Look around and you’ll see that most top-ranking articles highlight the same steps for dataset creation. General steps are full of gaps, leading to time loss and wasted resources during execution.
A well-structured and efficient dataset creation process yields an accurate, consistent, and reliable dataset. A dataset capable of fulfilling its purpose optimally—whether that’s revealing trends, operational inefficiencies, or customer behaviors.
Now, let’s see how this holistic data creation approach stands out from the rest. Go from second guessing what data creation process to follow to confidently creating a well-curated and effective dataset!
Comprehensive Step-by-Step Guide to Create a Dataset
- Define the purpose
Establishing a specific purpose is the first step in learning how to create datasets tailored to specific strategies.
To define a clear purpose, start by analyzing the goal that’s driving your need for a dataset. The goal should include the target audience.
For instance, your goal or use case may be to build a machine-learning model to help practitioners with cancer diagnosis.
When building the machine learning model, you need a dataset for various purposes like training, validating, and testing.
See how that works? You first define the overall goal and then trickle down to defining a specific purpose.
- Establish the data blueprint
With a clear purpose in mind, it becomes easier to now define the data requirements, helping you collect both relevant and focused data.
First, define the variables or features required, including patient ID, age, gender, or lab results. Next, outline the formats and categories of data as well as the extent of the data gathering.
For example, if you need to collect numerical data, will you work with floating-point numbers or integers? And, how many entries or data points do you need?
Once you have a clear understanding of what data you need and its nature, outline and document a clear standard procedure on how you’ll organize or structure the data.
The standard procedure should outline the data categories or column names/descriptions, data types, required and optional fields, valid categories or ranges, and relationships (where necessary).
- Select relevant or appropriate data-sourcing methods
Reference the defined data requirements and data collection scope to help with selecting a relevant data source. In addition this, you need to consider the factors such as time commitment, expenses associated with data collecting, and further limitations.
Some data sources may have access restrictions in place, requiring you to seek permission or partner with the owner to gain access.
Moreover, some data may simply be harder to obtain due to privacy laws, lack of accessibility, and proprietary ownership.
It is always critical to consider the cost, time needed, and feasibility of collection to help with decision-making. Here are some reliable sources to gather data from:
- Primary sources: These are the firsthand or original sources of information, including survey or interview participants. You must interact with these participants to collect the data.
Primary data collection is usually time-consuming, costly, and rigorous. However, it is a great option when the data you seek is not readily available or, when you need specific information.
- Secondary sources: These are existing data records that were originally collected by someone else for a particular purpose.
If you opt to obtain data from secondary sources, such as open-source data repositories or government sites, assess the source’s reputation and data quality.
These data sources are usually cost-effective and great for analyzing trends over time.
- APIs (Application Programming Interface) and web scraping: You can also use web scraping or APIs to get information from websites. Nonetheless, always review the terms of a site before scraping to avoid legal trouble. Moreover, stick to ethical scraping tactics.
- Define quality control measures
Here, you need to build up quality control procedures in order to produce a dataset that is correct and trustworthy. You should follow these guidelines when gathering, cleaning, and preparing the data.
Start by defining and documenting a standard data collection procedure. Within the procedure, define the data source and the data collection steps to reduce errors.
Make precise rules defining the allowed forms, values, and limitations for every feature or data variable. This makes it easier to monitor data entry and implement validation rules and consistency checks.
Moreover, include practical error detection mechanisms you’ll use to weed out errors.
If possible, define how you’ll automate error detection checks to detect missing values or outliers on the fly, optimizing data cleaning and preprocessing.
- Obtain the data
Referencing the standard data collection procedure, collect relevant data.
During this step, adhere to ethical data collection requirements such as obtaining necessary permissions and removing or encrypting sensitive information to avoid legal issues or compromising specific participants.
Document the collection dates and times for each data source and any deviation from the defined standard procedure. Monitoring changes in data collecting and comprehending the occurrence of data restrictions and variances will become easier with the use of these records.
Moreover, note that some data collection methods like web scraping may require you to implement real-time checks when obtaining the data.
- Clean and preprocess the data
Still referencing the standard data collection procedure, proceed to clean and implement the quality control measures. The goal is to adjust or get rid of problematic elements that may otherwise lead to biases or errors in the results.
To have an accurate and consistent data collection, correct obvious errors like unrealistic values, typographical mistakes, and incorrect spelling.
Then, remove duplicate entries, which are likely to skew analysis. Duplicate entries may arise when merging data from multiple sources, collecting data through an API, or at the data entry point.
Imputing missing values or removing incomplete records are two ways to deal with missing values.
Sometimes, deleting some values may lead to errors. That’s why you have the option of replacing missing values with plausible values such as mode for categorical data, and median or mean for numerical data.
- Organize and structure the data
Finally, review the standard data structuring procedure and organize the data to create your dataset.
Create clear, descriptive column headers and format data consistently for each column. Then, normalize the data if necessary and securely store the dataset in an appropriate file format.
Wrapping Up!
And that’s it! You no longer have to get stuck figuring out the gaps left out in most dataset creation procedures. Follow this holistic approach and you should have a purposeful, accurate, consistent, and reliable dataset.
