In the first of a three-part blog series, we look at how demographics can be used to move from # mentions to valuable analytical insight. Whoever says Twitter is just for teenagers? In fact, the fastest-growing demographic on Twitter is the 55-64 year old age bracket. Facts like these are critical to know as you plan your next social media strategy. But how do you analyse and segment social conversations based on detailed audience demographics?…

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With Human Data comes human fallibility, but awareness is first step to mitigating our error prone nature. In this piece, we will take a look at the most common mistakes we humans make when it comes to collecting, analyzing, and drawing conclusions on Human Data. 1. Mistaking correlation with causality. You might remember your college statistics 101 or 102 professor letting you know that just because something—let’s call it thing A—is related to or shows…

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It’s a fact. Social media produces huge volumes of data that can be used to detect a trending topic or brand sentiment. There are countless of agencies and application development houses that build successful businesses on social media monitoring. But beyond social media monitoring lies a semi-unchartered land of security and threat detection, fraud, supply chain and human capital management and more. The land of opportunity for those who know how to handle unstructured data….

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If you’ve read my previous blogs, you’ll know that our platform allows you to filter vast amounts of real-time data. You end up with all the interactions you’re interested in – but how do you get those interactions from our platform to your app or analytics tool? There are three ways: 1. Stream them from our platform in real time 2. Have our platform send them to a destination of your choice 3. Pull them…

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Humans are social creatures. That might even be truer when we go online. Consider the following recent (beginning portion of 2014) statistics about the most popular social network and blog platforms and their memberships: Twitter has 560 million registered users, and over half of them are active, and at least as of a couple of years ago, there were 200 million tweets sent every single day. WordPress powers 20% of the top global sites and…

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When we talk about Big Data, we’re not talking about a few petabytes of structured data which has been captured, tamed and sits caged in a database waiting to be queried. We work with Big Data in the wild. Untamed, unpredictable and unbelievably big. We have close to 30 data sources hurling vast quantities of unstructured data into our platform. Then we use 10 augmentations to add more metadata to every interaction. Then, as if…

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Today, we’re kicking off a new series of blog posts under the umbrella of ‘getting back to basics on data’. We want to tackle the most fundamental questions around data, in particular human-generated or as we like to call it – Human Data. What is it, what do you need to be able to work with it, what value can you extract from it and many more. First up – unstructured data. It’s an important…

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I had the pleasure of hosting a webinar panel earlier this month with our partners from WordPress and Disqus talking about how DataSift customers can unlock value and insights from blogs and comments using our platform. I want to give a big thank you to Min Wei, Director of Platform Partnerships at Automattic, and Ro Gupta, VP of Business Development at Disqus for their expertise in the panel. The key takeaway from our discussion? The…

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From helping the UN identify what matters to spotting trends minute-to-minute, social data has proven a powerful tool to data scientists eager to understand how humans think and behave. Unfortunately, many hurdles have impeded us from putting these data science in the forefront of our research efforts. According to a recent article in the The New York Times, “Data scientists, according to interviews and expert estimates, spend from 50 percent to 80 percent of their…

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One of the strengths of the DataSift platform, is the ease with which filtered interactions can be sent to a variety of destinations. Google BigQuery is a good example. It takes no more than 10 minutes to configure a database in Google BigQuery, then it takes no more than 5 minutes to configure a filter in the DataSift platform which sends matching interactions straight to BigQuery. Sounds too good to be true? Register for this…

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