How Data is Transformed into Information and Knowledge

Introduction

I would like to talk about how people make decisions in their lives. Decisions are made because people have enough information to where they can make a sound decision. When people make decisions, they are able to take action and do something. How do people get the information in order to make good decisions? We first have to have good data in order to have something to start with. Once we have good data then we need to give it context and meaning. We start with data, transform it into information and finally knowledge. I will discuss all three stages of the data transformation process. First comes data, information, and then finally knowledge.

Stage 1: Gathering and Collecting Data

Data is just large quantities of pieces of information without any meaning or context behind them. This is the very first step where you just gather information and collect it.You must gather data from many different sources. Then once all of the data is collected, we try to make sense of it. Since this is the first step in creating knowledge, gathering data is very crucial because you need to have as much data as possible to create great knowledge. At this point, you do not have to worry about how good the data is because you will end up filtering that out in the next stage. The goal here is just to gather as much data and “leave no stone unturned”. This early stage is the reason why we have information overload because there is so much information from many different sources. Efficient data collection involves having different fields for each piece of data. These fields are just short pieces of data that describe the kind of data that you are collecting, like a name, e-mail, phone number etc. A field can also be thought of as a label that you use to mark something so that you can remember what something is. These fields are used to keep track of the kind of data you are collecting. These fields will help you organize, filter and sort your data. The next stage of the data transformation process, information. Information is data that has been organized and given meaning and context. Before we move on to the next stage, I will discuss a couple of different methods on collecting data.

Passive vs. Active Data Collection

Data collection can be done either passively or actively. The difference between the two is whether you wait for data to come to you or you are actually going out and collecting it. An example of passive data collection is any type of form that you put out there for people to fill out. With a form, you set it up and wait for people to send data to you. Each field on the form collects one piece of data. This is why I mentioned fields earlier because you need them to keep your data organized. Forms can be either physical or digital. Forms include applying for a service, asking for help, or filling out a survey. Passive data collection is nice because if you are patient enough, you can just wait for form submissions to fill your data coffers. All of this data is content that users generate.

User Generated Content: Free Easy Data

We call this user generated content because the content that you collect comes from your users and not yourself. User generated content is very important for websites because they allow you to gather more content without having to actively create or look for it. So in this case, there is much less effort on your part to create new content for your website because you are relying on your users to generate it for you. Common forms of user generated content are comments and reviews. An inherent problem with this type of content is the influx of data that comes in since anyone can create it. In an effort to keep data clean, content moderation and spam prevention are necessary so that your data stays untainted. Content moderation allows you to check and approve content as part of the data filtration process. This prevents irrelevant data from being used as part of the knowledge creation process. We all know spam, no not the meat in the can. Spam is terrible because people use it to send all sorts of unsolicited messages and advertisements using your form. The reason why content moderation and spam prevention are critical is because the source of data is hard to trace and it is often anonymous. This is why people love sending their information out everywhere, they are hard to track and identify. Since we do not know who exactly is submitting our form, we have a higher chance of gathering poor data when collected passively.

Active Data Collection: Higher Quality Data

Active data collection is when you target certain users and gather data directly from them. This is different from passive data collection because passive data collection involves gathering data from anonymous users. So active data collection is targeted and is more meaningful because you actually know who your uses are. Passive data collection comes from anonymous users which means their data is not always reliable and trustworthy. You will gather more data passively but it will have to be filtered more due to incredible sources. With active data collection, the data comes from known users. So the quantity of data will be less but the quality of data will be better because you actually know who your users are. So this is the tradeoff between passive and active data collection. An example of active data collection is a newsletter. A newsletter is sent out to a mailing list of users who are subscribed to your newsletter. Since they are subscribed to you, you know who they are. You also know that they have an interest in receiving your newsletter because they signed up for it. When you know your target audience well, then you can use active data collection to gather quality data. In this case, you do not have to worry about anonymous data sources that are not reliable, which interferes with your data gathering. 

What is Better: Active or Passive Data Collection?

So there is not one that is better than the other, rather it is realizing the differences between the two and knowing when to implement one over the other. Since you have these two options, use both and see which one works better. Either method can be implemented depending on the situation. After you have finished collecting your data, you need to make sense of it. Now it is time to transform your data and give it meaning. In order to do this, you have to organize, filter, and sort your data. Once you have completed this sometimes lengthy process, there is then some context to your data. This second stage of data transformation is called information.

Stage 2: Transforming Data Into Information

Information is data with meaning. You can gain all kinds of different knowledge using the same set of information. What you do is, give your information meaning in different kinds of context. For example you might want to organize information for knowledge that you want to use for today, next week, next month, or next year. So in this case, the context of information is based on the date. So this means that you need to organize your data for short-term use for today and then again for long-term use for next year. The duration of information transformation depends mostly on how you gathered your information. So if you have a lot of information, but it is coming from unknown anonymous sources, then that information will take longer to organize. However, if your information came from reputable known credible sources, then the length of time needed to sort this information out will be much faster. The reason why is because you are starting out with quality data. So I mentioned the concept of garbage in and garbage out. Regardless, if you have a lot of unverified data compared to a little bit of verified data you still need to make sure that when it is time to transform your data, that you have a good reliable data to information transformation process. This is because when you go to the final stage, knowledge is only as good as the data you gathered in the first stage. So depending on how much time you want to spend on organizing your data into information will determine what kind of method you want to use in your data collection. The best thing to do is to try to see how much meaning you can get out of your data. If your information has very little contextual meaning then that means you might have to return to the previous step. Then you need to try to reconfigure your data collection method. However, if you find that your information has sufficient meaning and is usable, then you can proceed to the last step, which is knowledge.  

Multi-level Data Sorting

So I mentioned earlier that you sort your data by your fields, like date for instance. You can take multiple fields and sort them in a tiered order. So you can sort by date first and then sort by some sort of monetary value like a dollar amount. Combining fields while sorting data gives them more meaning because you are using more fields at once to make data easier to understand. The goal here is to use the data to answer questions. Questions like, “What were the latest products ordered?” Or, “What days had the most sales?”. These questions can only be answered with sorted data. For example, sort by date first, then by product second. If you sort by date first, then you will receive a list of products in the order that they were ordered. Then if you sort that same list again by product, you will get a list of products in alphabetical order. This will make the list easier to read if the list of products are ordered by A through Z. This is helpful if you have several products ordered on the same day. The list will contain the latest products in alphabetical order. Knowledge is basically the action that can be taken once your information has answered your questions. Now that you know which are the latest products ordered, you can decide what action to take. Should you promote those products because they are currently popular? Or should you take another course of action? These actions are part of the decision making process that can only happen with good, complete and meaningful information.

Reports: Lists of Organized Information

So what I described earlier was just a report. You take unorganized data and turn it into organized information. The purpose of these reports is to display your information in a way so that it is very easy to ingest and comprehend. When you have a good report available, then you can finally make a decision and take action using that report. That is when information transforms into knowledge. However, in order to obtain great knowledge you had to have started with good data, which then is converted into good information, which is then again converted even further into knowledge. Did that sound complicated? We are at the final step, knowledge.

Stage 3: Knowledge Is Power 

We have finally reached the last step on this journey, knowledge. Knowledge is just actionable information. So in order to make good decisions with information, your information organization skills have to be good. If we use poor data, then that will lead to poor decisions. This is why the entire process is so important. Many times, if we make a poor decision, we cannot undo it and go back. The best that we can do at this point to learn from a poor decision and revisit the entire process and try again. However, if we make a good decision with good data then we will reap the reward. Decision making can go both ways, either good or bad.

So to recap, we learned about how data gets transformed into information and knowledge. First we have data, which is just raw pieces of information without any meaning or context. Since we need quality data to produce quality knowledge, we need to have a good method when it comes to data collection because good information comes from good data. Depending if we use active or passive data collection will determine the amount of data and also the quality of it. Once you have gathered enough data, then you are ready to turn that data into information. Information is just data with meaning and context. This involves sorting, filtering and organizing data so that you are able to get some meaning out of it.  Then, once we have our information prepared, it becomes knowledge. Knowledge is just information that we use in order to make decisions. So this is why we need quality data because quality data produces good decisions. . 

I hope that you enjoyed another post from me about information. I am trying to write these posts in a way that anyone can understand. I know that there will be some technical jargon involved. I try my best to write this in a way that sounds like I am having a conversation with you. Due to the technical level of the content being shared, this might sound more like a lecture to you. So I will continue to write and see how the comprehension level of the articles go. Again, my goal is to share with you my opinions on information and the web.