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What is Data Collection | Step-By-Step Process

Last updated on 27th Oct 2022, Artciles, Blog

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Smita Jhingran (Big Data Engineer )

Smita Jhingran provides in-depth presentations on various big data technologies. She specializes in Docker, Hadoop, Microservices, MiNiFi, Cloudera, Commvault, and BI tools with 5+ years of experience.

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    • In this article you will learn:
    • 1.What is “data collection”?
    • 2.Why is it necessary that we collect data?
    • 3. Various Approaches to Collect Data.
    • 4.Methods of Data Collection.
    • 5.Data Collection Equipment.
    • 6.Obstacles to data collection.
    • 7. Choosing the Data to Be Collected.
    • 8. Managing Massive Data.
    • 7.Conclusion Of Data Collection.

What is “data collection”?

Data series are given because the process of collecting, measuring, and studying accurate information for investigations uses tried-and-true methods. A researcher can test their hypotheses by looking at the data they’ve collected. No matter the field of study, gathering relevant data is almost always the first and most important phase of the research process. The way to put together a list of facts is unique and can only be used in certain fields of study because it depends on what you already know.

Before trying to explain what data collection actually means, it is important to first ask, “What is data?” The concise explanation is that data is a collection of several types of organized information. Therefore, the process of gathering, measuring, and analyzing precise data from a range of relevant sources is known as “data collection.” This is done in order to find solutions to study problems, provide answers to questions, evaluate outcomes, and forecast trends and probabilities.

The fact that our society depends so much on data shows how important it is to keep collecting it. The collection of accurate data is essential for making educated business decisions, ensuring quality assurance, and maintaining the integrity of research.

The researchers are required to determine the types of data, the sources of the data, and the methods that are being employed when they are collecting the data. As we will see in a moment, there are a great many distinct approaches to the collection of data. Data collection is very important in science, business, and government.

Why is it necessary that we collect data?

Before a judge can issue a judgment in a case or a general can devise an offensive strategy, they are required to have as much pertinent data at their disposal as is humanly possible. Because information and data are the same thing, the best courses of action are the ones that come from well-informed decisions.

As we will see in the following section, the idea of collecting data is not a new one, but the world has evolved. There is an enormous amount of material that can be accessed today, and it can be found in formats that did not even exist a century ago. With the changing times and improvements in technology, the way data is collected has had to change and improve.

It doesn’t matter if you work in the world of academia and are attempting to conduct research or if you work in the world of commerce and are trying to think of ways to market a new product, you need data collection to help you make better decisions.

What Are the Various Approaches That Can Be Taken to Collect Data?

The seven basic techniques for gathering data in the field of business analytics are as follows:

  • Surveys.
  • Monitoring of Financial Transactions.
  • Conducting Interviews and Holding Focus Groups.
  • Observation.
  • Forms for Online Monitoring Available.
  • Social Media Monitoring.

There are two distinct approaches to the collection of data. As a side note, it’s important to keep in mind that many terms, such as approaches, methods, and types, can be interchanged depending on the context and the person using them. For instance, one source may refer to the processes for data collection as “methods.” Nevertheless, regardless of what labels we use, the overarching ideas and breakdowns are universally applicable, regardless of whether we are discussing a scientific research endeavor or a marketing analysis.

The two methods are as follows:

Primary: As the name suggests, this is the real, first-hand data that the data researchers themselves got. This procedure, which is the first step in gathering information, is done before anyone else does any related or additional research. If the researcher is the one to acquire the information, then the results from primary data are extremely accurate. However, there is a drawback, and that is the fact that a study conducted first hand may be both time-consuming and costly.

Secondary: The term “secondary data” refers to information that was gathered by third parties and has previously been subjected to statistical analysis. This data is either information that the researcher has looked up themselves or information that the researcher has given to other individuals to collect on their behalf. To put it another way, the information comes from a second source. Secondary information raises questions over its quality and validity, despite the fact that obtaining it is simpler and less expensive than obtaining primary information. The majority of the secondary data consists of quantitative information.

Methods of Data Collection

Methods of Data Collection That Are Particularly Relevant:

Let’s get down to the nitty-gritty of it. Here is a breakdown of some strategies, with a nod to the first and second steps that have already been explained:

The gathering of primary data:

Interviews: The researcher will conduct interviews with a large number of people, either directly or through other means of mass communication such as the phone or the mail, and will ask them questions. This is by far the most common way to gather information, and it is how most information is gathered.

Collecting Data Based on Projections:An indirect interview is what’s known as “projective data collection,” and it’s used when potential respondents are aware of the purpose of the questions and are hesitant to answer them. If a representative from the person’s cell phone carrier were to ask questions regarding the person’s phone service, for instance, the person might be hesitant to answer the inquiries. In projective data gathering, the respondents are given a question that is only partially answered, and it is up to them to finish the inquiry based on their own thoughts, emotions, and perspectives.

Delphi Technique:According to Greek mythology, the Oracle at Delphi was the high priestess of Apollo’s temple and was able to give wisdom, prophesies, and counsel to anyone who came to her for help. Researchers utilize the Delphi method for the purpose of collecting data. This method entails getting information from a group of knowledgeable individuals. Each expert answers questions about their own area of knowledge, and then the answers are put together to make a single view.

Discussion Groups:In the same vein as interviews, focus groups are a technique that is frequently utilized. A gathering of anywhere from a half-dozen to a dozen people, overseen by a moderator, is taking place in order to have a conversation on the matter at hand.

Questionnaires:Using questionnaires to gather information is a simple and straightforward process. The respondents are given a sequence of questions, pertinent to the topic at hand, which may or may not have open-ended responses.

Data Collected From Secondary Sources:In contrast to the acquisition of primary data, there are no predetermined techniques for collecting it. Instead, because the information has already been gathered, the researcher checks a number of different data sources, such as the following:

  • Financial Statements.
  • Sales Reports.
  • Personal information about retailer, distributor, and deal customers Retailer/Distributor/Deal Feedback.
  • The Various Business Journals.
  • Documents from the Government (e.g., census, tax records, Social Security info).
  • Trade/Business Magazines.
  • The worldwide web.

Data Collection Equipment:

Now that we’ve talked about all the different strategies, let’s focus on a few specific tools. For example, we said that interviews are a strategy, but there are different kinds of interviews, which we called tools:

Word Connection: The researcher provides the respondent with a list of terms and then asks what comes to mind for each word.

Sentence Completion: Researchers employ sentence completion to determine the nature of the respondent’s ideas. This technique involves providing an incomplete statement and observing the interviewee’s completion.

Role-Playing: Respondents are given a made-up situation and asked how they would handle it if it were real.

Personal Interviews: The researcher conducts interviews in person.

Online/Web Surveys: Even though these questionnaires are easy to fill out, some users may not be willing to answer honestly, if at all.

Mobile Surveys: These surveys make use of the growing use of mobile technologies. Mobile survey collection relies on mobile devices such as tablets or smartphones to conduct SMS or mobile app-based surveys.

Phone Interviews: No researcher can simultaneously contact thousands of individuals; hence, a third party must undertake the task. Nevertheless, many individuals have phone filters and will not answer.

Observation: Occasionally, the easiest option is the best. Researchers who conduct direct observations acquire data with minimal intervention and third-party bias. Obviously, its effectiveness is limited to small-scale scenarios.

Data Collection Tools

What are the most frequent obstacles to data collection?

Let’s examine a few of the most common obstacles encountered during data collection in order to better understand and prevent them:

Data Quality Issues: The main thing that could stop machine learning from being widely used and working well is bad data quality. If you want technologies such as machine learning to function for you, data quality must be your first focus. This blog post will talk about the most common problems with data quality and how to fix them.

Ambiguous Data: In big databases or data lakes, errors can still occur despite meticulous management. For rapidly streaming data, the problem gets more daunting. Unnoticed misspellings and formatting errors may occur, and column headings may be deceptive. This imprecise data may present a number of difficulties for reporting and analytics.

Duplicate Data: Modern organizations must manage a variety of data sources, including streaming data, local databases, and data lakes in the cloud. These sources are likely to have substantial duplication and overlap. For example, duplicate contact information has a significant influence on customer satisfaction. Marketing campaigns suffer if certain prospects are neglected while others are regularly contacted. When duplicate data are present, the probability of erroneous analytical conclusions rises. It can also result in skewed training data for machine learning algorithms.

Inaccurate Data: For highly regulated industries such as healthcare, data precision is essential. It is more crucial than ever to improve the data quality for COVID-19 and future pandemics in light of recent events. Inaccurate information cannot be used to determine the best course of action since it does not present a true picture of the issue. If your customer data is wrong, personalized customer experiences and marketing initiatives will not be as effective.There are a lot of causes for inaccurate data, including data degradation, human error, and data drift. Approximately 3% of global data is lost each month, which is extremely alarming. Integrity of data may be affected during transfer between systems, and data quality may decline with time.

Hidden Data: The majority of organizations only utilize a part of their data, with the remainder frequently being lost in data silos or deleted. For instance, the customer care team may not obtain client information from sales, so missing out on an opportunity to create more accurate and detailed customer profiles. Hidden data causes missed opportunities to develop new products, improve services, and streamline processes.

Choosing the Data to Be Collected:

Determining what data to collect is one of the most crucial and should be one of the initial considerations while collecting data. We must determine the topics that the data will cover, the sources that will be used to collect it, and the quantity of data required. Our responses to these questions will depend on our objectives, or what we hope to accomplish with your data. As an example, we may opt to collect data on the article categories that website visitors between the ages of 20 and 50 access most frequently. Additionally, we can decide to generate statistics on the average age of all customers who made a purchase from your company in the previous month.This issue, if left unaddressed, could result in duplicate effort, the acquisition of irrelevant data, or the overall failure of your research.

Managing Massive Data:

Big data refers to enormous data sets with increasingly complex and diverse structures. Typically, these characteristics increase the difficulty of storing, analysing, and utilising additional ways for retrieving findings. Big data specifically refers to data collections that are so large or complex that typical data processing technologies are inadequate. The excessive volume of unstructured and structured data that a firm must deal with daily.As a result of recent technological breakthroughs, the amount of data generated by healthcare apps, the internet, social networking sites, sensor networks, and several other enterprises is continuously increasing. Big data refers to the enormous volume of data generated at a rapid rate from multiple sources in a variety of forms. Dealing with this type of data is one of the several obstacles of Data Collection and a vital step in obtaining accurate data.

Conclusion Of Data Collection:

Data collection approaches can assist you develop strategies primarily based on insights as opposed to views.Whether you are an entrepreneur, data-driven marketer, researcher, or student, data collection must be at the core of your job.This article highlights the most important advantages and disadvantages of the top five facts collection strategies.They can help you select the optimal method for collecting qualitative and quantitative data based on your demands.Data collection approaches and strategies are beneficial for researching decisions, gaining competitive advantages, making improvements, and growing your firm.

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