In my opening remarks, I recounted what I believe lớn be seven basic principles that underpin the Io
T. I have derived these conclusions & observations over the last four years while working with end users and customers deploying a variety of Industrial Io
T solutions. I’ll detail these seven principles in subsequent blogs, and here is a summary:
1.Bạn đang xem: Predictive maintenance solutionsBig Analog Data
Analog data represents the natural and physical world and is everywhere or in other words is part of everything; light, sound, temperature, voltage, radio signals, moisture, vibration, velocity, wind, motion, video, acceleration, particulates, magnetism, current, pressure, time, and location. It’s the oldest, fastest, & biggest of all big data, but it represents an IT challenge in that it has more than two values that digital data has.
Bạn đang xem: Iot and big data
Simply put, in many ways analog data needs to lớn be treated differently than digital data. The question is, and will continue khổng lồ be, how can we efficiently unlock the business value of Big Analog Data?
2. Perpetual Connectivity
The Io
T is always connected, always on, & that “Perpetual Connectivity” to lớn products and users affords three key benefits:
I refer to these as the Three M’s, & the notion that an organization can be perpetually connected to lớn consumers và products is quite profound, with far-reaching implications và opportunities. For example, if your washing machine was connected lớn the Io
T, predictive analytics could sense when the machine would fail & schedule a repair, say, ten days before that unfortunate event occurred. This way you’re not standing in front of a defunct washer holding a basket of dirty laundry.
3. Really Real Time
The definition of real time differs from people who don’t understand the Io
T than from people who do. Real time actually begins back at the sensor or the moment the data is acquired. Real time for the Io
T does not begin when the data hits a network switch or computer system – by then it"s too old. If you want to lớn know if your house is going lớn catch on fire, how soon would you like to know that? Or if and when a crime may occur, mere seconds are crucial. Hence an alarm must go off in very real time, before the data even gets khổng lồ the cloud or data center, or it doesn’t help.
The point is, we’re seeking khổng lồ blend the world of operational technology (OT), sensors, và data measurement with the world of IT. The Io
T blends these two worlds for the first time in a major way, & the results will be profound.
4. The Spectrum of Insight
The “Spectrum of Insight” derived from Io
T data relates to lớn its place in a five phase data flow: real time, in motion, early life, at rest, and archive. Recall real time for the Io
T at the sensor or point of acquisition & analytics are needed to lớn determine the immediate response of a control system and adjust accordingly, such as in military applications or precision robotics. At the other end of the spectrum, archived data in the data center or cloud can be retrieved for comparative analysis against newer, in-motion data, lớn gain insight into the seasonal behavior of an electrical power generating turbine, for example. Hence insight from the big data in the Io
T can be extracted across a spectrum of time & location.
5. Immediacy Versus Depth
With today’s traditional computer và Io
T solutions, there’s a trade-off between speed & depth. That is, one can get immediate “Time-to-Insight” on a rudimentary analytic such as a temperature comparison or fast Fourier transform to lớn determine if rotating wheels on a tram will cause a life threating accident. Immediate Time-to-Insight is crucial here.
On the other kết thúc of the spectrum is the time required to lớn gain deep insight. The example here is from one of my former customers, the Large Hadron Collider at CERN in Europe, where they smash subatomic particles together to lớn seek insight into the make-up of such particles. The data collected here takes a long time lớn analyze, using large, back-end computer farms. Such depth of insight has resulted in the recent discovery of a new subatomic particle called the Higgs Boson.
6. Shift Left
Consider the mutually exclusive objective of deriving both immediate and deep insight, as discussed in #5 above. It’s really hard lớn get both today. However, engineers are good at resolving conflicting objectives & getting BOTH. James Collins has referred this phenomenon as “the genius of the AND”.
The drive khổng lồ get both immediate & deep insight from data will cause sophisticated high kết thúc compute and data analytics that is normally reserved for the cloud or data center (what I hotline Tier 4 in the Io
T solution), lớn migrate toward the left of the end-to-end Io
T solution infrastructure. That is, deep compute will be positioned closer khổng lồ the source of data, at the point of data acquisition và accumulation in sensors (what I call Tier 1) và network gateways (Tier 2).
In my opening remarks, I recounted what I believe to lớn be seven basic principles that underpin the Io
T. I have derived these conclusions and observations over the last four years while working with over users & customers deploying a variety of Industrial Io
T solutions. I’ll detail these seven principles in subsequent blogs, và here is a summary:
1.Big Analog Data
Analog data represents the natural & physical world & is everywhere or in other words is part of everything; light, sound, temperature, voltage, radio signals, moisture, vibration, velocity, wind, motion, video, acceleration, particulates, magnetism, current, pressure, time, và location. It’s the oldest, fastest, and biggest of all big data, but it represents an IT challenge in that it has more than two values that digital data has.
Simply put, in many ways analog data needs to lớn be treated differently than digital data. The question is, và will continue to be, how can we efficiently unlock the business value of Big Analog Data?
2. Perpetual Connectivity
The Io
T is always connected, always on, and that “Perpetual Connectivity” to lớn products and users affords three key benefits:
I refer khổng lồ these as the Three M’s, and the notion that an organization can be perpetually connected to consumers & products is quite profound, with far-reaching implications and opportunities. For example, if your washing machine was connected khổng lồ the Io
T, predictive analytics could sense when the machine would fail & schedule a repair, say, ten days before that unfortunate sự kiện occurred. This way you’re not standing in front of a defunct washer holding a basket of dirty laundry.
3. Really Real Time
The definition of real time differs from people who don’t understand the Io
T than from people who do. Real time actually begins back at the sensor or the moment the data is acquired. Real time for the Io
T does not begin when the data hits a network switch or computer system – by then it"s too old. If you want to know if your house is going to lớn catch on fire, how soon would you lượt thích to know that? Or if và when a crime may occur, mere seconds are crucial. Hence an alarm must go off in very real time, before the data even gets khổng lồ the cloud or data center, or it doesn’t help.
The point is, we’re seeking to lớn blend the world of operational công nghệ (OT), sensors, & data measurement with the world of IT. The Io
T blends these two worlds for the first time in a major way, & the results will be profound.
4. The Spectrum of Insight
The “Spectrum of Insight” derived from Io
T data relates to lớn its place in a five phase data flow: real time, in motion, early life, at rest, and archive. Recall real time for the Io
T at the sensor or point of acquisition and analytics are needed khổng lồ determine the immediate response of a control system và adjust accordingly, such as in military applications or precision robotics. At the other end of the spectrum, archived data in the data center or cloud can be retrieved for comparative analysis against newer, in-motion data, to gain insight into the seasonal behavior of an electrical power nguồn generating turbine, for example. Hence insight from the big data in the Io
T can be extracted across a spectrum of time & location.
5. Immediacy Versus Depth
With today’s traditional computer and Io
T solutions, there’s a trade-off between speed & depth. That is, one can get immediate “Time-to-Insight” on a rudimentary analytic such as a temperature comparison or fast Fourier transform khổng lồ determine if rotating wheels on a tram will cause a life threating accident. Immediate Time-to-Insight is crucial here.
On the other kết thúc of the spectrum is the time required to gain deep insight. The example here is from one of my former customers, the Large Hadron Collider at CERN in Europe, where they smash subatomic particles together lớn seek insight into the make-up of such particles. The data collected here takes a long time lớn analyze, using large, back-end computer farms. Such depth of insight has resulted in the recent discovery of a new subatomic particle called the Higgs Boson.
6. Shift Left
Consider the mutually exclusive objective of deriving both immediate and deep insight, as discussed in #5 above. It’s really hard to get both today. However, engineers are good at resolving conflicting objectives & getting BOTH. James Collins has referred this phenomenon as “the genius of the AND”.
The drive lớn get both immediate và deep insight from data will cause sophisticated high over compute and data analytics that is normally reserved for the cloud or data center (what I điện thoại tư vấn Tier 4 in the Io
T solution), khổng lồ migrate toward the left of the end-to-end Io
T solution infrastructure. That is, deep compute will be positioned closer to lớn the source of data, at the point of data acquisition & accumulation in sensors (what I hotline Tier 1) and network gateways (Tier 2).
Xem thêm: Sir isaac newton the english scientist and mathematician, was one of the most important figures
7. The Next ‘V’
Big data is commonly characterized by the infamous “V’s” --- Volume, Velocity, Variety, & Value. I propose a fifth “V” -- Visibility. When the data is collected, data scientists around the world should be able to lớn see and work with it, as needed. Visibility refers khổng lồ the benefit afforded by not having to lớn transfer large amounts of data to remote people or locations. I love this idea of access khổng lồ data & app “independent of time and place”. Mark Templeton, CEO of Citrix, adds a third independence: “independence of device”. My team & I are working closely with our partner Citrix as we deploy time, place, & device independent “Visibility” solutions.