Key differences between data vs information (With examples)
By Indeed Editorial Team
Published 27 June 2022
The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.
People often talk about data and information interchangeably, but the terms are actually different. Although they both deal with fact-gathering and problem-solving, there are variations between their purpose and component parts. Data describes facts and information presents these facts for understanding and decision-making. In this article, we look at data vs information, seek to understand their key points and look at how they are both vital for businesses and organisations.
Understanding data vs information
Learning the differences between data vs information can help you identify problems and gather intelligence to solve them. Researchers and organisations usually use data and information to add value at different stages of the problem-solving process. Here are differences to consider about data and information:
Data is a collection of facts or statistics. Most people think of data as figures and numbers. But data can come in a variety of forms, including text, images, observations, symbols and graphs. Data might be weights, individual prices, names, ages, dates, temperatures or distances. Data is a raw form of knowledge that isn't yet organised or processed for understanding. In that way, it doesn't have purpose or significance.
For data to have meaning, the data handler is required to interpret it. Data can be simple or complicated, but it appears to be useless until researchers organise, analyse and interpret it for understanding. This is where you determine the information stage. There are two main types of data that people work with, which are quantitative data and qualitative data:
Quantitative data is a numerical form of data. This could be a volume figure, a weight, a length or an item cost.
Qualitative data is a non-numerical, descriptive type of data, such as a person's gender, name, eye colour or preferences.
Information is knowledge. Researchers take raw data and apply different techniques to organise it and analyse it. From here, they can study the data, research it further, share instruction about the data or communicate it using a variety of approaches. By analysing the data and interpreting it, information becomes knowledge. To look at this in another way, data is a series of numbers, figures or graphs, and information is the way that researchers present those knowledge pieces, share and communicate them.
As an example, a data set could gather production line outputs in a manufacturing facility over the course of a week. But unless you have context, this raw data is meaningless. When you organise the information and analyse it, with comparative and supporting data sets that help to create a full picture of what we see, you can identify trends in output and see whether the performance was on track or above/below targets. Only when compiled, organised and presented with the necessary analysis can the data show its value to decision-makers.
Specific differences between data and information
There are various key points to know about data and information. Here are specifics to consider:
Nature: data describes collated facts, while information organises that data and contextualises it to provide knowledge.
Organisation: data is raw and without organisation, although you organise information into a useful format that aids decision-making.
Relationships: this includes points of data that are always individual and sometimes completely unrelated. When data maps into information, it provides the overarching picture of how the data links together.
Meaning: raw data is meaningless. It only becomes meaningful when organised, analysed and communicated.
Dependency: data isn't dependent on information, but information can only exist with data.
Format: data typically includes numbers, figures, graphs, statistics or numerate symbols, while information typically includes language, words, ideas, images or thoughts.
Decision making: People cannot make decisions based on data alone, but they can make good decisions using relevant, quality information.
Related: Examples of information application
Examples of data and information in practice
It's helpful to look at examples of data and information in practice, to understand how they work together, and how they apply to different situations. Here are examples of data and information in practice:
When a customer buys a pair of shoes from a shop, the receipt that shows the bill provides a point of sale data set. When the owner of that shoe shop collects till sale data over a period of time, this can provide information. For example, the shop owner could see how many pairs of that particular shoe have sold within the month.
The shop owner can use the data to see which sizes have sold in the shoe range and whether different colours of that design have sold better than others. They can also see if anything may have affected sales, such as a price promotion or change in weather, for example.
If a customer takes part in a survey about their recent restaurant experience, they provide a point of data. But when the responses from a range of customers collate over time, the survey host can collect information. This information could provide insights to change something. For example, the surveys might flag that the restaurant requires more staff at a certain time of the evening.
If you make a social media post and garner likes, these are a single data point. When researchers assess likes alongside other social media engagement measures, such as shares, comments and followers, the data sets can become information. Marketers can find out which social media platforms are working best when to post and what sorts of posts their target audience is most likely to engage with.
We can also apply data and information to competitor research. The prices that competitors charge are individual data sets. But when the researcher analyses the data, it can reveal a picture of the competitor's pricing strategy and advantages. This becomes information that the researching business can use to effectively position their own products and pricing strategy.
Related: How to become a data analyst
Ways that businesses can leverage data and information
Businesses can use data and information to make better and faster business decisions. They can enjoy the best success when they work with the readily available data and information, especially as most organisations have so many ready and available data sets through their myriad systems. Marketers are an example of how businesses can leverage data vs. information to create successful campaigns. A marketing team might gather data to assess the performance of an advertising campaign. They could collect a variety of data sets that relate to different ad formats across the campaign.
Data analysis and interpretation then provide insights into which phrases, products, graphics and designs were most appealing, which ad placements were most effective and which elements of the campaign maximised the ROI. These data sets might also extend to provide broader information about the target audience of that advertising campaign. This would help marketers to better understand how to target their future offerings, campaigns and brand activities. As more high-quality and relevant data accumulates for processing, marketers can gather more and more useful information and make stronger decisions for their future work.
Related: What is data mapping?
Common roadblocks to working with data and information
Although most modern businesses want to create data-driven management cultures, there can be roadblocks. For example, internal teams might work with their own different data sets and interpret them in different ways. Without a central data gathering and processing function, the business as a whole cannot benefit. Regulations exist regarding data collection and you may keep your business updated by consulting government websites about data protection.
If there is no individual or team responsible for overseeing corporate data in a consistent, quality way, the data might not be of the right type or quality level to be valid and of value. Poor quality data can be inaccurate or misleading. A good way around this issue is to create a knowledge management system and implement clear processes and protocols for data management, whilst training employees in data analytics skills across the organisation.
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