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Data, Information, and Organizational Knowledge
Data, Information, and Organizational Knowledge
Introduction
The increasing competitive business landscape in the market place needs organisations innovatively manages their organisational knowledge in a manner that is cognisant with their organisational objectives. For the optimal overall business strategy, organisations have a need to effectively and efficiently create, locate, capture, and share their organisational knowledge. This implies that the created knowledge should be preserved for future use and easy distribution. Data can be considered as facts or observations about a particular phenomenon, which might not be meaningful and useful since it has not been processed. There are various types of data such as scientific, analytic, attitudinal, and socio economic data, which could be primary (raw data) or secondary, and can be found from both external and internal sources.
According to Engeström, (1987), information can be referred to as data that have already been processed through purposeful analysis. Information has more subjective values than raw data, since it is associated with a particular view point or theory. The basis of data and information being applied to beliefs, values, context, and attitudes creates knowledge. Different people within an organisation may hold a variety of knowledge over the same topic or subject, and this is a form of organisational knowledge creation. Knowledge can be defined as what a group of individuals has come to believe and value based upon meaningful collection of information through experience, inference, or communication. Knowledge can be stored and manipulated; therefore, organisation requires that they manage knowledge in terms of as an object and as a process.
Information Systems
Every business organisation or company deals with information systems or sub systems either through a lesser or greater extent. Information systems enable the organisations to handle information by collecting data, and interpreting it. This also includes availing it to the workers via a distribution system. This information could be marketing information, accounting results, company reports, cost projections, which needs to be manipulated into useful knowledge to support decision making processes.
Information systems comprise of software, hardware, data, techniques, and people which are designed to create information that supports knowledge creation that is in line with the organisation’s short term and long term goals. The types of information systems that can be found in business organisations are management information systems, transaction processing systems, and decision support systems.
Organisational Knowledge
Organisational knowledge management involves efforts in information seeking, information sharing, tasks undertaken in the work place, collaboration, and construction of organisational memory. A knowledge management structure comprises of repositories of explicit knowledge, refineries for accumulating, managing, transferring, refining, and distributing the knowledge. Knowledge management is organisational processes and strategies that are designed through information technology management processes together with strategies that significantly enhance the organisation’s ability to apply the knowledge resources in solving organisation problems (Krishna, &Schrader, 2002).
A major problem with knowledge management systems in over indulgence and over emphasis on information technology, this occurs at the expense of creatively designed knowledge management roles and responsibilities. The organisation’s knowledge management architecture in many organisations consists of knowledge or expertise centres, organisational learning, knowledge mapping, and integrating the company’s technological resources. Organisational knowledge management must address the cross functional and cross organisational processes that enable knowledge to be created, shared, and applied.
Knowledge Based Theory
The knowledge based theory of the firm looks at knowledge as the ultimate significant resource strategy of the firm. Knowledge based resources cannot be replicated and socially complex, heterogeneous knowledge and capabilities among firms are the main factors that determine sustaining a competitive edge coupled with superior performance. Companies such as Honda, canon, and 3M are some real world examples that use organisational knowledge creation. The knowledge in organisations is integrated through multiple entities such as organizational culture and identity, documents, routines, policies, systems, and people. This type of resource based view in these firms considers the importance of knowledge in attaining a competitive edge, these systems considers knowledge as a generic resource (Bartlett, & Toms, 2005).
Knowledge based theory used by these firms interprets the intelligence of the firms as not coming from the chief executive officer, but comes from gathering of any knowledge by individual employees. These firms apply the theory to enhance their ability to adapt to the turbulent environment through knowledge creation. Knowledge creation forms the basis of innovation and the company’s capabilities. The flow of information within the organisation is such that all employees can obtain access to current information on the market, technology, and competitors. The firms emphasize enhancing mobilization of tacit knowledge, so that coordination of knowledge flow and coherent knowledge creation unit is maintained. The general goals of the knowledge creating firms are to construct new theories for organisational knowledge creation, provisions of reasons why certain companies are leading in innovation, and developing universal management models. The organisational knowledge comprises of the modes of knowledge conversion in socialization, externalization, combination, and internalization (Chatman, &Pendleton, 1995).
Conclusion
The data, information, and organisational knowledge management must be accurate, time bound, ensures security and confidentiality, and must possess integrity and reliability. The collection and transfer of organisational knowledge is relevant from the workers to the customers, suppliers, partners, and collaborators. In order for organisations to maintain a competitive advantage and leader in innovation, they have to build and manage their knowledge assets. This is useful for strategic planning.
References
Bartlett, J. & Toms, E. (2005). Developing a protocol for bioinformatics analysis: an integrated information behaviour and task analysis approach. Journal of the American Society for Information Science and Technology, 56(5), 469-482
Chatman, E. &Pendleton, V. (1995). Knowledge gap, information-seeking and the poor. Reference Librarian, (49/50), 135-145
Engeström, Y. (1987). Learning by expanding: an activity-theoretical approach to developmental research. Helsinki: Orienta-Konsultit.Krishna, A. &Schrader, E. (2002). The social capital assessment tool. In C. Grootaert &T. v. Bastelaer (Eds.), Understanding and measuring social capital (pp. 17-44). Washington, DC: International Bank for Reconstruction and Developments.
Conclusion-Internet Marketing
In conclusion, this discussion has touched on a few of the many positive aspects associated with marketing and advertising on the internet. The ease of use and the quickness of the service are of the utmost importance. When consumers make purchases through the internet, it results in cost savings for them since it eliminates the need for delivery services. In addition to this, the treatments are pretty reasonable in terms of price. The cost of advertisements in traditional media such as newspapers is much higher than the cost of advertisements on the internet. Technology has developed into the single most important component that must be considered in order to get access to anything on the Internet in the modern day. Social media platforms like those outlined above may benefit businesses in becoming more successful. In addition, companies have the opportunity to engage with their customers via the use of online advertising. When a customer buys a product from a company, the firm could send the customer an email, and the buyer might also be given the opportunity to provide their feedback on the website of the company. In spite of the positives that were discussed before, there are a few drawbacks that you should be aware of. When it comes to advertising on the internet, there are legitimate worries about infringing on copyrights and trademarks. Because of this, it changes with time, and something that is helpful for marketing right now could not be relevant in a few weeks’ time at all. Expenses can become more expensive as a consequence of these two problems. When it comes to marketing on the Internet, negative feedback about a company’s products or services is common, which may be detrimental to the reputation of the business. Overall, the benefits outweigh the drawbacks making internet marketing a must-have for businesses in the future.
Data Warehousing and Data Mining
Data Warehousing and Data Mining
Introduction
Information technology is growing every day, with new things that we can do with it to make life easier. Growth of IT has caused us to change the way we handle data from our businesses. Today managers have to make a decision on what to do with the data they have in their firms. To make these decisions, a manager has to be well informed on the advantages of having huge data banks and how he can use it to transform his firm. Thus the importance of data to a firm cannot be overlooked, especially data that is generated by the firm itself. Data warehousing is a profitable venture only when a firm knows the worth of its data. This paper will present new insights on business data and how a firm can use it for its benefits.
Data warehousing
Most people are familiar with a database, maybe the concept of a database and how it works or have heard the word database in conversations. To understand the concept of data warehousing, you must be familiar with databases. So, a database is like a store, where all data is kept in a particular order. All data that is entered in a database is stored in groups, using a particular relation.
Data warehousing uses the same concept of as a database, however more security measure are taken in a data warehouse. A unique database is created for major business purposes like analyzing data to get a trend and understand sessions.
Current trends and benefits of data warehousing
The main purpose of a data warehouse is to store data which is used for business analysis and in making the right managerial decisions. Data warehouses are an integral part in data analysis for the business, today a business stands a chance to save its operating costs using a data warehouse. Instead of conducting the same survey every year, a team can conduct one survey and have it stored in digital format. This survey will be analyzed at different angles and important managerial decisions can be made from it.
Managers now have a chance to access business data flexibly, and this data is not manipulated. The data is extracted from data sources by an independent system, this shows that the data is credible and can be used to make important decisions.
The main advantage of data warehousing is that data access is easier and flexible but at the same time it is secure. Look at it this way, a manager who does not have a firm understanding on how databases work can access information from them and make a decision without involving a number of workers to assist him.
Businesses have a chance to improve their strategies after they implement data warehousing. Business strategy is formulated by analyzing data from the past, this will now be possible. Different levels and departments of a firm can easily access each other’s data in a data ware house.
Data warehousing will bring a new perspective to your firm; data will be easily accessible but at the same time not manipulated thus credible. The main worry about data that is easily accessible by a firm is its credibility. Managers don’t have to worry about that when implementing data warehousing.
Data Mining
This is comparing trends or patterns in large data banks to understand the relation. In business it is the comparison of large customer data sets to understand how customers respond to different things in order to come up with marketing strategies.
Data mining is important for data comparison. The concept works by obtaining data from a large data set and making it understandable, getting the particular details that you requires or that are useful to you from a data bank. A data bank is a place where a lot of data is stored; data mining helps you get the relevant information from a pool of so much data.
Why should a business conduct data mining?
The main business advantage in data mining is creating a base for comparison. It is not a specific system that is created to conduct data mining. A business goes back to the data that it has collected over a period of time.
Let’s say sales records for the last six months, a company goes back to these records and learns who buys what from them. The business team is also able to learn what items are bought at certain times and at what prices do customers buy the items in large numbers. Such information is very important for a business to strategize on its marketing methods and supply method. They are able to learn what items to make available or to stock at what time seasons.
A business goes back to its records to obtain data and interpreted it. The advantage is that the firm has obtained all this data for free and they use it to increase sales and come up with new strategies. This is data that would have been deleted but now the business recycles it.
Often when a firm collects data from customers, they use it for a very short time, maybe only to perform a particular task. If the data was collected from a survey, the firm only uses it for analysis and dumps it or puts it in the archive. Now a firm has to maintain all its records because every piece of data is important.
Data mining for a firm is now as important as recycling, old data is as good as new data. A firm can implement data mining by installing software that goes through all their data gathering information for them. The software will help in making work easier; it works faster and requires less people to operate. I am not an advocate of downsizing or laying-off workers but with data mining, fewer workers will be required to conduct data searches. These workers can be assigned tasks where they will be more useful.
Examples of companies that use data warehousing
American Airlines
They use data ware housing to increase their sales by reducing the number of forged and fake airline tickets. Data of the genuine airline tickets that are generated by the company is made accessible to all departments in the company. This data cannot be altered by any person; it is used only for reference by workers to know the genuine tickets. This system enables the company to know which tickets they have sold once and cannot be used again and also which tickets they never sold.
With data warehousing the company is able to use the data they have to their advantage profitably.
Sears
The company aimed at increasing its storage space, to have all data that it collects in one central storage point. Creating a central storage point enabled them to create a data warehouse where they stored all data. This was easily accessed by managers and other workers who were interested in it. It was also not manipulated, thus it was credible to use in making business decisions.
This gave the store a chance to compare data from different geographical locations but was stored in one point. They were better placed to understand what their clients wanted and they were able to provide it at the right time.
Architecture used in data warehouses
Enterprise warehouses
This is a data store for all the information relating to the firm. It collects information which is generally linked to the firm, no specifics.
Virtual Data Warehouse
Many databases are put together to form one large data resource center. It becomes a data ware house because it combines different sources of data for information analysis.
In these architectures there are different models which are used to implement them;
A data model is a system for data flow; how data comes in and out of the ware house is presented by a data model. Models are presented in levels from the conceptualization to the actual implementation.
The conceptual level of a data model is where the idea and relationships between data is established. No implementation is made, however this level provides the details for design.
The logical level is where the data is analyzed and described to create a relationship which is used in the data warehouse. All data that will be in the data warehouse must be identified and a logical relationship established. How is one data item related to the other, this is what this stage seeks to establish.
The physical data model is the final stage. It is similar to implementation, as the data warehouse is now put into motion. This is the last stage, these stages are procedural, and you cannot do one before the other.
Optimization techniques for data warehousing and data mining
The company has already taken the first step by implementing the use of a data base. To optimize this, now data must be protected. The database provides for that. The company should store all its data in the databases that it runs; this will be the central point of storage i.e. the data ware house. Managers can access the data and use it to learn business trends and improve practice.
For data mining, the company will have to purchase the software which will assist them in the process. All data that is collected by the company should be stored and never disposed because it is valuable. The software will assist in data analysis and comparison. It also helps in making data searches easier for the firm.
In conclusion
It is time to use the available data resources to improve our management skill and decision making. The firm has a huge resource of data from its clients and should use it to its own advantage. Recycle your data and save the cost of searching and conducting surveys in your business.
References
Han, J., & Kamber, M. (2006). Data mining concepts and techniques (2nd ed.). Amsterdam: Elsevier ;.
Hobbs, L. (2005). Oracle Database 10g data warehousing. Amsterdam: Elsevier.
Munnelly, B., & Holden, P. (2002). Databases. London: Prentice Hall.