What Big Data Can Learn From Mobile Data

What Big Data can learn from Mobile DataThe following is a guest contributed post from Panos Papadopoulos, CEO and Co-Founder of BugSense.

Would you believe you can feed data coming from various sources (even thousands of different sources like mobile devices) into a system, describe what information to extract in a few lines of code, and have the results at your fingertips now? In real-time? While the system keeps running?

You can.

And you can thank the rapid growth of mobile data for this boom. Mobile apps are constantly producing a mountain of information like user behavior data (session starts, events, transactions) and machine generated data (crashes, apps logs, location data, network logs).  It’s the value in this constant stream of data that gives “Big Data” the mainstream recognition and constant chatter you’re seeing and hearing today.

Mobile Big Data was born of necessity. To capitalize on the wealth of mobile data from smartphones, the challenge of collecting, analyzing and acting on data while it was still relevant had to be met.  Businesses and mobile developers with the ability to leverage their mobile data have the competitive business edge. Because they can identify factors that impact user behavior as they happen, they can be more reactive, prioritize more effectively and meet customer needs more effectively.

The secret weapon in the race for real-time Big Data is in-memory databases. In-memory databases provide the “in-motion” part of Big Data – processing the tsunami of data at an exponential pace and providing results while they still matter.  In-memory databases provide in-motion, real-time, in-memory data processing from mobile devices, and will soon be collecting, analyzing and trending data from other sources like cars and home systems, all at the speed of business.

Distributed processing of large data sets across clusters of computers that scale up to thousands of machines like Hadoop have historically handled most jobs, but distributed processing isn’t the cost effective for the fast-moving, constantly streaming world of mobile. In-memory databases provide new tools for companies to leverage their data in real-time: analyze data as it’s coming in, spot trends, react faster, reduce server costs, and increase profitability.  Enterprise-level stream databases like StreamBase and KDB, to CEPs and hybrid, in-memory databases are stepping in to fill the real-time processing void with new ways to use algorithms and visualizations.   Mobile Big Data providers are bringing together in-memory databases, in-motion processing, algorithms and visualization to give companies access to mobile Big Data and making it a business driver.

Mobile apps teams understand how crucial it is to be able to analyze data as it comes in. To keep customers engaged and returning, developers need to see errors, see the impact errors have on user behavior, measure the effectiveness of a new release, identify user engagement trends and view most used and most affected devices so they can fix & release before problems show up in negative user reviews lost users.

Here are four trends we’re seeing with Mobile Big Data:

  1. It’s all about transactions – Mobile is all about transactions and the need to monitor them.  Customers use apps for specific reasons – play, buy, find, share; they have a low tolerance for anything that disrupts or slows their ability to do what they want to do. Monitoring transactions within apps gives companies what they need to assess and respond to user experience, managing customer experience before their customers dump their app or post a negative review.   Having a mobile strategy in place that focuses on monitoring both the transactional and the functional data streams is crucial.
  2. The three “Vs” will continue to be a driving force – In its latest report,Business Insider states that Big Data stands for three “Vs”: volume, variety, and velocity. It’s data that is generated quickly, comes in all shapes and sizes, and in great quantities. Mobile data is already living and breathing this. Mobile data volume grows exponentially. A recent report byCisco makes the point that as millions of people connect to the Internet via mobile only, it’s clear that the biggest chunk of data will be generated by the devices they use to connect.   Already there are many interactions that go untracked – and unanalyzed – as pointed out byKash Rangan. This represents lost opportunity. What is even more interesting is the variety of data created on mobile devices. Data ranges from user tracking to crash reporting, specific app data like commerce transaction, sentiment feeling, heartbeat measurement, check-ins or even wind reports. As lifestyle depends more and more on mobile apps, the velocity of the data generated is staggering. Consider how much is captured and shared every day via mobile phone for just one single user.
  3. Metrics are crucial – One of the challenges facing Big Data users is considering what matters to their business. Big Data can become a distraction if not targeted to achieve better outcomes.   What information can drive better business decisions and what information is just information?   Before jumping on the mobile Big Data bandwagon, companies need to define their key metrics, otherwise they risk being trapped in a garbage-in, garbage-out scenario.
  4. Monitor first, ask questions later – This sounds counterintuitive, but the reality is that companies should adopt a strategy to monitor apps and collect data first, then answer key business questions and explore new opportunities that arise through viewing data. Building a picture of what is happening with an app “in the wild” is a critical first step to harnessing the power of Big Data. With a baseline understanding, companies and developers can drill down to what matters.

Mobile Big Data providers are giving companies – Indie to Enterprise – the ability to let their mobile data work for them. Now that in-memory databases are here, mobile Big Data providers are working on the next iteration: Optimizing the mobile side of things by maximizing efficiency of how data is collected and transmitted, keeping up with new challenges like power consumption, 3G data usage, slow connections, privacy concerns and local storage, expanding traffic and managing expected huge spikes.   The business race isn’t about joining the mobile revolution anymore, it’s about reacting faster to the information mobile is generating.

About The Author

Panos Papadopoulos is CEO and Co-Founder of BugSense. BugSense helps thousands of developers worldwide, including Fortune 500 companies, create better mobile app experiences by harnessing the power of mobile data. BugSense is the leading operational intelligence service for mobile app developers providing real-time insights for Android, iOS, HTML5, WP and Windows 8 using big data analysis. To learn more, please visit bugsense.com.