# Data enrichment part IV: selecting data providers Post by: [Ed King](/content/blog/author/ed-king/index.html) in [Cleanse and unify data](/content/blog/category/cleanse-and-unify-data/index.html) This is part 4 of our blog series on data enrichment. Feel free to catch up if you missed the first three posts: [Introducing the Data Enrichment 101 Series](/content/blog/data-enrichment-blog-series-2/index.html), [Determining the Processes the Data Will Support](/content/blog/data-enrichment-blog-series-part-2/index.html), and [Determining Your Target Market](/content/blog/data-enrichment-blog-series-part-3/index.html). ## Factors to Consider Now that you’re clear on your own goals and needs, next you must decide on your preferred consumption model. Here are the factors to consider: ### 1. Single Data Vendor or Best-of-Breed Whether you’re looking for only contact data or comprehensive data that includes firmographic, contact, social, technographic, and intent, you’ll be faced with the choice of using only a single vendor that gives you the widest coverage or assembling a collection of best-of-breed vendors. **Advantages of a single vendor include:** - Lower cost - Less integration and automation requirements - Less confusion for users - Fewer data standardization issues **Benefits of the best-of-breed approach are:** - Highest possible data quality, depth, and coverage - Cross validation ### 2. Single Source or Multi-Source Match rate for data providers can range from under 10% to over 70%. We’ve seen most match rates tend to fall between 30% to 50%. So if your preferred provider doesn’t find a match or returns a low confidence match, would you like to try another provider? Another common reason for multi-sourcing the same data is to get the necessary geography, vertical, or company size coverage. ### 3. Cost Model Data vendors and providers use different pricing schemes. All else being equal, the best-fit cost model can help you maximize the ROI of your data investment. Here are the most common models and when they make the most sense: - **Credits / A-La-Carte** – You buy a basket of credits that can be used for different levels of services. The benefit of this model is that you can maximize your budget given your desired mix of data needs. - **Per Record** – You pay once for each record you receive. The benefits here are simplicity and that you will never pay for the same record more than once. - **Per Request / API Call** – You pay every time you make an inquiry. This is sensible for batch bulk enrichment. It simplifies cost tracking. - **All-You-Can-Eat** – You rent the entire database or a subset of the database allowing as many leads and inquiries as you want. This is ideal for keeping records up-to-date. ## Understanding the Data Supply Chain It is very common within the data business to source data from other data providers. In fact, the majority of data vendors and providers do not have original data they produce themselves, but are aggregators of other people’s data. Here are some examples: - If the data contains DUNS Number, it is guaranteed that the provider has sourced firmographic data from Dun & Bradstreet (D&B). - Salesforce’s Data.com’s firmographic data is D&B data. - Many providers’ technographic data is from HG Data. - Social media data are often sourced from SYNTHio and FullContact. - Email validation is often sourced from BriteVerify and FreshAddress. Whether you are going with a single provider or best-of-breed approach, you should always ask the providers which third-party data they have sourced. It is important to understand what the original data sources are because: - You should not unknowingly pay for the same data multiple times. - If you are using a multi-sourcing strategy to achieve more comprehensive coverage, you may not get the coverage you were targeting. - This has great implications for GDPR compliance, which we will discuss in the next blog. Each one of these sourcing relationships is likely unique. So ask not only which third parties they source data from, but also which data field is sourced from whom. Tags: [Data enrichment](/content/blog/tag/data-enrichment/index.html)