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Internet of Things (IoT)

What it is and why it matters

The Internet of Things is the concept of everyday objects – from industrial machines to wearable devices – using built-in sensors to gather data and take action on that data across a network. So it’s a building that uses sensors to automatically adjust heating and lighting. Or production equipment alerting maintenance personnel to an impending failure. Simply put, the Internet of Things is the future of technology that can make our lives more efficient.

History of the Internet of Things

We’ve been fascinated with gadgets that function on a grander scale for decades (think spy movie-type stuff) – but it’s only been in the past several years that we’ve seen the IoT’s true potential. The concept evolved as wireless Internet became more pervasive, embedded sensors grew in sophistication and people began understanding that technology could be a personal tool as well as a professional one.

The term “Internet of Things” was coined in the late 1990s by entrepreneur Kevin Ashton. Ashton, who’s one of the founders of the Auto-ID Center at MIT, was part of a team that discovered how to link objects to the Internet through an RFID tag. He said he first used the phrase “Internet of Things” in a presentation he made in 1999 – and the term has stuck around ever since.

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Building a connected world through the Internet of Things

Data is everywhere – at home, at work and in practically every facet of life. This video from SAS and Intel explains how analytics is helping organizations find new solutions through streaming, always-on data.

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The Internet of Things explained. Click to enlarge

Nİ veri akışını nasıl analiz edebilirsiniz?

Nİ ile ilgili tartışmalarda analitik teknolojilerinin, gelen kaynak verileri bilgilendirici, bilinçli ve yararlı bilgiye dönüştürmede kritik öneme sahip olduğu en başından beri kabul edilmiştir.

Fakat durmaksızın sensör ve cihazlardan gelen verileri nasıl analiz ederiz? Ve bu yöntem günümüzde yaygın olan diğer analitik metotlardan hangi yönüyle ayrılır?

Geleneksel analizde veri saklanır ve sonra analiz edilir. Fakat sürekli veriyle model ve algoritmalar saklanır ve veri analiz için buradan geçer. Bu analiz türü veri gerçek zamanlı oluşturulduğundan etki modellerinin tanımlanmasını ve incelenmesini mümkün kılar.

Bu yüzden veri bulut içinde veya herhangi yüksek performanslı bir bellekte saklanmadan önce otomatik olarak sizin tarafınızdan işlenir. Daha sonra cihazlarınız veri yaymaya ve almaya devam ederken veriyi deşifre etmek için analitiklerden yararlanırsınız.

Gelişmiş analitik teknikleriyle veri akış analitikleri, gelecek senaryoları öngörme ve karmaşık soruları incelemek için mevcut koşulların incelenmesi ve eşiklerin değerlendirilmesinden öteye gidebilir.

Bu veri akışlarından yararlanarak geleceği değerlendirmek için verilerinizdeki modelleri ortaya çıktıklarında tanımlayan yüksek performanslı teknolojilere ihtiyacınız vardır. Bir model saptandığında veri akışı içine gömülü ölçütler, bağlı sistemlere otomatik ayarlamalar getirir veya acil eylem ve daha iyi kararlara yönelik uyarıları başlatır.

Esasen bu, koşulları ve eşikleri incelemenin ötesinde gelecekteki muhtemel olayları değerlendirme ve sayısız olasılık senaryosunu planlamaya geçebilmeniz anlamına geliyor.

We should be thinking less about the Internet of Things, and more about the Intelligence of Things. We need to infuse analytics into our systems and applications, because collecting data alone is not enough.
Randy Guard
 Executive Vice-President and Chief Marketing Officer, SAS

The Internet of Things in Today’s World

The impact that the IoT has had on the world has been significant – and it’s only getting started. Learn more about what people are saying.

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Non-Geek's A-to-Z Guide to the Internet of Things

We all know the Internet of Things is big – so it's no surprise that the language used to describe it is expansive. This A to Z guide includes 101 IoT terms that provide a quick, go-to resource for data professionals.

Download the guide

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Understanding Data Streams in IoT

Learn how event stream processing technology helps you acquire, understand and use real-time, streaming data to make fact-based, automated decisions.

Read white paper summary

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SAS is a Leader in The Forrester Wave™: Streaming Analytics, Q3 2017

Enterprises increasingly demand analytics that can keep pace with the real-time, rapid-fire nature of an IoT world. This report cites SAS® Event Stream Processing as a Leader in this space.

Read the report

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SAS® Analytics for IoT

Integrate, analyze and visualize streaming data at the source

Learn more about SAS Analytics for IoT

Who's using it?

The IoT is more than just a convenience for consumers. It offers new sources of data and business operating models that can boost productivity in a variety of industries.

Health Care

Many people have already adopted wearable devices to help monitor exercise, sleep and other health habits – and these items are only scratching the surface of how IoT impacts health care. Patient monitoring devices, electronic records and other smart accessories can help save lives.


This is one of the industries that benefits from IoT the most. Data-collecting sensors embedded in factory machinery or warehouse shelves can communicate problems or track resources in real time, making it easy to work more efficiently and keep costs down.


Both consumers and stores can benefit from IoT. Stores, for example, might use IoT for inventory tracking or security purposes. Consumers may end up with personalized shopping experiences through data collected by sensors or cameras.


The telecommunications industry will be significantly impacted by the IoT since it will be charged with keeping all the data the IoT uses. Smart phones and other personal devices must be able to maintain a reliable connection to the Internet for the IoT to work effectively.


From predictive maintenance to multimodal transportation and shared mobility services, bring valuable services to market by combining analytics with IoT data. The IoT also impacts transportation on a larger scale: delivery companies can track their fleet using GPS solutions. And roadways can be monitored via sensors to keep them as safe as possible.


Smart meters not only collect data automatically, they make it possible to apply analytics that can track and manage energy use. Likewise, sensors in devices such as windmills can track data and use predictive modeling to schedule downtime for more efficient energy use.

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Real-World Examples

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Smarter Cars

Andreas Mai sheds light on the topic of connected vehicles.

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Safer Transportation

Trucking companies use IoT to make operations safer, more efficient and economical.

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Connected Grids

Duke Energy uses advanced analytics on sensor data to anticipate customer needs.

How It Works

In IoT discussions, it’s recognized from the onset that analytics technologies are critical for turning this tide of streaming source data into informative, aware and useful knowledge. But how do we analyze data as it streams nonstop from sensors and devices? How does the process differ from other analytical methods that are common today?

In traditional analysis, data is stored and then analyzed. However, with streaming data, the models and algorithms are stored and the data passes through them for analysis. This type of analysis makes it possible to identify and examine patterns of interest as data is being created – in real time.

So before the data is stored, in the cloud or in any high-performance repository, you process it automatically. Then, you use analytics to decipher the data, all while your devices continue to emit and receive data.

With advanced analytics techniques, data stream analytics can move beyond monitoring existing conditions and evaluating thresholds to predicting future scenarios and examining complex questions.

To assess the future using these data streams, you need high-performance technologies that identify patterns in your data as they occur. Once a pattern is recognized, metrics embedded into the data stream drive automatic adjustments in connected systems or initiate alerts for immediate actions and better decisions.

Essentially, this means you can move beyond monitoring conditions and thresholds to assessing likely future events and planning for countless what-if scenarios.

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