Rich Rovner, VP Marketing, MathWorks spoke to EM more about data analytics driven applications, and adds insights on technical computing. We have been investing heavily in data analytics capabilities for the past few years – that is, not just with R2016a but with releases since R2014b.
We are seeing MATLAB used for data analytics in a variety of industries like automotive, aerospace, industrial automation, medical devices, retail, life sciences, and healthcare management. For example, we have a customer creating a smart building management system that collects data on weather patterns, variable energy costs, thermal dynamics, and other factors affecting energy use. While general analytics is highly pervasive, we see that engineering data is really becoming critical in lots of applications.
We have a habit not to pre-announce specific features, but generally we are continuing to invest heavily in all these areas. UNL students will have free access to Adobe’s Creative Cloud software subscription under a campus license agreement starting August 24. Students who may have already purchased individual Creative Cloud subscriptions may cancel through Adobe and re-subscribe through the campus license. Here you'll find information, sample and massive resource for The Water Cycle Final Product, for every grade - Elementary, Middle School, or High School. Audio, image, real-time video, motion, machine performance metrics, and other sensor-generated data are being combined with traditional business, transactional, and other IT data.
We continue to add new algorithms and improve existing ones for statistics and machine learning, computer vision, neural networks, and other data analytics applications.
We have highly optimized libraries, just-in-time compilation, and a new MATLAB Execution Engine that came out last year. Some good examples are predictive maintenance for fleets in the automotive industry, operational logistics and supply chain analytics in industrial automation, and risk modeling in financial services. Lots of financial organizations around the world use MATLAB data analytics capabilities for this. These applications get smarter over time with the analytics and machine learning that goes back down to the devices.
It uses analytics and optimization to automatically control the temperature set point in commercial buildings throughout the day.
We certainly see our products used across automotive, aerospace, industrial automation, financial services, retail, healthcare management, life sciences, etc.  The other indicator of this growth is that a lot of people talk about the shortage of data scientists.


You can think of traditional business data as coming from transactional systems like financial and retail systems that record the products you might buy at a store or online retailer. Below is list of The Water Cycle Final Product.Click image to get bigger picture, and if you find The Water Cycle Final Product interesting, you might pin it to Pinterest. Small start-ups to major global organizations are able to apply these techniques for data analytics applications. Specific risk management examples include loan approval analytics and credit scoring analytics. In many IoT applications there could be thousands of sensors out in the world collecting an enormous amount of data in real time.
They’re not just changing the set point in the morning and then setting it back in the evening. That’s one source of data, but another critical source is engineering data generated by smart systems, smart vehicles, drones, unmanned aircrafts, all types of IoT applications where the machine is generating data in an always-on fashion. The flexibility to run those analytics – in massive data sets in IT, as cloud infrastructures, or even as the data are acquired on smart sensors and embedded devices – is enabling organizations in many industries to develop intelligent products, devices, and services that expand the business impact of their data and analytics. With 16a, you can train convolutional neural networks, which enables deep learning, using Neural Network Toolbox. With parallel computing, MATLAB runs on multiple computing architectures, from multicore desktops to GPUs, computer clusters, and grid and cloud services. That data is then aggregated in the cloud and the analytics can be applied against the data aggregator to build a predictive model.
With this application, they’re helping their customers save 10-25% in energy consumption, which is significant in large-scale enterprises.
There is also a large investment in big data, including MapReduce and Hadoop file structure support introduced in R2014b. This is an example where they are using cloud with sensor-based technologies to store the data and perform the analytics. The second is they understand computation and computing technology and they know how to apply the principals of computation to solve problems.
And applying analytics to that combination of engineering and business data really produces a better outcome or better predictive model, and a better set of analytics for wherever you want to deploy it.
In 15a, we released the Classification Learner app in Statistics and Machine Learning Toolbox.


So we have a heavy development investment in algorithm performance, big data support, and computing architecture. So what the cloud offers is flexibility around where the data needs to reside and, further, where analytics need to reside.
We continue to invest in exploring new domains and enhancing existing ones in every release. Then they deploy the algorithms in a variety of ways into a production IT environment, which is absolutely critical for them. The sensors and systems become smart because of the analytics that are created and deployed in those devices. So the flexibility of the computing architecture and the ability to move the algorithm and the computation anywhere  are among the technology value drivers that financial services organizations see when working with these types of tools. In many cases those are embedded devices and we are seeing more customers incorporate analytics with Model-Based Design for embedded systems workflows. It’s easier for customers to absorb rather than waiting a longer period of time and seeing this massive release every one or two years.
Now our perspective is to enable those who already have the domain expertise and understand computation. There are new sets of features, new capabilities customers can learn about, to figure out what’s right for them. If they are MATLAB users, we bring the data analytics and the domain-specific algorithms to them immediately. Events like MATLAB EXPO offer a great way for customers to stay current and learn about all the new features and capabilities. In the case of engineering-driven analytics, MATLAB and Simulink are designed around the engineering data analytics workflow.



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