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• Economics /

Class 11 Collection, Organisation and Presentation of Data

• Updated on  
• Jun 22, 2023

The collection of data aims to collect evidence for attaining a sound and comprehensible solution to a problem. To understand the inconsistencies in the output, we need the ‘data’ on the generation. It is a process which is conducted to measure and gather information. ‘Data’ is a device, which aids in the comprehension of problems by providing knowledge. Here is this blog, we will talk in detail about the Class 11 collection, organisation and presentation of data. 

Must Read: Business Services Class 11 Notes

This Blog Includes:

What are the sources of data, primary data, secondary data, preparation of instrument, mode of data collection, personal interviews, mailing questionnaire, telephone interviews, pilot survey, census and sample surveys, census , random sampling, non-random sampling, sampling errors, non-sampling errors, census of india and nsso.

To understand more about the chapter Class 11 collection, organisation and presentation of data, we fist need to know the sources of data. Statistical data can be obtained from two sources:

• Primary data

We further move on to the concept of primary data in class 11 collection, organisation and presentation of data. The important points of primary data are:

• The enumerator (person who assembles the data) may collect the data by administering an inquiry or research. Such data is called Primary Data , as it is formulated on first-hand information.
• Primary data are unique, do not require any modification, and are costly.

Next important form of data in class 11 collection, organisation and presentation of data is secondary data.

• If the data have been examined and analyzed by another agency, they are called Secondary Data . Usually, the issued data are secondary.
• They are already in the presence and therefore are not unique.
• It demands to be modified to satisfy the aim of the study at hand.
• Secondary data are low priced.

How do we collect Data?

Collection of data is important in class 11 collection, organisation and presentation of data. It is done by the following ways:

• The survey aims to describe characteristics like cost, worth, utility (in case of the product) and reputation, honesty, loyalty (in case of the nominee).
• The objective of the survey is to gather data and is a method of gathering information from individuals.

The most prevalent type of tool employed in surveys is a questionnaire/ interview schedule. The questionnaire is either self-directed by the interviewee or conducted by the enumerator or qualified investigator. While drawing-up the questionnaire/interview schedule, the following points should be kept in mind:

• The questionnaire should not be lengthy.
• The array of problems should move from indefinite to distinct.
• Questions should not be enigmatic.
• Questions should not use binary negatives. 
• Questions should not be leading.
• Questions should not indicate choices. 

Also Read: Emerging Modes of Business Class 11 Notes

The next important topic in class 11 collection, organisation and presentation of data is the mode of data collection. The aim of probing questions is to survey the acquisition of data. There are three ways of collecting data: 

• Mailing (questionnaire) Surveys

Personal interviews form an important part of the mode of data collection in class 11 collection, organisation and presentation of data. In this method, the researcher has the main role as he/she conducts the interviews face-to-face with the respondents. Personal interviews are preferred due to various reasons:

• Highest Response Rate 
• Allows use of all types of questions 
• Better for using open-ended questions 
• Allows clarification of ambiguous questions.

The personal interview has some demerits too:

• Most expensive 
• Possibility of influencing respondents 
• More time taking

Another important part of class 11 collection, organisation and presentation of data is the mailing questionnaire. In such a method, the data is collected through the mail. The questionnaire is mailed to each person and a  request is attached to complete and return it on time. 

The advantages of this method are:

• Least expensive 
• The only method to reach remote areas 
• No influence on respondents 
• Maintains anonymity of respondents 
• Best for sensitive questions

The disadvantages of mail surveys are:

• Cannot be used by illiterates 
• Long r esponse time  
• Does not allow an explanation of unambiguous questions  
• Reactions cannot be watched 

In telephone interviews, the investigator asks questions over the telephone. 

The advantages of telephone interviews are:

• Relatively low cost 
• Relatively less influence on respondents 
• Relatively high response rate.

The disadvantages of this method are:

• Limited use 
• Possibility of influencing respondents

Explore: Accountancy Class 11 NCERT Solutions

The pilot survey is another important tool in class 11 collection, organisation and presentation of data.

• After the questionnaire is ready, it is desirable to carry a try-out with a diminutive group, known as Pilot Survey or Pre-Testing of the questionnaire . 
• The pilot survey serves to give a preliminary impression of the survey. 
• It helps to pretest the questionnaire and know the lapses and drawbacks.
• It also aids to assess the appropriateness of questions, the accuracy of guidance, the administration of enumerators, and the expense and time required in the actual survey.

Census and sample surveys are an important tool in class 11 collection, organisation and presentation of data. 

• A survey, which encompasses every component of the population, is apprehended as Census or the Method of Complete Enumeration.
• The primary feature of this approach is that this comprises every individual unit in the whole population.

Sample Survey

• A sample refers to a section of the population from which information has to be taken. A good sample (representative sample) is usually short and competent in giving reasonably accurate information about the population at a lower cost and in less time.
• Most of the surveys are sample surveys and are preferable in statistics because of several reasons.
• A sample can give rationally secure and authentic information at a lower cost and in less time. 
• Now the question is how do you do the sampling? There are two main types of sampling:
• Non-random Sampling
• It is also known as the lottery method.
• Random sampling is where the specific units from the population (samples) are randomly selected. 
• In random sampling, each person has an equal possibility of being chosen, and the person who is selected is the same as the one who is not selected.
• Random number tables are generated to ensure an equal chance of selection of every single unit in the population.
• They are accessible either in an issued form or can be generated by employing relevant software packages.
• In this method, units of the population don’t have equal chances of being selected. 
• The convenience or interpretation of the investigator plays a crucial role in the adoption of the sample. 
• They are chiefly selected based on belief, purpose, ease, or quota and are non-random samples.

Sampling and Non-sampling Errors

While conducting surveys, in class 11 collection, organisation and presentation of data, sample and non-sampling errors find an important mention. 

• Sampling error applies to the variations between the sample estimate and the actual value.
• It is the error that transpires when you observe the sample taken from the population. 
• The point of differentiation between the actual parameter of the population and its estimate is known as sampling error. 

Non-sampling errors are more consequential than sampling errors. Sampling error can be minimized by taking a larger sample, on the other hand, it is difficult to minimize non-sampling error. Even a Census can carry non-sampling errors.

 Some of the non-sampling errors are:

• Errors in Data Acquisition: This type of error stems from recording inaccurate responses.
• Non-Response Errors: Non-response happens if an interviewer is incapable to contact a person listed in the sample or a person from the sample declined to respond. In this case, the sample research may not be representative.
• Sampling Bias: Sampling bias happens when the sampling plan is such that some portion of the target population could not possibly be incorporated into the sample.

Must Read: Class 11 Oscillations Notes

The census of India is a very important body of our country and is an important part in the chapter class 11 collection, organisation and presentation of data. 

• The Census of India and the National Sample Survey Organisation (NSSO), are two significant firms at the national level, which gather, manner, and tabulate data.
• The Census of India produces the most comprehensive and continuous demographic record of the population. 
• The NSSO was established by the Government of India to conduct nationwide surveys on socio-economic issues. 
• NSSO gives periodic measures of education, school enrolment, utilization of educational aids, employment, unemployment, manufacturing, and service sector enterprises, morbidity, maternity, child care, utilization of the public distribution system, etc.

Ans. Three methods exist for gathering data: Personal meetings. Telephonic Interviews, and mailing surveys with questions.

Ans. The term “presentation of data” refers to the display of data in a way that makes it easy for viewers to understand and examine it.

Ans. Based on the methods used to acquire them, data can be divided into four basic categories: observational, experimental, simulational, and generated. The kind of research data you gather may have an impact on how you manage that data.

Also Read: Class 11 Formation of a Company

We hope the Class 11- Collection of Data notes helped you understand the essential concepts covered in this chapter. Still unsure about which stream to choose after Class 12. Our Leverage Edu experts are here to guide you in selecting the right stream of study to make sure that you make an informed decision. Sign up for a free session with us now!

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Chapter 2 collection, presentation, and organization of data.

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Methodology

• Data Collection | Definition, Methods & Examples

Data Collection | Definition, Methods & Examples

Published on June 5, 2020 by Pritha Bhandari . Revised on June 21, 2023.

Data collection is a systematic process of gathering observations or measurements. Whether you are performing research for business, governmental or academic purposes, data collection allows you to gain first-hand knowledge and original insights into your research problem .

While methods and aims may differ between fields, the overall process of data collection remains largely the same. Before you begin collecting data, you need to consider:

• The  aim of the research
• The type of data that you will collect
• The methods and procedures you will use to collect, store, and process the data

To collect high-quality data that is relevant to your purposes, follow these four steps.

Table of contents

Step 1: define the aim of your research, step 2: choose your data collection method, step 3: plan your data collection procedures, step 4: collect the data, other interesting articles, frequently asked questions about data collection.

Before you start the process of data collection, you need to identify exactly what you want to achieve. You can start by writing a problem statement : what is the practical or scientific issue that you want to address and why does it matter?

Next, formulate one or more research questions that precisely define what you want to find out. Depending on your research questions, you might need to collect quantitative or qualitative data :

• Quantitative data is expressed in numbers and graphs and is analyzed through statistical methods .
• Qualitative data is expressed in words and analyzed through interpretations and categorizations.

If your aim is to test a hypothesis , measure something precisely, or gain large-scale statistical insights, collect quantitative data. If your aim is to explore ideas, understand experiences, or gain detailed insights into a specific context, collect qualitative data. If you have several aims, you can use a mixed methods approach that collects both types of data.

• Your first aim is to assess whether there are significant differences in perceptions of managers across different departments and office locations.
• Your second aim is to gather meaningful feedback from employees to explore new ideas for how managers can improve.

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Based on the data you want to collect, decide which method is best suited for your research.

• Experimental research is primarily a quantitative method.
• Interviews , focus groups , and ethnographies are qualitative methods.
• Surveys , observations, archival research and secondary data collection can be quantitative or qualitative methods.

Carefully consider what method you will use to gather data that helps you directly answer your research questions.

When you know which method(s) you are using, you need to plan exactly how you will implement them. What procedures will you follow to make accurate observations or measurements of the variables you are interested in?

For instance, if you’re conducting surveys or interviews, decide what form the questions will take; if you’re conducting an experiment, make decisions about your experimental design (e.g., determine inclusion and exclusion criteria ).

Operationalization

Sometimes your variables can be measured directly: for example, you can collect data on the average age of employees simply by asking for dates of birth. However, often you’ll be interested in collecting data on more abstract concepts or variables that can’t be directly observed.

Operationalization means turning abstract conceptual ideas into measurable observations. When planning how you will collect data, you need to translate the conceptual definition of what you want to study into the operational definition of what you will actually measure.

• You ask managers to rate their own leadership skills on 5-point scales assessing the ability to delegate, decisiveness and dependability.
• You ask their direct employees to provide anonymous feedback on the managers regarding the same topics.

You may need to develop a sampling plan to obtain data systematically. This involves defining a population , the group you want to draw conclusions about, and a sample, the group you will actually collect data from.

Your sampling method will determine how you recruit participants or obtain measurements for your study. To decide on a sampling method you will need to consider factors like the required sample size, accessibility of the sample, and timeframe of the data collection.

Standardizing procedures

If multiple researchers are involved, write a detailed manual to standardize data collection procedures in your study.

This means laying out specific step-by-step instructions so that everyone in your research team collects data in a consistent way – for example, by conducting experiments under the same conditions and using objective criteria to record and categorize observations. This helps you avoid common research biases like omitted variable bias or information bias .

This helps ensure the reliability of your data, and you can also use it to replicate the study in the future.

Creating a data management plan

Before beginning data collection, you should also decide how you will organize and store your data.

• If you are collecting data from people, you will likely need to anonymize and safeguard the data to prevent leaks of sensitive information (e.g. names or identity numbers).
• If you are collecting data via interviews or pencil-and-paper formats, you will need to perform transcriptions or data entry in systematic ways to minimize distortion.
• You can prevent loss of data by having an organization system that is routinely backed up.

Finally, you can implement your chosen methods to measure or observe the variables you are interested in.

The closed-ended questions ask participants to rate their manager’s leadership skills on scales from 1–5. The data produced is numerical and can be statistically analyzed for averages and patterns.

To ensure that high quality data is recorded in a systematic way, here are some best practices:

• Record all relevant information as and when you obtain data. For example, note down whether or how lab equipment is recalibrated during an experimental study.
• Double-check manual data entry for errors.
• If you collect quantitative data, you can assess the reliability and validity to get an indication of your data quality.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Likert scale

Research bias

• Implicit bias
• Framing effect
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

When conducting research, collecting original data has significant advantages:

• You can tailor data collection to your specific research aims (e.g. understanding the needs of your consumers or user testing your website)
• You can control and standardize the process for high reliability and validity (e.g. choosing appropriate measurements and sampling methods )

However, there are also some drawbacks: data collection can be time-consuming, labor-intensive and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

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Home » Data Collection – Methods Types and Examples

Data Collection – Methods Types and Examples

Table of Contents

Data Collection

Definition:

Data collection is the process of gathering and collecting information from various sources to analyze and make informed decisions based on the data collected. This can involve various methods, such as surveys, interviews, experiments, and observation.

In order for data collection to be effective, it is important to have a clear understanding of what data is needed and what the purpose of the data collection is. This can involve identifying the population or sample being studied, determining the variables to be measured, and selecting appropriate methods for collecting and recording data.

Types of Data Collection

Types of Data Collection are as follows:

Primary Data Collection

Primary data collection is the process of gathering original and firsthand information directly from the source or target population. This type of data collection involves collecting data that has not been previously gathered, recorded, or published. Primary data can be collected through various methods such as surveys, interviews, observations, experiments, and focus groups. The data collected is usually specific to the research question or objective and can provide valuable insights that cannot be obtained from secondary data sources. Primary data collection is often used in market research, social research, and scientific research.

Secondary Data Collection

Secondary data collection is the process of gathering information from existing sources that have already been collected and analyzed by someone else, rather than conducting new research to collect primary data. Secondary data can be collected from various sources, such as published reports, books, journals, newspapers, websites, government publications, and other documents.

Qualitative Data Collection

Qualitative data collection is used to gather non-numerical data such as opinions, experiences, perceptions, and feelings, through techniques such as interviews, focus groups, observations, and document analysis. It seeks to understand the deeper meaning and context of a phenomenon or situation and is often used in social sciences, psychology, and humanities. Qualitative data collection methods allow for a more in-depth and holistic exploration of research questions and can provide rich and nuanced insights into human behavior and experiences.

Quantitative Data Collection

Quantitative data collection is a used to gather numerical data that can be analyzed using statistical methods. This data is typically collected through surveys, experiments, and other structured data collection methods. Quantitative data collection seeks to quantify and measure variables, such as behaviors, attitudes, and opinions, in a systematic and objective way. This data is often used to test hypotheses, identify patterns, and establish correlations between variables. Quantitative data collection methods allow for precise measurement and generalization of findings to a larger population. It is commonly used in fields such as economics, psychology, and natural sciences.

Data Collection Methods

Data Collection Methods are as follows:

Surveys involve asking questions to a sample of individuals or organizations to collect data. Surveys can be conducted in person, over the phone, or online.

Interviews involve a one-on-one conversation between the interviewer and the respondent. Interviews can be structured or unstructured and can be conducted in person or over the phone.

Focus Groups

Focus groups are group discussions that are moderated by a facilitator. Focus groups are used to collect qualitative data on a specific topic.

Observation

Observation involves watching and recording the behavior of people, objects, or events in their natural setting. Observation can be done overtly or covertly, depending on the research question.

Experiments

Experiments involve manipulating one or more variables and observing the effect on another variable. Experiments are commonly used in scientific research.

Case Studies

Case studies involve in-depth analysis of a single individual, organization, or event. Case studies are used to gain detailed information about a specific phenomenon.

Secondary Data Analysis

Secondary data analysis involves using existing data that was collected for another purpose. Secondary data can come from various sources, such as government agencies, academic institutions, or private companies.

How to Collect Data

The following are some steps to consider when collecting data:

• Define the objective : Before you start collecting data, you need to define the objective of the study. This will help you determine what data you need to collect and how to collect it.
• Identify the data sources : Identify the sources of data that will help you achieve your objective. These sources can be primary sources, such as surveys, interviews, and observations, or secondary sources, such as books, articles, and databases.
• Determine the data collection method : Once you have identified the data sources, you need to determine the data collection method. This could be through online surveys, phone interviews, or face-to-face meetings.
• Develop a data collection plan : Develop a plan that outlines the steps you will take to collect the data. This plan should include the timeline, the tools and equipment needed, and the personnel involved.
• Test the data collection process: Before you start collecting data, test the data collection process to ensure that it is effective and efficient.
• Collect the data: Collect the data according to the plan you developed in step 4. Make sure you record the data accurately and consistently.
• Analyze the data: Once you have collected the data, analyze it to draw conclusions and make recommendations.
• Report the findings: Report the findings of your data analysis to the relevant stakeholders. This could be in the form of a report, a presentation, or a publication.
• Monitor and evaluate the data collection process: After the data collection process is complete, monitor and evaluate the process to identify areas for improvement in future data collection efforts.
• Ensure data quality: Ensure that the collected data is of high quality and free from errors. This can be achieved by validating the data for accuracy, completeness, and consistency.
• Maintain data security: Ensure that the collected data is secure and protected from unauthorized access or disclosure. This can be achieved by implementing data security protocols and using secure storage and transmission methods.
• Follow ethical considerations: Follow ethical considerations when collecting data, such as obtaining informed consent from participants, protecting their privacy and confidentiality, and ensuring that the research does not cause harm to participants.
• Use appropriate data analysis methods : Use appropriate data analysis methods based on the type of data collected and the research objectives. This could include statistical analysis, qualitative analysis, or a combination of both.
• Record and store data properly: Record and store the collected data properly, in a structured and organized format. This will make it easier to retrieve and use the data in future research or analysis.
• Collaborate with other stakeholders : Collaborate with other stakeholders, such as colleagues, experts, or community members, to ensure that the data collected is relevant and useful for the intended purpose.

Applications of Data Collection

Data collection methods are widely used in different fields, including social sciences, healthcare, business, education, and more. Here are some examples of how data collection methods are used in different fields:

• Social sciences : Social scientists often use surveys, questionnaires, and interviews to collect data from individuals or groups. They may also use observation to collect data on social behaviors and interactions. This data is often used to study topics such as human behavior, attitudes, and beliefs.
• Healthcare : Data collection methods are used in healthcare to monitor patient health and track treatment outcomes. Electronic health records and medical charts are commonly used to collect data on patients’ medical history, diagnoses, and treatments. Researchers may also use clinical trials and surveys to collect data on the effectiveness of different treatments.
• Business : Businesses use data collection methods to gather information on consumer behavior, market trends, and competitor activity. They may collect data through customer surveys, sales reports, and market research studies. This data is used to inform business decisions, develop marketing strategies, and improve products and services.
• Education : In education, data collection methods are used to assess student performance and measure the effectiveness of teaching methods. Standardized tests, quizzes, and exams are commonly used to collect data on student learning outcomes. Teachers may also use classroom observation and student feedback to gather data on teaching effectiveness.
• Agriculture : Farmers use data collection methods to monitor crop growth and health. Sensors and remote sensing technology can be used to collect data on soil moisture, temperature, and nutrient levels. This data is used to optimize crop yields and minimize waste.
• Environmental sciences : Environmental scientists use data collection methods to monitor air and water quality, track climate patterns, and measure the impact of human activity on the environment. They may use sensors, satellite imagery, and laboratory analysis to collect data on environmental factors.
• Transportation : Transportation companies use data collection methods to track vehicle performance, optimize routes, and improve safety. GPS systems, on-board sensors, and other tracking technologies are used to collect data on vehicle speed, fuel consumption, and driver behavior.

Examples of Data Collection

Examples of Data Collection are as follows:

• Traffic Monitoring: Cities collect real-time data on traffic patterns and congestion through sensors on roads and cameras at intersections. This information can be used to optimize traffic flow and improve safety.
• Social Media Monitoring : Companies can collect real-time data on social media platforms such as Twitter and Facebook to monitor their brand reputation, track customer sentiment, and respond to customer inquiries and complaints in real-time.
• Weather Monitoring: Weather agencies collect real-time data on temperature, humidity, air pressure, and precipitation through weather stations and satellites. This information is used to provide accurate weather forecasts and warnings.
• Stock Market Monitoring : Financial institutions collect real-time data on stock prices, trading volumes, and other market indicators to make informed investment decisions and respond to market fluctuations in real-time.
• Health Monitoring : Medical devices such as wearable fitness trackers and smartwatches can collect real-time data on a person’s heart rate, blood pressure, and other vital signs. This information can be used to monitor health conditions and detect early warning signs of health issues.

Purpose of Data Collection

The purpose of data collection can vary depending on the context and goals of the study, but generally, it serves to:

• Provide information: Data collection provides information about a particular phenomenon or behavior that can be used to better understand it.
• Measure progress : Data collection can be used to measure the effectiveness of interventions or programs designed to address a particular issue or problem.
• Support decision-making : Data collection provides decision-makers with evidence-based information that can be used to inform policies, strategies, and actions.
• Identify trends : Data collection can help identify trends and patterns over time that may indicate changes in behaviors or outcomes.
• Monitor and evaluate : Data collection can be used to monitor and evaluate the implementation and impact of policies, programs, and initiatives.

When to use Data Collection

Data collection is used when there is a need to gather information or data on a specific topic or phenomenon. It is typically used in research, evaluation, and monitoring and is important for making informed decisions and improving outcomes.

Data collection is particularly useful in the following scenarios:

• Research : When conducting research, data collection is used to gather information on variables of interest to answer research questions and test hypotheses.
• Evaluation : Data collection is used in program evaluation to assess the effectiveness of programs or interventions, and to identify areas for improvement.
• Monitoring : Data collection is used in monitoring to track progress towards achieving goals or targets, and to identify any areas that require attention.
• Decision-making: Data collection is used to provide decision-makers with information that can be used to inform policies, strategies, and actions.
• Quality improvement : Data collection is used in quality improvement efforts to identify areas where improvements can be made and to measure progress towards achieving goals.

Characteristics of Data Collection

Data collection can be characterized by several important characteristics that help to ensure the quality and accuracy of the data gathered. These characteristics include:

• Validity : Validity refers to the accuracy and relevance of the data collected in relation to the research question or objective.
• Reliability : Reliability refers to the consistency and stability of the data collection process, ensuring that the results obtained are consistent over time and across different contexts.
• Objectivity : Objectivity refers to the impartiality of the data collection process, ensuring that the data collected is not influenced by the biases or personal opinions of the data collector.
• Precision : Precision refers to the degree of accuracy and detail in the data collected, ensuring that the data is specific and accurate enough to answer the research question or objective.
• Timeliness : Timeliness refers to the efficiency and speed with which the data is collected, ensuring that the data is collected in a timely manner to meet the needs of the research or evaluation.
• Ethical considerations : Ethical considerations refer to the ethical principles that must be followed when collecting data, such as ensuring confidentiality and obtaining informed consent from participants.

Advantages of Data Collection

There are several advantages of data collection that make it an important process in research, evaluation, and monitoring. These advantages include:

• Better decision-making : Data collection provides decision-makers with evidence-based information that can be used to inform policies, strategies, and actions, leading to better decision-making.
• Improved understanding: Data collection helps to improve our understanding of a particular phenomenon or behavior by providing empirical evidence that can be analyzed and interpreted.
• Evaluation of interventions: Data collection is essential in evaluating the effectiveness of interventions or programs designed to address a particular issue or problem.
• Identifying trends and patterns: Data collection can help identify trends and patterns over time that may indicate changes in behaviors or outcomes.
• Increased accountability: Data collection increases accountability by providing evidence that can be used to monitor and evaluate the implementation and impact of policies, programs, and initiatives.
• Validation of theories: Data collection can be used to test hypotheses and validate theories, leading to a better understanding of the phenomenon being studied.
• Improved quality: Data collection is used in quality improvement efforts to identify areas where improvements can be made and to measure progress towards achieving goals.

Limitations of Data Collection

While data collection has several advantages, it also has some limitations that must be considered. These limitations include:

• Bias : Data collection can be influenced by the biases and personal opinions of the data collector, which can lead to inaccurate or misleading results.
• Sampling bias : Data collection may not be representative of the entire population, resulting in sampling bias and inaccurate results.
• Cost : Data collection can be expensive and time-consuming, particularly for large-scale studies.
• Limited scope: Data collection is limited to the variables being measured, which may not capture the entire picture or context of the phenomenon being studied.
• Ethical considerations : Data collection must follow ethical principles to protect the rights and confidentiality of the participants, which can limit the type of data that can be collected.
• Data quality issues: Data collection may result in data quality issues such as missing or incomplete data, measurement errors, and inconsistencies.
• Limited generalizability : Data collection may not be generalizable to other contexts or populations, limiting the generalizability of the findings.

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• Data Collection Methods | Step-by-Step Guide & Examples

Data Collection Methods | Step-by-Step Guide & Examples

Published on 4 May 2022 by Pritha Bhandari .

Data collection is a systematic process of gathering observations or measurements. Whether you are performing research for business, governmental, or academic purposes, data collection allows you to gain first-hand knowledge and original insights into your research problem .

While methods and aims may differ between fields, the overall process of data collection remains largely the same. Before you begin collecting data, you need to consider:

• The  aim of the research
• The type of data that you will collect
• The methods and procedures you will use to collect, store, and process the data

To collect high-quality data that is relevant to your purposes, follow these four steps.

Table of contents

Step 1: define the aim of your research, step 2: choose your data collection method, step 3: plan your data collection procedures, step 4: collect the data, frequently asked questions about data collection.

Before you start the process of data collection, you need to identify exactly what you want to achieve. You can start by writing a problem statement : what is the practical or scientific issue that you want to address, and why does it matter?

Next, formulate one or more research questions that precisely define what you want to find out. Depending on your research questions, you might need to collect quantitative or qualitative data :

• Quantitative data is expressed in numbers and graphs and is analysed through statistical methods .
• Qualitative data is expressed in words and analysed through interpretations and categorisations.

If your aim is to test a hypothesis , measure something precisely, or gain large-scale statistical insights, collect quantitative data. If your aim is to explore ideas, understand experiences, or gain detailed insights into a specific context, collect qualitative data.

If you have several aims, you can use a mixed methods approach that collects both types of data.

• Your first aim is to assess whether there are significant differences in perceptions of managers across different departments and office locations.
• Your second aim is to gather meaningful feedback from employees to explore new ideas for how managers can improve.

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Based on the data you want to collect, decide which method is best suited for your research.

• Experimental research is primarily a quantitative method.
• Interviews , focus groups , and ethnographies are qualitative methods.
• Surveys , observations, archival research, and secondary data collection can be quantitative or qualitative methods.

Carefully consider what method you will use to gather data that helps you directly answer your research questions.

When you know which method(s) you are using, you need to plan exactly how you will implement them. What procedures will you follow to make accurate observations or measurements of the variables you are interested in?

For instance, if you’re conducting surveys or interviews, decide what form the questions will take; if you’re conducting an experiment, make decisions about your experimental design .

Operationalisation

Sometimes your variables can be measured directly: for example, you can collect data on the average age of employees simply by asking for dates of birth. However, often you’ll be interested in collecting data on more abstract concepts or variables that can’t be directly observed.

Operationalisation means turning abstract conceptual ideas into measurable observations. When planning how you will collect data, you need to translate the conceptual definition of what you want to study into the operational definition of what you will actually measure.

• You ask managers to rate their own leadership skills on 5-point scales assessing the ability to delegate, decisiveness, and dependability.
• You ask their direct employees to provide anonymous feedback on the managers regarding the same topics.

You may need to develop a sampling plan to obtain data systematically. This involves defining a population , the group you want to draw conclusions about, and a sample, the group you will actually collect data from.

Your sampling method will determine how you recruit participants or obtain measurements for your study. To decide on a sampling method you will need to consider factors like the required sample size, accessibility of the sample, and time frame of the data collection.

Standardising procedures

If multiple researchers are involved, write a detailed manual to standardise data collection procedures in your study.

This means laying out specific step-by-step instructions so that everyone in your research team collects data in a consistent way – for example, by conducting experiments under the same conditions and using objective criteria to record and categorise observations.

This helps ensure the reliability of your data, and you can also use it to replicate the study in the future.

Creating a data management plan

Before beginning data collection, you should also decide how you will organise and store your data.

• If you are collecting data from people, you will likely need to anonymise and safeguard the data to prevent leaks of sensitive information (e.g. names or identity numbers).
• If you are collecting data via interviews or pencil-and-paper formats, you will need to perform transcriptions or data entry in systematic ways to minimise distortion.
• You can prevent loss of data by having an organisation system that is routinely backed up.

Finally, you can implement your chosen methods to measure or observe the variables you are interested in.

The closed-ended questions ask participants to rate their manager’s leadership skills on scales from 1 to 5. The data produced is numerical and can be statistically analysed for averages and patterns.

To ensure that high-quality data is recorded in a systematic way, here are some best practices:

• Record all relevant information as and when you obtain data. For example, note down whether or how lab equipment is recalibrated during an experimental study.
• Double-check manual data entry for errors.
• If you collect quantitative data, you can assess the reliability and validity to get an indication of your data quality.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organisations.

When conducting research, collecting original data has significant advantages:

• You can tailor data collection to your specific research aims (e.g., understanding the needs of your consumers or user testing your website).
• You can control and standardise the process for high reliability and validity (e.g., choosing appropriate measurements and sampling methods ).

However, there are also some drawbacks: data collection can be time-consuming, labour-intensive, and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research , you also have to consider the internal and external validity of your experiment.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

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Collection and Presentation of Data

We come across a lot of information every day from different sources. Our newspapers, TV, Phone and the Internet, etc are the sources of information in our life. This information can be related to anything, from bowling averages in cricket to profits of the company over the years. These facts and figures are often numerical and are called Data. Statistics is the study of data. Let’s look into this in detail. 

Statistics – Collection and Presentation of Data

Before going into Statistics, first, let’s define what is Data. 

“Data are units of information, often numeric, collected through observation.”  It is plural form of the Latin word “Datum”.

Our world has become very information-oriented in the past two decades. So, it becomes essential for us to extract meaningful information out of data. For that we need statistics. Let’s see what statistics mean in formal terms. 

Statistics is derived from Latin word “Status” which means “a state”. It concerns with the nature, meaning and distribution of the data. 

Collection of Data

Collection of data refers to collecting information about something with an objective to analyze it or extract some meaningful information from it. Some examples of activities involving the collection of data are: 

Students collecting data from their localities about the number of people with Covid Vaccines. A Football fan collecting information about the goals scored by his favorite player. A record company collecting information about album sales by their artists.

Types of Recorded Data

Most of the time when we collect data for our experiment with an objective. It usually falls into one of these two categories: 

Categorical Data

• Numerical Data

This data represents the characteristics of something entity. For example, if we are collecting data about some people. Categorical data related to this information might be, gender of the person, marital status, etc. These things will have values that are not numerical, often “Yes/No” or in this case “Male/Female”. Since they are not numerical, they cannot be added together. 

Numerical Data 

This data comes out of measurement and is numerical in nature. For example, Weight of the person, stock prices, marks of students of class XII, etc. This data is also called quantitative data. It can be broken down further into types: 

• Continuous Data
• Discrete Data

Continuous Data : This data can take any value between intervals. The number of possible values for this data cannot be counted. For example Length of a ruler can take any length between 0-100cm. It can be either 30cm, 30.11cm and so on. There are infinitely many possible values. 

Discrete Data: This data takes only certain values. For example: If a coin is tossed three times, and we want to count the number of heads. There are only a handful of values that are possible. 0,1,2 or 3. It cannot take 2.2 or any other value. So, there are only finite possible values. 

Presentation of Data

After collecting the data, we need to present it in a meaningful way. Let’s take an example, 

Suppose we have the data of heights of students in a class, 

140, 161, 152, 184, 135, 168 and 144.

We need to answer the following questions related to the data: 

• What is the height of the longest student in the class?
• What is the height of the shortest student in the class?
• What is the average height?

It is a little difficult to analyze the data in this format. The data in the form is called raw data. Analyzing the data in this form might take more time if the data is big. It can be made a little easier if sort the data in ascending or descending order.  Thus, in this way, the presentation of data affects the information and the time taken to extract it from the data. 

Suppose if this data was even bigger, then it would be very difficult to organize the data in sorted order. In such cases, we might use a frequency table. Let’s see this through an example. 

Un-Grouped Frequency Distribution

In this type of frequency table, we consider the values as it is and then count their number of occurrences in the data. We don’t group the data. Let’s see this through an example. 

Question: Let’s say we have marks of students of class XII. The marks are out of 40. 

Represent this data using a frequency table. 

Solution: 

Let’s take marks of some student in one column and frequency of such marks in another column.  Marks Frequency 5 1 7 1 8 1 10 1 11 1 13 2 15 2 20 2 21 1 24 2 29 1 33 1 38 1 40 1 Notice that in this table, we have not grouped the data instead we have taken exact values and their frequency. So, this type of representation is called ungrouped frequency distribution. 

Grouped Frequency Distribution

The previous kind of representation is definitely an improvement over previous representations but as seen in the above example, tables can get pretty big in such representations. Tally Marks and grouping can also be used to represent this data. 

Question: We have the data for the number of covid cases on a particular day in 20 cities. 

In the previous example we saw that ungrouped frequency distribution is cumbersome and very long to look at. So now, we will divide the data into groups. This kind of frequency table representation is called grouped frequency representation.  Let’s divide the numbers of cases in the groups like, 0-5, 5-10, 10-15 … and so on.  Then the frequency table will become,  Group Frequency 0-5 2 5-10 3 10-15 1 15-20 3 20-25 2 25-30 2 30-35 1 35-40 1

The intervals like 0-5, 5-10 .. And so on given in the above example are called class intervals. The larger number is called higher limit and the lower number is called the lower limit. 

Let’s see some sample problems on these concepts 

Sample Problems

Problem 1: The table below represents the data. Represent this data in the form of suitable frequency distribution. 

We can see from the data given above, that there are only three values – 2,3 and 4. These values occur multiple times throughout the data. Since there are very less number of values, we can represent this kind of data in the form un-grouped frequency table.  Value Frequency 2 4 3 5 4 3 Total – 12

Problem 2: The data given below represents the blood groups of the 20 students of class XI. 

Represent the data given above in the table in the form of a frequency table. Which of the following blood group has the highest frequency among the students?

We know there are four types of blood groups in the table.  O, A, AB and B So, we will use ungrouped frequency distribution table to represent the data.  Blood Group Frequency  O 5 A 5 AB 4 B 6 Total  20 From the frequency distribution table we can tell the B is the blood group which most commonly occurring in students. 

Problem 3: The table represents the weights of the students of class X. 

Answer the following questions: 

• What is the range in which most students lie? 
• Suppose students weighing more than 70 are considered overweight and those weighing less than 50 are considered as underweight. How many such students are there in the class? 

Let’s make a grouped frequency distribution table for this data.  Assuming intervals like 0-10,10-20…and so on. Let’s divide the data into these intervals are count the frequency.  Weight Group Frequency 0-10 0 10-20 0 20-30 0 30-40 0 40-50 3 50-60 4 60-70 6 70-80 2 80-90 1 Total  16 This above table represents a grouped frequency table. Now answering the questions.  1. Most students lie in the range from 60-70.  2. For overweight students, we need to count the number of students with weight greater than 70. It can be observed from the table that there are three such students.  For underweight students, the number students with weight less than 50 are also three students. 

Problem 4: Three coins are tossed 20 times. The number of heads that occurred each time is recorded and given in this data below. Prepare a frequency distribution for the given data. 

We know there are maximum of three heads possible at each turn in this experiment. So we can actually make an ungrouped frequency distribution for such data Number of Heads Frequency 0 3 1 5 2 8 3 4 Total  20 Thus, the table above represents the frequency table for this data. 

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7 Data Collection Methods in Business Analytics

• 02 Dec 2021

Data is being generated at an ever-increasing pace. According to Statista , the total volume of data was 64.2 zettabytes in 2020; it’s predicted to reach 181 zettabytes by 2025. This abundance of data can be overwhelming if you aren’t sure where to start.

So, how do you ensure the data you use is relevant and important to the business problems you aim to solve? After all, a data-driven decision is only as strong as the data it’s based on. One way is to collect data yourself.

Here’s a breakdown of data types, why data collection is important, what to know before you begin collecting, and seven data collection methods to leverage.

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What Is Data Collection?

Data collection is the methodological process of gathering information about a specific subject. It’s crucial to ensure your data is complete during the collection phase and that it’s collected legally and ethically . If not, your analysis won’t be accurate and could have far-reaching consequences.

In general, there are three types of consumer data:

• First-party data , which is collected directly from users by your organization
• Second-party data , which is data shared by another organization about its customers (or its first-party data)
• Third-party data , which is data that’s been aggregated and rented or sold by organizations that don’t have a connection to your company or users

Although there are use cases for second- and third-party data, first-party data (data you’ve collected yourself) is more valuable because you receive information about how your audience behaves, thinks, and feels—all from a trusted source.

Data can be qualitative (meaning contextual in nature) or quantitative (meaning numeric in nature). Many data collection methods apply to either type, but some are better suited to one over the other.

In the data life cycle , data collection is the second step. After data is generated, it must be collected to be of use to your team. After that, it can be processed, stored, managed, analyzed, and visualized to aid in your organization’s decision-making.

Before collecting data, there are several factors you need to define:

• The question you aim to answer
• The data subject(s) you need to collect data from
• The collection timeframe
• The data collection method(s) best suited to your needs

The data collection method you select should be based on the question you want to answer, the type of data you need, your timeframe, and your company’s budget.

The Importance of Data Collection

Collecting data is an integral part of a business’s success; it can enable you to ensure the data’s accuracy, completeness, and relevance to your organization and the issue at hand. The information gathered allows organizations to analyze past strategies and stay informed on what needs to change.

The insights gleaned from data can make you hyperaware of your organization’s efforts and give you actionable steps to improve various strategies—from altering marketing strategies to assessing customer complaints.

Basing decisions on inaccurate data can have far-reaching negative consequences, so it’s important to be able to trust your own data collection procedures and abilities. By ensuring accurate data collection, business professionals can feel secure in their business decisions.

Explore the options in the next section to see which data collection method is the best fit for your company.

7 Data Collection Methods Used in Business Analytics

Surveys are physical or digital questionnaires that gather both qualitative and quantitative data from subjects. One situation in which you might conduct a survey is gathering attendee feedback after an event. This can provide a sense of what attendees enjoyed, what they wish was different, and areas in which you can improve or save money during your next event for a similar audience.

While physical copies of surveys can be sent out to participants, online surveys present the opportunity for distribution at scale. They can also be inexpensive; running a survey can cost nothing if you use a free tool. If you wish to target a specific group of people, partnering with a market research firm to get the survey in front of that demographic may be worth the money.

Something to watch out for when crafting and running surveys is the effect of bias, including:

• Collection bias : It can be easy to accidentally write survey questions with a biased lean. Watch out for this when creating questions to ensure your subjects answer honestly and aren’t swayed by your wording.
• Subject bias : Because your subjects know their responses will be read by you, their answers may be biased toward what seems socially acceptable. For this reason, consider pairing survey data with behavioral data from other collection methods to get the full picture.

Related: 3 Examples of Bad Survey Questions & How to Fix Them

2. Transactional Tracking

Each time your customers make a purchase, tracking that data can allow you to make decisions about targeted marketing efforts and understand your customer base better.

Often, e-commerce and point-of-sale platforms allow you to store data as soon as it’s generated, making this a seamless data collection method that can pay off in the form of customer insights.

3. Interviews and Focus Groups

Interviews and focus groups consist of talking to subjects face-to-face about a specific topic or issue. Interviews tend to be one-on-one, and focus groups are typically made up of several people. You can use both to gather qualitative and quantitative data.

Through interviews and focus groups, you can gather feedback from people in your target audience about new product features. Seeing them interact with your product in real-time and recording their reactions and responses to questions can provide valuable data about which product features to pursue.

As is the case with surveys, these collection methods allow you to ask subjects anything you want about their opinions, motivations, and feelings regarding your product or brand. It also introduces the potential for bias. Aim to craft questions that don’t lead them in one particular direction.

One downside of interviewing and conducting focus groups is they can be time-consuming and expensive. If you plan to conduct them yourself, it can be a lengthy process. To avoid this, you can hire a market research facilitator to organize and conduct interviews on your behalf.

4. Observation

Observing people interacting with your website or product can be useful for data collection because of the candor it offers. If your user experience is confusing or difficult, you can witness it in real-time.

Yet, setting up observation sessions can be difficult. You can use a third-party tool to record users’ journeys through your site or observe a user’s interaction with a beta version of your site or product.

While less accessible than other data collection methods, observations enable you to see firsthand how users interact with your product or site. You can leverage the qualitative and quantitative data gleaned from this to make improvements and double down on points of success.

5. Online Tracking

To gather behavioral data, you can implement pixels and cookies. These are both tools that track users’ online behavior across websites and provide insight into what content they’re interested in and typically engage with.

You can also track users’ behavior on your company’s website, including which parts are of the highest interest, whether users are confused when using it, and how long they spend on product pages. This can enable you to improve the website’s design and help users navigate to their destination.

Inserting a pixel is often free and relatively easy to set up. Implementing cookies may come with a fee but could be worth it for the quality of data you’ll receive. Once pixels and cookies are set, they gather data on their own and don’t need much maintenance, if any.

It’s important to note: Tracking online behavior can have legal and ethical privacy implications. Before tracking users’ online behavior, ensure you’re in compliance with local and industry data privacy standards .

Online forms are beneficial for gathering qualitative data about users, specifically demographic data or contact information. They’re relatively inexpensive and simple to set up, and you can use them to gate content or registrations, such as webinars and email newsletters.

You can then use this data to contact people who may be interested in your product, build out demographic profiles of existing customers, and in remarketing efforts, such as email workflows and content recommendations.

Related: What Is Marketing Analytics?

7. Social Media Monitoring

Monitoring your company’s social media channels for follower engagement is an accessible way to track data about your audience’s interests and motivations. Many social media platforms have analytics built in, but there are also third-party social platforms that give more detailed, organized insights pulled from multiple channels.

You can use data collected from social media to determine which issues are most important to your followers. For instance, you may notice that the number of engagements dramatically increases when your company posts about its sustainability efforts.

Building Your Data Capabilities

Understanding the variety of data collection methods available can help you decide which is best for your timeline, budget, and the question you’re aiming to answer. When stored together and combined, multiple data types collected through different methods can give an informed picture of your subjects and help you make better business decisions.

Do you want to become a data-driven professional? Explore our eight-week Business Analytics course and our three-course Credential of Readiness (CORe) program to deepen your analytical skills and apply them to real-world business problems. Not sure which course is right for you? Download our free flowchart .

This post was updated on October 17, 2022. It was originally published on December 2, 2021.

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Data and Data Presentation

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Planning is a process that designs a plan of action or evaluates the impact of a proposed action to achieve a desirable future. During this process planners often obtain the necessary data from different sources, analyze them efficiently and comprehensively, and present the results in easily understandable forms. The rationale for such a process is that public policy and decision makers derive their decisions based on the anticipated future from knowledge about the present and the past of a community. The three-step procedure—data collection, analysis, and presentation has the goal of accurately presenting the information to reflect what has happened and what may happen.

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Used tesla cybertruck price continues to crash, new and dangerous android attack warning issued, visibility: the foundation for transformation.

Real-time visibility enables manufacturers to see, track or monitor operations as they happen. It drives efficiency, productivity and quality. Yet only 16% of manufacturers have real-time, work-in-progress (WIP) monitoring across the entire manufacturing process, according to Zebra’s study.

Imagine attempting to manually monitor hundreds of machines and workers spanning a large complex factory. Without technology, the status of equipment, raw materials and workers in the production process isn’t visible or traceable. This explains manufacturers’ pressing need for technologies enabling visibility, real-time monitoring and product tracking to drive quality and efficiency throughout operations .

Visibility is the foundation of digital transformation. It provides a real-time line of sight—and ideally an annotated data archive—so manufacturers can identify, analyze and make operational decisions. A recent National Association of Manufacturers (NAM) study reports that 44% of manufacturers say the amount of data collected has doubled over two years and anticipate it tripling by 2030. Yet, most say they have only moderate confidence in their analytics capabilities.

Manufacturers can begin to pave the way to full visibility by evaluating current pain points and identifying visibility gaps. Even in manufacturing plants where ID systems have been fully adopted, there is still an opportunity to digitize inspections, which are often painstakingly tedious, prone to human error and crucial to the manufacturing process. Machine vision systems powered by artificial intelligence (AI) handle inspections with speed and accuracy well beyond human capacity.

Manufacturers are also looking to AI to use data more effectively. Zebra’s study found 61% of manufacturers expect AI to drive growth by 2029, a 41% increase from 2024. Successful implementation involves careful planning, training and a phased approach to avoid overwhelming the workforce.

To start your digital transformation, ensure the foundation is solid with visibility solutions. They are fundamental to forging your path to transform into an agile, resilient connected factory.

Upskill Labor: Augment Workers With Technology

According to the NAM Outlook Survey , more than 65% of manufacturers report attracting and retaining quality workers remains a top challenge. A gap exists between the latest manufacturing technology and the workers equipped to use it. The World Economic Forum reported that 60% of the global workforce needs significant training to bridge this gap.

Manufacturers are augmenting workers and integrating AI and tech tools to build a workforce as skilled as the technology they use. By 2029, Zebra reports that 73% of manufacturers expect to reskill labor with data and technology to improve workflows, and 7 in 10 expect to augment workers with mobile devices and technology. Half of the manufacturers report they’re implementing tablets, mobile computers and workforce management software. More than 60% plan to leverage wearables and computer vision.

Workplace flexibility, meaningful work and a safe (secure and respected) workplace were the top reasons Gen Z cited to remain in a job according to McKinsey. Manufacturers can better attract the growing Gen Z workforce by using technology to improve the worker experience, offering ongoing development and training, and creating career advancement paths.

Increasing wages is simply not enough to attract and retain Gen Z. This generation of workers is pervasively using technology outside the work environment and is therefore comfortable adopting technology augmentation in their work.

Optimize workflows and workers to empower them to focus on high-value, customer-centric tasks and build the relationships and career paths they desire. You’ll also boost productivity and efficiency and open new opportunities to attract future talent.

Visibility + Augmentation + Automation = Quality

Quality in manufacturing can’t exist without real-time visibility and skilled workers. In Zebra’s study, leaders cite real-time visibility and timing in identifying and resolving issues, keeping up with new standards and regulations, maintaining traceability, and integrating data as their most significant quality management issues.

But what if you’re a manufacturer without the budget, bandwidth or time to invest in advanced digital transformation right now? You can still take practical steps to move forward. Start with fundamental data collection and analytic tools to lay the groundwork. Leveraging visibility solutions like barcode scanning, wearables or other basic Internet of Things (IoT) devices can help monitor machines and provide insights and improvements.

Quality is the final piece of the equation. Once you’re further down the path to transformation, implement visibility solutions and augment and upskill workers with technology to optimize quality. To drive quality even further, add advanced automation solutions. You don’t have to boil the ocean on your digital transformation journey—take it one step at a time from wherever you’re starting.

An Ongoing Journey

Most manufacturers (87%) in Zebra’s study agree it’s a challenge to pilot new technologies or move beyond the pilot phase, yet they plan to advance digital maturity by 2029. With the right technology tools and solutions in place to advance visibility, augment workers and optimize quality, they will get there.

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