Imagine a sensor inside an offshore drilling rig. The sensor checks for damage to a critical valve. To do so, the sensor regulates pressure in the oil well 7,000 feet below the ocean’s surface. This sensor generates data that might have gone unnoticed half a decade ago. Back then, the rig operator had no way to tap into this ground-level information.
That’s not the case anymore. With the right technology, organizations can now find everyday data instantly, no matter where it is created—right away, on the fly—exactly when this data is most useful.
The Era of Big Data
What is different now from five years ago? It’s the Internet of Things (IoT), of course. IoT universally connects people, mobile devices, machines, and devices via networks. The implications of this sea change for business, government, and all of society are only just beginning to be revealed. IDC predicts that IoT’s installed base will be roughly 212 billion machines and devices worldwide by the close of 2020. This includes 30.1 billion installed "connected (autonomous) things."
Before IoT, organizations generated most of their data: files, presentations, spreadsheets, and databases. They sent this self-generated data to a centralized repository. They stored it until they could analyze it. During the data warehouse era, organizations ran their operations and made decisions based only on this warehoused data. It could take days, weeks, or even months to attain intelligence from this centralized data. It could take even longer to make decisions based on these findings. In the age of IoT, that’s not fast enough, and it leaves out too many additional sources of valuable information.
During the last half-decade, there has been an explosion of unstructured data, giving rise to the term “big data.” It’s coming from Google, Amazon, Facebook, and Twitter. It’s also coming from mobile devices such as phones or tablets, and from machines like smart oil wells.
IoT generates massive amounts of big data every instant about how people are living, working, and purchasing, and how machines and networks are operating. Today, organizations must use big data; it is critical unstructured data they need on top of the data they create. Based on this data, organizational decision makers can ask questions they weren’t even capable of posing before, let alone answering.
Computing Close to the Edge
IoT has ushered in a new way of processing data known as edge, or “fog,” computing. (For more about fog computing, see our introductory post on the topic, “Fog Computing 101: A Quick Look at Life Out on the Edge.”) Fog computing takes place close to the edge compared with traditional cloud computing. It takes place right where people are using mobile devices, and right where sensors are tracking and reporting performance and conditions within industrial systems. The oil rig sensor’s signal is an example of an edge activity captured in the big data torrent, and it can be tracked and analyzed using fog computing.
Big data does not have a long shelf life, however. Even if it takes only hours to go to a data center before it is analyzed, big data risks becoming obsolete. If the rig’s sensor reports a sudden change in pressure, the valve might fail before the rig operator knows there’s a problem.
Big data has to be analyzed on the network edge, right where people, devices, and machines are generating it—right when a decision based on the intelligence from that data can make a difference. Storing data in a warehouse and waiting on decisions doesn’t cut it anymore.
Connected Data Offers Rich Opportunities
IoT “hyper-connects” people, processes, data, and things. This is what makes big data so significant. Hyper-connection alters the role of information and promises tremendous opportunity.
Organizations must focus on improving the quality of each decision that they make. How much value they obtain from IoT depends on how well they secure, aggregate, automate, and draw insights from their data and big data … and do it with lightning speed. Over time, the results can pay off in a big way. If the rig operator discovers the valve flaw in time to replace it during routine maintenance, it averts the huge cost of an unexpected shutdown.
Digitalization Presents Challenges
All of this digital transformation brings technology challenges. These include the need for stronger engines to accelerate applications and power data-intensive analytics, and an operating environment that is common to the sources of computing demand in both the data center and at the edge. This operating environment must bridge traditional and emerging applications and manage varying workloads better.
In a 2014 Cisco study, 40 percent of companies surveyed identified big data as an area that most needed improvement within their organization. And almost 40 percent said that within three years, smart devices at the network edge would process most data. They also identified analytics tools for big data as the most important enablers of connected device networking.
To make effective use of IoT, the survey participants highlighted four areas for improvement:
- Data: Capturing, storing, and analyzing data from connected machines and devices.
- Process: Updating business and operational processes.
- People: Enabling workers to exploit IoT through training and easily used systems.
- Things: Connecting the right machines, devices, and equipment to capture truly useful data.
IoT is akin to the New World in the day of Christopher Columbus. Its potential is undiscovered. Organizations that upgrade their technology for the journey will sail toward unlimited rewards.
Click here to learn more about what Cisco is doing to train folks in the area of big data and analytics.
Learning@Cisco product manager Neeraj Chadha has more than 20 years of experience in the networking industry. Over that time, he has functioned as a software developer and network engineer, and in various aspects of product management. Currently, he guides the overall product strategy and evolution of Cisco courseware and certifications around Wireless, Collaboration, and Big Data and Analytics. Neeraj's primary areas of focus include technology trends, digital transformation, continuing education, and product strategy.