The four current biggest trends in IT are Social, Mobile, Cloud, and Big Data. In this post, I will discuss the intersection of Mobile and Big Data. Without going into too much detail, I’ll first review what Big Data is. The first aspect of Big Data is gathering and storing extreme volumes (often in petabytes) of data from databases, log files, social media, connected devices, external data marketplaces and internal systems. Data is “deep,” meaning it is transactional in nature and nothing is thrown away.
Once all data is brought into a Big Data environment (like a Hadoop HDFS), it is quickly made available for data exploration and analysis. The results of the analysis provide insights that were previously not available. These insights help businesses understand their customers better, get a more complete view of their risk, and predict what and how customers will behave. Insights are then turned into actions and the impact on the business is measured.
Big Data is just coming out of its infancy now. At this same time, Mobile Computing has been growing leaps and bounds. In 2013, more than half a billion mobile devices and connections were added. Smartphones are getting cheaper ($25-$30 smartphones have been recently announced), faster (now with 64-bit processors and octo-cores), smaller (wearable mobile devices is the hottest trend), and networks are getting faster (we are talking 5G now). This means more and more data is being created and consumed by mobile devices. More business functions will be performed on mobile devices. At the end of 2013, mobile devices generated 1.5 exabytes per month of traffic.
When people think and talk about Mobile and Big Data, they mainly view mobile devices as a source of data. People are leaving behind more and more of a data trail through their mobile devices – data that, when combined with other data and mined, can provide insights into people’s actions, locations, movements, behavior and attitudes, However, the real value of convergence of these two technologies is in using mobile devices as the “consumer” of the analytics and insights derived from Big Data.
Let’s take a couple of examples. Results from Big Data analytics can be used to optimize and customize the mobile experience. Marketing and advertising functions have been working on targeting mobile users using analytics to create micro-segments and deliver hyper-targeted advertisements. Big Data can expand these initiatives by allowing this data to be combined with loads of weblogs, social media data, insights gleaned from CRM notes and call records. The insights gained from this advanced analytics is then put into action right at the mobile device – when a user accesses a mobile site, reaches a particular location, or uses a mobile application.
From a healthcare perspective, let’s look at patient readmission analytics. Our solution will first involve aggregating and integrating millions of data points gathered from hospital’s clinical, operations and financial systems. Predictive algorithms will then correlate this information to identify the probability of a patient getting re-admitted – a readmission score. How do we use this valuable insight? During a patient admission or a discharge, nurses armed with mobile tablets can be notified quickly of the “readmission score” of the patient so that proper protocols can be followed to reduce readmission. Healthcare providers can also use the mobile channel to quickly and efficiently reach patients with chronic diseases and provide reminders and other valuable information in a easily consumable manner, which leads to better health outcomes. In these scenarios, mobile devices and technology allows for the quicker, easier and more real-time consumption of analytics results and insights from Big Data.
How else can mobile enable Big Data? Share your thoughts in the comments.