For a major oil and gas exploration company using complicated algorithms to compute millions of data points taken with sophisticated sound waves, technology is vastly improving their ability to predict natural resource opportunities in their exploration efforts. They conduct these investigations from a ship at sea and use supercomputers to analyze it in a way that provides them with a predictable course for their exploration and discovery.
Welcome to the strategic age of Big Data — a growing method of building business. Big Data computes huge amounts of data points for applications that range from condition monitoring at a sprawling chemical process plant, to calculating weather patterns in regions where shipments travel. Big Data is on every business leader’s mind as companies use the power of information to better compete globally. Tantamount to this effort is a parallel concern of maintaining data integrity and security in an often risky global environment of hacks and cybercriminals.
“Big Data can be fed into many parts of the design process. For example, data from real-world operations of equipment is being used as inputs and boundary conditions for simulations. We’re also seeing organizations looking to improve their test coverage with Big Data by making sure that the tests they run are capturing all of the conditions the design may encounter in the field,” says Seth DeLand, product marketing manager of Data Analytics at MathWorks. “More data is going to be available to design engineers. They’re going to have more and more opportunities to leverage real-world data to drive decisions during the design process. Engineers are also going to see their companies looking to build new services based on their data. For example, equipment manufacturers are looking to provide predictive maintenance as a service to their customers.”
Internet of Things
The Internet of Things (IoT) is accelerating the use and application of Big Data. Sensor technology and its reliability has improved greatly over the past decade; data attained may be fed into user interfaces and software computing programs all the way around the world via the Internet. As sensor technology becomes more advanced, so too do the possibilities for Big Data applications.
“One area where it is becoming prevalent though is in sensor systems,” says Mike Haley, senior director, Emerging Products and Technology at Autodesk. “In this case, physical products — buildings, machines, batteries, etc. — are instrumented with sensors that track aspects of their performance over their lifetime. By downloading and correlating all of this information using Big Data technologies, it is possible to give designers more informed design choices based on real-world performance statistics.
Haley points out that certain industries adopt data picked up via IoT better than others. The utilization of such data and its incorporation into business strategy isn’t fully mature. There’s still a way to go.
“Specific industries where this is becoming prevalent include: buildings — sensors in concrete and beams as well as components such as HVAC (heating, ventilation and air conditioning) systems; battery technology — understand battery chemistry and performance in difference usage scenarios; and automotive, which probably has the longest history of this,” says Haley. “That said, true Big Data analysis of sensor data is in its earliest stages as a large volume of data has to be acquired over time then appropriately analyzed to discover patterns and predictors that will be useful to designers.”
For design engineers and companies relying on technology to drive success, IoT and Big Data relies on several things. “To understand the implications of Big Data on engineering firms, it’s useful to first examine the components of IoT and how each will affect design engineers,” says Ray Milhe, vice president of Enterprise Solutions for ANSYS Inc. “Designing products within this framework will have unique implications on engineering firms.”
Milhe says that by 2020, industry experts estimate there will be as many as 200 billion wireless connected devices. This will provide an enormous onslaught of data. Harnessing this data is both a challenge and an opportunity for design engineers and software engineers. Customers will aggressively seek solutions to capitalize on this.
“The last component of the IoT infrastructure is Big Data and the cloud,” says Milhe. “Whether performance data is sampled every millisecond or every hour, 200 billion connected devices are going to generate a tremendous amount of data, in some cases hundreds of terabytes or petabytes. Some of our customers are looking at new ways to store, index and search vast amounts of data. Some companies are using predictive analytics and trending and will apply it to simulation and test data, which they can use internally or as part of a service to their customers — for example — preventive maintenance.
The IoT and more prevalent data has cultivated a concern about all data, including Big Data, in cybersecurity circles. Data in all forms is vulnerable to hacker attacks and breaches of security. Security poses a serious business risk for those enterprises hoping to capitalize on Big Data in the future. A breach of security can lead to a tacit hijacking of a company’s intellectual property; a company may fall target to industrial espionage and other cybercriminal acts that endanger their business operations. Businesses are struggling to keep up with the needed security to protect volumes of data across its many disparate data silos.
According to research firm Gartner Inc., more than 80% of organizations will fail to develop a consolidated data security policy across silos, leading to potential noncompliance, security breaches and financial liabilities over the next few years. At its Security and Risk Summit last year, Gartner stated that while much data is at risk, the advent of Big Data and its interchange via cloud computing is fueling the overall concern for data security.
“Businesses have traditionally managed data within structured and unstructured silos driven by inherent requirements to deploy relational database management systems, file storage systems and unstructured file shares,” said Brian Lowans, principal research analyst at Gartner. “However, the advent of Big Data and cloud storage environments is transforming the way in which data is stored, accessed and processed, and CISOs need to develop a data-centric security approach. Unfortunately this is not common practice today, and its planning is critical to avoid uncoordinated data security policies and management.”
Big Data and cloud storage environments, as Lowans notes, are likely to fuel IT investments in the short run. Research recently presented by CompuCom based on its survey results revealed that 26% of IT professionals expect their organizations to focus most of their 2015 technology investments on cloud technologies, followed by security (24%), and Big Data/analytics (23%).
“Enterprises understand the enormous potential of the cloud to integrate social, mobile and Big Data,” says Sam Gross, chief technology officer at CompuCom. “With the agility challenges that IT faces, we are not surprised that cloud [computing] is top of mind, even as security remains an ongoing consideration. Secure cloud-scale application and mobility solutions both drive and consume Big Data, and it is no surprise that the focus on cloud, security and Big Data technology investments rises to the top.”
As Big Data investment continues to rise to the top of IT spending, technology should become even more advanced and reliable continuing a trend where smart devices, remote monitoring and data transmission have been vastly improved. This will fuel design engineers to seek more advanced software tools and technology to utilize these capabilities and optimize the performance of data on the road ahead.
According to Milhe, the future computation of more data and better data will rely on software development tools more than ever. “Over the past 10 years, we’ve all seen devices get smarter. Embedded software is what makes devices smart, and there has been an explosion of the lines of code in today’s products,” he says. “We see this trend continuing. It will be commonplace for discrete products to have millions of lines of embedded software. Engineering firms will not be able to create, test and certify this volume of code using traditional, manual methods and still get to market on time. They will have to adopt model-based software development tools to design and simulate their embedded systems and then use certified code generation to produce the actual embedded software.”
Ultimately, leveraging Big Data as part of the design process is something firms should start preparing for now on a smaller scale so their resources can evolve.
“The astute engineer and progressive design firm will start small, invest in currently available comparative information about critical assets or equipment and engage experts in numerical and statistical methods to put that information into appropriate design context,” says Roy Whitt, senior vice president and general manager for Asset Answers for Meridium Inc.
Whitt posits that his company will ease into Big Data computing to allow for development of effective techniques to utilize the ever-increasing amounts of data sure to come over the next decade.
“Identifying a few key or critical pieces in a design that can be improved if optimized in the short term will offer an early competitive advantage, while positioning the individual engineer and design firm for success in the future,” he says.