The age of big data: what mobile data can do for us

If I told you that you could do the job of extracting data from massive data sources (including a wide variety of mobile devices) into a single system, and then make the results easy to present with only a handful of lines of programming describing the required information, and also do the job of processing the data in real time and keeping the system running at the same time, would you believe that?

Don't doubt it, you can do it.

This is first and foremost due to the rapid growth of mobile data in the age of the information explosion. Mobile apps are constantly generating large amounts of information, such as information about user behavior (including conversation starters, event occurrences, transactions, and so on), and then devices generate data (crash data, app logs, location data, web logs, and so on). The significance of these data is that they give Big Data a constant source of information to identify and analyze what mobile users see and hear throughout the day.

It must be said that the era of mobile big data was born. And in order to collect data from smartphones, one has to face the challenge of collecting, analyzing and running the data. There is no doubt that companies and mobile device developers who are able to utilize mobile data are more competitive in the market and have a business advantage. This is because they can accurately identify the factors that influence user behavior at the outset, effectively hierarchize customer needs, and thus be able to realize them both creatively and efficiently.

And the key to winning the race for real-time big data analytics is the in-memory database. In-memory databases ensure dynamic analytics for big data -- processing large amounts of data generated in spurts at exponential speeds and then producing timely results. In-memory databases enable real-time and dynamic in-memory data processing for mobile devices at varying speeds, as well as importing data from other data sources such as automobiles and home systems.

Distributed processing of big data enables cross-cluster operations on computers that scale to thousands of devices, such as Hadoop, which accomplishes multiple tasks with distributed processing. However, for this high-speed, non-stop information eruption of the mobile era, decentralized processing is not the most effective and economical way. The creation of in-memory databases has certainly given organizations new tools to take advantage of real-time data: analyze it as quickly as possible at the outset of its creation, spotting trends and reacting to them faster, with the goal of lowering service costs and increasing revenues. Those enterprise-class streaming databases, such as StreamBase and KDB, including CEPs and hybrid, in-memory databases are beginning to fill the gap in real-time processing technology with new algorithms and visualization techniques. Providers of mobile big data are trying to merge in-memory databases, dynamic processing technologies, algorithms and visualization techniques to enable organizations to use mobile big data and make it a business driver.

Mobile app teams are better able to understand the importance of synchronizing and analyzing data. In order to retain users, developers need to be able to anticipate errors, understand their impact on user behavior, measure the benefits of new products, identify user engagement trends, and detect clients so they can eliminate problems before they are exposed to negative users.

Here are four trends we've observed in mobile big data:

1. Transactions matter most

The most critical aspect of "mobile" is the interactions and the monitoring of them. Users choose applications for a variety of purposes: entertainment, shopping, learning, sharing, etc., and it's easy for users to become negative when anything interferes with or slows down their experience of achieving their purpose. Using applications to monitor transactional processing allows organizations to evaluate and respond to the user experience, and try to prevent users from uninstalling software or giving bad reviews. Monitoring both transactional and functional data is important today, and can't be done without a strategy that adapts to the era of mobile development.

2. Troika, the three "V"

The latest report from Business Insider points out that big data is characterized by three things: volume, variety, and velocity, which we'll summarize in three "Vs". "V". The data itself is generated very quickly and in a variety of forms, sizes, and also in large quantities. Not to mention mobile data, which is growing exponentially in volume. And with Cisco's recent report showing that millions of people are connecting to the Internet only through their mobile devices, it's clear that these devices are generating a lot of data.Kash Rangan says that there are a lot of interactions that are being overlooked and not analyzed, and these are the opportunities that are being overlooked. What's even more interesting is that the diversity of data is created precisely by mobile devices. From user tracking to crash reports, there's all sorts of assorted and detailed app data, including business transactions, emotional responses, heartbeat measurements, lodging records, and even wind reports. Mobile apps are increasingly affecting the way people live their lives, and as a result, the rate of data growth is on the rise. One only has to think about a cell phone user such as you and I being firmly ensnared by our phones on a daily basis to understand.

3. Measurement is key

One of the challenges of facing big data users is to consider the operational influences. If it is poorly positioned and profitable, big data may instead become a tether. How do you identify which information can help make better business decisions and which is useless? Before organizations take the plunge into the mobile data craze, it's important to figure out what their key metrics are, or they'll be stuck in a dilemma with a pile of data that doesn't come in handy.

4. Monitor first, ask questions later

This may sound like a different intuition than ours, but it's actually a strategy that organizations should be adopting, monitoring apps and collecting data first, then answering key business questions before exploring new opportunities for growth uncovered from the data. Getting to the bottom of application development is a decisive step in being able to harness big data. After a basic understanding, businesses and developers can then dive into the key factors. Mobile big data providers also give companies of all sizes the ability to make mobile data work for them, whether they are independent operators or large enterprises. Now that in-memory databases are available, mobile big data providers are back to work on the next goal: optimizing the mobile side of things by maximizing the efficiency of data collection and transmission, while focusing on new challenges such as battery consumption, 3G data usage, slow connections, privacy issues, and localized memory, as well as scaling up communications and controlling the predictable surge in traffic. The race is no longer about who is faster at innovating mobile devices, but who is faster at reacting to the data they generate.

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