Big Data and Mobile were meant for each other. Big Data allows for nearly instantaneous processing of large amounts of information from disparate sources while mobile creates a real time, always-on channel for the distribution of information. As we think about the intersection of these technologies, several notable use cases come to mind. All of these use cases can transcend any industry or category of app, but to illustrate, I’ve provided a specific example for each.
Big Data Contextual Push
Big Data technologies can analyze users' past behaviors and push suggested new behaviors. The new behaviors being suggested can be based on new business initiatives from the app owner or previous goals set by the user. Nike’s recent incorporation of “Move Reminders” is a good example of a Behavioral Push.
2) Environmental Push
Utilizing Big Data stores, mobile can collect information about a user's environment (i.e. location, weather, calendar, activity) and push a suggested action based on that context. For example, a sales enablement app can pull information about a customer in advance of a sales reps scheduled call with them. Information can include past customer activity, weather in the customer’s city and last night’s sports scores. This information can be pushed to the sales rep during a break in their calendar a few hours before the call, so they can prep. If the sales rep is driving, the information can by default, be read aloud to them.
3) Historical Push
Big Data sources can use mobile to push suggested actions to a user based on what actions they (or other, similar users) have taken in the past. For example, let’s consider an investment trading app. Suppose an analyst downgrades Apple to “Sell” and many users on the trading system begin selling Apple. The system pushes this trending information to the users that currently hold Apple shares. The system can also suggest buying Samsung shares based on a trend that others selling Apple shares are buying Samsung shares. Amazon’s predictive shipping patent is another example of a Historical Push.
4) MicroLocation Push
Beacons (low power blue tooth sensors) can create sensor-aware environments, allowing Big Data to collect micro-location information about users. Mobile can push suggested actions based on where a user is inside a store, branch, warehouse, factory or hospital. For example, in facilities maintenance, a beacon can record every time a custodian enters a restroom for cleaning. Big Data can determine if a restroom hasn't been cleaned by a scheduled time and push a ticket to the nearest custodian to clean the restroom.
5) Social Push
Similar to historical push, social push can make a suggestion based on what social networking friends have/haven’t done given a particular environment or context. Facebook pushes a notification when friends are physically nearby. The same model can apply for coworkers in an intranet mobile application.
6) Cross-Platform Push
Big Data can aggregate user analytics across handset, tablet and desktop platforms and push suggested actions to one platform based on a user's activities on a different platform. For example, if a banking user is looking up ATM locations on their laptop, Big Data can push a notification to their phone when they are nearby an ATM.
Big Data Decision Making
8) Financial Decisions
Product owners can use Big Data to couple user analytics along with transactional data and performance goals to determine if specific financial metrics are going to be met. Analytics being gathered can be further refined to analyze where breakage in user conversions are occurring.
9) Location Decisions
Utilizing beacons and sensor-aware environments, Big Data can collect information about where mobile pieces of machinery are. For example, in a hospital environment, it can track which patient’s room a missing ultra-sound machine is located in. Over time, it can track the patterns of usage of the ultra sound machine and suggest the optimum number of ultra sound machines for the hospital and where they should be stored.
10) Gamified Decisions
Big Data can translate user actions into badges/rewards/leveling to heighten engagement levels in mobile applications. There are many examples of gamification in fitness and social apps but, where appropriate, gamification concepts can be applied to any type of app.
It is not simply serendipitous that Mobile and Big Data are the two hottest technology trends right now. The two technologies are driving the need for one another, as they both race to realize their fullest potential.