Information vs data vs knowledge

n 1597, Sir Francis Bacon published the first appearance of this well-known, widely-used phrase: “Knowledge is power.”  Certainly, he wasn’t talking about information systems as we know them  in the tech industry, but the phrase still holds a tremendous amount of  significance as it acknowledges the valuable potential and capacity that  comes with insightful information that becomes knowledge.

But  where does information come from? The technology ecosystem is  data-driven and finding value in data is becoming critical for  successful businesses, which is the topic at hand for this article: data  and information. We cover data vs information to better understand  their interdependence, their points of difference, and how one cannot  exist without the other. Let’s begin by defining each concept.

What is data?

Regardless  of industry, data is driving the future and a massive number of  technologies across multiple industries heavily depend on it to thrive.

Based on the definition of data from TechDifferences,  data is “raw, unanalyzed, unorganized, unrelated, uninterrupted  material which is used to derive information after analyzation.”  Essentially, data is plain facts, observations, statistics, characters,  symbols, images, numbers, and more that are collected and can be used  for analysis. Data left alone is not very informative and in that sense,  it is relatively meaningless, but it gains purpose and direction after  it is interpreted to derive significance.

Whether qualitative or  quantitative, data is a set of variables that help construct outcomes.  Another key characteristic of data is that it’s freestanding and does  not depend on any other concept to exist, unlike information which only  exists because of data and is entirely dependent on it.

Data and  information are measured in bits and bytes. It can be represented in  structured/unstructured tables, graphs, trees, etcetera, and it doesn’t  have significance until it is analyzed to meet a specific user’s needs.

Now, let’s move on to information.

What is information?

If  data is the atom, information is the matter. Information is the set of  data that has already been processed, analyzed, and structured in a  meaningful way to become useful. Once data is processed and gains  relevance, it becomes information that is fully reliable, certain, and  useful.

According to this Forbes article,  information is “prepared data that has been processed, aggregated and  organized into a more human-friendly format that provides more context.  Information is often delivered in the form of data visualizations,  reports, and dashboards.”

Information addresses the requirements  of a user, giving it significance and usefulness as it is the product of  data that has been interpreted to deliver a logical meaning. As we’ve  stated, information cannot exist without its building block: data. Once  data is transformed into information, it doesn’t contain any useless  details as its whole purpose is to possess specific context, relevance,  and purpose.

Ultimately, the purpose of processing data and  turning it into information is to help organizations make better, more  informed decisions that lead to successful outcomes.

To collect  and process data, organizations use Information Systems (IS) which are a  combination of technologies, procedures, and tools that assemble and  distribute information needed to make decisions.

What is Knowledge?

Knowledge means the familiarity and awareness of a person, place,  events, ideas, issues, ways of doing things or anything else, which is  gathered through learning, perceiving or discovering. It is the state of  knowing something with cognizance through the understanding of  concepts, study and experience.

In a nutshell, knowledge connotes the confident theoretical or  practical understanding of an entity along with the capability of using  it for a specific purpose. Combination of information, experience and  intuition leads to knowledge which has the potential to draw inferences  and develop insights, based on our experience and thus it can assist in  decision making and taking actions.

What is the difference between data and information?

The  terms are sometimes mistakenly used interchangeably when in reality  there is a clear distinction between the two. The major and fundamental  difference between data and information is the meaning and value  attributed to each one. Data is meaningless in itself, but once  processed and interpreted, it becomes information which is filled with  meaning.

To put it into context, think of data as any series of random numbers and words that hold no meaning whatsoever. For example:

4a 61 6e 65 20 44 6f 65 2c 0a 34 20 53 74 72 65 65 74 2c 0a 44 61 6c 6c 61 73 2c 20 54 58 20 39 38 31 37 34 0a

Once  the aforementioned data is processed, interpreted, formatted, and  organized, you can see that it is the contact information of Jane Doe:

• Jane Doe,
• 4 Street,
• Dallas, TX 98174

Another  clear example of the distinction between data and information are  temperature readings from across the globe. A long list of temperature  readings mean nothing of true significance until organized and analyzed  to unearth information such as trends and patterns in global  temperatures. Once data is analyzed, users can identify if the  temperature has been on the rise over the last year or if there’s a  regional trend for specific natural disasters. Those types of  discoveries are information that is extracted by analyzing data.

Here’s a comparison table to help pinpoint the key differentiators between data and information.

CriteriaDataInformationMeaningRaw facts, that are the building blocks for information.Combined data filled with relevance and significance.FormUnorganized.Organized.BasisRecords and observations.Analysis.DependencyDoes not depend on information.Depends on data.MeasurementsBits and bytes.Meaningful parameters such as time, quantity, dates, etc.Significance and usefulnessData alone has no significance.Information is always significant, useful, and relevant.SpecificNo.Yes.

One bit and one byte

As  the base of measure for digital information, bits and bytes play a  fundamental role in the subjects of data and information. Computers,  with their millions of circuits and switches, use the binary system to  represent on and off or true and false, using bits and bytes.

A  bit, which is short for binary digit, is the most basic and smallest  unit of data measurement in computer information and it contains only  two values: 0 and 1. Bits are usually designed to store data and execute  instructions in strings of 8 bits, which is called a byte.
The term byte was first coined by Werner Buchholz in 1956 and it represents this unit of data measurement, which is eight binary  digits long. All computers use bytes to represent all kinds of  information including letters, numbers, images, audio, videos, and more.  Given that all information in computers is larger than a bit, the byte  is considered the universal and smallest measurement size listed in  operating systems, networks, etc.

To put this in perspective and according to statistics from TechJury, by 2020, every person will generate 1.7 megabytes of data in just a second. And what is a megabyte? It is 1,048,576 bytes.

Here are some helpful references for units of data measurement:

Bits.

• 8 bits constitute 1 byte.

Bytes.

• 1,024  bytes constitute 1 Kilobyte. (Please note that in 1998, the  International Electrotechnical Commission (IEC) created the prefixes  kibi, mebi, gibi, and so on to denote powers of 1024. The kibibyte came  to represent 1024 bytes. These prefixes are now part of the  International System of Quantities. Furthermore, the IEC specified that  the kilobyte should be used only to refer to 1000 bytes.)
• 1,048,576 bytes constitute 1 Megabyte.
• 1,073,741,824 bytes constitute 1 Gigabyte.
• 1,099,511,627,776 bytes constitute 1 Terabyte.
• 1,125,899,906,842,624 bytes constitute 1 Petabyte.
• 1,152,921,504,606,846,976 bytes constitute 1 Exabyte.
• 1,180,591,620,717,411,303,424 bytes constitute 1 Zettabyte.
• 1,208,925,819,614,629,174,706,176 bytes constitute 1 Yottabyte.
• As of 2018, there’s no recognition for anything bigger than the yottabyte.

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With these figures in mind and according to this article from Visual Capitalist,  the digital universe is expected to reach over 44 zettabytes by 2020.  If that number becomes a reality, it will mean there will be 40 times  more bytes than there are stars in the observable universe. By 2025,  it’s estimated that 463 exabytes of data will be created worldwide, on a  daily basis.

As you can see, bits and bytes are incredibly  significant in the modern technology landscape as they help organize  data in a standardized way that in turn helps boost data processing  efficiency of network equipment, disks, and memory. For example, it is  fairly common to hear the terms 32-bit and 64-bit as they define the  fixed-size of data that a processor can transfer to and from memory.

What is raw data and how is it transformed into information?

Now  that we understand better the intricacies of data and information,  let’s examine raw data and how it is transformed into useful information  that ultimately leads to insights.

Based on the definition provided by TechTerms,  raw data is “unprocessed computer data. This information may be stored  in a file, or may just be a collection of numbers and characters stored  somewhere in the computer’s hard disk.” Typically, data that is entered  into a database is referred to as raw data and it can be user-generated  or entered by the computer itself.

Raw data comes from numerous  sources such as relational databases, machine-generated data, data  mining tools that extract data from the web, real-time data, data from  the Internet of Things (IoT) devices, human-generated data, and more.

Given  that it is raw, this type of data, which is also oftentimes referred to  as primary data, is jumbled and free from being processed, cleaned,  analyzed, or tested for errors in any way. As stated, raw data is  unprocessed and unorganized source data that once it’s processed and  categorized becomes output data.

Because raw data is messy, it’s  important to use deconstruction analysis techniques to process it  accordingly since structured data allows easy retrieval and raw data  requires cleaning, preparation, and formatting before data analysis can  begin and lead to the extraction of information.

Filtering,  reviewing, and interpreting raw data leads to the extraction of useful  information that is relevant, useful, and valuable.

There is a  procedure in computing known as extract, transform, load (ETL) that  combines these aforementioned functions in a single tool to harness data  out of a database and place it into another database. Typically, it is  used to build data warehouses by extracting data from a source system, transforming it into an easy-to-analyze format, and loading it  into another database, data warehouse or system. For many years, ETL  has been the de facto procedure to collect and process data as it gives  organizations the opportunity to capture and analyze data quickly.

Once  data is normalized through the use of a procedure such as ETL, there  needs to be a robust information system in place to understand and give  meaning to the extracted data.

Information systems best practices to gain value from data

As  proven, once data is normalized through the use of procedures such as  ETL, it is ready to be leveraged by an information system to give it  meaning and utility. By employing a comprehensive information system,  users can leverage the available tools, technologies, and techniques to  help transform data into information that will eventually become  insights/knowledge. As Techopedia defines  it, an information system is the “collection of multiple pieces of  equipment involved in the dissemination of information. Hardware,  software, computer system connections and information, information  system users, and the system’s housing are all part of an IS.”

These components come together to store, retrieve, transform, and disseminate information.

• Hardware:  The computer itself along with its peripherals such as servers,  routers, monitors, printers, storage devices, keyboard, mouse, etc.
• Software:  The software system is what instructs the hardware what to do. The  software collects, organizes, and manipulates data to carry out  instructions.
• Data/databases: The information part of any information system. Data is critical.
• Network/communication: Devices that communicate with each other to share information and resources.
• Procedures: Strategies, descriptions, policies, instructions, methods, and rules to use information systems.
• Users/people:  This component is what glues together all the other components as they  combine hardware, software, data, network, and procedures to generate  valuable information.

Information systems require a  comprehensive strategy to deploy best practices that drive actionable  insights. Some of these best practices include data integration, data  virtualization, event stream processing, metadata management, data  quality management, and data governance, to name a few.

• Data integration: Combining data from several sources into a centralized view.
• Data virtualization: Retrieving and manipulating data to deliver a simple, unified, and integrated view of data in real time.
• Event  stream processing: Analyzing time-based data as it’s created and before  it’s stored, even as it streams from one device to another.
• Metadata management: Administration of data that describes other data.
• Data  quality management: Practice of identifying data flaws and errors and  simplifying the analysis and remediation of data flaws.
• Data governance: Management of availability, usability, integrity, and security of data.

Key Differences Between Information and Knowledge

The points given below are important, so far as the difference between information and knowledge is concerned:

1. Information denotes the organised data about someone or something  obtained from various sources such as newspaper, internet, television,  discussions, etc. Knowledge refers to the awareness or understanding on  the subject acquired from education or experience of a person.
2. Information is nothing but the refined form of data, which is  helpful to understand the meaning. On the other hand, knowledge is the  relevant and objective information that helps in drawing conclusions.
3. Data compiled in the meaningful context provides information.  Conversely, when information is combined with experience and intuition,  it results in knowledge.
4. Processing improves the representation, thus ensures easy  interpretation of the information. As against this, processing results  in increased consciousness, thus enhances subject knowledge.
5. Information brings on comprehension of the facts and figures. Unlike, knowledge which leads to the understanding of the subject.
6. The transfer of information is easy through different means, i.e.  verbal or non-verbal signals. Conversely, the transfer of knowledge is a  bit difficult, because it requires learning on the part of the  receiver.
7. Information can be reproduced in low cost. However, exactly similar  reproduction of knowledge is not possible because it is based on  experiential or individual values, perceptions, etc.
8. Information alone is not sufficient to make generalisation or  predictions about someone or something. On the contrary, knowledge has  the ability to predict or make inferences.
9. Every information is not necessarily a knowledge, but all knowledge is an information.

Conclusion: information vs data

In  the last couple of years, information science and the technology  associated with it have made significant leaps forward. From local  servers that transitioned to the cloud, smarter databases, key-value  data stores, and more, data is being processed and analyzed at  break-neck speed.

Along with speed, another key factor that plays a  big role in the success of processing data and information is the  relatively low cost associated with the use of hard disk drives,  solid-state drives, and the cloud. For instance, organizations store  information in the cloud in raw format and then use procedures such as  ETL along with information systems to generate insightful information.

Data  and information solve real-life problems with the many applications  they impact by injecting knowledge into the decision-making process.  From space programs, medical applications, education, retail, financial  services, and software development, just to name a few, there is no  limit to the number of industries that benefit by the second from the  value extracted from data and information.

To  sum up, these two interrelated concepts are the cornerstone of valuable  insights that drive intelligent decisions and successful outcomes for  businesses and organizations alike.

Conclusion: information vs knowledge

To sum up, we can say that, information are the building blocks, but  knowledge is the building. Processing of data results in information,  which when further manipulated or processed becomes knowledge.

Suppose a person possess plethora of information about a particular  subject, but this does not mean that he/she can make a judgement or draw  inferences on the basis of the available information because to make  a sound judgement, one should have ample experience and familiarity with  the subject, which is possible through knowledge.