It’s no news that unstructured data has been a highly sought after source since its inception, first for determining public topical insights and now for training machine learning algorithms. The critical question to answer is whether you should outsource the collection to overcome business challenges or not? Mike Madarasz explains why it could be worth it!

Welcome to the new world of analytics! Brands are hiring data scientists to overcome challenges and shortcomings of single-point solutions. However, better algorithms and better data are not enough. There is no magic button that drives actionable insights to solve business challenges. At least not yet. Combine the right technology with experienced operators and subject-matter expertise and now you have something of substance. Technical skills are not enough to satisfy market expectations. We need data scientists, engineers, designers, people who understand data, people who are creative with data and who have empathy for the insight-challenged community that’s now represented and shaped by experienced practitioners. The tide is now turning toward making data and sophisticated algorithms king. 

Social media data, for instance, is truly a global phenomenon. Everyone uses a few popular sites but you have many regionally dominant platforms. Even without different apps, each culture and region have its own social media nuances, and you have to account for that when analyzing data. Furthermore, incorporating social data, adds substantial depth to a variety of use cases in practically any research question and can be informed by at least one of these data sets.

But what sources are relevant? Twitter or Facebook? Reddit or YouTube? Or maybe various forums, blogs and reviews? Maybe it’s just one source or maybe it’s all of them. Many factors contribute to the relevance of a data source as it pertains to a specific use case. However, regardless if you are using these datasets for research or training your machine learning algorithms, they can be invaluable.    

The data analytics community essentially builds cars, which require gas. And data is the gasoline that fuels these sophisticated engines. So, the million-dollar question is, “how can I access it?” Understanding how to access unstructured data sources like online conversation, is an integral, yet tricky, part of the equation. With today’s compliance and access standards more scrutinized than ever before, knowing how to best prepare for that from a licensing and technical perspective in order to maximize the opportunity for successful analytics is essential. For example, social media today looks nothing like it did 15 years ago. Data has become more complex, more global, and has more uses than anyone could have predicted. We are talking about hundreds of millions of data points from millions of sources. According to market experts, more data has been created just in the past two years than in the entire previous history of the human race. And within five years there will be over 50 billion smart connected devices in the world, all developed to collect, analyze and share data. Just accessing the raw data isn’t enough anymore because it’s so nuanced that you need to have it sifted and parsed to make any real use of it. 


Which begs the question, buy vs. build?

Data aggregation challenges ensue as the activity can be time-consuming, costly and very difficult to do effectively. I’d compare it to renting an office. Do you want to have to find your own source of water? Electricity? Of course not, but gathering those things is not in your wheelhouse and your energy is best used on other things. The insights from social media data today is right up there with water and power when it comes to keeping a business functioning. You need it, but just like you don’t want to run your own pipes and your own power plant, you shouldn’t have to find social media data relevant to your requirements.

There are viable options to identify, index and make unstructured data available in a structured way, and enable access to social media content from a vast array of sources in near-real to real-time delivery mechanisms. Standardized data sets support business intelligence algorithms and predictive modelling by providing on-demand access to years of historical data.

 Social data is a basic necessity and should be delivered to a business already mined and sifted so that analytics systems can do their work. Even if you have a truly huge business, and want full vertical integration, you can do your own social media mining but it’s still probably not worth it. At the end of the day, if you are a Data Scientist or Analyst, your time is best spent focusing on your core competency rather than tedious data collection.  

With the rise of machine learning, the ability to analyze data is only getting better. That’s why we’ll be able to look at information much more quickly and organize it more precisely, which is great because there will be so much more useful data coming in. Our whole world is online, and IoT alone is going to load-up systems worldwide with more data than we can conceive. Better machine learning and heuristics are necessary just to keep up with the flood of information we’re expecting to see, and the companies that are best positioned to thrive in this digital tomorrow are the ones who make the best use of all this information. To do that you need the best, smoothest access to well-organized data.

 

 

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