Mark Ghuneim

Using real-time data to drive business intelligence and creative, engaging user experiences and to ultimately to break emotional ground…oh yeah and annotating dead things

The mantra “data is the new creative” has been bantered around for a couple of years now.  As I work closely each day with real-time data, I have seen how it can help us inform creative, glean business intelligence, and grow clients’ businesses.  What I am truly excited about is a new realization that data can provide an even bigger utility: it can be at the forefront of innovative, creative, engaging, and ultimately valuable user experiences with the winning ideas breaking emotional ground.

To better understand this    philosophy, I wanted to break-out the many ways in which we are processing real-time data:

· Business Intelligence – Optimization and Effectiveness
· Creative / Experiential – Activity Streams
· Engagement – Broken Emotional Ground
· Live Data – Drives Dead Things

Collecting data is a cumbersome process that relies on processing real-time information and activity streams, a deep understanding of prerequisite business and technological matters, the ability to make this data actionable, as well as a strong infrastructure for inhaling massive volumes of information real-time (drinking from the proverbial fire hose).  This is no small feat – and something that 99% of the marketplace should rely on third parties like us for – but for the purpose of this post we will stick to what to do once the data is in place.

While there is a lot that can be done with this data, I want to emphasize the importance of looking at it through a lens that best suits business logic as everyone has different goals, key wins, and ROI.  Additionally, I must note the urgency of real-time data as while it is highly actionable, it has a shelf-life of hours and minutes versus the weeks, months, and years of over-time data.

With that said, let’s look at the different applications of data:

I. Business Intelligence – Optimization and Effectiveness

Key types of business intelligence data includes:

· Demographic
· Geographic
· Psychographic
· Volume of Activity
· Velocity of Activity
· Sentiment and Intent
· Shared Links and Hash Tags

Processing this intelligence requires several actions all yielding important insights:

1. Listening: Events drive conversations, what is being said? How is the messaging being shaped and informed by market factors?
2. Measuring: What is the velocity around the messages being shared? Does that meme have game?
   
3. Understanding: What insights can we gain from this messaging?
4. Optimizing and Refining: How can we change what we are presenting and promoting from these insights?  For example – advertising effectiveness
5. Communicating / Responding: How can we personalize our communications, customer support and information flow? (ala FedEx track your package)
6. Activating: Using this data almost recursively to inform and improve outreach activation programs

II. Creative / Experiential – Activity Streams

Another way “data as creative” is being rendered is the taking real-time information and molding it into compelling user experiences. In the last year, we have seen important developments in data visualization – moving from information graphics (over-time insights) into to experiences driven by real-time activity streams.

Two examples of this:

1. MTV VMA’s Red Carpet – visualizing celebrity heads in a bubble with their corresponding size determined by the volume of real-time conversation attributed to that personality
2. NBC Olympic’s – webpage manipulating and realizing the real-time conversation around the Winter Olympics

While both projects were rendered amazingly, they are more art and design realizations rather than immersive engagements.  Very likely by design to be more of a production aesthetic.  The ongoing challenge  is the balance of that design aesthetic  and business logic coupled with a compelling user experience that will engage users.  A pretty high bar in doing something like this effectively.

We have built a number of innovative live data experiences, the most recent being an approval metrics application based on Twitter sentiment around a high-profile network series.

Emotional data can be characterized as data-powered creative that breaks emotional ground in the form of truly engaging user experiences.  The current challenge here is how to move from data powered art projects that have a nice “oh gee that’s cool” factor into something that really resonates and can provide ongoing value.

Location based services is where I think we will finally see the power of emotional data as we now have the ability to enable real-life connections between people though location based real-time data.

We started Wiredset with a product called tourio.us which was a location based application that allowed users to create a localized online tour which could be pushed to a mobile device to allow for shareable excursions.

I remember looking at the location based landscape at that time just five short years ago and seeing several key barriers to entry, primarily the lack of small, affordable chip sets, licensing data from navtech, application ecosystems or app stores, participating carriers, and/or enabled devices.  It was a daunting view.

Today we can look at path history, illuminate migration patterns, and drive compelling user experiences both online and off with new, easy data flowing from location based services, porting them right into applications.  This is all without talking to Verizon, having to get a WAP deck “brew certified”, or following any other type of misguided production process (may we never see that landscape again!)

The marketplace (and more importantly getting to market) has changed for the better and the process of making an application for the iPhone or other smart devices is now faster and smarter.  These developments allow us to leverage this new data in compelling and creative ways with fewer barriers to entry.

Each day we are seeing new and exciting case usages around real-time data. There are examples of machines driving their own real-time data, literally in the case of Big Ben Clock pushing its time data into a twitter account (@big_ben_clock).  Both push and pull lanes exist and the relationship around each still being defined.  Companies like stickybits are annotating objects via bar codes and pachube real-time sensor data from objects, devices, buildings, etc.

We are always off from this data fully annotating things and in turn becoming building blocks for the above mentioned experiences, but I would be remiss in not mentioning them.

End Game

Right now as a company we are focused on maximizing the aggregate value of measurement and rendering – identifying programs that can ultimately result in making business being more effective while also creating engaging user experiences that will resonate with consumers.