Founder and CEO of Amazon.Com
Jeff Bezos is the founder and CEO of Amazon.com, a company which is consistently ranked as one of the top retail sites on the internet. After graduating from Princeton in Electrical Engineering and Computer Science in 1986, Bezos worked for FITEL, Bankers Trust Company and then D.E. Shaw and Co. in New York where he helped build one of the most technically sophisticated and successful quantitative hedge funds on Wall Street. At Amazon.com, not only has he built one of the most successful e-tailers but he has showed how to build an online customer base so loyal that other e-tailers will pay Amazon big dollars to reach customers on the Amazon.com site.
Amazon.com opened its virtual doors in July 1995 with a mission to use the internet to transform book buying into the fastest, easiest, and most enjoyable shopping experience possible. Today, Amazon.com is a public company and is the place to fi nd and discover anything you want to buy online. Millions of people in more than 220 countries use the online shopping site to peruse a huge selection of products, including free electronic greeting cards, online auctions, and millions of books, CDs, videos, DVDs, toys and games, electronics, kitchenware, computers and more.
What is interesting is that Amazon.com did not return a profi t until 2002!! Mind you, since then, it has remained profi table and in January 2004 posted its fi rst full-year net profi t (of US$35.3 million on revenues of $5.65 billion in the calendar year 2003). Much of the growth of the company was due to its international division.
A TIME Magazine Person of the Year in 1999, Jeff Bezos’s approach to the scepticisms surrounding the concern as to whether Amazon.com would ever turn a profi t was to take the view that it would be short-sighted to optimize for short-term profi ts. He strategized that in the long term, investment in building great customer experience and introducing new customers to the company was more likely to create a sustainable competitive advantage. It seems he was correct – Amazon’s share value at IPO in 1997 was US$18.00 – today it is US$41.40...Slow growth and attention to the customer has obviously paid off big time!
Source: Time Magazine (www.time.com), Wired.com (www.wired.com) and Wikipedia (www.wikipedia.org)
63
The KID Triangle – Knowledge, Information and Data in the ‘E’ World
Knowledge is power
This chapter examines a fundamentally important issue for successful businesses in the 21st century – managing the information coming into and out of the organization as well as that within the organization. The chapter also introduces the concepts of information ecology, the information economy and the information supply chain which have particular implications for electronically enabled business. Finally, the chapter takes a look at knowledge and knowledge management exploring the questions of what is knowledge, how do you (or the business) acquire it, is it really possible to manage it, and why is it important to attempt to do so from a business perspective. Nonaka’s SECI model of knowledge creation is discussed as is the idea of knowledge as ‘capital’ in a business.
Chapter Objectives
After reading this chapter you should understand and be able to describe:
1. The differences between data, information and knowledge
2. What business information and information management in organizations are about 3. What the information ecology approach is and why it is fundamentally linked to good
business management practices
4. What the information economy and the associated information supply chain mean 5. The implications for electronically enabled business of managing information online 6. What portals are and their evolution to the enterprise information portal (EIP)
7. The importance of knowledge acquisition and knowledge management to the business.
Distinguishing between Data, Information and Knowledge
Data, information and knowledge are not easy to separate in practice and essentially form a continuum, with the amount of human intervention increasing along that continuum. However, the following defi nitions adopted from Davenport (1997) simplify the essential question of what is the difference between the terms ‘data’, ‘information’, and ‘knowledge’.
• Data can be defi ned as a simple observation of states of the world. Data is easily structured, easily captured in machines (i.e. it is inert), often quantifi ed and easily transferred.
Examples include the raw results of a soil test such as a pH value or a clay content value or – from a business perspective – an individual share price of a company on the stock market.
• Information can be defi ned as data endowed with relevance and purpose. Information creates patterns in the data and activates meaning, it requires a unit of analysis, is dynamic, involves a lot of human mediation, and is easily transferred. For example, you could create information by using the soil test values noted above to create a spatial variability map of a paddock for, say, pH – or in the stock market example, by creating a graph of a company’s share price over time.
• Knowledge is valuable information from the human mind and involves refl ection on, and synthesis of, information in context. Knowledge is hard to structure, diffi cult to capture on machines, is often tacit (exists uniquely in a person’s mind), is hard to transfer from person to person and forms the basis of intelligent action. For example, using the spatial variability map of pH talked about above to determine application rates of lime to manage the paddock for a particular crop requires knowledge. Similarly, a knowledgeable person will use share price trend information to help make decisions over buying and selling stock in that company.
The KID (Knowledge, Information, Data) Triangle shown in Figure 3.1 conveys the inverse difference between quantity and value of data, information and knowledge within an organization. The remainder of the chapter will follow the structure and fl ow of the triangle, dealing fi rst with data and information and then knowledge issues.
Fig. 3.1. The KID Triangle: information management basics
Types of Data and Information Commonly Found in Organizations
Data can be defi ned in many ways and includes numbers, characters, images or any other method of recording, in a form which can be manipulated by a human or via computer.
Information science defi nes data as unprocessed information which is then converted into processed information and then into knowledge (Alter, 1999).
There are essentially two types of information in modern organizations: unstructured informationandstructured information.
1. Unstructured information. This is unorganized information and according to some analysts at least 80% of most organizations’ critical information resides in this form. It includes any text-based content such as market research information, information on customer reactions to a new product, information on a competitor’s business, or socio-political and legislative environments, along with rumours, gossip, stories, etc. Classically, in an electronically enabled business, it is scattered in various types of fi le formats across a company’s network, thus very often being inaccessible or extremely diffi cult to fi nd by the individuals who need it.
Gathering this type of information is largely an ad hoc activity and is very labour intensive. Little, if any, value adding goes on as it is gathered, and it is diffi cult to control and manage – librarians being the only workers extensively trained in this type of information management.
Intellectual capital is a specialized form of unstructured information since it encompasses the intangible assets and information sources of a company that can be expected to generate revenue, either directly or indirectly (Burton-Jones, 1999). It includes intellectual property (patents, trademarks, copyrights), employee know-how, and business rules incorporated into computer programs or databases.
2. Structured information. Structured information includes both paper and computer- based information that is logically and strategically organized to ensure that it is easily accessible. It includes paper fi les and computer printouts, microfi lm, diskettes, computer tapes and CDs, optical disks, audio and video tapes.
This type of information has proliferated with the Business Systems Planning (BSP) approach where a top-down identifi cation of business information requirements and a bottom-up identifi cation and detailing of all computerized information items or elements used in business takes place. In such situations, an alignment of key data entities and classes with the processes using them within the organization and a grouping of data class and process relationships into specifi c computer applications and databases has proliferated.
Organizing and Accessing Information through Databases
There are many texts and books that address data and information management in great depth (e.g. Alter, 1999; Elmasri and Navathe, 2000), and it is not the intention of this text to go into great detail on databases; however, for the sake of completeness, this section briefl y outlines the major issues involved in managing business data and information which a manager should be aware of.
Information plays an essential role in business. Maximizing its benefi ts in the business always requires careful organization and good access methodologies. There are three main questions relating to organizing and accessing information in a business:
1. What information is in the information system?
2. How is the information organized?
3. How can users obtain the information they need?
In traditional information management the focus is very much on managing computerized data and information. The process of identifying the types of entities in a situation (specifi c
‘things’ the system collects information about), the relationship between those entities, and the relative attributes of those entities (specifi c information related to those entities) is called data modelling. Data modelling also includes the decisions involved in structuring the information in computerized information systems or databases (McLellan, 1995). As such, databases today are a component of everyday life. A generalized defi nition of a database is that it is a collection of related data (facts that have explicit meaning). A more detailed defi nition includes the following:
• A database represents some aspect of the real world. Changes associated with this aspect of the real world are refl ected by changes in the database.
• A database is a logically coherent collection of data with some inherent meaning (i.e. a random assortment of data is not a database).
• A database is designed, built and populated with data for a specifi c purpose. It has an intended group of users and some preconceived applications in which these users are interested.
• A relational database is a set of two-dimensional tables in which one or more of the key fi elds in each table is associated with a corresponding key or non-key fi eld in another set of tables.
Traditional databases and applications accessed by most people include:
• Bank accounts where funds may be deposited or withdrawn from a bank
• Hotel/airline reservation systems which enable the booking of a hotel reservation or airline seat
• Library systemswhich enable library catalogues to be accessed online.
Non-traditional database applications include:
• Multimedia databases which store pictures, video clips and sound messages
• Geographic information systems (GIS) which store and analyse spatial information such as weather maps, land use maps, bushfi re hazard maps, satellite images, taxi routes, etc.
• Data warehouses and online analytical processing (OLAP) systems which are used in many companies to extract and analyse information from very large databases for decision making
• Real-time and active database technology which is used in controlling industrial and manufacturing processes
• Internet search engines which have been built up using ‘spiders’ to scour the web for appropriate (see section later in this chapter on information quality) information to populate the database behind the search engine.
A database may be generated and maintained manually (e.g. library card catalogue), or it may be computerized (e.g. an online library catalogue). A database that includes a database management system (DBMS) is a database system.A database system includes a complete defi nition or description of the database structure and constraints which is stored in a catalogue.
The catalogue describes the structure of the primary database by containing information or metadata (data on data) on:
• The structure of each fi le (number of fi elds, characters per fi eld, etc.)
• The type and storage format of each data item (e.g. text such as RTF, HTML or graphics such as JPEG, TIF, GIF)
• Constraints to data (e.g. currency, completeness, doubts on collection technique, etc.).
Users obtain the information they need by querying the database via graphical user interfaces (GUI) developed using software to create the ‘look and feel’ a user encounters when using a system (e.g. Windows).
Text Databases, WWW Hypertext Databases, Search Engines, Spiders and Bots
A text database is a collection of related documents assembled into a single searchable unit. The individual documents can be large or small, but they should bear some relation to each other and should be stored on computer so that individual documents and information within the documents can be retrieved. They have become increasingly more important as computers and searching techniques have become fast enough to fi nd information within text documents.
Text database management differs substantially from standard DBMS software, which will include some rudimentary text/text string search and retrieval functions for locating specifi c records, in that its focus is on searching for, retrieving and categorizing specifi c words, phrases or combinations of words.
AWWW hypertext database is a database in which information is stored as a document written in the hypertext markup language (HTML) which enables it to be displayed onscreen and accessed directly from the display. Very often, these documents contain interactive links that a user may select in order to move directly to other parts of the current document. The use of the World Wide Web (WWW) has focused on this type of access because the information on it is organized as a set of hypertext documents that can be downloaded on request from computers around the world. The WWW is accessed using software called a browser (e.g.
Netscape) which provides a user’s interface to the web and displays web pages to the user.
Browsers either access the WWW via a URL or via a hypertext link within a document.
Search engines are software programs that fi nd documents or web pages that seem to be related to groups of words or phrases supplied by the user (Tyner, 2001). The GUI (graphical user interface) front-end to a search engine allows the user to enter keywords that are run against a database (most often created automatically by ‘spiders’ or ‘bots’ – see below). Based on a combination of criteria (established by the user and/or the search engine), the search engine retrieves WWW documents from its database which match the keywords entered by the searcher. It is important to note that when you are using a search engine you are not searching the internet ‘live’, as it exists at this very moment. Rather, you are searching a fi xed database that has been compiled some time previous to your search.
While all search engines are intended to perform the same task, each goes about this task in a different way, which leads to very different results. Factors that infl uence results include the size of the database, the frequency of updating, and the search capabilities. Search engines also differ in their search speed, the design of the search interface, the way in which they display results, and the amount of help they offer. In most cases, search engines are best used to locate a specifi c piece of information, such as a known document, an image, or a computer program, rather than a general subject. Table 3.1 lists some of the many search engines that exist with their URLs.
Table 3.1. A selection of popular search engines and their URLs
Search Engine URL
Google www.google.com/
Altavista www.altavista.com/
HotBot www.hotbot.com/
Fotosearch www.fotosearch.com.au/
Game Spy Arcade www.gamespyarcade.com/
Australian Jobsearch www.jobsearch.gov.au/
Spiders and Bots are software programs that move across and around the WWW automatically (Venditto, 2002). They access the WWW hypertext structure by retrieving a document, and recursively retrieving all documents that are referenced. Some are more
‘intelligent’ than others – that is, they carry out tasks automatically although you must always remember that ‘someone’ has programmed them to be so. See the Byte Idea – The Big Four to get an idea of the way in which so much depends on so few in this data and information rich world.
Information Ecology
In 1997, Thomas Davenport proposed a way of looking at information management that takes into account the total information environment within an organization. Davenport argued that the information that comes from computer systems may be considerably less valuable to managers than information that fl ows in from a variety of other sources. He suggested an approach that puts people, not technology, at the centre of the company’s information world and called the management of this approach ‘Information Ecology’ (Davenport, 1997).
There are three environments associated with managing information ecologically and all three can be involved in any one project. These are:
• The information environment
• The organizational environment
• The external environment.
A change in one will affect the others – Figure 3.2 shows the interactions between the three environments.
Fig. 3.2. The Ecological Model of Information Management
(adapted from Davenport, T. (1997), Information Ecology, Oxford University Press, p. 34)