Introduction
The accuracy of the data collected/data underpins all forms of analytics, artificial intelligence (AI), and strategic management decisions across industries. Collecting data inaccurately can lead to deceptive conclusions and poor forecasting models, as well as costly mistakes for businesses or organizations. According to research, 47% of all newly created record entries include at least one significant error caused by the ineffectiveness of the Data Collection Methodology used on the record entries.
In today’s fast-paced business world, companies gather data through multiple avenues simultaneously rather than focusing on a single method. This guide provides an overview of available data collection methods, real-world examples, and guidelines for choosing the best method for your organization’s unique needs.
What Is Data Collection?
Data Collection is systematically carried out by gathering Data from a variety of sources for analysis, research, or operational use. In addition to the above, Data is used within business intelligence platforms, for Market Research Studies, for training Artificial Intelligence (AI) and Machine Learning (ML) Models, and for developing operational plans.
Data collection can be done manually or automatically. Choosing which method to use depends on how much data you need to collect and how often you need it. The Data collected for research may be structured (data organized into a Database) or Unstructured (data in the form of Text, Images, or video).
The method(s) chosen to Collect Data will impact the quality, completeness, and Usability of that data for downstream analyses.
Types of Data Collection Methods
Primary Data Collection Methods
Its business needs or project objectives drive the primary data that an organization collects. An organization has complete control over all aspects of data collection and can ensure the data collected directly addresses the questions they want answered.
Surveys & Questionnaires
Using surveys to gather standardized responses is one way to collect structured responses from specific target audiences. Customer satisfaction surveys measure the quality of service provided and identify areas for improvement. Market research forms measured product demand, pricing sensitivity, and brand awareness.
They are also often used to collect customer feedback, gauge how customers perceive a brand, and rank customer needs for product features. You can use digital survey platforms to deploy surveys quickly and track responses in real time.
Interviews & Focus Groups
Through individual discussions with participants, interviews provide extensive qualitative insight. Through in-depth conversations with customers, interviews identify customer motivations, pain points, and the decision-making stages of a customer’s journey. Having discussions with key stakeholders allows a company to align its organizational priorities and get stakeholder input about expert opinions.
Both methods for collecting qualitative insights and validating the product through research are effective. However, they typically require greater time investment than traditional quantitative research methods and are most effective with small sample sizes.
Observational Data Collection
Observational approaches analyze a consumer’s behavior without requiring the consumer’s involvement. It includes observing the shopping experience in-store by monitoring how they shop for items and what products they purchase, and by tracking customer behavior on websites and mobile apps (e.g., how they navigate the site/app).
LocationsCloud’s approach to using daily observational data models to understand shopping behaviors in retail better better will also help them analyze the foot traffic of their businesses through these models, which will provide them with information about how customers behave when shopping that is based upon what customers actually do rather than what they state they will do in surveys or focus groups.
Secondary Data Collection Methods
Existing secondary data and its repurposing can be accomplished quickly, without the need for additional data-collection resources (both Physical Collection Resources and Time). Thus, this data collection method is generally more efficient than primary data collection.
Public Datasets & Reports
Organizations such as the government release statistical data on population, the economy, and demographics; industry associations publish reports on market trends and performance; and academic institutions provide researchers with data sets that enable further research and analysis.
Public source data provides insight into industry growth, competitive analysis, and industry change. However, public data sources may not provide the specific information needed to address niche business concerns. They will likely not be the most current source available.
Web Data & Online Sources
There is a lot of accessible information about businesses on the World Wide Web, including directories with listings for different types of companies, including address/contact information, locations, etc. Many of these sites also offer customer feedback and ratings of their experiences with that company, allowing potential customers to gauge how much trust to place in them.
These same location-based services aggregate POIs and include information such as operating hours, category, amenities, and more. The collection and structuring of Internet data for competitive intelligence, market mapping, and place-based analytical purposes are the specialty of LocationsCloud. When processed accurately and adequately, it transforms secondary data into primary intelligence.
Automated Data Collection Methods (Most Scalable)
Web Scraping
Web scraping is the automated extraction of publicly available data from websites. Because of how this automated process works, web scraping can produce millions of records at one time while maintaining a consistent data structure. Scrape directory listings of businesses to build a comprehensive database of companies on those sites. In the case of restaurants, scraping restaurant listings enables the collection of menus, reviews, and hours of operation.
By scraping Point of Interest (POI) attributes, organizations can automatically identify their coordinates, category, and facility details. Automated Data Collection is used by many organizations, including LocationsCloud, for Location Intelligence, Competitive Analysis, and Market Research. With automated web scraping, dynamic content, pagination, and complex website structures, these can be handled with minimal effort.
APIs (Application Programming Interfaces)
APIs (Application Programming Interfaces) provide a structured, programmatically accessible way to get data with a standard set of endpoints. Point of interest (POI) data is made available via an API as a JSON- or XML-formatted document, providing consistency across all APIs that offer this type of data. Location APIs allow users to access locations, boundaries, Geographic and location-level attributes.
APIs can be used in real time or for dashboards, where an application needs access to live data; for web-based analytic applications that require frequent updates; for location-based Mobile Applications that need to provide location-related services. While APIs may be of lower quality than scraping, they typically limit the number of requests per day and charge per-use fees.
Sensor & Device-Based Collection
Connected devices and physical sensors generate significant amounts of data, creating large data streams. GPS signals track where things go and how people travel. Environmental monitoring (IoT) devices track environmental conditions, usage, and operations.
By using these different ways to collect data, we can analyze how people are moving in relation to transportation and how companies are moving products through their supply chains (logistics). However, when collecting this kind of information from people or businesses, it is essential to be sensitive to privacy issues.
Data Collection Methods by Data Type
Location & POI Data Collection
Because the geography is complicated, we need to use different methods to collect location data. To build a comprehensive database of location data, you can collect business listings via web scraping from directories such as Google Maps and Yelp, as well as industry-specific sites such as state department stores.
To compose an appropriate location aggregate, you can collect public location information from government records, business registration records, and various mapping services, or via APIs from Points of Interest (P.O.I.) databases.
Furthermore, there are countless uses for this information. These uses include (but are not limited to) mapping retail chain store locations to gain a competitive advantage, mapping a brand’s presence for competitive intelligence, and providing a database of facilities that meet site-selection requirements.
LocationsCloud offers automated web scraping that extracts millions of POI locations, normalises attributes, and leverages geocode validation.
Foot Traffic & Mobility Data Collection
To grasp how people move about, Mobility Signals from Devices and Sensors need to be put together (aggregated). Observational Data Models can quantify foot traffic without tracking the individuals whose feet are moving. These methods can help collect data to measure retail store performance and to analyze how people’s movement in the city has changed over time.
By leveraging multiple sources of Mobility Data, LocationsCloud provides insights into Visitor Behaviour and Location Performance, as well as Analytics-Ready, Privacy-Compliant Data.
Comparing Data Collection Methods
| Method | Data Type | Best For | Limitations |
| Surveys | Primary | Qualitative insights | Small scale, bias risk |
| Public Data | Secondary | Macro analysis | Often outdated |
| Web Scraping | Automated | Scalable data | Needs validation |
| APIs | Automated | Real-time access | Limited scope, costs |
| Sensors | Automated | Movement tracking | Privacy concerns |
Choosing the Right Data Collection Method
The most crucial part of determining the best data-gathering method is identifying key considerations. Will your project require real-time or historical data? Real-time scenarios will rely on Application Programming Interfaces (APIs) and/or Sensor data, whereas historical scenarios utilize data that was collected and saved as a “batch” process.
Is your project qualitative or quantitative? Qualitative studies typically use interviews and/or focus groups to gather data, while quantitative research relies on surveys and/or automated methods.
Are you gathering Data for use at Scale or in an Automated Manner? Manual methods are sufficient for conducting small studies, whereas researching Business Intelligence is best accomplished through automated means.
Will the data you gather be utilized for the analysis of the data, or will the data be used in Machine Learning models? There is typically a significant need for large datasets to train Machine Learning models. Consistent, large data sets are best obtained through automated data collection techniques. LocationsCloud enables organizations to match their data collection methods to their specific analytics needs.
Challenges in Data Collection (and How to Solve Them)
When organizations implement data collection strategies, many obstacles can impede their progress. Data Inconsistency occurs when each source uses a different format for its information, naming conventions, or units of measure. Standardization Processes and Normalization Rules can help with this issue.
Incomplete Coverage is a result of geographic areas, market segments, and/or attributes being missed by the method of collection. Organizations can improve their collections and overall performance by using multiple sources of information together. By aggregating multiple data sources, organizations will have a single, accurate, complete, and reliable source on which to base future decisions.
Organizations face challenges with data validation and accuracy when they use outdated, duplicate, or erroneous records.One way to maintain accurate data is to use Automated Validation Checks and Manual Quality Reviews.
As data volume continues to grow exponentially, manual processes may not keep pace with the volume of data generated. Organizations can overcome this challenge by using automated data scraping and API Integration to manage larger datasets more efficiently.
Managed data providers like LocationsCloud can assist by providing reliable systems for data collection, quality checks, and continuous data updates.
How LocationsCloud Supports Scalable Data Collection
LocationsCloud collects data on points of interest (POI) and business locations using a proven method. We gather thousands of locations from millions of sources every day using automated data-scraping technology. We ensure this data is consistent, accurate, and complete by normalizing it, making it easier to compare and verify.
You can access our data in different formats. We provide APIs that give you real-time access to data and bulk datasets for analysis. All datasets available for use with LocationsCloud are fully compatible with Analytic Systems, Business Intelligence Tools, Digital Mapping Solutions, and Machine Learning Applications. They are quick, easy to use, and compatible across multiple platforms. Using our managed service lets customers skip building and managing their own data-collection systems. Instead, they get a high-quality dataset that we regularly update and verify for accuracy.
Conclusion
Data collection methods serve distinct purposes within an organization. For example, an enterprise may use a survey to capture subjective opinions about a specific product. At the same time, a public data set would provide an overview of trends at a broader level. However, the ability to rapidly collect large amounts of data is critical for today’s analytical capabilities and therefore requires automated processes.
Automation is critical not only because it enables businesses to collect large amounts of data and process frequent updates efficiently, but also because it enables them to scale operations and deliver results with greater accuracy. For example, a business may use location (latitude/longitude) or point-of-interest (POI) information and need a unique collection process that leverages multiple sources to ensure accurate results and validate that results are current.
LocationsCloud provides businesses with a reliable, scalable way to obtain location data through automation, validation, and flexible delivery methods.
By investing in high-quality data collection today, businesses can achieve greater analytics accuracy, a competitive edge, and improved AI model performance.
