Mobile devices play a dual role in the context of Big Data. Mobile devices – namely traditional mobile phones, smartphones and tablets – are both sources of Big Data and delivery mechanisms for Big Data.
Mobile as Source of Big Data
There are over 6 billion mobile phones in use worldwide. China alone supports over one billion mobile phones in active use, and that’s with a penetration rate of just 75%. (In contrast, there are 326 million mobile phones in use in the U.S. – 1.03 phones for each man, woman and child in the country.) Each mobile phone, non-smart phones included, creates numerous types of data every day. These data include call detail records, Short Message Service (text message) data, and geo-location data.
In the case of smartphones, such devices also generate log data via the use of mobile applications, financial transaction data associated with mobile banking and shopping, and social media data from updates to Facebook, Twitter and other social networks. The volume of mobile data and the speed at which it is created is only going to increase as both the global population and mobile device penetration rates rise, and the use of social media increases.
When analyzed effectively, this data can provide insight on user sentiment, behavior and even physical movement patterns. Due to the sheer number of mobile devices in use, Big Data practitioners can tap mobile Big Data analytics to better understand such trends across vast populations and sub-segments of users to improve engagement tactics and optimize the delivery of services.
Mobile device data becomes particularly useful for analytics purposes when combined with outside data sources, such as weather data and economic data, which allow practitioners to correlate macro-level trends to targeted sub-segments of users (potentially down to the individual user level.)
Mobile as Big Data Delivery Mechanism
Analyzing mobile device data is just part of the equation, however. Big Data practitioners should also leverage the near-ubiquity of mobile devices to deliver relevant products and services to users based on insights gleaned from analysis of mobile device data (which should also include non-mobile device data sources for additional context, as mentioned above.) This requires the use of Big Streaming technology to apply analytics to data in motion, the results of which trigger appropriate actions in near-real time.
In the real world, this could take the form of a retailer providing targeted product offers via mobile phone alerts to users based on a combination of their current physical location, time of day, weather conditions and historical buying behavior patterns, for example. The key is to perform the data analysis and take relevant actions based on the related insights in time to influence the behavior of the customer.
Internal to the enterprise, mobile devices should also serve as delivery mechanisms for Big Data analytics to frontline workers that require access to timely information to perform particular functions. In most cases, such frontline workers do not require access to fully-functional analytic mobile applications for data exploration, but rather are better served by targeted applications and alert-based services that put the right insight into the right hands at the right time.
Mobile devices are ideal delivery mechanisms for Big Data insights to field workers in the utilities industry, for example. Such workers – those tasked with repairing and maintaining energy pipelines and substations – can be alerted to potential and active system failures via mobile devices to speed the time to repair, and in some cases, to take preventative action to stop malfunctions before they occur. These alerts should be derived from underlying analysis of streaming machine-generated data, combined with other relevant streaming and historical data sources.
Privacy, Security and Governance
There are, however, significant privacy, security and data governance challenges and risks associated with mobile devices and Big Data.
From the mobile-device-as-data-source perspective, while many consumers understand the ramifications of mobile device use and data creation, many do not realize that the smartphone in their pocket is akin to a tracking device continuously relaying location, communication and behavior data. When enterprises use that data to craft targeted messages and services, the uninformed consumer can be turned off by the results, viewing them as invasions of privacy.
As delivery mechanisms for Big Data analytics in the enterprise, mobile devices pose the same security and governance challenges they always have … only now the stakes are higher. By its very nature, Big Data is often particularly valuable and/or sensitive. Therefor, the implications for Big Data falling into the wrong hands due to a lost or stolen mobile device are greater. Big Data practitioners must invest in identity and authentication management tools and technologies to ensure that Big Data on mobile devices is available only to authorized users.
Enterprises should also carefully consider which workers will have access to Big Data via mobile devices. In some cases, industry regulations and legal restrictions limit which types of workers may access sensitive data, so mobile Big Data analytics should be rolled out only after carefully evaluating the governance implications of any new project.