Data, Information, Knowledge, and Wisdom, Essay Example

Introduction

The below essay is focusing on the difference between four different terms used to describe what people perceive as valuable snippets of truth. The author of the current essay will analyze how data can be turned into information, transformed into knowledge, to create wisdom that will be used for creating guidelines, principles, and theories. The current essay is arguing that without data, there is no information, and knowledge cannot be obtained without information. Learning for organizations, therefore, is important to create a wisdom that can help create guiding principles, frameworks, and practices.

According to Bellinger, Castro and Mills (2004), data, information, and wisdom are built upon each other, and the best representation of the connection between the four definitions is through the Wisdom Pyramid.

The Necessity of Data

Without data, or symbols that can be turned into information, no knowledge that is reliable can be obtained. As an example, in a marketing firm, the company would not have a knowledge of their customers’ preferences without collecting data. Data collection can be completed in different ways. One of the simplest ways of collecting data about customers is through surveys (online and offline). However, due to technological advancements and the spread of the internet, now it is possible to collect data about usage and searches of customers at a real time. The data itself is a single information, and without assigning values, references, and significance to them, it is invaluable for a company. Knowing how much time users spend on a site, for example, would not provide information and knowledge for the decision-making team. Signing up for a Google Analytics account, and analyzing data that tells the company which pages of the website the customers are most likely to enter the company’s domain, and which ones they leave the site will provide important information. Likewise, knowing how many purchases were made online would be one single set of data, meaning nothing. However, comparing the data with the number of visitors to the site will provide important information, such as “conversion rate”, calculated by dividing the number of purchases with the number of visits. The knowledge, however, cannot be obtained without first collecting data.

Gaining Information from Data

Data becomes information when a meaning is assigned to the sets of figures. As an example, for a marketing company, information gained from data can be that there are more people looking for holidays in the winter than in the summer. This means that customers have a preference for shopping for holidays when it is dark and cold outside. The data is translated into information, however, it is still not knowledge. Understanding data transforms it into information. Specific rules of data analysis need to be assigned, for example, and it is also important that those collecting data have a specific purpose. For example, without breaking down website usage data to days, months, or seasons, the information cannot be obtained. Likewise, only knowing that women there are 60 women and 25 men visiting the site each day will not translate into information. Information is created when a meaning and understanding is given to the dataset, for example that there are more women looking at the site than men, or that there are more holiday purchases made in February than in May. As Bellinger et al. (2004) Confirm, information is: “data that are processed to be useful; provides answers to “who”, “what”, “where”, and “when” questions”. Answering questions, however, does not necessarily lead to knowledge, as the answers need to be specific to the goal of the organization, and provide answers to the company’s other questions related to alternatives and potential strategies.

Knowledge

According to Bellinger et al. (2004), knowledge is created when the information that is gained from data can translate into answers to the company’s “how” questions. As an example, in the case of the marketing company selling holidays, the most obvious question would be: “how to sell more holidays?”. Knowing that the company’s website is visited by more women than men, and people are more likely to make purchases in February, the answer can be created using a logical approach. If the company would like to sell more holidays, they have to create advertisements that appeal to women, and put them online and in electronic media in the winter. Simply put, “knowledge is the appropriate collection of information, such that it’s intent is to be useful” (Bellinger et al., 2004). Likewise, when the company creates the advertisement, they will possibly collect data on how many people saw it, how many men and women liked it, and how many decided to visit the site based on the ad. This data can then be turned into information that gives the company an idea about the effectiveness of the advertisement. The information can then be turned into a knowledge, based on the preferences of customers and feedback received. For example, when customers say that they liked the dogs on the ad, the company’s marketing management will know that their customers have a strong preference for dogs in advertisement. Based on the knowledge, the company can create wisdom, which will set some marketing guidelines and principles based on experience and the “big picture”.

Wisdom = The Final Step

Unlike the previous processes, wisdom is abstract, and requires creative association. Being an “an extrapolative and non-deterministic, non-probabilistic process” (Bellinger et al., 2004), it requires humans to use various levels of consciousness. Wisdom focuses on the large correlations and problems, such as telling the difference between good and bad marketing, ethical and unethical business behavior, and positive or negative attitude. It can also be used to create values for the company. As an example, from the pool of data, information and knowledge obtained through market and customer research, the company that sells holidays has found a pattern of purchase, and got to know customers. However, in order to create a full marketing strategy, they need to align market conditions, competition level, regulatory and ethical requirements, past experience statistics, and sometimes the leader’s own judgment to create a strategy for the long term. Focusing on the really big picture and synchronizing different snippets of knowledge will lead to organizational wisdom. Strategies, therefore, cannot be simply explained by statistical data and trends: the human element of judgment is needed to create it.

Conclusion

The relationship among data, information, knowledge, and wisdom can be best described though a pyramid graph. The foundation is data, of which information can be gained through understanding. Further understanding will lead to knowledge, and this can be turned into wisdom (understanding principles). Through the process of turning data into wisdom, the level of connectedness and understanding gradually increases.

References

Bellinger, G., Castro, D., & Mills, A. (2004). Data, information, knowledge, and wisdom. Systems Thinking. Retrieved from http://www.systems-thinking.org/dikw/dikw.htm

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