1. Getting data metrics to the right people
Often, social media is treated like the ugly stepchild within the marketing department and real-time social media analytics are either absent or ignored.
2. Visualization
Visualizing real-time social media analytics is another key element involved in developing insights that matter.
Simply displaying values graphically helps in making the kinds of fast interpretations necessary for making decisions with real-time data, but adding more complex algorithms and using models provides deeper insights, especially when visualized.
3. Unstructured data is challenging
Unlike the survey data firms are used to dealing with, most (IBM estimates 80%) is unstructured — meaning it consists of words rather than numbers. And, text analytics lags seriously behind numeric analysis.
4. Increasing signal to noise
Social media data is inherently noisy. Reducing noise to even detect signal is challenging — especially in real time. Sure, with enough time, new analytics tools can ferret out the few meaningful comments across various social networks, but few can handle this in real-time.
5. A wait and see attitude
Again, businesses are used to a certain operational model that makes real-time social media analytics challenging. For instance, we listed to a presentation by an analyst from NPR.
Often, social media is treated like the ugly stepchild within the marketing department and real-time social media analytics are either absent or ignored.
2. Visualization
Visualizing real-time social media analytics is another key element involved in developing insights that matter.
Simply displaying values graphically helps in making the kinds of fast interpretations necessary for making decisions with real-time data, but adding more complex algorithms and using models provides deeper insights, especially when visualized.
3. Unstructured data is challenging
Unlike the survey data firms are used to dealing with, most (IBM estimates 80%) is unstructured — meaning it consists of words rather than numbers. And, text analytics lags seriously behind numeric analysis.
4. Increasing signal to noise
Social media data is inherently noisy. Reducing noise to even detect signal is challenging — especially in real time. Sure, with enough time, new analytics tools can ferret out the few meaningful comments across various social networks, but few can handle this in real-time.
5. A wait and see attitude
Again, businesses are used to a certain operational model that makes real-time social media analytics challenging. For instance, we listed to a presentation by an analyst from NPR.
