Choosing a good data provider: 5 Questions to Ask Before You Buy

Finding the right data provider is one of the most critical steps in working with public web data. The products you're building or the insights you extract are only as good as the data it's based on.

Because collecting public data and its maintenance is challenging due to constant changes and the volumes of data involved in the process, it's essential to work with a reliable provider.

What defines a good data provider?

A good data provider focuses on offering the most suitable data solution for your use case, provides necessary resources for making an informed decision, and helps you get the most out of the data you're buying.

The fundamental principle of building a successful partnership with a data provider is relatively straightforward: take your time, do your homework, and don't settle for anything less than a top-notch option.

There are multiple layers to assessing data providers and securing a successful partnership. The key ones are selecting the right data, ensuring that the provider is reliable, and testing service quality.

Additionally, consider factors like experience, flexibility, stability, and pre-purchase and post-purchase stage support.

Most importantly, a good data provider will be happy to assist you in choosing the most suitable data solution, integrating it into your data pipeline, and then maximizing the value of your data with the help of dedicated account managers or technical support.

How to choose data provider: 5 key things to consider

To help their clients get the most out of their data, providers need to be experienced, adaptable, and consistent. We're sharing 5 questions that will help you choose the right vendor for your business.

1. What data do you offer?

Now this might seem like an obvious question, but as the big data analytics market keeps growing, there's a myriad of web data solutions and even more ways to process and package the same web data.

When choosing B2B data, in our experience, the most important aspects to consider are:

• Sources
• Coverage
• Quality
• Data delivery options/formats
• Freshness

First, find out what you can do with this data by getting familiar with the product and its use cases in the B2B market.

Say you need raw datasets. The definition of "raw" may vary between companies because of how it's collected, how it's processed, and how it's presented.

Before purchasing data, you need to know if you want to handle all the analytics yourself or if you are open to working with data that went through additional processing and transformations. For instance, are you interested in using data that combines multiple sources or contains derivative data fields.

In some cases, analytics is done on the provider's side. If the data provider specializes in working with companies in your industry, it might result in additional value for your business. However, in some cases, it may not suit your use case.

If you're interested in very specific data, for example, scraped data from one source, you will care about how truthful this data is. It's important to know if the data you're getting is as close to the original data source regarding its completeness and structure.

The next thing you want to ask about is what data processing the data vendor is doing. For example, Coresignal offers both raw and clean data.

Our clean datasets contain filtered, unified, and standardized data, which is also enriched with the help of AI. Clean data offers benefits like quicker time to value, easier processing and onboarding, and additional data fields.

Raw datasets, on the other hand, are heavier, contain more records, and include elements like special symbols—valuable to some companies and completely unnecessary to others. That's why asking about this is so important.

We also offer other data solutions like our Startup data, which contains clean and aggregated data on startup founders.

It's an excellent option for investors who need a quicker way to get insights. Still, some investors want to build their analytics process with raw data from scratch, meaning that any logic that goes into creating a specific dataset might not suit them. Those investors should choose raw datasets.

2. How fresh is your data?

In the web data industry, needing a one-time purchase of a large-scale web dataset that hasn't been updated for a while is rare because information about businesses and professionals changes fast. That's why most companies are looking for fresh and regularly updated data.

Suppose you need to track changes and see updates and/or new data records in data on business professionals working in specific industries. In that case, this is an essential criterion to apply to your selection process when looking for a data vendor.

Besides checking if data is fresh, it's essential to know how and how often the data provider updates datasets.

Here are some additional questions that might help you get more information about how the company is ensuring data freshness:

• What part of your datasets do you update, and how often?
• How many new records do you add with every update, and how often?
• How do you prioritize what to update?

Keep in mind that keeping data fresh requires expertise and experience; therefore, by getting information on freshness, you're also getting a closer look at the provider's capabilities.