in knowledge management

1.Making it easy for people to find what they are looking for

Nobody likes searching for something that they need for a long time, so as a manager in charge

of knowledge sharing, you need to make sure that all information is put together in one place,

but it’s also easily accessible.

By doing this, you’re helping users find what they’re looking for in an instant. And this saves

serious amounts of time, which can eventually be used for completing other tasks.

Wondering how you can overcome this challenge? It may be simpler than you think: add a

search bar in your internal knowledge management platform.

2. Managing community engagement

If you’re one of the forward thinking companies that have an internal knowledge sharing

platform, it doesn’t mean that it will solve all your problems by itself. The next step in your

mission is to start engaging with your team by answering questions and recognising those who

play an important role in the growth of the platform. After all, they’re those who add value to

the community.

Keep in mind: several studies have proven that gamification is one of the best ways to recognize

efforts and promote engagement.

3. Encouraging people to share their knowledge

There is one core principle in this case: the more people participate in and receive benefit from

the knowledge management platform, the more they will contribute. People like helping

others, so they will also like the idea of sharing knowledge and information of value.

But how can you convince your team to start sharing their experience? Again, the answer is

simple: people like having their efforts recognized.

Put together a simple gamification process, where the most active users are recognised for their

knowledge and efforts, and you will see participation and involvement rates grow. This is also a

great way of keeping an eye on your most valuable team members.

4. Facilitating collaboration among team members and different teams

Having a physical place where people from different departments of your company can

collaborate is a great idea. But with an increasing number of remote workers, one of the

knowledge management challenges you will constantly come across will be facilitating

collaboration for team members, regardless of their location.

A great example of solving this is to customize the ideation process by identifying and clearly

outlining its different stages. This can keep members updated and engaged, no matter what team

they’re part of.

5. Measuring knowledge contribution and rewarding active users

Last but not least, you should be able to track whether the goals and objectives of

your knowledge management efforts were met. And if the answer is positive, you also need to

reward the most active users in such a way that everybody will be encouraged to take part and

add content.

In order to do this, our recommendation is to start by defining your goals, then use advanced

analytics to help you keep track of both your progress and the return of your knowledge

investment. As you observe goals being met, make sure that those who helped to reach these

points get the praise they deserve. This will encourage more and more members of the company

starting to contribute.

As a manager in this fast-paced business environment, you will eventually deal with

some knowledge management challenges. The good news is that you can gradually eliminate

most of them by having a knowledge management platform.

This will make it easier for employees to add content and generate a huge database of intrinsic

knowledge, not to mention how easy it will be to find the information they need. Collaborating

will be easier, you can quickly see who the top contributors are and find out through analytics if

your achieve your goals.

• Measuring Knowledge Management

Measurement has always been a divisive topic in KM. Some knowledge managers insist that

anecdotal evidence is more powerful than data and that the energy involved in calculating KM’s

business impact would be better spent improving the organization’s KM offerings. This attitude

is understandable. It takes a lot of energy to prove KM’s worth, the exercise does not in itself

generate any value for the organization, and executives who are skeptical of KM may

rationalize away even the most carefully constructed metrics.

But despite the challenges inherent in measuring something as intangible as KM, APQC

strongly recommends that you do so. Here are a few of the reasons for measuring levels of

knowledge management:

1. By keeping you honest about the goals you’re trying to accomplish, measurement

reminds you of what’s most important and prevents you from straying too far from

the big-picture vision laid out in your KM business case.

2. Measures paint a picture of progress during the early stages of a KM

implementation, before tangible results can be discerned.

3. Data helps you recognize problems and course-correct when a KM tool or approach

is not working as planned.

4. Analysis reveals less-engaged groups so that you can design targeted strategies to

build awareness and motivate participation. (You can read some good

examples here.)

5. If you are able to show evidence of KM’s effect on the business, your KM program

will be better protected during future organizational changes or cost-cutting efforts.

What the Data Says

APQC’s knowledge management metric of the month shows great diversity in the methods used

to evaluate KM programs. The highest proportion of organizations use feedback from leaders

and users, combined with success stories and other anecdotal evidence, to show that KM is

making a difference for the business. A majority also use activity and satisfaction metrics to

gauge whether KM is reaching its target audience(s) and achieving its intended purpose.

Business impact and ROI measures are less common, with 36 percent and 24 percent using

these methods respectively.

While impact and business value measures are less widespread than other forms of evaluation,

KM programs that use these methods tend to get higher marks from decision makers and more

robust financial support. For example, KM programs that track either ROI or KM’s impact on

business outcomes are 68 percent more likely to be viewed as effective by leaders and 52

percent more likely to anticipate an easy next KM budget approval.

Not surprisingly, KM teams that tie knowledge sharing and reuse directly to the bottom line

have the easiest time getting the funds necessary to continue and expand their programs. Among

KM programs that track business value measures, those that gauge cost savings from KM are 63

percent more likely to expect their next budget approval to be easy or very easy. Similarly, KM

programs that link KM activities to increased revenue are 46 percent more likely to anticipate an

easy budget approval.

However, non-financial business impact measures such as cycle time reductions, time savings,

and quality improvements appear to be equally valuable in building leaders’ confidence in KM

and convincing them that it is fulfilling its intended purpose. KM programs that track any

business value measures are more than twice as likely to be rated as effective by leaders when

compared to KM programs that assess none.

Acting on the Data

The case for KM value measurement is compelling, and we urge knowledge managers not to

neglect this aspect of their programs, even if sponsors and leaders have not demanded the data.

Activity measures and success stories may be enough to demonstrate the promise of early

efforts and secure funding for the expansion of small-scale implementations. But as your KM

program grows and matures, someone will eventually ask for hard evidence that KM is worthy

of further investment.

And don’t delay: Starting your measurement program early will make it easier to assess KM’s

business impact down the road. For one thing, developing key performance indicators as part of

your initial business case will ensure that your measures are meaningful to leaders and align

with KM’s strategic objectives. Initial measurement also provides accurate baselines for KPIs,

which will help you compare costs, cycle times, quality levels, and other outcomes before and

after KM implementation. And the longer you track your metrics, the more longitudinal data

you have, which bolsters the credibility of any trends you detect.

Even if your current crop of leaders believes in the mission of KM and does not require

statistical “proof” that it is working, remember that the only constant is change itself.

Organizations merge, are acquired, reorganize, shift strategies, cut costs, and assign new

executive teams all the time. Value measures may seem like optional “nice to haves” today, but

you will be much better equipped to sell your KM program to new (or newly frugal) leaders if

you can support your argument with solid metrics.

• The centrality of data analysis and data scientists

Data Analytics vs. Data Science

While data analysts and data scientists both work with data, the main difference lies in what

they do with it. Data analysts examine large data sets to identify trends, develop charts, and

create visual presentations to help businesses make more strategic decisions. Data scientists, on

the other hand, design and construct new processes for data modeling and production using

prototypes, algorithms, predictive models, and custom analysis.

Is data science important? It’s a term that’s talked about a lot but often misunderstood. Because

it’s a buzzword it’s easy to dismiss; but data science is important. Behind the term lies very

specific set of activities – and skills – that businesses can leverage to their advantage. Data

science allows businesses to use the data at their disposal, whether that’s customer data,

financial data or otherwise, in an intelligent manner. It’s results should be a key driver of

growth.

However, although it’s not wrong to see data science as a real game changer for business, that

doesn’t mean it’s easy to do well.

In fact, it’s pretty easy to do data science badly. A number of reports suggest that a large

proportion of analytics projects fail to deliver results. That means a huge number of

organizations are doing data science wrong. Key to these failures is a misunderstanding of how

to properly utilize data science. You see it so many times – buzzwords like data science are

often like hammers. They make all your problems look like nails. And not properly

understanding the business problems you’re trying to solve is where things go wrong.

What is data science?

But what is data science exactly? Quite simply, it’s about using data to solve problems. The

scope of these problems is huge. Here are a few ways data science can be used:

• Improving customer retention by finding out what the triggers of churn might be

• Improving internal product development processes by looking at points where faults

are most likely to happen

• Targeting customers with the right sales messages at the right time

• Informing product development by looking at how people use your products

• Analyzing customer sentiment on social media

• Financial modeling

As you can see data science is a field that can impact every department. From marketing to

product management to finance, data science isn’t just a buzzword, it’s a shift in mindset about

how we work.

Data science is about solving business problems

To anyone still asking is data science important, the answer is actually quite straightforward.

It’s important because it solves business problems. Once you – and management – recognise

that fact, you’re on the right track. Too often businesses want machine learning, big data

projects without thinking about what they’re really trying to do. If you want your data scientists

to be successful, present them with the problems – let them create the solutions. They won’t

want to be told to simply build a machine learning project. It’s crucial to know what the end

goal is.