Consider a keynote address that needs to be delivered to a large profes- sional conference (or any other large presentation). Consider how much more effective a presentation could be if the speaker refers to topics that he or she knows are of interest to the audience. Sounds obvious, right? But we all know that, as human beings, our priorities, interests, and concerns are always in flux. Even the projects we work on are often moving targets. So how do we prepare to present a topic to an audience and address the topics
currently on our (collective) minds ?
One way to do this is to monitor social media around the event and look for common themes or topics. Figure 8.10 shows some data from a recent trade show where we monitored the topics of conversation over time.
Popular Topics as of 10:00am Category Security Cloud IoT Mobile Apps 40% 26% 19% 15% Percent of Mentions
Popular Topics as of 11:30am (noon) Category Cloud IoT Mobile Apps Security 48% 26% 14% 15% Percent of Mentions
Figure 8.10 Topics of conversation from a trade show (10 a.m. versus 11:30 a.m.).
Most conferences today use a Twitter hashtag to promote their event to allow participants to post comments or observations. So imagine you’re about to deliver an address to this large crowd. In the morning, the hot topic
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of conversation at the conference was security (with over 40% of the tweets discussing some aspect of the topic), followed by cloud computing with 26% of the discussion. But by mid-afternoon, the topic of conversation has changed (somewhat drastically) to indicate that almost half of the conversa- tion is centered around the topic of cloud computing (at 48%) with the sec- ond topic shifting from security to the Internet of Things (IoT). With this information in hand, you can shift the emphasis of your talk from security to cloud computing given that it is still relevant to your original topic. So if your plan was to focus on security (or even mobile applications), it’s clear (at least from a social media discussion) that these topics may not be the uppermost in your audience’s minds. A slight tweak to your presentation to highlight areas around the Internet of Things focusing on cloud technologies would, it appears, be much more appealing.
Another application of this technique could be in the context of panel discussions, talk shows, or any live event. If, indeed, you monitor the con- versation in social media and aggregate the results (as we did in the previous example), questions to panelists or topics of discussion become much more relevant (and useful) to those listening, thereby increasing the value of your participation. One key point to remember is that you need to understand your audience and determine if what is happening in socia l venues is truly a representation of their views.
Summary
There are a number of reasons we might want to capture and analyze data in real time. In this chapter, we focused on two:
■ Sense and respond ■ Early warning
In most cases, early warning systems entail the timely collection and anal- ysis of data, which, when understood, trigger prompt interventions on the part of those watching. In the case of social media, this means getting in front of a potential public relations issue, company-related scandal, or a competitor’s breaking news. Sometimes how we respond in the early min- utes or hours can have a lasting effect on the impact on the public’s percep- tion of an issue.
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While early warning is more like “sense and react,” “sense and respond” is slightly different in that we want to sense a condition and respond in such a way as to change that condition. In today’s world, competition is based on nonstop strategic maneuvering among competitors, product or service positioning, and competition to protect an existing product or market. Agile teams understand this concept of iterations quite well. They iterate through many options, responding to change while discovering the best solution through continuous experimentation or change. In the world of mining social media, a real-time analysis gives us that view into what needs to be changed and the (instant) feedback and reaction allowing us to continually make course corrections along the way.
Finally, in this chapter, we also explored one specific social analytics sys- tem, leveraging the concepts of stream computing, to extract value from real- time and near real-time information.
Endnotes
[1] Maraboli, Steve. Life, the Truth, & Being Free. A Better Today, 2009.
[2] Davis, Colin J., Jeffrey S. Bowers, and Amina Memon, “Social Influence in Televised Election Debates: A Potential Distortion of Democracy.” PLoS One, 6, no. 3. (March 30, 2011): e18154.
[3] The Naked Scientists, “Science Interviews,” April 3, 2011. Retrieved from http:// www.thenakedscientists.com/HTML/content/interviews/interview/1606/.
[4] TechTarget, “REST (Representational State Transfer) Definition.” Retrieved from http://searchsoa.techtarget.com/definition/REST.
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Up to this point in our discussions, we’ve looked at how to find data, where to look for it, how to clean it, and now how to process it. Much of what we have discussed thus far involves the process of building a model (or a definition) of what we want to look for and then running an analysis to see if our model fits or provides relevant results. If the data seems to fit the model, we take the output, calling it information, and build our insights or knowledge from there. If the data doesn’t fit our model, perhaps we modify the model and try again, or go look for different sources of data that may be more relevant than what we have already gathered.
In this chapter, we discuss ad hoc analysis. We introduced this concept in
Chapter 6, where we defined it as an analysis that is produced one time to answer a single specific business question. Specifically, we use this type of analysis when dealing with situations as they occur rather than ones that are repeated on a regular basis. As a result, often we are searching for an answer in dataset.
This ad hoc method assumes we are looking for a number of different insights to emerge from our assembled data and that a model will be able to accurately, or at least somewhat accurately, represent the information