Spotless Data version 9 includes data validation, substitution and lookalike improvements for better data quality.
We've just launched Version 9 of our unique Data Quality web-based API solution, which includes a new rule type, known as a Data Validation Rule, as well as significant enhancements to our rules engine, which have been driven by Machine Learning.
Here are the five basic improvements to Spotless Data Version 9:
- Data Validation Rules: These are a new rule type which we have developed in order to check and make sure that your data are always well-formatted and within certain well-defined ranges, and accompany our already existing reference, regex, duplication and session rules.
- Substitutions: We have found that sometimes the data companies submit, typically those gathered by them from a 3rd party source, have the same consistent errors occurring repeatedly. In order to address this problem we now allow you to hard-code a substitution which can always be made in a particular situation. For instance if a TV Listings service consistently receives information about a show called Mr & Mrs but it actually refers to the show Mr and Mrs, a substitution would be to always replace & with and when the parameters are that the show title starts with Mr and ends with Mrs. We can also suggest substitutions when we see that an error is consistently occurring when you regularly submit your data to us. If you would like guidance in hard-coding such a substitution rule yourself please email us, see below.
- Lookalike Matches Sometimes blank entries in your data files can really mess up the data quality, eg if you have two columns of data then a blank entry in one column could result in all the data below this entry in both columns being mismatched, resulting in wildly inaccurate data. By comparing any blank entries which appear in your data with other rules we can now automatically fill in the blanks to ensure data consistency throughout.
- Reporting and Quarantining One reason why it makes so much sense to use our API data quality service is for better reporting within your business or organisation. However, we recognise that our own reporting of the cleansing of your data batches is also extremely important in ensuring that your data are now exactly what you require in order to make them quality data you can consistently trust in. With this end in mind we have improved our reporting so that you understand exactly what we have done in improving your data, and can then flag us via email if there any anomalies in our report or if we have done things which don't exactly fit you requirements. We have also improved the quarantining of bad data, which can is then flagged so that it can either be fixed manually or through a further automation, eg imposing a new data validation rule.
- Security We recognise that your data are both valuable and highly confidential and that the last thing you want is for a malicious 3rd party to be able to access them while they are in Spotless Data's care. While we have long used the https protocol for secure communications, we take the security of your data extremely seriously and so have put new measures in place to ensure that our commitment that nobody will be able to access your data when they are with us is a cast-iron guarantee.
If you or your organisation have data that need a good clean-up then get started and find out how Spotless can save you time and money to ensure your data is clean and that you know the quality of your data over time.
A quick link to our FAQ. Please do sign up for our service using your email address, facebook, google or github accounts. You can also view our videos on cleansing an EPG file and cleansing a genre column which explain how to use our API. You can also check out our range of Subscription Packages and Pricing.
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If your data quality is an issue or you know that you have known sources of dirty data but your files are just too big, and the problems too numerous to be able to fix manually please do log in and try now