Spotless Data's machine learning filters are ideal for guaranteeing the data which is at the heart of data governance, freeing up the time of your data steward, data scientists and IT experts to focus on important business tasks at a time when most businesses are worried about GDPR.
One of the big changes that the EU's GDPR will bring in May is that company bosses will be required to document a data compliance strategy for their businesses. This is a data governance issue. While the EU won't be checking everyone's data compliance strategy, if your company is unlucky enough to be accused of a GDPR breach you will have to go to the Information Commissioner’s Office (ITO). There you will be expected to explain what compliance strategies you had in place to protect the digital rights of EU citizens and visitors about whom you had data. And at stake is not merely the possibility of paying up to 20 million euros or 4% of annual turnover but losing the even more valuable company's reputation as well. However, the ITO will have discretion so not only will a good data compliance strategy defend your business from being reported in the first place but will also enable a better defence of what the company were doing to prevent this violation from occurring. Gartner estimates that 50% of businesses are still unlikely to be GDPR compliant by the end of 2018 and this gives your business a fantastic opportunity to be ahead of the pack.
A data governance strategy begins by assigning a data steward. The fundamental task facing the steward is assuring that the data in the company's possession have data quality which can be trusted in again. Unquestionably the best way to achieve this is through the data validation and data cleaning of your data by using Spotless Data's API solution. We can also integrate your data sets, which is excellent if the steward isn't a data scientist, and the businesses which are not currently GDPR compliant will tend to be those who don't employ data scientists, and where hence the data steward is also not one. So if you are a data steward with an IT background but no explicit training in data science we can solve this headache of ensuring data quality with our easy-to-use API where your data can be validated from any device. But equally businesses who do employ data scientists should also consider, and take a look at our service, given that the majority of data scientists spend 60-90% of their working lives on data cleaning, which can now be automated with Spotless, allowing these employees to get on with new projects and tasks with more significant business value.
Once the data steward has validated all the company's data and set processes in motion so that all the new data also get validated by Spotless, then she or he is in a position whereby they can order data in terms of importance. This means that essential data, such as those on any EU citizens, or, given that GDPR is likely to be copied by other nations and governing bodies in the coming years, data about any individuals which could in any way identify them. You will also be able to organise the data more efficiently and swiftly once they have been validated. For instance, creating a single customer view for every customer about whom you have data will make it easy to comply with any requests that any individual to see the data a company has about them. Remember that according to GDPR these data actually belong to the person whom the data are about and, when GDPR comes into force, will have every right to see the data you have about them. With a single customer view, you already have that data at your fingertips.
If your data are full of rogue data, you may well have a problem when it comes to giving a customer an accurate picture of the data you have about them. We have developed several rules which can be applied by the person using our API after we have swiftly analysed your data, and based on our report so that the person who submits the data remains entirely in control of their modification. These include rules for strings (such as removing blanks); for numbers; for dates (which might be inaccurate, fixable by limiting the possible dates so, say, all dates need to be from March 2018 or fail validation, or might be inconsistent, fixable by imposing a date format); for lookup (where data are checked against a separate dataset); for session data (typically fixing gaps and overlaps); and for uniqueness, which, like the session data rules, are for multiple as opposed to single columns (typically used to ensure that each field within a set of data columns is unique).
You can read our introduction to using our API to validate your data. You can take advantage of our free offer of 500Mb of free data cleaning to see how much you like our service. If you haven't already done so, you can sign-up using your email address, Facebook, Google or GitHub accounts. You may also view our video on data cleaning an EPG file, which also explains how to use our API.
We use the https protocol, guaranteeing your data are secure while they are in our care and that they are not accessible by any third party, a responsibility we take very seriously.
Here is a quick link to our FAQ. You can also check out our range of subscription packages and pricing. If you would like to contact us, you can speak to one of our team by pressing on the white square icon with a smile within a blue circle, which you can find in the bottom right-hand corner of any of the web pages on our site.
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