The exponential rise in data has led forward-thinking companies to bank on big data analytics tools to gather actionable insights that help them structure their business and make strategic plans. Today, big data analytics tools have become essential in decision-making across the operational spectrum. Decision-makers are making use of next-generation analytics tools to sift through massive amounts of data and make predictions about their business' future. In an exclusive interaction, Sunil Motwani, Industry Director at MathWorks India tells how the company offers a high-performance environment for working with big data and delivers solutions that drive innovation and enhanced learning.
Using Data Analytics to turn large volumes of complex data into actionable information can help to improve engineering design and decision-making processes. However, developing effective analytics and integrating them into business systems can be challenging. Engineers, analysts, and data scientists interested in developing analytics, managers interested in using data analytics to improve design and design-making and IT managers and systems engineers interested in integrating analytics into business systems are using mathematical computing software to support:
• Accessing, exploring, and analyzing data stored in files, the web, and data warehouses.
• Techniques for cleaning, exploring, visualizing, and combining complex multivariate datasets.
• Prototyping, testing, and refining predictive models using machine learning methods.
• Integrating and running analytics within enterprise business systems and interactive web applications.
We see growth coming in both engineering and business analytics.
Engineers and data scientists work with large amounts of data in a variety of formats such as image, video, telemetry, databases, and more. The ability to create analytics that processes massive amounts of business and engineering data enables designers in many industries to develop intelligent products and services. Designers can use analytics to describe and predict a system's behavior, and further combine analytics with embedded control systems to automate actions and decisions. With machine learning techniques, they can find patterns in data and build models that predict future outcomes based on historical data. With diverse data sources and big data, companies are building increasingly complex and vital applications.
There are three key trends to track when it comes to growth in these areas:
Impacts of predictive analytics systems on industries like manufacturing and medical devices:
• It's well-known at this point that data analytics technologies can bring significant business benefits in areas such as predictive maintenance. However, the system architecture for such applications remains an open question. Customers are hesitant to share their data with vendors, logging all of the data from a machine is often impossible given the volume of data created, and responses to events may be needed in milliseconds – much too short of a time to wait for a response from an internet server. All of these will drive innovation at "the edge", or on the equipment itself. This will entail data reduction techniques such as signal processing algorithms that can transform high-frequency sensor data into a compressed form that can be easily transferred over a network. Design constraints may also result in running a machine learning model directly on the equipment. Software that makes it easy to develop an algorithm once and deploy it into these different scenarios will enable design teams to implement the optimal architecture for their systems.
• Medical Devices: Predictive analytics systems will allow for more informed and personal relationships between patients and physicians and more effective diagnoses at point-of-care. It is quite possible that predictive analytics will also drive the progress of both preventative and therapeutic care with the data collected from wearables and shared on personal devices.
Machine Learning and Deep Learning:
• As it becomes easier and easier to apply machine learning techniques, more products and services will incorporate machine learning models. Embedded systems, typically used for controls and diagnostics, will incorporate machine learning models that can detect previously unobservable phenomena (eg. detecting a driver's style of driving, or classifying whether a machine is likely to break down or not). In 2018, we'll continue to see machine learning models being incorporated in new places, especially in edge nodes and embedded processors.
• While deep learning continues to look promising, there is still a lot of design and tuning necessary to train a useful deep network. Techniques such as automated hyperparameter tuning appear well-positioned to reduce this work, which should ramp-up the pace of adoption of deep learning.
Domain Experts Take on Data Science:
• As engineering and IT teams become more integrated, there will be increased demand for domain experts that understand the core products and services of the business. Partnering with (or serving as) data scientists, these domain experts will be critical to identifying areas where data science technologies can benefit the business. Empowering these domain experts to apply data science methodologies will enable the rapid integration of big data and machine learning technologies into the services and operations of the broader organization, ultimately establishing a significant competitive advantage when offering the products and services that customers are demanding.
MATLAB Integrates Workflows
Major engineering and scientific challenges require broad coordination across teams to take ideas to implementation. Every handoff along the way adds errors and delays. MATLAB helps automate the entire path from research to production.
MATLAB Is Trusted
Engineers and scientists trust MATLAB to send a spacecraft to Pluto, match transplant patients with organ donors, or just compile a report for management. This trust is built on impeccable numerics stemming from the strong roots of MATLAB in the numerical analysis research community. A team of MathWorks engineers continuously verifies quality by running millions of tests on the MATLAB code base every day.
MATLAB Is Designed for Engineers and Scientists
Everything about MATLAB is designed specifically for engineers and scientists:
• Function names and signatures are familiar and memorable.
• The desktop environment is tuned for iterative engineering and scientific workflows.
• Documentation is written for engineers and scientists, not computer scientists.
Data is everywhere and each year we store more and more of it. Huge datasets present an amazing opportunity for discovering new things about our world, the products we make, and how people interact with them. However, big datasets also present some real challenges. How do we understand them? How do we interrogate them? How do we even read them?
Engineers need to pre-process the data and then learn from it using the right algorithm so that they can create the right model to be deployed on real data. Key to this is access to the right hardware and software tools that can handle big data. A software platform that supports complete workflow from handling of big data, pre-processing, building training models and deploying analytics will be of tremendous help to domain experts wanting to use data analytics for applications in their respective domains.
MATLAB provides a single, high-performance environment for working with big data. MATLAB enables engineers and domain experts to develop their own data analytics applications. Easy-to-use functions and interactive apps help them design accurate predictive models quickly. You can then deploy these to both enterprise and embedded systems. There's only one algorithm to develop and maintain, that engineers can run where they choose to. MATLAB is increasingly playing a leading role in the design and development of new technologies because it is:
• Easy — use familiar MATLAB functions and syntax to work with big datasets, even if they don't fit in memory.
• Convenient — Work with the big data storage systems you already use, including traditional file systems, SQL and NoSQL databases, and Hadoop/HDFS.
• Scalable — Use the processing platform that suits needs, from local desktop machine to Hadoop — without rewriting algorithms.
Engineers and scientists are finding new and creative applications for data analytics technologies. They use machine learning, big data, and optimization to make critical decisions in medical diagnosis, stock trading, energy load forecasting, and more. In MATLAB they can acquire and pre-process data from a variety of sources, build predictive models, compare machine learning algorithms, and integrate algorithms into production systems.
Engineering and IT teams are using MATLAB to build today's advanced Big Data Analytics systems ranging from predictive maintenance and telematics to advanced driver assistance systems and sensor analytics. Teams select MATLAB because it offers essential capabilities not found in business intelligence systems or open source languages:
• Physical-world data: MATLAB has native support for sensor, image, video, telemetry, binary, and other real-time formats. Explore this data using MATLAB MapReduce functionality for Hadoop, and by connecting interfaces to ODBC/JDBC databases.
• Machine learning, neural networks, statistics, and beyond: MATLAB offers a full set of statistics and machine learning functionality, plus advanced methods such as nonlinear optimization, system identification, and thousands of prebuilt algorithms for image and video processing, financial modeling, control system design.
• High-speed processing of large datasets. MATLAB's numeric routines scale directly to parallel processing on clusters and cloud.
• Online and real-time deployment: MATLAB integrates into enterprise systems, clusters, and clouds, and can be targeted to real-time embedded hardware.
A significant challenge for us has been helping customers get data from source into the hands of end users, which is a common barrier for engineers who need data to formulate requirements for new products, troubleshoot field problems, and come up with new technologies. Connectivity technologies such as CAN and high-speed mobile communication removed this barrier in many situations. With more and more streaming data, we are faced with a data science challenge. We need to ensure that the speed of data analysis is keeping pace with data intake and, equally important, provide the capability to zoom into and extract insight from stored data throughout the engineering community. To address this new challenge, one often looks for those who have computer science skills, knowledge of statistics, and domain expertise relevant to their specific engineering problems. Such instinct is not wrong, but these types of candidates are rare. You may find success by focusing on domain expertise. Domain expertise is often overlooked, yet it is essential for making judgement calls during the development of an analytic model. It enables one to distinguish between correlation and causation, and between signal and noise. Domain knowledge is hard to teach. It requires on-the-job experience, mentorship, and time to develop. This type of expertise is often found in engineering and research departments that have built cultures around understanding the products they design and build. These teams are intimately familiar with the systems they work on. They use statistical methods and technical computing tools as part of their design processes, making the jump to the machine learning algorithms and big data tools of the data analytics world manageable. Instead of searching for elusive data scientists, we're now working with engineering teams by helping their engineers to do data science with a flexible tool environment like MATLAB, which enables engineers to become data scientists.
About Sunil:
Sunil Motwani is working as Industry Director at MathWorks India office managing sales related activities for commercial customers in India. He has been at MathWorks since 2008 from the time it started operations in India. Prior to joining MathWorks, Sunil worked at Hewlett Packard & Agilent Technologies managing sales of test instruments for various industry segments in India including Aerospace & Defense, Communications, Semiconductor, Automotive and Industrial Automation. He has more than 25 years of experience in sales of technology products across various regions within India having been based at Mumbai, Delhi, Hyderabad & Bangalore during this time. Sunil has a Bachelor's Degree in Electronics Engineering from Visvesvaraya National Institute of Technology (VNIT), Nagpur and a Post-Graduate Diploma in Software Technology from National Centre of Software Technology ( NCST ), Mumbai.