Data may be the new oil but Zuckerberg defending Facebook's actions in US Congress show that they can have an immensely negative affect on one's business. Getting your data straightened out with Spotless Data's machine learning filters is a great first step.
With the value of Facebook having fallen by tens of billions of dollars in the last few weeks due to loss of reputation caused by the way it has been handling its data, i.e. making them available to third parties who, while appeared to be writing innocent apps on Facebook such as psychology test quizzes, were in fact exploiting the data given to them by Facebook's to affect the outcome of important elections, such as the Brexit referendum and the 2016 US presidential election.
So big has this story become that some might believe that all companies which exploit big data are, like Facebook, seeing the users of their services and the data they have on them as the product, to be sold to advertisers, or those who pretend to be advertisers, who can then target ads at their users. Many companies with entirely different business models also exploit the data they have.
Also the majority of people would probably be happy to see ads that they are genuinely interested in rather than irrelevant ones "spamming" their web-pages. However, they atre clearly not so happy when they hear that their data were used to help swing an election so that the political party or proposal which they opposed then won, and it is the political misuse of their data which has caused Facebook so many problems and not their business model itself.
The new EU GDPR, which comes into force next month, demands that companies worldwide seek consent from anyone (located within the EU) before exploiting and selling or passing on their data. This may actually come as a relief to many companies, who observe that Facebook has never tried to hide its business model of offering a free service to users and then exploiting the data obtained about them, although they naively thought such data would only be used by third parties to sell advertising!
And, with YouTube now facing a possible lawsuit for having allegedly targetted ads in their videos to under-13s, all companies are becoming aware of how data can be a liability as well as an asset, and how catastrophic even their accidental misuse can be to the companies exploiting them. Which is why it is crazy to ignore the issue and leave your data dirty and ungoverned. And with most companies currently engaged in reviewing their GDPR compliance, there has never been a better time than this moment to develop a new, lasting data strategy which will successfully take your company into the 2020s.
There is still a great market for those wanting to engage in data exploitation, albeit companies need to tread extremely carefully. The first and most critical step in handling your data is to make sure that those which you already have in your platforms undergo a thorough data cleaning and data validation process by using Spotless Data's machine learning filters, through our easy-to-use API. It is advisable to do this even before developing a data strategy simply because when your data are invalid, rogue data it is challenging to know which data demand the highest levels of data security, which data pertain to which people or where pieces of data will end up.
Validating and cleaning your data, ensuring the data integration of the different datasets, and removing inconsistencies within both meta tags and the data themselves are the basis from which you can then develop a data strategy. A well thought out strategy is not merely for GDPR compliance but because data are the new oil, and those with the greatest success in exploiting them will emerge as the new winners in the digital age, as both Facebook and Google have already demonstrated.
The two keys to successfully using the data one has on individuals in the future are protecting their privacy and that those executives at the highest level of the company need to be able to justify their enterprise's data strategy, not the least to ensure the desired reputation of their company. With Zuckerberg having to testify personally in Washington earlier this week, it is the CEO or owner of any company who will most likely be directly in the spotlight if the way a business uses its data is generating negative attention, whether through an alleged GDPR violation or simply negative media coverage.
With all your data now validated by using the business rules which Spotless has developed, and any new data automatically cleaned by us before they enter your platform, this means that you now have data quality you can trust. With this done you can now start to devise your data strategy, taking care with whom you share your data and being aware of what purposes they might want to use them for in their businesses.
You can read our introduction to using our API to validate your data and here is a quick link to our FAQ. We offer business rules for number, string, date and lookup validation checks as well as uniqueness and session rule checks.
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 videos on data cleaning an EPG file and data cleaning genres in an EPG file, which also explain how to use our API.
We use the https protocol to guarantee that 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. We are also GDPR compliant and offer offline processing to businesses which request it, and at no extra cost.
You can also check out our range of subscription packages and pricing. If you would like to contact us, you can chat to one of our team by pressing on the white square icon with a smile within a blue circle located 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