In the IT world, the terms data and information are often used as if they were synonyms. However, that’s incorrect! In fact, these two notions are very different from each other. Where data is a collection of raw facts and figures, information is data that has been processed and contextualized for a user. In this article, discover the sometimes subtle differences between data and information, and their definitions.
In an ever-changing digital age, everything is data and everything is information…Understanding the difference between both terms is much more than a semantic subtlety; it’s the key to harnessing the full potential of modern technologies. This distinction illuminates the path to informed decision-making, impactful innovation, and skillful navigation in a world saturated with seemingly chaotic data streams.
What does the word data (really) stand for?
You encounter this term all day long on your computers, in your reading, and on television. But, the word data, in the IT sense of the term, represents an elementary unit of information, often in binary form (0 or 1), captured and stored in computer systems.
Data can take many different forms, such as text, images, videos, or numerical values. It serves as the raw material for analysis, processing, and communication processes, enabling software and systems to make decisions, generate reports, and provide a wide range of functions in the digital world.
What is information?
In the field of computing and information technology (IT), the notion of information refers to organized, meaningful, and interpretable data, processed and stored by computer systems.
Information encompasses elements such as facts, figures, texts, or media, which are used to make decisions, generate knowledge, or facilitate processes. It results from the transformation of raw data by algorithms and software, playing a crucial role in communication, management, analysis, and automation of operations in the digital environment.
What are the differences between data and information?
To present the differences between data and information, we need to start by defining a principle: data are the basic, raw, uninterpreted elements; while information is the result of transforming data into something meaningful and comprehensible.
The main difference, then, is that data are objective representations of facts or observations, but they have no meaning of their own. For example, the binary sequence “01001000 01100101 01101100 01101100 01101111” is meaningless data until it is interpreted.
Information, on the other hand, is the result of processing data through algorithms, analysis, and interpretation. Thus, once interpreted, the binary sequence referred to above is revealed as the ASCII code for the word “Hello”. The raw data then becomes comprehensible and intelligible information. In the same way, you can collect the data: 25, 33, 46, 63. If your interpretation reveals that these are the ages of your customers, you can deduce that the average age of your customers is 41.75. For instance, in a financial table containing the following list of amounts in Euro: 100, 150, -50, 200, and -30, you can draw the information that income and expenses have been recorded. The resulting information is that the total income is €450 and the total expenditure is €80, leaving a positive balance of €370.
How to transform your data into reliable information
Transforming data into reliable information involves contextualizing, analyzing, and interpreting it. To do this, you’ll need to use various algorithms, analysis tools, statistical methods, and so on. In this way, you can make your data speak for itself, refining it in such a way as to extract… information. This refinement of the data is intended to bring out trends and patterns, to give meaning to the raw data. This transformation requires checking data quality, eliminating errors, and considering their source.
Collect your data from all your sources
Ensuring the collection of all your data, emanating from different sources requires a methodical approach. To start with, make sure you identify and select relevant sources, such as databases, sensors, or social media. Then use APIs and extraction tools to gather data automatically. Aggregate, cleanse, and normalize them to ensure consistency. Then apply filters to attenuate and eliminate noise. Finally, store the data on an analysis platform.
Store data in a single directory
If you aspire to turn your data into information, it’s essential to inventory data in a single directory. There are several reasons for this:
- A single directory facilitates access to all data, eliminating the need to search in various locations. This speeds up the transformation process.
- Data from different sources may have different formats. By bringing them together in one place, you can more easily normalize their structure and simplify subsequent analysis.
- By centralizing data, it’s easier to identify missing, erroneous or redundant data. As a result, you can improve the quality of the information you generate.
- Having all data at your fingertips reduces the time spent on searching and preparing data, speeding up the transformation process. In this way, information from multiple sources facilitates informed decision-making, as it reflects a complete and accurate view of the situation.
- Finally, centralizing data enables you to better manage regulatory compliance and enhance security by controlling access to sensitive information.
Document data to give context
The context provided by documentation helps to interpret data correctly, avoiding analysis errors due to misunderstandings. Clear documentation is your best guarantee that data is interpreted consistently by different people, ensuring consistency in results. But that’s not all! Documentation allows you to track data history and modifications, providing valuable traceability for analysis and decision-making. Finally, the context provided by documentation enriches analysis, helping to transform data into relevant, useful information.
Make data accessible via discovery tools
Turning data into information means first and foremost making it usable for the greatest number of users in your company. That’s why discovery tools enable you to explore data intuitively, quickly identifying patterns and trends. They also offer the possibility of interacting with data in real-time, facilitating rapid analysis and adjustment. Finally, the advanced exploration features built into discovery tools can reveal hidden information or correlations that traditional analysis would be unable to identify.
Between refining, domesticating, and adding value, transforming your data into information is a major imperative for developing and accelerating your data strategy and culture.
