with the right tools working together, any organization can make social data work for them.


There’s no question about it, social media has completely changed the way organizations communicate. Brands from all industries are posting and sharing, and tweeting and pinning. They’re interacting with their customers in a way – and on a scale – that, until relatively recently, was unimaginable. But what does all that interaction mean?


Today, more and more companies are delving into the often overwhelming world of big data analysis to answer just that question. Unfortunately, if you are a small- to medium-sized organization like Environics, entering that world can pose some problems. In our recent post, How to Implement Data-driven Decision-making, we laid out a road map to guide small businesses through the difficult process of implementing big data analysis. Here, though, we will be focusing on one particularly problematic aspect of data integration: the tools.


The biggest issue with acquiring the right tools for your social media analysis is the investment. Unless you’re a large organization, the software development and hosting infrastructure is simply too costly. It’s an issue that Environics is all too familiar with – but, by uniquely combining the tools that are available to us, we’ve found that social media analysis can work for any sized business. We’ve broken down the process we use into three key steps, and outlined the specific tools we used in combination with one another to tackle each.


1. Collecting and Augmenting Data with DataSift




DataSift is a pretty incredible software platform that essentially aggregates and processes social media data across various channels. It gives us access to an enormous amount of data and allows for a high level of customization. By using DataSift’s CSDL Code Editor, for example, we can collect data with a variety of augmentations, such as gender identification, klout score, topic and sentiment. It allows us to either stream in live data from a variety of social media channels, or run historical searches on Twitter, Facebook and Tumblr. We can even collect basic demographic information to identify the approximate age, gender, region and profession of Twitter users, and whether the account is a person or an organization.


Once the data has been successfully collected and augmented, we need a way to store and access it. For that we look to another one of DataSift’s major advantages – its ability to pair and integrate with our second big data tool: Google BigQuery.


2. Storing Data with Google BigQuery



Tools like Google BigQuery are crucial for any small business working with big data. This cloud-based platform has allowed us to take advantage of Google’s server infrastructure, rather than investing in our own, saving us a major expense. By pushing all of our social media data from DataSift into Google BigQuery, we can easily store and access the data while running customized post-processes as needed. In addition to being able to handle the volume we’re throwing at it, BigQuery reshapes the data coming out of DataSift, so that it integrates well into third party platforms and software. This allows us to easily connect to our social media data using Tableau, our third big data tool, and to present our results internally or to clients in near real time.


3. Presenting Data with Tableau


Tableau is a powerful data visualization software tool that, on top of working seamlessly with Google BigQuery, allows us to simplify large or complex datasets from multiple sources into easy-to-understand visual representations. In some cases, these visualizations allow us to find patterns that lead to insight and, in other cases, they are the only way to even begin making sense of datasets. Ultimately, if you are unable to effectively convey the insights you gain from your data analysis, no action will be taken and those insights will go to waste. With Tableau, we can present rich data on a dynamic dashboard that tells a story, answers a business question and spurs on evidence-based change.


You may have noticed that our entire data analysis process, save authoring the dashboards, takes place in the cloud, meaning that we don’t need a large infrastructure on-site. Everything is scalable, allowing us, as a smaller organization, to integrate powerful social data into our workflows as needed.


Working with social data can be a difficult process, especially if you don’t have the budget for major infrastructure development, but as you can see from our own example, with the right tools working together, any organization can make social data work for them.

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