The Internet of Things
The Internet of Things (IoT) was first coined in 1999. The concept was made popular by the Auto-ID Center at MIT and related market analysis publications. Radio frequency identification (RFID) was seen as a prerequisite for the Internet of Things. The concept was that all non-human objects could be equipped with identifiers and communicate with one another. When first conceived, the main benefits were centered on retail to better manage inventory, reduce waste and keep up with client demand.
Today, the Wall Street Journal reports that more than 30 billion devices will be wirelessly connected to the Internet of Things by 2020. Machine data generated in the IoT comes from thousands of sources with applicability much broader than retail.
Some narrowly define machine data as logs or sensor output; certainly these sources are a part of the IoT, but it’s much broader. Machine data is any information which is automatically created from a computer process, application or other machine without the intervention of a human. This includes logs and sensors, but also sources such as power grids, health monitors, security cameras, computer networks, call detail records, financial instrument trades and more.
Let’s take an example from the airlines. A Boeing 747 aircraft will generate data across 100’s of parameter every second. That means a three hour flight generates more than two million pieces of data and that’s just one flight. Most planes take multiple per day. Aggregated over weeks, months and years, the amount of data is astonishing.
The bottom line is that data automatically generated in the IoT provides a fantastic fuel source for the era of big data. Speaking of fuel, it is estimated that the auto industry will be the second largest generator of data by 2015. This estimate is not surprising, considering that some plug-in hybrid vehicles generate 25 GB of data in just one hour.
What to do about the rise of the machine? Analyze it!
Emerging big data technologies enable a new generation of applications to analyze large volumes of multi-structured, often in-motion machine data to gain insight. Performing analytics on machine data will help you answer the following questions:
- Do you have real-time visibility into your business operations such as customer experience and behavior?
- Are you able to analyze all your machine data and combine it with enterprise data to provide a full view of business operations?
- Are you proactively monitoring end-to-end infrastructure to avoid problems?
But how? Machine data brings unique challenges. Formats vary and are complex, plus there are few standards. A mix of streaming and at-rest data creates challenges in correlating and visualizing data sets. Data is also likely to be time sensitive with a mixture of data refresh rates and data may or may not have context.
The answer? Enable continuous, extremely fast analysis of massive volumes of data-in-motion. Big data has always been with us, what’s new is our ability to capture and analyze more of it to achieve results faster.
Fredrik Tunvall (@tunvall), analyst and consultant at Ovum, Barry Devlin (BarryDevlin), founder 9sight consulting and Alex Philp (@BigDataAlex) founder of GCS, join us in an open-forum Twitter chat, to discuss some of these factors on Wednesday, April 30, 12:00 p.m. ET. You can follow along and join the discussion using the hashtag #BigDataMgmt. Here are the questions we’ll be discussing as well as reference articles to help inspire the conversation:
#BigDataMgmt chat questions
- How is the Internet of Things and the constantly connected world influencing and transforming business strategy?
- What are the possible hiccups and security threats in the Internet of Things? Can we avoid? How?
- What are the challenges of leveraging the Internet of Things technology?
- How do businesses handle the task of processing even more data from the Internet of Things in real time?
- What are the benefits of the Internet of Things technology for businesses and which industries (if any) is it best suited for?
- How can organizations leverage data from connected devices to benefit users and customers?
- How do you define the consumer privacy line while advocating for the disruption that is the Internet of Things?
- What can we do to prepare for the impact of the Internet of Things?
- What are your predictions for the Internet of Things in a 2020 world?
- Big data on wheels | http://bit.ly/PxDayd
- Securing the Internet of Things: Where do you start? | http://bit.ly/PxDUmS
- Koby's predictions for the Internet of Things in 2014 and beyond | http://bit.ly/QCgUEH
Fredrik Tunvall (@tunvall) is an analyst in Ovum’s Information Management Software group. With a deep understanding of business drivers and technology he researches, speaks, delivers advisory services and consults on trends and strategy in business intelligence, analytics, data visualization and big data.
Prior to joining Ovum, Fredrik served in several technical, consultancy and project management roles. Throughout his career he has gained a broad technological and strategic understanding of everything from application development and data management to the more high-level human-computer interaction.
Fredrik has been quoted in the Financial Times, Forbes, CIO Magazine, ComputerWorld, InformationWeek, PCWorld and is an experienced speaker with keynotes for organizations such as CIO.com, Computerworld and SAP.
Dr. Barry Devlin (@BarryDevlin) is among the foremost authorities on business insight and big data, and is one of the founders of data warehousing, having published the first architectural paper on the topic in 1988. With over 30 years of IT experience, including 20 years with IBM as a Distinguished Engineer, he is a widely respected analyst, consultant, lecturer and author of the seminal book, “Data Warehouse—from Architecture to Implementation” and numerous white papers. His new book “Business unIntelligence—Insight and Innovation beyond Analytics and Big Data” is now available.
Barry is founder and principal of 9sight Consulting. He specializes in the human, organizational and IT implications of deep business insight solutions that combine operational, informational and collaborative environments. A regular contributor to BeyeNETWORK, TDWI, and more, Barry is based in Cape Town, South Africa and operates worldwide.
|Alex Philp (@BigDataAlex), Ph.D., is the founder and president of GCS, an advanced geospatial solutions company headquartered in Missoula, Montana. Alex and his partners have also launched two additional high tech companies, Adelos, Inc. and TerraEchos, Inc., pioneering advances in fiber optic sensor systems and streaming cyber analytics. Prior to launching GCS, Alex worked at the University of Montana on a variety of Earth System Science information technology projects. Alex holds a Ph.D. in Interdisciplinary Studies (Geography, Ecology and History) and serves as a Faculty Affiliate in the Department of Geography at The University of Montana. He loves working on cutting-edge problems and creatively developing solutions to these problems.|
What is #BigDataMgmt chat?
#BigDataMgmt chat is a weekly conversation every Wednesday at 12:00 p.m. ET, on Twitter. Each week we discuss a different topic around big data management.
How do you join in?
If you use a Twitter client like Tweetdeck or HootSuite, create a search column for the term ‘#BigDataMgmt’. Then as participants tweet with the #BigDataMgmt hashtag, those tweets will appear in your column. Or you can follow with Tweetchat – http://tweetchat.com/room/bigdatamgmt – and it automatically adds the #BigDataMgmt hashtag.
How do you participate?
Just jump right in! Review the discussion questions posted in advance so you can prepare your thoughts and answers. When the question is posed, begin your response with A1: for question 1 and A2: for question 2, etc. This makes it easier to follow the conversation throughout the chat. No answer is wrong! We look forward to seeing you at the #BigDataMgmt water cooler hosted by @IBMbigdata.