Data quality startup Superconductive today said that it has secured a $40 million funding round led by prominent technology investor Tiger Global.

Index, CRV and Root Ventures participated in the Series B round as well. Salt Lake City-based Superconductive has raised a total of $64.5 million in funding since launch. 

Superconductive develops Great Expectations, a popular open-source tool that helps companies ensure the reliability of the data processed by their applications. The tool is downloaded nearly 3 million times every month, the startup says.

Data reliability is a major priority in the enterprise. A finance team working on a quarterly revenue forecast has to ensure that the information used to create the forecast is accurate. Similarly, marketing departments must filter duplicate records from ad performance data before using it to evaluate their advertising campaigns. 

Many data quality issues relate to the way that information is formatted. For example, a retailer’s e-commerce purchase records may consist of three data points: the name of the product that was purchased, its price and the date of the transaction. If an e-commerce purchase record contains only two of the data points instead of all three, or contains one of the data points twice, then it’s not suitable for analysis. 

Because inaccurate information can negatively impact data analytics initiatives in the enterprise, companies need a way to ensure that the information they are processing is accurate. That’s the task Superconductive’s Great Expectations tool simplifies.

Great Expectations enables developers to specify a set of requirements that the data being processed by an application must meet. A developer could create a rule specifying that an e-commerce purchase record must always include the value of the sale it describes. Similarly, it’s possible to specify that records should contain a specific number of data points or use a certain data format. 

Companies can use the rules they create with Great Expectations to ensure the reliability of the information they are processing. Before an application analyzes a dataset, Great Expectations can check that the dataset meets all the rules specified by developers. That makes it possible to prevent erroneous information from finding its way into business applications.

Another use is to validate the results of data processing workflows. For example, an application may have a feature that takes two separate spreadsheet columns and merges them into a single column. Great Expectations can detect if the application fails to merge two columns in one of the spreadsheets that it processes and help developers troubleshoot the issue.

Data quality rules created in Great Expectations take the form of  relatively simple code snippets. Developers can write the code manually, or have Great Expectations generate it. To ease developers’ work further, the tool creates a natural-language description of each data quality rule.

“Great Expectations is one of the fastest-growing open source communities in the data ecosystem,” said Superconductive co-founder and Chief Executive Officer Abe Gong. “It’s … rapidly becoming the de facto shared open standard for data quality.”

Superconductive is currently developing a cloud-based version of Great Expectations that will make it easier for companies to incorporate the tool into their data analytics projects. The startup’s newly closed round will support the development effort. Superconductive will also use the funding round to support the open-source ecosystem around Great Expectations. 

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