The Differences between Data, Information, and Knowledge, and why you never find it when it’s needed!

It is a common misconception for people to use terms such a data, information, and knowledge interchangeably, but the truth is that they all mean very different things. In an organization, where conversations make most of the operations the basis of their work, storage of such details is often a great concern. Where do these conversations land up and how are they used? Today’s article is based on understanding data, information, and knowledge as well as why they are nowhere to be found when needed the most. We hope to bring you to a solution that will help you gain insights on how to use your storage records more efficiently with the help of Artificial Intelligence.

Definition of Data:

Data is understood differently in various sectors. In its basic form, data is a set of different symbols and characters whose meaning only becomes clear when they connect with context. Collecting and measuring observations generates data. Usually, machines send, receive and process data. The confusion between data and information often arises because the information is made out of data. In addition, data often gets interpreted as facts in the context of the colloquial meaning and are therefore regarded as information. It can be noted that computers are very good at crunching data; they are only now learning how to make sense of it to derive information with the help of Machine Learning.

Definition of Information:

Data reaches a more complex level and becomes information by integrating them into a context. The information provides expertise about facts or persons. Example of information: The information about a date of birth still has very little value when it is unknown to which person it belongs. By adding more information like the name, inter-linked pieces of information and context represent knowledge.

Definition of Knowledge:

Knowledge thus describes the collected information that is available about a particular fact or a person. The knowledge of this situation makes it possible to make informed decisions and solve problems. Thus, knowledge influences the thinking and actions of people. Machines can also make decisions based on new knowledge generated by information. To gain knowledge, it is necessary to apply such information.

Implicit Knowledge?– Knowledge that isn’t written down or stored digitally. It is procedural or part of the practice, and not dependent on an individual’s context. Most institutional knowledge is implicit, usually just muscle memory of the people in the organization. Tribal knowledge that isn’t documented

Explicit Knowledge?– Knowledge that is written down and accessible. It may be in paper or digital form. Examples include training manuals, return policies, or documented product information.

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Key Differences Between Data, Information, and Knowledge

1. Data is fragmented pieces of symbols and characters strung together, information is refined data whereas knowledge is useful information. Additionally, data can lack context when looked at singularly, whereas information gives context to data and knowledge brings depth in understanding?to such information.
2. It is noteworthy that data is incomprehensible independently, but the outcome of information is comprehension while the outcome of knowledge is understanding. Data is meaningless without being compiled into a sensible structure, while information improves representation and knowledge amplifies consciousness.
3. Data and Information alone are not sufficient to make any predictions while knowledge prediction is possible if one possesses the required experience.
4. You can’t use Data to make any statements, while information is data strung together, forming statements. Knowledge brings the ability to have a deduced conclusion using pieces of information together.
5. Data cannot independently be a basis for question formation; Information is a text that answers the questions a who, when, what, or where while knowledge is a text that answers the questions of why and how. The final difference we can take into consideration is that data and information are easily transferable while transferring knowledge requires learning.

Why Knowledge and Wisdom are Often Hidden

Most of the acquired knowledge and information acquired by employees through conversations with fellow employees, clients, customers, and stakeholders are not stored upfront. Interactions between such parties tend to be sliced and stored in siloed systems of record.

However, considering that there are heaps of such conversations, the records become meaningless quickly. The interaction context is usually lost or quickly turns into dark data that is stored in an archive. If these pieces of insights, information, and conversations through texts, phone calls, emails, and more are not stored aptly, they can get turned into dark data, which is as good as lost. Organizations therefore will need to keep reinventing the wheel as it were and ask the same questions as well as discover who is an expert at what.

Dark Data and Dark Analytics

According to a study made by IBM in 2018, over 80% of all data is dark and unstructured and this will increase to 93% by the year-end of 2020.

However, don’t let the name fool you: there’s nothing dark about dark data. It may be the light at the end of a tunnel for many businesses. Much like Big Data, Dark Data is a buzzword and you may hear a lot about it today.

To help you understand its importance, we’re going to provide you with all the important details related to dark data, starting with a detailed explanation of the term.

So, what is dark data?

Dark data can be defined as “information assets organizations collect, process and store during regular business activities, but generally fail to use for other purposes.”

To put it simply, organizations collect a vast amount of unstructured data, which includes everything from raw survey data to previous employee profiles and customer information, and most of this data is never utilized. Today, most companies have a significant amount of dark data stored in their repositories but only a few realize that this treasure trove exists, or can derive value from it.

Unstructured, untapped data that is yet to be processed or analyzed, dark data is kept in data repositories. It is found within data archives and logs files stored within data storage locations. A tricky situation for any company is when every interaction, transaction, and engagement gets captured. This is when companies need to prioritize — which data to utilize and which data to push aside for safekeeping. Often, this results in vast amounts of unstructured or semi-structured data being stored in log files or data archives in case it is required in the future.

How can Teams and Employees of Organizations Use Dark Data Mining?

For many companies, dark data represents a sizable portion of all data stored. This makes it crucial to understand the use cases of dark data.

There is a lot to derive from dark data and some ways this data could be used are mentioned below:

Customer Assistance Logs

If you’re like most businesses, then you probably maintain records of customer-support interactions.

It is important not to keep data in these records such as when a customer contacted your business, which channel he/she used, how long the interaction lasted, and so on in the dark for too long or using it only when a customer issue arises.

Instead, leverage the data to understand when your customers are likely to contact you, what their preferred methods of contact are.

Networking Machine Data

Large amounts of machine data related to network operations are generated by servers, firewalls, networking monitoring, and other parts of your environment. By using this information to analyze network security and monitor the activity patterns of the data, you can avoid dark data in the network and ensure that the network structure is never under or over-utilized.

Can it be used for Data Analysis?

Organizations can analyze dark data to develop greater context and unveil trends, patterns, and relationships that miss them during normal business intelligence and analytics activities. Analyzing valuable dark data could give your business insights you don’t currently have.

Conclusion

Now that the connotations of data, information, and knowledge are clear, it can be understood why data goes dark. The key lies in bridging the gap to help employees access such information that can be utilized for the organization’s growth through better customer interactions.

By: Mohamed Saber