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The difference between data, information, and insight

In general discussions, data, information, and insight are three terms that are often used interchangeably. However, they are not synonymous. Business strategies cannot be founded on raw data, and information that is not analysed in the right context does not provide a solid base for making strategic business decisions. To recognize the value of insights, and how they help people reach their desired business outcomes, it is important to understand the difference between data, information, and insight.

What is data?

Data is raw facts, figures, and statistics collected according to pre-decided standards. Raw data, also known as primary data, does not explain anything on its own.

It is a cluster of words, numbers, images, or symbols that do not convey any meaning when first viewed. Data needs to be put into context and processed into information before it can become useful. 

Let us look at an example!

Imagine that you own a confectionery company and track brand mentions on social media every day through a social listening tool. Your goal is to learn who mentions your products (demographics), how your products are perceived online (sentiment), and what drives engagement (trends). 

When you open the dashboard, you can see all the brand mentions that the listening tool has crawled following the standards you set (for example, your search string). Simply reading the mentions on your dashboard or in your Excel exports will not give you a clear idea of ongoing trends or overall sentiment towards your brand. You will just be looking at a bunch of words, photos, time stamps, usernames, etc. For this data to make sense, it needs to be processed into information.

What is information?

Information is the process of transforming data into knowledge. Data processing involves examining and identifying patterns in the data you have collected.

These patterns help you come up with some conclusions about how things work, and these conclusions become information. Think of it like this: you extract meaning from your data and turn it into something that drives business decisions, whether that is identifying trends, making predictions, or deciding what actions to take on next.

Let us continue with our confectionery business social media monitoring example. To convert data into meaningful information, we need to define what we are measuring. Are we looking at demographics? Posting times? Most active users? Sentiment? The number of product mentions per period? Information is data displayed in a more understandable format. Many social listening tools make this easy for you by automatically grouping and structuring similar data in graphs and tables. 

Information would be a pie chart showing the percentage of users who mentioned one chocolate product per country or a timeline that illustrates the days and months when mentions of that chocolate product peaked. Information can be a table containing the percentages of positive, neutral, and negative mentions measured over a given period. Information is also the calculation of the average engagement rate of posts by your brand’s owned accounts. All this information is valuable, but it does not tell you exactly what caused the fluctuations in your data set. That is where insights come into play.

What are insights?

An insight is a deeper understanding of something through analysis of facts and events—that is, turning them into more meaningful pieces of knowledge that can help guide decision-making processes in your business or organisation.

Insight is what happens when information comes full circle. You cannot make informed decisions without first having access to relevant facts. However, if those facts are not connected or interpreted correctly, you will not be able to make better choices in the future.

Insights are not just data wrangled into a neat shape. They do not just summarise what has already happened so we can understand it better. They reveal new patterns, relationships, and possibilities, and allow us to use what we know about the past in order to imagine how things might be different in the future—and how those changes could affect us all.

In our confectionery business example, an insight would be the explanation behind a certain timeline peak. Understanding why one of your chocolate products was frequently mentioned on 14 February, for example, and having a summary of the overall sentiment and discussion around it, can help you create better marketing campaigns that support your business goals.

How do we get from data to insights?

First, let us reiterate:

Data is raw material (facts, figures and statistics) that gets processed into information. Insights happen when you interpret that information. In other words:

Data

Contains the facts.

Information

Shaped by those facts.

Insights

The new knowledge gained from it all.

But how do we turn these disparate bits of information into something meaningful? That is where data analysis comes in!

What is data analysis?

Data analysis transforms raw data into valuable information and insights, allowing you to make informed decisions about your business. The first step in performing data analysis is to identify the purpose of the analysis. Why are you looking at this data? What questions do you want answered? Once you have an idea of what kind of information you need, you can decide which tools will help you get there.

The right tools can help you make sense of your data, but you have to know how to use them effectively. There are a wide variety of tools out there—from simple spreadsheets to complex statistical software—and each one has its strengths and weaknesses. Some tools may be better suited for certain types of analyses than others.

For example, if you are looking for trends or patterns in large amounts of unstructured text (such as social media posts in our confectionery company example), then an automated keyword extractor might be what you need. But if your goal is to compare two different datasets using complex statistical models and algorithms, then a specialised statistical software program might be more appropriate.

Figuring out which tools to use can be complicated, and the exponential proliferation of data analysis tools on the market does not make it any easier. Some of them are built for specific industries or applications and only work with certain kinds of data, while others are more general-purpose—but even those can have different features that make them better suited to some people’s needs than others.

How can we help?

Our multilingual teams at A Data Pro have experience in working with various tools and can help you turn your big data into actionable insights. We know which tools work the best for your needs and we can easily sort through large datasets and identify patterns, trends, and other useful information that will help you target your marketing efforts more effectively.