Simply put, a dataset is a curated collection of data points, formatted or organized to serve a defined purpose. From enhancing machine learning and analytics to refining AI models, datasets enable businesses to capture opportunities, optimize operations, and effectively tackle challenges. 

Even so, not all datasets yield such positive results. Some datasets have led to the downfall of giants like Enron, Target, and Sears. For instance, Sears failed in part due to relying on outdated customer datasets for decision-making.

To ensure you are working with an effective dataset, dive into this comprehensive guide to understand the process of creating one. If you decide to outsource, the traits of an effective dataset outlined here will lead you to the appropriate dataset.

How Do I Develop an Effective Dataset?

Datasets differ in structure, types, size, sources, and purpose. However, with this approach, you should develop effective datasets for almost any purpose:

1. Specify the purpose and data requirements

Datasets serve different purposes including training, testing, and validating AI models, benchmarking algorithms, and facilitating data-driven decision making. Understanding the end goal from the start guides your decision making throughout the dataset-building process, including defining the data requirements. 

Data requirements are the traits that data must meet to be useful for a specific purpose. They include the data type, format or structure, primary variables, data volume, and frequency. For instance, machine learning datasets are known for being large, structured, unstructured, and diverse. 

To pinpoint the purpose, clearly document the problem or questions you want to address. If you are working on a project that requires various datasets, engage with the stakeholders to uncover critical questions, ensuring that the information you collect is relevant and actionable.

2. Acquire raw data

Having defined the purpose and data requirements, collect the defined data type from various sources. In case you don’t find the needed data from internal or external databases, opt for primary data collection techniques like interviews and questionnaires. Common sources or methods to obtain data include:

• Web scraping: Involves the use of automated data collection scripts to extract data from a target website. It is especially useful for competitor analysis.
• Data providers: These are organizations that collect data with a particular purpose in mind and resell for profit. This option is optimal for obtaining huge volumes of data like those needed to create machine learning datasets.
•  Internal databases: This includes the data collected while running the business like the number of available products, sales, and more.
• External sources: Include publicly available data or proprietary data sold by research institutions or organizations. For example, data marketplaces or subscription-based research databases.

3. Clean the data

Before turning the raw data into a purposeful dataset, you must clean it. Some data cleaning activities include resolving missing data, correcting errors, getting rid of duplicates, and standardizing formats.

During the data collection process, errors do occur like typos or numerical inconsistencies. Sometimes, the data may be in inconsistent formats, especially when collected from multiple sources. Standardizing the data format, such as currency units, dates, and more, improves reliability. 

Remember, it’s okay to delete missing values as long as they are not critical. Moreover, you can use advanced algorithms to predict missing values if estimation techniques like median, mode, and mean do not work.

4. Preprocess or transform, and structure the data

Still guided by the purpose, preprocess or transform, and structure the data. Preprocessing comes in when you are preparing a dataset to train, validate, or test an AI model. 

It involves data transformation techniques like normalization and encoding categorical variables, data splitting, and feature engineering.

For data analysis with spreadsheet applications, you are to structure the data into rows and columns. And, since spreadsheet applications are limited to numeric, text, booleans, and dates/times — then you must eliminate data points that are not of this type. \

Overall, the purpose dictates how the data should be structured to achieve optimal results.

5. Store the data securely

Finally, format the dataset ready for storage and use. Select the format based on the dataset’s purpose, the nature of the data, and the systems and tools planned to interact with it. Common data storage formats include CSV (Comma-Separated Values), JSON (JavaScript Object Notation), and Excel. 

Have a reliable security and backup system in place to keep unauthorized parties out. And, in case of an accidental deletion, data breach, or system failure, you can restore the data thanks to the backup and recovery system. 

Following these five steps generally results in a dataset ready for use. However, if developing a dataset is time-consuming and resource-intensive, outsourcing is the way to go. Look for these traits to distinguish a quality dataset from a subpar one.

Qualities of a reliable dataset

1. Closely aligned with the target purpose or objective

A reliable dataset must have been made with a clear purpose in mind. Examine the dataset’s documentation to ascertain that its purpose closely aligns with your target objective or purpose. Also, find out when the dataset was last updated to ensure it is still relevant to your objectives.

2. Accurate and low bias

An accurate dataset is free from various types of errors, distortions, and inconsistencies. Go over the methods used to collect the data to evaluate the correctness of the dataset in representing reality. 

Remember to also examine how balanced the data is. Biases in datasets, especially those used for machine learning, can result in incorrect or unfair insights, categorization, or predictions.

3. Complete

A reliable dataset must include all the data points that were needed to achieve the set objective. Review the dataset’s documentation to find out what data was planned for collection. The dataset should not have missing values, else it is likely to lead to inaccurate or skewed results once you apply it to another purpose.

Conclusion

For years, datasets have powered machine learning and analytics, supporting decision-making, problem-solving, and research. On the flip side, erroneous or outdated datasets have also been the reason for the failure of great business. 

Thanks to this comprehensive beginner’s guide, you can now avoid the negatives of datasets. Get to learn how to develop an effective dataset and how to evaluate the effectiveness of various datasets from this guide, and enjoy the benefits that come with a reliable dataset.