Four-point overview of big data and mobile devices

Foreign media reports that the world today is full of big data. According to the forecast of Commercial Bank of Canada, the growth of information will increase by 50 times over the next ten years. Market research firm IDC also made a similar prediction, saying the amount of data from 2009 to 2020 will grow 44 times.

Mobile devices, social networks, wearables and the Internet of Things will play an important role in driving data bloat.

Six years ago, Apple released the iPhone, causing an uproar in the e-commerce world. The birth of the iPhone marks the arrival of "the design, hardware and software become the most important concept of science and technology" era.

So the data may lead to the next revolution? Many people think it is very possible.

BI Intelligence, a research firm run by Business Insider, a consulting site, recently released a report on "Big Data and Mobile Devices." The report defines big data, examines the links between mobile devices and big data, analyzes its potential, its practical applications and possible difficulties, takes into account the path of big data collection, and finally answers people's questions about big data and Some of the most frequently asked questions about mobile devices.

The following is a summary of the relationship between big data and mobile devices:

Big data needs to be defined: Big data is usually defined as a data set that meets the requirements of three attributes: quantity, type, and rate. But there's more to it. Jeep-Jones, vice president of marketing services at Skyhook, a map service provider, said: "There's a fourth attribute, I value, that's value." To make sense, data needs to be efficiently captured and stored, and then some people want Manage these data, analyze them and extract valuable parts of them. Data, whether large or not, are not worth the time if they are of no value to people.

Mobile devices are great for capturing big data. Moving big data is not just a function of the popularity of mobile phones and consumer usage patterns. Apps and other services also generate data as the user is using it. Technically, the process does not differ much from the process of generating data from traditional networks. The difference between the two is that consumers create more data on their mobile devices as they move their behavior into digital channels, leaving behind a long list of data that records consumer behavior. Even though we do not use cell phones on the surface, we are still creating data streams.

3, these data can be used to optimize and personalize the mobile device experience. Mobile big data can be used in many ways, but it is often used to optimize and personalize mobile services. For example, application developers might use Flurry's analysis of the U.S. application analytics company to improve their applications. For developers, retention time is a key metric. Developers can compare how much their app's user retention time and all other apps' user retention time are used to seeing how improvements can be made to increase those numbers.

4, may be able to help boost the mobile advertising business surge. A major part of mobile big data is local data, which is expected to help transform the mobile advertising industry. Its ability to conduct real-time, hyper-local, targeted advertising represents a potentially important evolution in the advertising marketplace.