Data Collection: a step-by-step guide on how to help your company get better data

Accurate and relevant data is critical for organizations to make educated decisions in today's data-driven environment. Incorrect data can lead to revenue loss, poor efficiency, and missed opportunities. According to Experian Data Quality, erroneous data has a direct impact on the bottom line of 88% of businesses, with the typical firm losing 12% of revenue. That is why data sourcing—the act of gathering and analyzing data from multiple sources—is such a vital component in any corporate plan.

Databases, spreadsheets, questionnaires, social media platforms, and other digital and non-digital sources can all be used to collect data. However, merely gathering data is insufficient. It is critical to ensure that the data collected is pertinent to the business concerns being addressed.

What is Data Collection?

Data collection is the process of gathering, evaluating, and analyzing data from multiple sources in order to acquire insights and information through observation and experimentation.

Key Steps in Data Collection Process

1. Define the purpose and scope of data collection

Specify the goal of the data collection. This involves identifying the problem to be solved as well as the information needed to solve it. This will aid in ensuring that the information gathered is relevant and usable for the analysis.

Every research endeavor requires a goal or purpose to guide it. Writing a concern statement is one method to get started. This is often one or two phrases that describe the issue and the intended goal.

2. Choosing Data Collection Method

The next stage is to decide on the data collection methods that will be employed. Surveys, interviews, observation, and secondary data collection are all examples of this. The approach used will be determined by the aim and extent of the data gathering, as well as the available resources. Make sure to select procedures that directly address the study topic.

3. Collect the Data

Data may be gathered in a variety of ways. Whatever approach is used, it is critical to stick to the data collection strategy and ensure that the data is gathered consistently and precisely. This might entail training data collectors, utilizing standardized instruments, and putting in place quality control mechanisms. When gathering the final data, make a note of anything that is relevant. Pay attention to details when collecting data. Businesses can ensure their research project produces vital insights by collecting data thoroughly and planning each stage of the process.

Example

A marketing team at a software company wants to gather information on client satisfaction with their product. They plan to conduct a poll utilizing an online form and send it to their customer email list. They construct a standardized survey with a clear set of questions and instructions for respondents to guarantee that data is collected consistently and properly. Furthermore, they also train their survey distribution team members to guarantee that they are following the same process.

4. Clean and organize the data

The preliminary assessment of the data to discover any flaws or inconsistencies is the first stage in cleaning and organizing the data. Once mistakes and inconsistencies have been detected, they must be corrected or removed. Finally, it is critical to organize the data in a logical and easy-to-navigate manner. It may be necessary to rename variables, label missing data, or create new data files to group relevant variables.

Example

The marketing staff at the software business begins the process of cleansing and categorizing the data after it has been collected. They begin by performing a preliminary examination of the data to find any defects or discrepancies. This might include looking for missing values, mistakes, or anomalies in the data. They take efforts to rectify or remove any flaws that are discovered. The marketing team may guarantee that their analysis is based on correct and dependable data by taking the effort to clean and arrange the data. This eventually leads to more meaningful insights and better business decisions.

5. Analyze the data

This entails exploring the data with statistical methods and tools, identifying patterns and correlations, and drawing conclusions based on the results. Data visualization, in addition to statistical analysis, may be a valuable tool for analyzing and conveying data. Overall, data analysis is an important phase in the data gathering process because it helps data analysts to derive meaningful inferences from the data and make educated decisions. Data analysts may find trends and patterns in data, test hypotheses, and provide evidence-based recommendations by employing a variety of statistical approaches and visualization tools.

Example

After cleaning and organizing the data, the marketing team moves on to the next stage of the data collecting process: data analysis. They investigate the data with various statistical approaches and tools in order to uncover patterns and correlations that may be utilized to derive relevant conclusions.

6. Interpret and report the findings

Interpreting and reporting the findings is a significant phase in the data gathering process because it allows data analysts to convey the outcomes of their study and make data-based suggestions. Data analysts may assist stakeholders in making educated decisions and taking appropriate actions based on the data by presenting findings in a clear and understandable manner. It is critical to ensure that the findings are presented in a suitable manner for the audience and are supported with relevant context and limitations.

Example

Finally, the team prepares a comprehensive report that clearly presents their findings, recommendations, and limitations to the company's management team. They ensure that the report is tailored to the audience's needs and presented in a way that is easy to understand. The software company is able to use the report to make informed decisions and take actions that lead to increased sales and customer satisfaction.

What are common challenges in data collection?

Data collection is an important phase in the research process, but it may also be difficult. Here are some frequent data collection issues that researchers may face:

Data Quality Issues

Data may be inadequate, inconsistent, or include mistakes, which might jeopardize the conclusions' validity and dependability. Errors, inconsistencies, and other difficulties are common with raw data. In an ideal world, data collecting procedures would be intended to eliminate or reduce such issues. However, in most circumstances, this is not infallible. As a result, gathered data typically requires data profiling to discover flaws and data cleansing to resolve them.

Data Grouping

Data collection can be complex, especially when trying to capture information about marginalized communities. One data point may not provide adequate insight into a person's background, leading to underrepresentation and confusion. For instance, grouping diverse communities under one label, such as 'LGBTQ,' fails to recognize their unique identities and experiences, leading to decreased data accuracy.

Choosing What Data to Collect

Collecting unnecessary data increases the time, cost, and complexity of the data collection process, whether for raw data or analytics. However, excluding relevant data can reduce the business value of the dataset and affect the accuracy of analytics outcomes.

Low Response Rate

A paucity of answers or willing participants in research investigations raises concerns about the validity of the data acquired. It is important to employ strategies to increase response rates, such as offering incentives, tailoring communication to the target audience, and following up with non-responders.

Conclusion

In today's data-driven world, firms must make smart decisions based on accurate and relevant data. However, data collection might be difficult owing to concerns with data quality, insufficient resources, and a lack of experience. It is critical to ensure that the data gathered is relevant to the business issues being addressed. With that, businesses may guarantee that their data that yield crucial insights that lead to improved business decisions.

IMPROVE YOUR DATA CLEANING ABILITY

Are you ready to take your business to the next level with data-driven decision-making? It all starts with the key step of selecting the right data collection method. By understanding the variety of data collection methods available, you can choose the one that fits your timeline, budget, and specific research question, and ultimately gain valuable insights for better business decisions.

Are you ready to dive deeper into the world of business analytics? Our academy is designed to help you develop and get better trained in data cleaning.