Thingalytics describes how to use real-time analytics and algorithms to seize the opportunities that flow from IoT (Internet of Things), while simultaneously minimizing threats. This composite solution manages and combines volumes, velocity and variety of data from the ever-increasing number of information sources presented by cloud, mobile, IOT and social media, among others.
Using real stories from some of the most tech-savvy retailers, banks, transportation and soft drink providers, thingalytics delves into the world of analytics and algorithms to realize how to seize the opportunities buried within IoT.
Opportunities for Thingalytics and Internet of Things
- It presents an opportunity for innovative businesses: As each device from tractors to refrigerators to ships is digitized and connected to the internet, it presents an opportunity for innovative businesses to learn from (and take advantage of) the digital data it creates.
- Machine learning can now be applied to huge quantities of data: Driverless cars are just one example of machine learning. It’s used in countless applications, including those that predict fraud, identify terrorists, recommend the right products to customers at the right moment, and correctly identify a patient’s symptoms in order to recommend the appropriate medications.
- More powerful computers and analytical opportunities: The concept of machine learning has been around for decades. What’s new is that it can now be applied to huge quantities of data. Cheaper data storage, distributed processing, more powerful computers and the analytical opportunities available have dramatically increased interest in machine learning systems.
- Retail space personalization made possible through analytics: In the retail space, websites collect data such as purchase history and customer profiles to make recommendations just as an automated personal shopper would. Personalization through machine learning is made possible through analytics, sifting through millions of rows of browser data and profile data.
- Big data streaming analytics: You are not asking the sensor for the data; the sensor is always reporting. A traditional app is more the request/response model. When the streaming data comes in, technology has to digest the information it contains, determining what it means and whether it fits into a pattern that has business significance.
- Streaming analytics engine can be programmed to take autonomous actions: The key difference between a streaming analytic engine and a database is that the former is event driven and is always on. Unlike a database, a streaming analytics engine can be programmed to take autonomous actions, immediately driving a decision when detecting a problem.
- Fast big data acquisition and integration: It is recommended by John Bates (CTO, Software AG) as a "highly scaleable messaging architecture to stream real-time events; to support communications among multiple streaming engines; in-memory caches and visual dashboards; and to communicate events out to applications and processed. This architecture requires adapters to connect to common APIs -- such as machine-to-machine connectivity protocol (MQTT)." It also ensures perfect data acquisition and integration.
- Cloud: The optimal strategy to navigate these peaks and troughs is for the platform to run in a cloud with the ability to provision computing resources automatically in response to the changing load. In the coming years, more and more.
Thingalytics looks at how the Internet of Things can save lives, stop fraud and reduce carbon emissions across the globe. Put simply, the Internet of Things represents an emerging reality where everyday objects and devices are connected to the Internet, most likely wirelessly, and can communicate with one another at some intelligent level. It is also assumed that, as the physical and digital worlds integrate more closely with each other, and the number of connected devices is predicted to reach 25 billion by 2015, the IoT will enhance and evolve our ability to manage and process information.
The Internet of Things is about digitizing everything in the real world and integrating it into the Internet of Things, in which everything from your bed to your refrigerator gathers and analyzes data that can improve your life. It’s a more context-oriented world, because there is better data. By effecting better decision making through a better under-standing of data, we can tackle socioeconomic issues like poverty and dis-ease, education, and quality of life around the world.