As the Internet of Things starts to find its footing, big data analytics will play a critical role in the emerging market as more and more data is being produced by IoT. The global IoT market will grow to $1.7 trillion in 2020 from $655 billion in 2014 as more devices become connected, according to IDC research, with platforms and services growing around them.

IoT acquisitions are also ramping up, as Santa Clara, Calif.-based Intel unveiled plans recently to acquire manufacturer Altera to bolster its IoT business. 

Solution providers need to start building big data analytics practices in order to capitalize on the trillion-dollar market opportunity, according to John Loiacono, CEO of the real-time big data analytics start-up Jolata of San Jose, Calif.
Loiacono talked to CRN about the large role big data analytics will play over the next several years and how solutions providers can create new incremental revenue opportunities.

Why should solution providers/VARs start building a big data analytic practice?

There is simply so much data being produced, customers are overly-burdened with trying to figure out how to extract the pertinent metrics that actually matter to their business. The challenge with so much data is not just being able to sift through it to find the proverbial needle, but doing so in seconds, not minutes or hours.

Customers are desperate to have better, continuous visibility into exactly how their network is performing -- the area we play in. Service providers and VARs who are able to leverage ground-breaking powerful solutions to quickly and intuitively assess existing issues, rapidly locate and diagnose increasingly difficult problems or proactively predict future bottlenecks can create new, incremental revenue opportunities. It's very powerful and lucrative to know more about a customer's network and services than they know themselves.

What are three problems businesses are facing that big data analytics can solve and how?

(1) Seeing the detail that matters: There is an overwhelming amount of data, much of which is not relevant to enable optimal/peak performance.

(2) Business performance optimization: New, interesting analysis and correlations in real-time, across an entire network.

(3) Ultra-fast response: Enabling access to this big data in real-time versus batch mode response times.