Big Data On the Move: Everywhere You Need It To Be

Big Data Evangelist, IBM

Mobility is freedom, flexibility and self-determination. If you can maintain seamless access to all your personal resources from anywhere on the planet, you have the power to be more completely yourself in every place, at every moment and in every context.

Mobility is most fulfilling when you travel light but have a heavy-duty support system to ensure that you get where you want to go. Smartphones and other modern gadgets are the essence of traveling light: they put pocket access to everything we need right at our fingertips while relying on network-centric infrastructure—databases, application services, identity management, access portals, etc.—to power our mobile experience.

Smartphones and other mobile gadgets have become important sources of the data pouring into Hadoop, NoSQL and other big-data platforms. Your ability to personalize mobile service delivery increasingly depends on your ability to capture, correlate and analyze massive streams of gadget-sourced data at the device, application and user levels. Every transaction, interaction, event, signal, ambient, behavioral, geospatial and other datum that you can acquire from employee and customer gadgets will be crunched by big-data platforms. And the trend is toward organizations moving most of their transactional, productivity and e-commerce applications to mobile devices.

Most of us don’t think of big data as a personal resource for mobility, but, clearly, that thinking will need to change.

Enterprises can ensure exceptional, consistent and secure experiences across all mobile devices by implementing the following big-data-powered infrastructure services:

  • Cloud services ensure big data is always there for your mobile: Most business users and consumers won’t be storing petabytes on their smartphones anytime soon. Rather, they will be maintaining growing volumes of information in the cloud, accessing it from various mobile devices—selectively synchronizing and caching what is needed locally. As we conduct more of our lives on cloud services, we will persist more of our data there as well, on massively parallel file systems, databases and other big-data repositories. As we track, quantify and log more aspects of our lives—for medical reasons or simply as a hobby—the sheer volume of personal data we keep, locally and/or in the cloud, will grow well beyond the 100s of gigabytes that most of us now keep at our disposal.
  • Stream computing enables real-time mobile experiences: Users depend on continuous real-time connectivity to all big-data and other services used by their mobile devices. Stream computing will become standard on all mobile services—it will leverage complex event processing, distributed cache and guaranteed subsecond end-to-end latency on all big-data applications. Stream computing will ensure a continuous flow of alerts, notifications, events, sensor data, transactions, social media updates, video and audio streams—other types of information between all endpoints and infrastructure services. Bidirectional streams will be necessary both between mobiles and big-data clouds—between the mobiles themselves.
  • Machine data is what your mobile feeds to big-data cloud analytics: The typical user won’t be manually pushing data from their mobiles into the big-data cloud. Instead, the gadgets will be feeding data automatically, silently—in the background into the cloud, under policy controls defined and enforced within mobile device management tooling. Much of this will involve voluminous “machine data” —such as geospatial coordinates, sensor readings and event logs—that the devices generate continuously. Before long, machine-to-machine mobile connectivity will be embedded into every artifact, possession and environment in our world. Wearable and implanted devices will generate machine data on user vital signs, helping people to monitor their lifestyles or alerting emergency services to urgent life-or-death situations.
  • Location analytics use big data to orient your mobile on the ground: Users won’t be performing resource-intensive geospatial analytics locally on most mobile gadgets. Typically, they will be feeding streams of geospatial data from those devices to big-data cloud services. The cloud-based services will help devices to track users’ precise locations and to recalculate the best route to wherever they need to be, based on dynamic conditions in their environment. To realize the promise of intelligent location services, the cloud-based big-data infrastructure will need to continuously correlate real-time feeds of traffic, weather, event and other dynamic environmental data.
  • Next best action leverages big data analytics for continual mobile guidance: When you’re mobile, you need all the automated guidance you can get. You’ll be busy enough trying to not crash your car or walk into brick walls. Users won’t be constantly interacting with mobile devices to determine the optimal road to take, the optimal recommendations to heed, the optimal commercial offer to accept, the optimal streaming media to consume—the best course of action to take in every situation. Instead, users will frequently lean on big-data-powered cloud services with embedded decision-automation capabilities to recommend their next course of action. Next-best-action infrastructures will continually provide contextual guidance that is personalized to each mobile endpoint. They will continually calculate guidance by leveraging segmentation, propensity, graph, semantic, experience and other advanced analytic models built by data scientists.

Most of us don’t think of big data as a personal resource for mobility, but, clearly, that thinking will need to change. Smarter mobility depends on the ability to serve all of our mobile devices from an intelligent big-data infrastructure.

To find out more about managing big data, join IBM for a free Big Data Event

Want to join the conversation about big data on the move? Join James Kobielus for a Twitter chat on Wednesday, March 13 at 12:00 p.m. ET , using the #bigdatamgmt hashtag.