Post by account_disabled on Feb 22, 2024 0:14:58 GMT -5
Do you know what data quality consists of ? Do you know to what degree it is necessary for social media analytics? Does credibility seem relevant to you as one of its attributes? More than data quality itself, or its qualities, the debate within the information management industry is about the possible consequences of poor data quality in operational and analytical environments. What quality means is not as important as the perception that companies have of it. data quality Photo credits: And there are many different dimensions of data quality, perspectives that, realistically, it is assumed that it is impossible to cover 100% (the investment required would not justify the results). However, one of these facets is credibility, one of the most frequently analyzed dimensions.
But what does it consist of? From data quality to information credibility Data credibility is the degree to which it can be verified or known for certain that the data actually represents what it is supposed to represent. This attribute of data quality is linked to the source or data provider, in whom there must be no indication of bad faith or evidence Chinese Student Phone Number List of intention to falsify. Given the impressive advances in technology and the volume of information available, data credibility has become more important than ever and social media is in the crosshairs of those who value credibility above all else. Social media is, today, an important catalyst for big data projects but, unfortunately, it is also the most vulnerable area when it comes to data quality , an example of this is how simple it can be, for example , artificially increasing the numbers on social networks: Buying followers on Twitter. Playing with Facebook "Likes.
Making a "free" interpretation of the number of impressions a blog receives (multiplying the number of Tweets by the number of followers of each tweet). The concern for credibility must be at the same level as that for the search for data quality . Data generated from social media feeds provides organizations with a powerful way to connect with current and potential customers to create interest in their products. Interest generates clicks, likes, comments, opinions and all this generates income, which in turn determines the success or failure of a business and its sustainability. But of course, this rule is fulfilled if and only if the data received from social media, which will later be analyzed to provide knowledge and vision to decision making; They are credible... and, all too often, that is not the case. Dirty data is as dangerous as unreliable data and the consequences of both for an organization can have a devastating impact, especially considering that social media analytics, like other advanced analysis techniques, are based on quality . of that data to support business action-taking.