The Internet of Things (IoT) generates an unprecedented amount of actionable data. Here are five things you need to know more about to thrive in this new landscape.

  1. The 3 V’s

To really understand big data, you need to understand what many are referring to as the 3 V’s: Volume, Variety and Velocity. Volume, of course, refers to the sheer amount of data coming in—as you know, it’s going to approach tsunami levels in the next few years. But there’s also the issue of variety. You don’t have simple data streams from single sources. Instead, you’re dealing with intel that streams in from multiple silos, from online customer portals, to social media, to the Internet of Things. And the third V, velocity, is the lynchpin. All that data isn’t doing you much good if your data in/action out processes can’t keep up.

  1. The Data Lake

Obviously, the unprecedented volume of data coming in can overwhelm legacy processes and systems. As a result, companies are increasingly investing in big data architecture. Your chief concern should be the ability to move beyond accumulating data, and integrate it. Being able to do this in real time, and to generate analysis, is likewise crucial. One popular solution is to use an architecture referred to as a “data lake,” a storage solution that holds a wide variety of raw data until you need it. Many companies gravitate toward popular open source platforms such as Hadoop, though some find it useful to employ Hadoop in tandem with a separate database application.

  1. Stream Processing

Unlike complex event processing (CEP) or other traditional real-time processing, stream processing is designed to handle data on the fly. It employs a continuous query process, processing each data event as it’s received. It also includes streaming analytics. It’s scalable and very fault-tolerant. This movement away from batch-based processing has empowered solutions in an array of fields including healthcare, finance and manufacturing.

  1. Talent pool

The Internet of Things still fundamentally runs on people. And the fact of the matter is, the solutions we’ve discussed above are cutting edge. For this reason, there’s a bit of a talent deficit right now. Outsourcing can be a good option.

  1. Privacy Issues

Privacy is likely to be feature in the majority of mainstream discussions about the IoT, so it’s something to be mindful about during the data collection and analysis processes. One way some companies are addressing this concern is to adopt a “much, but not many” approach, delving deeply into data for which they have a clear application in mind, and discarding or bypassing information that’s not immediately actionable. Regardless, privacy concerns should be at the forefront of your decision making when it comes to big data.