Hoorcollege 12 Flashcards
Data
Data is a collection of non-random symbols, numbers, words, images, and sounds. Data
represents something and can be stored somewhere.
Characteristics of data are:
- Represents facts
- Recorded by observation or research
- Not organized to convey specific meaning
Information
Information is data related to other (contextual) data.
Characteristics are:
- Processed data
- Contextually relevant
- Meaningful and useful to human recipients
Knowledge
Information becomes knowledge when you do something with the information. Knowledge
= skills + experience + accumulated learning + judgement.
Characteristics of knowledge are:
- Result of activities and related information processing.
- Needed for:
o Decisions
o Understanding and relating data or information
Knowledge as a production factor
Knowledge economy: Knowledge as a fourth production factor (next to labor, land, capital)
- Intellectual capital
o People’s skills and knowledge - Especially important in the service sector
o Marketing, Communication, Consultancy, IT, Non-profit, …
o Increasing proportion of the economy - Especially nowadays:
o Flexible work force (freelancers, no life-time jobs)
o Globalization
o Leaner organizations (more outsourcing, cooperation, integration)
o Technology: Social media
Knowledge Management
Knowledge is an important production factor in organizations! But…
- Knowledge resides in people’s minds, therefore…
- Difficult to manage!
Knowledge management is aimed at managing this important source for organizations through the following processes
- Acquiring knowledge (externally and internally)
- Accessing knowledge (who knows what, where is it stored)
- Sharing knowledge (how to get info from one person/team to another)
- Application of knowledge (what to do with knowledge)
- (Evaluation)
Two views on knowledge management
- Objectivist perspective (explicit knowledge)
- Subjective perspective (tacit knowledge)
Objectivist perspective (explicit knowledge)
Objectivist perspective (explicit knowledge)
- Knowledge is objective
- Convert tacit to explicit knowledge
- Knowledge can be codified and stored
- Key role for ‘old-fashioned’ IT (databases, outlook)
Subjective perspective (tacit knowledge)
Subjective perspective (tacit knowledge)
- Learning is doing
- Knowledge is embedded in practice, socially constructed culturally determined, both tacit and explicit
- Social interaction and collaboration
- Shared understanding
- Key role for ‘new’ IT (wiki’s, blogs, Teams)
1st generation KM
1st generation KM: top-down, focus on ICT, extracting knowledge from individuals
1st Generation Knowledge Management (KM) focuses on capturing, storing, and sharing explicit knowledge within an organization. It relies heavily on technology, such as databases and document management systems, to codify knowledge for easy access and reuse. The primary goal is improving efficiency and reducing redundancy in knowledge work.
2nd generation KM
2nd generation KM: bottom-up, focus on interaction, social networks & communities of practice
2nd Generation Knowledge Management (KM) emphasizes the importance of tacit knowledge—the personal, experience-based knowledge that is harder to document. It focuses on fostering collaboration, knowledge sharing, and learning through human interaction, such as communities of practice, social networks, and storytelling, alongside technological tools.
Objective perspective: solution
- Formalization
- Hierarchy & control systems
- Rewards & punishments
- Rules and codification
- Databases
-> Make knowledge explicit
The objective perspective on KM focuses on rules & regulations and technology to store and manage knowledge. You try to formalize rules for people to share knowledge. For example, in some organizations, you are forced to write down what you did that day. This is compulsory. For example, in healthcare, people who take care of people who need help (e.g., healthcare professionals in hiospitals or people giving care at home or in facilities) have to write down in a system for every client what they did that they and how the client is doing. This is compulsory. In this way, for each client, information is stored in a database
Three categories of knowledge management systems (objective perspective)
-Enterprise-Wide Knowledge Management Systems
-Knowledge Work Systems
-Intelligent Techniques
The first, relates to ERP systems and other company databases that are used to store information about sales, workers, finances, etc. The second are personal software/apps that people use to work with information: Think Microsoft office, google scholar for us, but also more complex packages such as CAD.
Finally, there are knowledge management systems that help us in analyzing and processing information to make decisions, and may sometimes make decisions for us. We call these systems: Business Intelligence Systems.
Subjective perspective: Solution
- Horizontal coordination
- Social capital
- Communities, Virtual Organizations
- Facilitate interaction: e-mail, social media, coffee conversations
- Facilitate learning: wiki’s, discussion boards, social bookmarking, etc.
-> Support knowledge sharing
-> Harnessing collective intelligence
In many organizations, people do not need to keep records, they just have to do the work. Or the organization is a professional organization, where people have large autonomy. In these cases, you want to focus on sharing tacit knowledge. You do this by horizontal coordination, i.e., stimulating communication across hierarchies, so more peiple interact with each other. You can also build social capital. Social capital means the benefits you derive from your social network. Basically, if you are connected to (many) other people, and you like and trust each other, you are much more likely to share information. In general thus you want to stimualte interaciton with others. Again, this is where many organizations hope that social media will enable more communication between peope (but we know that does only work if the structure, culture, etc. of the organization also align with this).
Explicit vs Tacit knowledge
-Socialization: sharing of experiences, imitation, etc.
-Externalization: articulation personal knowledge
-Combination: exchange of explicit knowledge
-Internalization: making explicit knowledge your ‘own’
You can try to make tacit knowledge explicit and all other combinations.
From tacit to tacit: is about how to share tacit knowledge: this is best done by sharing and communication. Socializatoin
From explicit to explicit: this is managing and combining data from different sources (combination), e.g. storing and managing all data in an ERP.
From tacit to explicit: externalization (because it goes from inside someones head to externally). This is about trying to store tacit information somewhere else than in people’s minds. For example, interviewing people and storing that information. Having people write down what they know.
This is where social media comes in. For example, WhatsApp groups are an example of externalization. The group history contains a lot of conversations, and these can be searched (and maybe stored elsewhere). This also workds this way for organizations: (some) Teams convesrsations are stored and available for everyone online to check and search through.
From explicit to tacit: internalization. This basically is learning. In this course, you receive a lot of explicit info (slides, summaries, literature, your notes) that you then learn for the exam and if you’re good you are able to combine that information into new ways.
Social media & knowledge management
Social media may really help sharing tactit knowledge in organizations. All the functionalities of social media help sharing knowledge in an organization (and between organizations and customer/other organizations).
Identity: when all employees create a profiule page with their interests and experience, you know who to contact for which issues.
Sharing: stimulate information sharing in organizations, e.g. by giving rewards.
Conversations: socia media stimulate communication between people.
Presence: know wo works where and on what.
Relationships: build connections between people in an organization, also outside of their normal team.
Reputation: allow people to like, react, acomment on each oether posts to enable knowledge building.
Groups: create groups of experrts who can work on certain tasks.
Consequences of Social Media for KM
-Less control, predictability from the side of the organization,
-Greater power and involvement on the side of the consumer/employee
-More control, less formality from the side of the employees
Social media is difficult to manage. You cannot just force people to post on social media. And it leads to much informtaion that may be difficult to filter.
Business Intelligence
BI means that you use data to make decisions in an organization about sales, marketing, strategy, operations, etc. etc. It involves information and information systems, but of course also people who work with BI apps and the processes and practices related to BI in organizations (e.g., management who requires daily reports).
-Business Intelligence (BI) refers to skills, processes, technologies, applications and practices used to support decision making
-Improving organizations by providing business insights to all employees leading to better, faster, more relevantdecisions
Business Intelligence Systems
Information Systems that help employees (mostly management and executive layers) to make decisions are…
Usually called Business Intelligence (BI) systems, but also
-Executive Information Systems
-Decision Support Systems
All these systems use data gathered from different sources, and combine these data in order to help making decisions.
These systems often make use of a ‘Data warehouse’, a large database systems containing detailed company data on sales transactions, business processes, etc.
BI systems usually gather information from multiple sources. Think of the AH bonuskaart. They collect information about your sales in a database, They combine this with other data from you (demographic, maybe even social data based on social media use, e.g., bought from Facebook), and external data (e.g., they combine sales data with weather data to predict sales of e.g., icecream, BBQ supplies, etc.). All this information is aggregates and combined in one application (A BI system), that people then can tap into to do analyses.
This is used a lot in marketing of course, but also used within organizations to predict manufacturing (who works best, which supplies do we need, how to optimize factory, etc.).
Business Intelligence: Uses
There are two uses of BI. The first, is operational BI. This refers to the basic reporting in an organization: how many sales in a certain time period, how many units produced, what error messages, etc. This is standard for day to day business.
The second one is more strategic/tactical use, this is using BI to make strategic decision. This can be a human process: humans using data to make strategic decisions, but can also be supported by IS, for example statistical software, of AI that advices or makes decisions. Most well-known (and relatively simple) is forecasting: making predictions about the future based on previous data. Very simple example, if you see that a lot of beer and oranje tompoezen are sold around the 27th of April, predictive modeling may be used to advice on keeping extra stock in store next time. Buit this can be waaaaay more complicated of course. You can use advanced statistical techniques to predict sales over time, for example.
Types of BI: Reporting
There are two types of BI. The first is reporting BI, This is linked to operational AI (mostly but not necessarily, data can also be used for strategic decisions). This BI collects and sorts, filters, and visualizes/reports data, but provides no conclusions, just the data. The interpretation is up to the analyser.
A reporting application is a BI app that inputs data from one of more sources and applies reporting operations to produce business intelligence:
-That is: They collect data from different sources and sort, filter, group, calculate and format the data in order to produce reports.
-Examples:
0 RFM Analysis (YouTube)
0 OLAP (Example)
-Reporting applications leave the decision making to the business analyst
Types of BI: Data mining applications
Second type of BI is data mining. These are BI applications that help make decisions. Refers to not only reporting data, but analyzing the data in order to come with new information and sometimes make decisions. Basically what this means is statistical analysis. Many of the statistical tests you learn are part of data mining: regression, factor analysis, chi-square. But there are also way more advanced techniques, such as cluster analysis, machine learning, etc.
Exploratory data mining: is unsupervised: looking for patterns in the data, but no statistical testing
Confirmative: is what you learn in statistics: this is data mining in order to confirm or test expectations.
Data mining is the application of statistical techniques to find patterns and relationships among data for classification and prediction
-That is: Data mining is not only reporting but also analyzes the data in order to come up with new information and sometimes even decisions.
-Exploratory data mining (unsupervised)
0 Cluster Analysis
0 Neural networks / Machine learning
-Confirmative data mining (supervised)
0 (logistic) Regression analysis
0 Confirmatory factor analysis
