Miniature cleaners cleaning a laptop symbolises the importance of Data Cleaning.
The need for data cleaning has never been so important in a world dominated by big data, which are of no use at all unless they have been suitably cleaned, and by Artificial Intelligence, which is about as smart as a wet blanket if it is working with data which have not undergone a thorough data cleaning process. Data scientists spend between 60 & 90% of their working lives engaged in data cleaning precisely because they know how important clean data which have undergone data validation are to the projects in which they work.
Given the vast nature of big data, the solution to the dirty and corrupted data lying within it is undoubtedly for an artificial intelligence solution to clean the data at the speed of business, the velocity which companies nowadays require. This is precisely the conclusion we at Spotless Data reached, which inspired us to design and patent our Machine Learning filters data cleaning solution, which roots out rogue data and replaces them with spotlessly clean, quality data.
Probably the biggest false myth about data cleaning is that only tech giants such as Google, Microsoft, Amazon and Facebook have to deal with the realities of really big data and thus have sufficient data to require some concerted effort to ensure their cleaning on a day-to-day basis. "Surely my company doesn't require a professional data cleaning service?" This may be a question in the minds of countless thousands of business owners and high-level executives.
Even ignoring the rise of the Internet-of-Things and its explosion of inconsistent data from multiple sources, this is not so. The majority of companies of all sizes already have vast quantities of big data. If they are not already ensuring the data integrity of these big data through data cleaning, they need some type of solution to manage this big data successfully and thus keep their firms competitive in a world where data is rapidly becoming the new king, just as oil was a century ago.
Our own Artificial Intelligence API filters solution is a very narrow-minded focus on data cleaning. We believe that if you use our service to ensure your data are clean and have data integrity before they enter your platforms you can then focus on all the other issues that large quantities of data require you to skillfully manage so that business can reap the tremendous benefits that well-organised and clean data bring in their wake. And that once you start to use our data cleaning machine learning service, your company will be able to guarantee data quality you can trust to offer better services and products to your customers. Once you have started using Spotless Data, you won't want to stop.
Our data cleaning API solution is easy enough for anyone to use. This is why when you submit your data to us we immediately give you a report based on what our machine learning filters believe are the best solutions for the data cleaning of any corruptions, mismatches or other inaccuracies and inconsistencies. It is then for the human being who has submitted the data to set the specifications for the actual data cleaning so that your data themselves are easy to understand.
What we find scary, in a world where the quantity of data is unstoppably exploding at an almost unimaginable pace, is dirty, rogue, inconsistent and opaque data. These dirty data can cause things to go wrong in ways that anyone should worry about. For instance, Internet-of-Things data which are so dirty that they are insecure, meaning your fridge or the electricity supply or a driverless car could be hacked. With clean data, such nightmare scenarios become much less likely, whether through accidents or due to the actions of malicious criminals.
There are many examples of dirty data which need cleaning. An email address list full of errors which might result in your email shot being sent to the wrong people (never a good idea as it could be then seen as spam, rightly seen as a 21st Century scourge) and not sent to the right people, who might be expecting your email or who perhaps, on the basis of it, decide to purchase your products and services. The monitoring of the latest expensive marketing and advertising campaigns, which might be a flop, in which case, the sooner you realise this the better, or might just need tweaking based on the initial data as to how they are going to turn them from mediocre into great successes. Or you might need some great internal reporting as your top-level executives have some very hard decisions to make in the coming quarter and need to have the best possible picture of the company in order to make these decisions. No matter how good your analytics software they cannot convert raw rogue data into any sort of useful business intelligence until the data have been cleaned.
All these examples and many more besides require clean data. Dirty data are not required for anything and just portray false pictures when they are not directly sabotaging your website, products or services. However, they are a fact of life and have a number of causes. These include blending data from different sources where the data or the metadata are inconsistent due to using different meta tags or descriptions as well as mistakes caused by both computer and manual errors, whether the cause of the latter are employees or users of a website.
You can read our introduction to using our API and then try out our service on your My Filters page but, for this, you need to log in first. 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.
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