The concept sounds easy enough. Get all your data in one location, known as a data lake, buy some expensive data analysis software tool, run it through your data and hey presto you now have the data mining and business intelligence which will allow your company to stand out among its rivals.
In practice, if your data quality is so poor, so untrustworthy, that the data analysis software fails to deliver, you probably shouldn't blame the software and start looking for an alternative but instead take a good look at the data you have and make sure that your dreamed of data lake is not in fact a mud pit. And because this untrustworthy and failing data is a data management issue does not mean that the solution is that you should replace your CDO, or whoever is in charge of your data, as that will likely not solve the problem either but instead waste six months, firing one person and then employing and training up a second, merely to discover that nothing has improved because the problem is in the quality of the data itself.
A classic example of how this unfortunate situation might occur could be getting the location data of your potential customers wrong, resulting in a data-driven marketing campaign which targets the wrong people. The worst of it is that you don't know that the problem is caused by the untrustworthy data and so you fire your marketing team, only to discover, again six months down the line, that your new marketing team, working with the same poor quality data mud pit, have done no better than the original team. Meanwhile your competitors, with their better quality data, are grabbing all the potential customers, and unless you act quickly will be impossible to remove from the top spot in your particular industry.
What is actually required is to data cleanse your mudpit and turn it into the pristine data lake which it should have been in the beginning. There are two ways of doing this. You can hire some data scientists, who will expect a salary of at least the equivalent of $100,000 a year and will then complain because they spend at least 60% of their working lives cleansing your data mud pit, and who as a result will be much harder to hang on to, as they start to dream of working for another company with cleaner data so they can, for the same salary, do the types of things they imagined themselves doing when they trained to become data scientists.
Or you can choose to cleanse your data lake and have quality data that you and your team can trust to do what you want it to, by using Spotless Data's unique web-based API solution, which will ensure that your data analysis software does what it is supposed to, giving you the much needed business intelligence and other useful information your business requires, and ensuring that your data-driven marketing campaign is a success.
In order to ensure the ongoing success of your data analysis you can build Spotless into your data projects to guarantees their data quality in your workflow and indeed throughout their lifecycle. You can integrate Spotless API with all your systems using our rule driven, proprietary, data cleansing algorithm. When your files are cleaned to a spotless quality you will receive a notification that they are now ready for analysis and use. Given that poor quality data is costing businesses $611 billion each year in the US alone, Spotless Data focusses entirely on the creation of quality data through cleansing in order to allow your company to focus on the business values and the data analysis so essential for success in the competitive modern world.