Data Vs. Information (Types, Application And Differences)

Updated 24 October 2022

Many industries, like health care and marketing, use data and information to analyse findings and present them with infographics. These raw statistics and critical observations enable analysts and business experts to make informed decisions. Understanding the differences between data and information can allow you to gather accurate data and use the information to take necessary actions. In this article, we discuss the differences between data vs. information, describe their features and highlight how their applications can benefit multiple industries:

Importance Of Data Vs. Information

Learning the differences between data vs. information is essential because they are valuable in assessing business processes and outcomes. Data can provide you with unorganised statistics that alone hold no value. But you can study these values and produce information to compare a company's rate of progress with the market standard. This can help you determine a company's potential.

What Is Data?

Data refers to facts or statistics that represent values in their raw forms. It can be figures or images that researchers and analysts use to interpret situations. For instance, data might signify the number of objects a business sold or the scores of a group of students in an examination. You can analyse these statistics to better understand the sales process or student performance and determine ways to improve them. Analysts often represent these figures as graphs and pictorial explanations. Researchers at an organisation may study these observations and present written reports concerning the organisation's operational procedures.

Types Of Data

The following are four basic types of data and their functions:

Quantitative

Quantitative data is figure that signify quantifiable amounts, such as the number of products a business sold or the mean weight of a particular age group. Researchers can use these values to perform mathematical calculations or statistical analyses to form meaningful conclusions. Quantitative data can simplify measuring various parameters and analysing survey or poll results. Quantitative statistics have two types: discrete and continuous. Discrete figures refer to finite numbers that you cannot divide into smaller parts, while you can divide continuous data into any value within a range.

Qualitative

Qualitative data, or categorical data, articulate the characteristics and quality of an item. For instance, it can include the names of products a particular group of people use, birth months or professions. This type of data is non-numeric, and experts may collect it from focus group discussions or interviews and arrange it according to its attributes and properties. Researchers can use these values to determine the frequency of a particular trait and form parameters to analyse large data sets.

Internal

Internal data refers to the private details that an organisation owns or controls. It can include reports and details from sales and services or various customer information. Professionals with insightful knowledge of research methodologies can collect these quantitative and qualitative facts. Internal data can have two different types: primary and secondary. You can collect primary data from surveys and interviews, and you can get secondary data from other sources like analytics software.

External

External data refers to values and details from third-party sources. These can include quantitative and qualitative figures, such as competitor analyses or customer demographics from external organisations. It can help you better understand the market in which a company operates. Utilising a diverse range of third-party sources allows data analytics professionals to deduce a company's progress and rate of production. Some external sources that data analysts use include government censuses, social media platforms and popular search engines.

What Is Information?

Information refers to a collection of data that researchers can use to identify and interpret patterns and deduce the resultant impact. Extensive studies and analyses of a variety of data can produce information about a particular subject. Information can depend on context. For instance, different people may perceive the recorded temperature readings over a specific week as high or low. You can get information from the detailed explanations they provide regarding their individual interpretation of the weather for that week.

Researchers can calculate averages from a set of data to get information. For example, suppose a meteorologist examines the number of tropical cyclones in summer. To better understand and study the context, researchers can analyse the number of cyclones every summer and determine how the current season's data compares.

Related: Types Of Graphs And Charts

Differences Between Data And Information

You can differentiate data from information in the following ways:

Purpose: Data provides raw values. Information assigns calculated values by organising and interpreting them in appropriate contexts.
Structure: Data and information have different forms. Data consists of unorganised figures, while you can use information to form infographics from gained values.
Dependence: Data exists as raw values and requires information to interpret it. Information depends on data to represent the findings of research.
Inference: Information from a data set can help you make conclusions. You may not conclude anything from the raw observations of an experiment.

How Do Data And Information Concern Businesses?

Companies can enjoy significant benefits when they collect data and interpret it periodically. Data and information can allow them to make intelligent and informed business decisions. For instance, a business can collect data about its advertising campaigns through a survey. Organising and analysing this data can provide insightful information regarding the type of graphics or products that appeal to its clients. Companies can use analytical reports to plan their future offerings and branding and improve output.

To incorporate a data-driven work culture, ensure that the data you collect is standardised and in a consistent format. This can help you avoid the disparate generation of information. You could build a central database to benefit everyone in the company with accurate and specific information. In addition, you can implement policies and regulations to oversee the consistency in data quality and encourage analytical skills in all departments. Both data and information can critically influence decision-making in business. Understanding the functions of these two elements can lead a business towards a data-centric culture.

Examples Of Data Vs. Information

Here are some examples explaining how different industries use data and information in multiple ways:

Health care

The medical industry can collect data from patients and interpret it to produce useful information. Such information can help in diagnosing a patient's illness or detecting an outbreak of a disease. Medical professionals record body temperature and other vital readings like blood pressure and heart rate. For example, nurses store the results of pathological tests as data. Physicians study this data to determine if a patient is improving. Similarly, if data shows that thirty hospitalised patients have similar symptoms, then the medical team can infer that there is an outbreak.

Finance

Financiers can use internal data to calculate net expenditure or financial allotment for a new fiscal year. In a crucial sector such as finance, accurate and specific data collection is essential to helping experts make effective monetary policies or plans. When creating a company budget, a finance expert may work with large numerical figures. These values may represent expenditure or the funds available for the remaining fiscal term. Data analysts can interpret this data to determine the amount of money to allot to each fund.

Business

Businesses may use a data-centric approach to understand the market situation and analyse the efficiency of their processes. Company owners frequently buy or sell businesses and services to one another. They can gather data to study the cost of expansion, and the information can enable them to create a graphical representation of expected organisational growth. Professionals can also compare their company's growth index with that of others. For instance, if data shows that a particular company owns considerable capital, then experts can interpret it to determine the size of the company.

Marketing

In the marketing industry, professionals can apply the value evaluating strategy to observe consumer buying behaviour and the efficiency of the products on the market. For example, marketing experts might collect data from consumers from a particular age group who purchased a particular hair product in a specific year. Analysts can study the data and create a graph representing the comparison between that year and the current year, along with detailed explanations for the difference between the figures. Promotional experts can also use data to monitor customer engagement on social media platforms.

Technology

In the technological sector, where engineers and coders design new products, data and information can help them assess their applications and make necessary changes. Data can explain how effectively a product operates, while information can allow them to compare their products with those of competitors. Troubleshooting experts can determine the efficiency of their code using data-driven assessments. In this context, data can refer to the number of times a system crashes, while information may prove the reliability of the system based on previous crash history.