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The Key Aspects of Dataset Pricing: Best Practices for Data Buyers

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The process of buying datasets can be quite complex, with pricing being a crucial consideration.

Nowadays many companies heavily invest in datasets. They are increasingly turning to external data providers to enhance their operations, gain valuable insights, and make informed decisions.

Why are datasets from providers so expensive?

According to recent stats, the total market value of the data broker industry will reach nearly 365.71 billion USD in 2029. This means that more and more companies are seeking out data providers for access to crucial business data. Buying datasets from providers can be expensive for several reasons:

  • Data collection and maintenance costs: Collecting high-quality data often requires significant resources, including data collection infrastructure, technology, personnel, and maintenance. Data providers invest in these resources to ensure the accuracy and reliability of the data they offer, which can drive up the overall cost. 
  • Data cleaning and validation: According to MarketingSherpa, B2B data decays at a rate of 2.1% a month or 22.5% annually. To ensure data quality, providers go through rigorous processes of cleaning and validating the data. This involves fixing data errors, duplicates, and inconsistencies, which require time and effort, contributing to the cost.
  • Customization: Customizing datasets to meet specific client needs can increase costs. Tailoring a dataset to include specific variables, geographic regions, or time frames requires additional work and may result in higher prices.
  • Scalability: Data providers often need to scale their infrastructure and operations to accommodate the growing demand for data. Scaling comes with its own set of costs, which may be reflected in the dataset pricing.

The key factors influencing dataset pricing

The cost of buying datasets from providers reflects the investment required to collect, clean, validate, maintain, and deliver high-quality data. It also accounts for factors such as source complexity, customization, and the reputation of the provider.

While datasets may seem expensive upfront, their value lies in their ability to inform strategic decisions and provide a competitive edge to organizations.

  • Data quality: The primary aspect of dataset pricing is the quality of the data. High-quality data that is accurate, up-to-date, and comprehensive is generally priced higher. Data providers invest in data cleaning, validation, and maintenance, which directly affects the final cost. 
  • Data rarity: Rarity plays a significant role in dataset pricing. Rare or unique datasets that are not readily available in the market command higher prices due to their exclusivity and potential to provide a competitive advantage. Assess the uniqueness of the dataset in your specific industry or use case for a better understanding of what to expect.
  • Data coverage and depth: The breadth and depth of the dataset's coverage impact pricing. Datasets that encompass a wide range of variables, locations, or time periods typically come with a higher price tag. Evaluate your specific needs and determine whether extensive coverage is essential. A more focused dataset with relevant variables may be sufficient and cost-effective.
  • Provider reputation: The reputation of the data provider also influences pricing. Established providers with a track record of delivering reliable data often charge premium rates due to their credibility. Reputable data providers often charge higher prices because they have a track record of delivering reliable, accurate, and high-quality data. Businesses are willing to pay a premium for data they can trust.
  • Data licensing complexity: The terms of the data licensing agreement can affect pricing. Exclusive or more permissive licensing terms may come at a higher cost than non-exclusive or limited-use licenses. Understand the licensing terms thoroughly. Ensure they align with your intended use of the data and factor in any additional costs, such as royalties or renewal fees.

The best practices for buying datasets

Here is what you need to pay attention to when purchasing a dataset from a provider: 

1. Clearly define your goals and objectives

86% of medium and large companies considered first-party data to be the most significant aspect of a business’ media strategy. Before purchasing any dataset, have a clear understanding of your objectives and the specific insights you aim to gain. 

Is it for marketing the new product or is it to optimize internal procedures? A better understanding of your goals will help you choose the right dataset and avoid unnecessary expenses.

2. Evaluate total cost of ownership (TCO)

Consider the entire cost of owning and using the dataset, including maintenance, integration, and any additional services required. Sometimes, a higher upfront cost may result in lower TCO.

3. Keep an eye on data performance

Don’t forget to regularly assess the performance of the dataset in meeting your objectives. If data quality deteriorates or becomes outdated, it may be time to reevaluate your data provider or source.

Summing up

By following best practices and conducting thorough due diligence, businesses can make cost-effective and strategic decisions when acquiring data to fuel their success in today's data-driven landscape. Purchasing datasets involves a careful assessment of various factors, from data quality and rarity to provider reputation and licensing terms. 

October 2, 2023
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