Beyond sentiment analysis: social data analytics
How can companies get thier arms around the rapidly changing arena of social media? Gone are the days where sentiment analysis or micro-targeted marketing could meet business needs--today's businesses require social data analytics.
What is social data analytics?
Social data analytics comprises two main constiuent parts: 1) data generated from social networking sites (or through social applications), and 2) sophisticated analysis of that data, in many cases requiring real-time (or near real-time) data analytics, measurements which understand and appropriately weigh factors such as influence, reach, and relevancy, an understanding of the context of the data being analyzed, and the inclusion of time horizon considerations. In short, social data analytics involves the analysis of social media in order to understand and surface insights which is embedded within the data.
Current examples of social data analytics
Social data analytics exists today: when the Annenberg Innovation Lab decided to track and understand how sentiment evolved and impacted the Arab Spring movements sweeping the Middle East, they employed social data analytics. Further, when the same lab decided to understand how sentiment might be able to predict box office potential for yet-to-be-released movies, they also performed social data analytics. When one of the world's leading research institutions wanted to understand how social media could impact how their clients conducted public awareness campaigns, social data analytics also played a role. Most companies understand that social media may have important roles to play in how they conduct business...the problem is that most are unaware of how to go about tackling the problem.
Solutions exist today
- BigSheets - to handle massive amounts of data typical in social data analytics
- Text Analytics tooling - to process the data and understand it's context and content
- IBM Watson - the team's expertise on Watson technology has proven useful when tackling social data analytics
- Semantic Enrichment - our background with semantic enrichment also lends itself to tackling social data analytics challenges
IBM's Social Sentiment Index
Interested in understanding the buzz that's occuring in social media? IBM recently established the IBM Social Sentiment Index to track interesting trends occuring in the social media sphere. You can check it out here.
Key concepts to understand in social data analytics
When talking about social data analytics, there are a number of factors it's important to keep in mind (which we noted earlier):
- Sophisticated Data Analysis: what distinguishes social data analytics from sentiment analysis is the depth of the analysis. Social data analysis takes into consideration a number of factors (context, content, sentiment) to provide additional insight.
- Time consideration: windows of opportunity are significantly limited in the field of social networking. What's relevant one day (or even one hour) may not be the next. Being able to quickly execute and analyze the data is an imperative.
- Influence Analysis: understanding the potential impact of specific individuals can be key in understanding how messages might be resonating. It's not just about quantity, it's also very much about quality.
- Network Analysis: social data is also interesting in that it migrates, grows (or dies) based on how the data is propagated throughout the network. It's how viral activity starts--and spreads.
Practical Business Value
As you might imagine, the business value created by such systems would be significant. Imagine a climate system which pulls data from every weather station on the globe, and which continues to gather data which may be relevant to environmental forecasts (amount of man made structures from mapping software, pollution projections based on demographic and power generation requirements, cross referential data which combines information from numerous sources--perhaps salinity levels, ocean albedo/diffuse reflectivity, etc.--to project evaportation trends and add that as a factor in a precipitation forecast, etc).
In short: it's not just about the amount of data you have. It's about understanding what that data is trying to tell you--and revealing non-obvious factors which influence that understanding. Semantic enrichment is yet another tool to do this--and which can be applied in almost any business scenario in which big data is a part of the equation.
talk to jStart...
Think you have a social data analytics challenge? jStart is looking to explore the concept of social data analytics with companies who feel they have a social data analytics challenge, and who are interested in getting started tackling that need, today. Think you might be a good fit? Schedule a Workshop.
Resources and Links
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