I’ve been getting lots of great questions in response to my data vendor series, so I wanted to take the next few weeks to answer them directly. There’s clearly a strong interest in how to navigate and get value from multiple data providers, and this feedback is driving the conversation.
One of the most common questions I’ve received is:
“I am using—or want to use—multiple data sources. How can I compare them?”
There are really two aspects to this question:
1. Overall Data Quality Comparison
First, there’s the (seemingly clear-cut) comparison of overall data quality to help with purchasing decisions.
2. Case-by-Case Comparisons
Second, there’s comparing sources in specific situations, like when a particular instrument or issuer presents inconsistent or unexpected information.
Speaking from a Data Management solution provider perspective (and as we do at GoldenSource), we deliberately maintain a data vendor-agnostic approach, collaborating closely with all our partners in the data provider space. This lets us focus on delivering unbiased value for our mutual customers.
It’s important to note that ‘data completeness’ and ‘data quality’ aren’t as cut-and-dried as they may appear. The same data product could be seen as high quality by one subscriber, yet fall short for someone else’s use case. Plus, data products (and the subject areas they cover) are constantly evolving, sometimes quickly and sometimes not, causing shifts in which vendors lead in quality.
That’s why having a data management platform built for full multi-sourcing is so impactful. It lets you perform ongoing, situational comparisons—supporting continuous improvement and ROI as needs change and new challenges arise.
For individual data challenges, the comparison process is much more straightforward if your platform:
- gives you full visibility into where each data point originated;
- provides a clear audit trail from intake to consumption;
- tracks all sources (even if a source’s value isn’t ultimately selected); and
- presents all this information in a user-friendly way.
With the right tools, determining which data value is ‘right’ or ‘wrong’ can be quick and decisive. And, in our experience, vendors with strong support and transparent challenge mechanisms are usually very responsive to data issues—so getting things resolved is much easier.