Big Data => Less Information

Image byPetar Pavlov (

We literally swim in a sea of data. Google tracks my saunter down the street, determines I’m walking, not driving, feeds the data to my Fit application. I’m also emitting data to many other applications on my phone every moment of the day. I frequently have this electronic tether in my hand, either talking to someone to get the latest update on her day, reading a social site, or scrolling through a news reader. In short, I’m constantly pulling and pushing information. So how does big data lead to less information? It doesn’t directly, but if you build products you must strive to reduce data overload. Exactly because of our immersion in data, our technology needs to crystallize the data into the most succinct and pertinent interactions. By presenting users less information, solutions break through the chatter, grab a person’s attention, and initiate engagement.

The challenge for technology solutions centers on harnessing the wealth of information available to assist users. Today’s access doesn’t mean we deliver all the available data to our users like a hose that has simply accumulated the streams of multiple faucets. Aggregating information provides the most basic value. Turning data into a compelling call to the user that moves them closer to solving a problem demonstrates a solution that actually works with the data to enrich its value. This move from being a passive tool to an active partner defines the opportunity of big data. Let’s look at a few hypothetical examples of how this works.

I have a coffee shop with a mobile application. I know a number of things about people who install the application. I have the application setup to push a 25% off eCoupon to the user when they get within 3 miles of my shop. Well, that does provide a call to action, trying to lure the user into the store based on relevance triggered by proximity. Now, let’s say I augment and enrich that data by looking at the weather. If the day is cold, I could make the offer more specific and say 25% off a hot coffee and make it an iced tea for hot days. That is better, but let’s go one step further and tap into the sales history for the client. Now I can offer $1 off a medium iced latte with an extra shot because the application knows that the customer orders that drink at this time of day, especially with this weather. Finally, let the push notification have a button to order the drink and it now partners with customer to solve the desire for a coffee. This example plays on the well established marketing practices developed to leverage proximity and context to make a personal offer, but adds value by simplifying the fulfillment of it. Let’s look at an example further from the marketing sphere and more clearly distilling information.

Reporting dashboards provide fertile ground for this conversation. I’ve previously written about the risks with dashboards. Let’s look at how less information can create a more valuable reporting user experience and avoid some of those risks. Rather than presenting the user with a grid of visuals showing trends and snapshots of values, use domain expertise and user behavior to tailor a more meaningful first interaction. As the reporting solution provider, I have history of the user’s data and also what and when the user interacts with it. In addition, I have industry wide knowledge from other clients and their use of my solution. Replace the dashboard with an initial view that shows the top one or two items based on analysis of the data and modeling of the user’s behavior. In doing so, the application focuses the user on what should be the most valuable pieces of information. “Most valuable” can result from unusual variance (analysis,) every Wednesday a.m. the user drills into a particular view (behavior,) or for 6 weeks the user hasn’t looked at a report the broader market uses on a weekly basis (domain expertise.) This shifts the application from forcing the user to filter the information, propelling the user past that initial analysis to a point of engagement.

In short, provide real value by using data to create a richer context and predict value for the user interacting with your application. Applications should not passively present information but rather initiate the action implied by the information. The user can always pull back the curtain to examine the reasoning/data that led to the recommendation, or even reject it altogether. Remember, today’s users don’t read, they react. You have to capture that reaction to get a longer interaction. Without it, your rich and detailed presentation will fall into the rag and bone shop of modern interaction.

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