Global smartphone subscriptions have reached 2.6 billion as of 2014 (as referenced in the Ericsson Mobility Report for 2015). The report further notes that smartphone subscriptions are set to breach the 6 billion mark by 2020 at their current projected CAGR of 15%.
From a market cap of $27.36 billion in 2014 (Wikibon research), the market cap for big data analytics solutions is projected to reach $125 billion globally (IDC Predictions 2015). IoT analytics have a five-year Compound Annual Growth Rate (CAGR) of 30%.
Additionally, driven by approximately 15 billion devices, the 2015 spending in the Internet of Things (IoT) market is projected to exceed $1.7 trillion, a 14% rise from 2014. By 2020, these numbers are further expected to touch the $3 trillion mark as devices more than double in numbers to 30 billion.
Now, you might want to ask how are these numbers along with their projected growth rates of any consequence here. Think of it – data generated by smartphones, wearables and other connected devices, not to forget the unstructured data streams, has entered the era of range of zetabytes – at an overwhelming 44 zettabyes. In this deluge of big data, smartphones and IoT, how does a business manage to stay relevant? How do they streamline and synchronize their processes to implement insights drawn from all the data that they encounter on an everyday basis? Nevertheless, it goes without saying that enterprises encounter numerous challenges in implementing and realizing the true value of big data.
The answer to solving this apparently humongous challenge is to revolutionize the way analysts, businesses and statisticians analyze data. And, despite everyone talking about the “problem”, very few are actually offering solutions. There are numerous organizations that rely on traditional data analytics tools to glean insights on the “what” (metrics like number of users, demographic or geographic breakdown) of the data rather than emphasizing on the “why” (reasons behind these metrics).
Consider an example: A web or a mobile app can be experiencing high bounce rates and low user retention rates. These attributes are evident to even the most non-technical individuals with simple datasets; however, what is unclear is the reason behind users not returning to the app. Although a mobile app company can give businesses/ clients a fair idea as to what might be a cause of low user retention rates, it cannot be termed conclusive. This is where the implementation of the right big data analytics solutions, more so the visual analytics, becomes effective.
Moreover, as the traffic from mobile devices surpassing that of their desktop counterparts, it is high time the data analytics solutions deployed by businesses consider this aspect. Mobile big data analytics solutions and tools dive deep into user experience and behavior trends, while presenting reports in the visual format making it easier for the analysts or the marketer to understand. In the future, these visual mobile analytics tools will empower a whole generation of marketers in enhancing productivity and increasing ROIs with insights that will not only indicate the issues but also suggest measures to solve them.