Look around you! Datasets aid in decision-making, risk management, product research, scientific research, and more. However, only accurate, relevant, complete, bias-free, reliable, valid, and consistent datasets yield quality results despite the purpose of the dataset.
So, how do you create a dataset with these qualities while saving lots of hours?
Yes, you create a dataset with a particular strategy in mind. Strategy facilitates clarity. That’s clarity in what you want to achieve (goals and objectives), the steps to assemble the dataset and the questions you want to answer.
Understanding the unique traits of various dataset-building strategies makes it easier to select a suitable one or even combine two or more. So, let’s see what these five dataset-creation strategies have in store for you.
1. Leveraging Public Data Sources
This strategy involves using readily available and free public data sources to create a dataset.
Research institutions, government agencies, open data platforms, and other entities provide specific data and datasets to the public for various reasons. Some data serves as a historical record, some highlight specific trends and patterns in an industry, and some describe the economic status of the people.
If you choose this path, you first identify platforms offering public datasets relevant to your objectives. Such platforms include Google Dataset Search and Kaggle.
From identifying a suitable platform, you go fishing for the desired dataset. And, once you find the data, you check for factors including relevance, quality, and the dataset’s documentation.
A well-documented dataset saves you time and effort as the documentation details the source of the data, variables, collection methods, analysis techniques, and more.
Advantageously, most public datasets are usually of high quality. They are mostly curated and validated by authoritative sources, assuring the public high levels of reliability and accuracy.
Moreover, public data sources are often standardized, making them pretty convenient to integrate with other datasets.
2. Using APIs
Application Programming Interfaces (APIs) are precisely detailed routines, tools, and protocols for developing a software application. They specify how select software components interact with each other or with external software systems, enabling data access, integration, and exchange.
You can learn how to use APIs to access data from various web services, or you can entrust a third party to get you the desired data. The third-party dataset providers do the heavy lifting for you, creating custom datasets for online research and other purposes.
Using APIs involves identifying the API that can provide you with the needed data. For instance, Google Trends API. Then, you register on the platform offering the API. With a valid platform account, you can then review the API documentation before you can start collecting data to create a dataset.
Remember, as you use an API to collect data, you must adhere to the platform’s terms of service and usage limits. Also, ensure your data collection configurations comply with the outlined user privacy regulations.
In contrast to public data resources, APIs grant you access to real-time data. This means you can create a dataset from the most relevant and up-to-date data. Moreover, you can scale the creation of datasets through API automation and the help of custom queries to collect data based on your needs.
3. Web Scraping
At some point, you may come across a platform without an API for data access. This is where web scraping comes in handy, even though you can still scrape a platform with an API.
Essentially, web scraping does not limit you to a specific format or way of accessing data from a platform like an API does.
You can write custom automated scripts to scrape a website of your choice, including an e-commerce website, social media platform, or news portal.
To scrape a website, you first select a web scraping tool. You don’t have to write web scraping scripts from scratch, as there are several web scraping libraries and tools available based on your needs and technical expertise.
After selecting a web scraping tool, you use it to develop a scraping script that navigates the selected website, gets you the data, and structures it in a useful format. If you find the web scraping process challenging, you can create a dataset by sourcing data from a pool of already scraped data.
4. Collaborating With Others
If you want to create a large and more complex dataset, you can collaborate with others. Approach other institutions, businesses, or researchers and propose a collaboration. Let the partners know the mutual benefits, too!
Collaborative efforts can lead to more robust analyses and insights compared to going in alone. Moreover, you can cut down on the cost of the resources, tools, and expertise required to create a complex dataset.
If you want to collaborate with others, you should first identify businesses, institutions, or researchers that align with your objectives and interests. Then, you should initiate contact with them and present a concise proposal.
Once you land partners interested in a collaborative effort to build a dataset, you establish clear terms defining how the data would be allocated and handled. Finally, you get to work creating the dataset while ensuring all partners are kept informed and involved in the whole process.
5. Conducting Interviews and Surveys
Do you desire a dataset of direct feedback and qualitative insights? Conducting interviews and surveys could be the right strategy for you.
Through interviews and surveys, you get to gather firsthand information directly from respondents.
Moreover, based on the quality of the questions you ask the respondents, you can get profound and insight-filled answers that are difficult to obtain through the other strategies.
For instance, the NHIS (National Health Interview Survey) has been active since 1957 due to the insights it provides about healthcare access and the health status of various groups.
To carry out a survey, you must design one using tools like SurveyMonkey and distribute it through channels including Email or social media. You must have a defined time for which the survey should run. And, once the time elapses, you can proceed to build the dataset.
For interviews, you prepare a list of questions based on the target group and your objectives. Then, you get relevant respondents to answer the questions while recording the interviews.
Closing Words
From analyzing a dataset, you can understand trends and patterns, forecast future trends, and more. However, the qualities of a dataset impact the quality of results you get from analyzing it.
That is why you ought to build a dataset following a specific strategy. This way, you can track the dataset creation process, allowing you to spot instances of bias or mistakes and make necessary adjustments. Explore this blog post to understand the unique traits of five of the most effective dataset-building strategies.