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By Mike Flannagan
We hear terms like digitization, digital business, or digital transformation all over the place. But they are more than just buzz words. As Gartner defines it, “Digital business is the creation of new business designs that not only connect people and business, but also connect people and business with things to drive revenue and efficiency.”
Organizations across a range of industries are striving to become digital enterprises. Yet many are struggling with today’s hyper-distributed environments brought on by the Internet of Things (IoT). In fact, four of the top ten incumbents in each industry will be displaced by digital disruption in the next five years according to Digital Vortex – How Digital Disruption Is Redefining Industries, a report by IMD and Cisco Initiative published in June 2015. While digital transformation can enable companies to innovate faster, its connection to data helping to fuel digital businesses, and analytic software is a foundational element in digital transformations. Companies who invest in analytics as part of their IT strategy will accelerate this process.
Many businesses are turning towards data and analytics solutions to see not only what is happening now, but also to help predict what will happen next. This ability to “see around the corner” is helping businesses become more agile as they learn new ways for operational efficiency or to increase revenue. By 2020, organizations that are able to analyze all relevant data and deliver actionable information will achieve an extra $430 billion in productivity benefits over their less analytically oriented peers, according to IDC FutureScape: Worldwide Big Data and Analytics 2016 Predictions.
The ability to quickly pull data from everywhere for real-time insights and engage people in context will define value in the IoT.
Hyper-Distributed Data Environments are Increasing
The term hyper-distributed data environments describes the large volume of data within an organization’s environment and wide array of locations in which that data exists, like distribution.
Data can originate in a flash, whether it is from IoT devices, web clicks, transactions, or mobile application (app) usage. In almost every industry, data is created in places it never has before, which is producing hyper-distributed data environments. But data itself is no longer the number one problem; connected data is the problem. It is becoming increasingly difficult to reach that data, secure that data, much less draw insight and enable a person or process to take action on the data. For example, data created by a retailer’s in-store video camera can be highly beneficial to learn about customer behavior and buying preferences in real time. Yet, a store employee can only act to help influence a purchase if he or she is empowered with insight while customers are in the store.
To overcome this challenge, organizations are adding edge analytics to enhance their existing strategy, analyzing data close to its source instead of sending it to a central place for analysis.
Going to the Edge
The IoT gives us an unprecedented ability to collect data from a nearly unlimited quantity and variety of sources, from drill bits to lightbulbs. To pull together all of this data into a centralized location—a cloud or data center—for processing requires lots of infrastructure, time, and money. To do this in an IoT environment the strategy needs to be reverse engineered; it requires a new approach and the capability to capture, store, and analyze data in the place where it is actively created.
The ability to query billions of data records instantly and run hyper-distributed analytics will be essential for new technical capabilities to deliver the insights and experiences customers, business partners, and employees expect. A streaming analytics platform is uniquely suited for this new paradigm. Organizations need a fast streaming engine that’s distributable, open, and can scale to process and enrich any type of data that is on the move. Speed is critical to generate value from data in hyper-distributed environments.
Businesses are deploying streaming analytics embedded in hardware at the edge of the network to get actionable value in near real time. Streaming analytics solutions capture perishable insights on real-time data to bring immediate context to all IoT, mobile, web, and enterprise apps.
Analytics Help Accelerate IoT
Many examples of hyper-distributed data environments can be found in the IoT. From terabytes of data created by sensors in offshore oil wells to extremely time-sensitive data created by robots in manufacturing facilities. IoT devices and sensors can highlight failing machines or dangerous conditions before they become serious issues. The next step is to analyze the data. Looking for patterns in it could illuminate ways for employees to improve operations, such as doing more preventive maintenance or designing more efficient processes. When data is combined with analytics, real opportunities arise.
A great example of this is Mazak and its SmartBox. The SmartBox is a mini electrical cabinet mounted on the side of a manufacturing machine enclosure. Inside of the SmartBox is Cisco Streaming Analytics embedded on a Cisco Industrial Ethernet (IE) 4000 switch. This enables the measurement of things like vibrations and temperature on the manufacturing floor in real time. By analyzing this data, manufacturing personnel are able to identify and easily fix downtime-related inefficiencies to improve overall equipment utilization.
The Race to Become Digital is On
What if organizations could empower employees to make a better decision each day based on having the right knowledge when it’s needed? That is what digital transformation and deploying edge analytics is all about. If every single employee is a decision-maker, organizations must focus on enhancing the quality of each decision taken. The integration of things, connected and intelligent, with people and business enables better and faster decision making. SW
As VP/GM for Cisco’s Data & Analytics Group, Mike Flannagan is responsible for the company’s data and analytics strategy, and leads multiple software business units. He joined Cisco in 2000. He previously held IT leadership positions in consulting and global media companies, and founded several start-ups. Flannagan is a Cisco Certified Internetwork Engineer, patent holder, and published author. He attended the University of Texas at San Antonio, and earned a Masters in Business Administration from Auburn University, where he now serves as Chairman of the Advisory Board for the College of Business’s graduate programs.
Jun2016, Software Magazine
